The shopping experience is ever-changing. After a booming ecommerce app market that was accelerated by the pandemic, in-person shopping returned in a big way in 2022 as people flocked back to shopping in-store. In today’s economic conditions, the importance of a stellar in-app experience is higher than ever and can be the difference between a sale and a missed opportunity.

Not only that, it’s become more challenging to retain customers’ brand loyalty, with shoppers far more likely to make a purchasing decision based on price than on their past relationship with a brand or company. In 2023, customers use apps to compare prices and product reviews from a range of retailers and find the best deal for them. 

That’s not to say brand loyalty is dead: it’s just harder to earn, and harder still to retain. It remains an immensely valuable asset for any business who can build that strong relationship with their customers. And with roughly 70% of shopping carts abandoned at checkout across the industry, strong brand loyalty can be the difference-maker in your customers seeing their shopping experience all the way through to a completed sale.

Customers who buy your products may not always be engaging with you online. Finding ways to engage with them and nurture their loyalty is a difficult task – but one you can overcome.

Strong product experience for stronger loyalty

In 2022, 53% of ecom apps were uninstalled within a mere 30 days of download. That number rises to 80% after three months. Consider how much time, effort and advertising budget is invested in acquiring users in the first place – and yet four out of every five customers will have moved on with their lives within 90 days.

What can be done to arrest the exodus of users? A strong product experience is key to holding onto your customers and reaping the rewards. Removing disruptions to the user journey and ensuring a seamless experience throughout will keep your users engaged – and we’ve seen the remarkable effects this can have on LTV. 

Engaged users produce 3.5x more revenue than non-engaged shoppers, and are 3x more likely to make a repeat purchase. Brands are utilising an array of tools at their disposal to ensure their users’ experience with their app is seamless.

Businesses with an in-store presence are capitalising on the growth of in-person shopping by looking for offline opportunities to engage their customers, such as adding dedicated QR codes to physical items in their stores. When scanned, these QR codes – which some brands are printing directly onto the product tags themselves – lead straight to the app product page. The customer can then order again, browse or leave a review, and shop complementing products.

Other brands use deep linking to deliver users directly to a relevant part of the app, and can be used in everything from mobile push notifications to email messaging and influencer campaigns. Deep linking can even be used to solve the issue of cart abandonment at checkout, by sending the user an email or push notification with a reminder of their abandoned purchase and a call-to-action that lands on the app’s checkout page via a deep link – letting them complete the purchase in just a single click.

Smart Banners also allow brands to migrate customers from web. While shopping on mobile web a user is served a Smart Banner which redirects to the app installation screen on their device. Once launched, deferred deep linking ensures that users land immediately on the relevant page – Smart Script can also automatically pull through the items they had in their basket on web to their cart in the app for a frictionless user journey.

A strong product experience should remove any obstacles and pitfalls in the user journey, and strip out any and all disruption. In many respects, brand loyalty and user retention are describing the same thing. Loyal users stay retained, and retained users become increasingly loyal to your brand. Underpinning it all is a seamless and engaging user experience.

Personalization enhances user experience

Personalisation is a key aspect of driving brand loyalty, increasing conversion rates and raising LTV. It creates emotional connections between you and your customers, and increases the likelihood that they’ll return to your business again and again.

In 2023, shoppers flock to brands where in-app experiences are tailored to them. Leveraging the data at your fingertips, you can apply a personal touch to every interaction with your customers. Everything from personalised campaigns, offers and product listings can help build stronger relationships with your customers.

The power of personalisation is even more important than ever in today’s economy. Having an understanding of which customer segments you may be about to lose to competitors, and then being able to address that with personalised offers or messaging, can make a huge difference to your bottom line.

Personalisation gives you a pathway to engage with customers who may be about to “lapse” from your app, or even those who are still active but have not made a recent purchase. By keeping in contact with these users, you can stay forefront in their minds, making them feel like part of a community and potentially draw them back in to spend again in future.

Reward your existing users

In today’s economic conditions, don’t fall into the trap of taking your existing users for granted and focussing all your efforts on user acquisition. Promotions targeted at new customers, such as a discount for the first six months of a contract, or free delivery on a first order, can be incredibly frustrating for an existing customer to see. Enter loyalty programs and reward schemes.

Shoppers’ increasing cost-consciousness should be viewed as a business opportunity: a chance for you to stand above your competitors. Bear in mind that value-for-money isn’t simply a race to offer a lower price point than others. Instead, it’s a chance to demonstrate value through loyalty programs and reward schemes, tailored to your customers through audience segmentation and personalized messaging. 

Existing customers are willing to stick with brands if the right incentive and experience is there, and it’s why brands are prioritising retaining and rewarding loyal customers rather than struggling to find new ones.

Mobile apps have become essential tools for cost-conscious customers looking to save money, find a bargain, or make money for themselves. Downloads for coupons and reward apps have climbed 65% year-on-year, with resale apps like Vinted and Twig also growing 23% in that span.

With engaged users delivering significantly higher LTV for brands, we’re seeing more and more businesses turn to loyalty schemes to reward and retain their customers. Last year, for example, ASDA launched their standalone Rewards app to complement their in-store shopping experience. It swiftly became the fastest-growing shopping app in the UK, with over 5 million downloads since mid-2022.

ASDA Rewards, like many other loyalty apps, offers loyalty programs and rewards that are exclusive to the app itself. In doing so, they funnel in-person shoppers onto mobile, boosting their customers’ engagement with their brand and opening up the opportunity for further sales further down the line.

Key takeaways

Retaining your customers is key to growing brand loyalty; growing brand loyalty is key to retaining your customers. In tough economic conditions, there are plenty of ways to ensure that your users stay loyal and provide high LTV:

• Disruptions reduce brand loyalty: provide a strong product experience and seamless user journey via deep links, Smart Banners and more.
• Personalization adds value: consumers expect to shop with brands where the in-app experience, marketing and messaging is tailored specifically to them.
• Don’t take existing users for granted: loyalty programs and reward schemes keep users engaged and produce significantly higher LTV for brands.

The post Driving loyalty and lifetime value (LTV) in challenging economic conditions appeared first on AppsFlyer.

AppsFlyer uses existing revenue data to model SKAN data, and then merges unique installs from both datasets to create a unified view.

The post Modeled data for revenue LTV D7 with ad revenue data appeared first on AppsFlyer.

What is predicted lifetime value (pLTV)?

To better understand how pLTV serves your measurement and performance goals, we first need to nail down what LTV means, the immense added value of predictive analytics, and the mighty potential of their power combo — pLTV. 

Lifetime value — aka LTV — is an estimate of the average revenue a customer will generate over the time that they use your app or service. But, given the recent data privacy revolution, how does one measure LTV without the same level of access to granular and especially long-term performance data?

This is where predictive analytics or modeling comes into the picture. It leverages machine learning and artificial intelligence (AI) to examine historical campaign data, past user behavior data, and additional transactional data — in order to predict future actions. 

By creating different behavioral characteristic clusters, your audience can then be segmented not by their actual identity, but by their interaction with your user funnel in its earliest stages, which can indicate their future potential to drive meaningful value to your business.

Why is it important?

Armed with knowledge, predictive modeling enables you to make rapid campaign optimization decisions without missing a heartbeat. Nip unsuccessful campaigns in the bud, or quickly double down on investment that can drive even better results — without compromising your users’ privacy. 

In other words, pLTV allows you to leverage data science horsepower, and predict how much money your customers will spend in your app over a predefined window of time based on their past behavior. 

It also enables you to segment users by their acquisition source and forecast projections accordingly, making it an ideal tool for determining which of your marketing channels will produce your highest spending users now — and in the future. 

Especially during the acquisition and re-engagement stages, understanding user behavior patterns and the typical milestones that separate high-potential users from low potential — can be incredibly valuable. 

Creating a pLTV model

While the average marketer will only measure a maximum of around 25 metrics, an app could have well over 200 available for measurement. Take a machine, on the other hand, and it will be able to ingest all this data in a matter of milliseconds and process it into actionable marketing insights. 

Calculate all these indicators based on your definition of success and LTV logic, a machine learning algorithm can apply all that to a significant amount of data, while finding correlation between early engagement signals and eventual success.

This means that advertisers no longer need to know who the user is, but rather which pLTV profile and characteristics they fit into. 

Two key guidelines to keep top of mind in this context:

• Your pLTV profile should be as accurate as possible, and made available during the campaign’s earliest days.
• It should represent your LTV requirements for it to be considered valid and actionable, and allow the algorithm to cluster general audiences into highly granular, mutually exclusive cohorts.

Because there are obvious differences in the way various apps retain and monetize their users, a universal cookie-cutter LTV model is not a real thing. This makes building an LTV model to predict mission-critical metrics like Return on Ad Spend (ROAS) somewhat challenging — yet still doable.

To learn more about how to set up predictive models for LTV calculations — visit our guide.

5 tips for building and maintaining LTV prediction models

1 – Success is a constant feeding loop

When building data models that are designed to guide significant decisions, it’s not only important to build the best system possible — but also to constantly test and tweak it to ensure effectiveness and accuracy.

For both purposes, make sure that you continuously feed your pLTV prediction model to keep it trained on the most relevant data, and always check whether your model’s predictions come to fruition based on new or near-new observations. 

Not following these steps could mean that a model with an initial useful prediction power could go off rails because of seasonality, macro auction dynamics, your app’s monetization trends or other factors. 

By observing your leading indicators or early benchmarks and looking for significant changes in data points, you can gauge when your own predictions are likely to break down. 

2 – Choose the right KPIs

There are several options to choose from, each with a set of trade-offs in viability, accuracy, and speed to produce recommendations. Go ahead and test different KPIs (e.g. more or fewer days of ROAS / LTV). You might be surprised at how poorly correlated the standard measures prove to be.

3 – It’s all about segmentation

Segmenting users into groups is a proven method to reduce noise and improve the predictive power of your pLTV model. 

Additionally, by creating different behavioral characteristic clusters, your audience can then be categorized not by their actual identity, but by their interaction with your campaign in its earliest stages. This interaction can indicate their future potential with your app.

For example, a gaming app developer can predict their 30-day LTV based on a tutorial completion (engagement), number of returns to the app (retention), or the level of exposure to ads across each session (monetization). 

4 – Timing is everything

Acquisition cost trends during the first week after a new app launch will be very different during the fifth month, or the second year for that matter.

So, although the influence of seasonality on breaking down predictions is a given, the lifecycle of your app / campaign / audience / creative could also influence the ability of your model to make accurate predictions. 

5 – Define team responsibilities

Whether opting for an in-house, strategic-minded analyst that can lead decisions around which model should be used and how, or a more junior analyst that owns day-to-day pLTV calculations, or outsourcing to a 3rd party — allocating pLTV ownership is completely up to your business needs and budgetary constraints. 

If you’re looking to hit the ground running and don’t sport tons of subject matter expertise, outsourcing pLTV analysis could very much jumpstart the process. In the long run, however, after viability and ROI have already been established, moving this in-house could enable more in-depth analysis and scale.

Key takeaways

• Leveraging data science horsepower, pLTV allows you to predict how much money your customers will spend in your app over a predefined window of time based on their past behavior. 

• Harnessing pLTV you’ll be able to segment users by their acquisition source and forecast projections accordingly, making it an ideal tool for determining which of your marketing channels will produce your highest spending users now — and in the future. 

• To ensure accuracy, be sure to continuously feed your pLTV prediction model to keep it trained on the most relevant data, and always check whether your model’s predictions come to fruition based on new or near-new observations.

• Also, be sure to choose the right KPIs, segment your audience to reduce noise and improve predictive power, factor in timing and seasonality, and lastly – predefine team responsibility based on your business needs and budgetary constraints.

Mobile marketing dictionary from A-Z

Thanks for your download!

The post Predicted lifetime value (pLTV) appeared first on AppsFlyer.

Background

Co-founded by Sai Srinivas and Shubh Malhotra in 2018, Mobile Premier League (MPL) has grown to become Asia’s largest esports and skill gaming platform, with over 75 million users in India and 4 million in Indonesia.

Having worked with a number of Indian and international game developers, MPL has on-boarded over 70 games on its app, available for Android and iOS. In less than three years, the company has grown to over 700 employees across Bengaluru, Pune, Singapore, and Jakarta, raising $95M in Series D funding at a valuation of $945M.

Challenge

MPL’s hyper-growth and ambitious targets brought about a host of challenges such as launching and managing complex marketing campaigns at scale. To appropriately optimize campaigns, the marketing team needed to keep track of data from a variety of sources, including app stores, platforms, and channels, thereby requiring one single platform to capture and analyze the data in one place.

As a Gaming app, customer engagement and retention were of primary importance. The team wanted to continually touch base with the app’s users on their key milestone achievements, encouraging them to play more in order to upskill these players. Since upskilling users represented the best pathway to retention, MPL would actively engage its players to get better at their game of choice. Cross-selling was also a priority, and the app also encouraged players to look at games within similar genres.

MPL needed an integrated engagement platform that could help them improve their onboarding process, get players hooked early on, encourage repeat usage, and build positive user habits. To achieve this, it was important for MPL to dive deep into analyzing user behavior patterns and creating real-time segments.

MPL also needed to more effectively attribute their web campaigns, as well as implement a better user onboarding process for users in order to more seamlessly stitch together the user journey from the various pre-install touchpoints.

Solution

MPL chose AppsFlyer and CleverTap, an AppsFlyer integrated partner, to achieve its multiple objectives, which included implementing a one-stop attribution platform; creating a solution to reach out to players at the right time to sustain engagement or get started; develop more personalized targeting of users, and kick off a user referrals program.

Accurate attribution and user referrals

To enhance and streamline the user onboarding process, MPL rolled out AppsFlyer’s deep linking and user invite referral program. The team set up App Store attribution to get installs attributed from the various app stores, media sources, and channels. OneLink Smart Script was put in place to secure accurate attribution from the mobile landing page as well as to measure all important UTM parameters.

The team was also able to unearth deeper insights into their customer base through raw data. With AppsFlyer’s Raw Data reports, MPL was able to measure attribution and in-app engagement data and thereby measure the impact of its various marketing and customer-focused strategies.

With raw data, the team could go beyond an understanding of user engagement on Day 1 and plot an overall lifetime value (LTV) model that could determine a customer’s overall value to the app.

Renewed user journeys

The growth marketing team knows that successful engagement begins with a robust onboarding process. Using an onboarding journey, they get users to log in, play, and make a successful deposit. An important KPI is converting newly activated users into depositors. Following onboarding, a Day-7 conversion journey encourages users to start accessing paid games. There are various milestones before a user starts depositing. The ultimate goal of these journeys was to compel users to log in, play, and ultimately to make a successful deposit.

For the team, converting new users into depositors represented a key KPI, with various preparatory steps designed to increase the chances of a deposit. MPL used CleverTap’s Journeys to nudge the user to achieve certain milestones along their path to a deposit. Journeys also allowed users to be targeted differently depending on their previous engagement actions.

In addition, CleverTap allowed MPL to run multiple campaigns targeted at either active or lapsed us and referrals. Campaigns help inform users about upcoming matches, encourage them to form teams, promote their referral program, increase their user base, encourage users to upload stories, provide timely transactional updates, and win back inactive users.

Enhanced segmentation

MPL primarily uses an RFM model to target users based on the recency of their gameplay and the amount generated. Players are further segmented based on the type of games played. They also use custom lists/CSV uploads to target specific user cohorts that have performed key actions during important sporting events like IPL. This user segment receives personalized messages.

Further, live segments are created based on user actions and inactions. Users would also be reminded through a triggered campaign when they’ve dropped off midway while creating a Fantasy team.

Results

Through the use of AppsFlyer’s user referrals feature, MPL dramatically increased the efficiency with which it onboarded its users.

In the 30 days after these referrals, the uninstall rate was a low 58.65% (compared with an industry average of 62%) – lowest amongst all non-organic media sources – due to the intelligent and automated deep links. In addition, these referrals yielded the highest average revenue per user, as well as the highest loyal user/install ratio, at 4.73% (not to mention the zero cost per install).

The time savings and overall success of the CleverTap and AppsFlyer integration also allowed MPL’s marketing team to focus on new growth initiatives through the AppsFlyer platform. This included event attribution of specific cohorts to maximize user engagement, build lifetime value models through Raw Data reports, and reduce user churn rate by processing App Store data to create cohorts and perform RFM analytics and predictive modeling.

“Being one of the largest esports and online gaming companies, we needed advanced platforms that could do justice to our scale of marketing and user engagement campaigns. We partnered with AppsFlyer & CleverTap, which are the best in business, and they helped us to improve our growth metrics across businesses and geographies.” – Arpit Awasthi, VP Growth Marketing

And via the OneLink Smart Script, MPL could more effectively convert and attribute website visitors (from any source) into mobile app users. This was achieved by correlating their app traffic with the web to achieve a better understanding of how the web factored into their portfolio mix. MPL was able to attribute their Google web campaigns with key parameters such as source, campaign and other sub-parameters, then send it back to Google for further optimization.

MPL also saved money. The team trimmed user acquisition budgets through AppsFlyer’s platform by attributing data from multiple sources, platforms, app stores, and channels, and then reducing any overlap.

The post Scoring record high ARPU while improving LTV with AppsFlyer and CleverTap appeared first on AppsFlyer.

Lifetime value, or LTV, is an estimate of the average revenue a customer will generate over the time that they use a given product or service.

What is LTV?

 

 

 

 

 

Click for sound

2:23

Lifetime value, or LTV for short, is a core metric in mobile business growth, often used to determine how valuable a user is over the span of time that they’re using an app. 

LTV helps marketers properly optimize revenue streams like in-app purchases, in-app advertising, and subscriptions, by pinpointing exactly how much money they can afford to spend on user acquisition while remaining profitable.

LTV vs CLV

You may also have heard the term CLV, or customer lifetime value, and be wondering how it differs from LTV. In truth, the two metrics are very similar, and in some companies the terms are used interchangeably. 

If you want to get technical about it, though, it’s generally agreed that CLV measures the value an individual customer will bring over the time they spend with you, whereas LTV looks at the average value across your entire customer base. 

What about ARPU?

ARPU — average revenue per user — is another metric you’ll hear mentioned a lot in the context of customer value. 

Where it differs from LTV is in the timeline. Whereas LTV looks at the value a customer brings over their entire interaction with your brand (whether that’s days or years), ARPU focuses on a defined time period. You might want to measure revenue over a particular month, or examine how customers behave 30 days after install, for example. 

Here’s how to work it out:

ARPU = total business revenue over a set period / average number of users in that period

How to calculate LTV

There are lots of formulas around for calculating LTV. Here are a few simple steps you can follow to quickly pinpoint your number:

1. Determine your average purchase value. If you haven’t been measuring your average purchase data for a while, consider looking at a one- or three-month period as a proxy for the full year.
2. Calculate the average number of purchases during a defined period. Frequency of app usage is a major driver of LTV.
3. Measure your user retention. Figure out how long the average user sticks with your brand. Some brands manage to inspire lifelong loyalty, but most struggle to keep users hooked because of a poor user experience (UX) or a brutally competitive landscape.
4. Calculate away! Now that you have the inputs, it’s time to multiply the three numbers together and put all that goodness into a simple formula:

Or try this one instead:

Why is LTV important?

Combined with the average revenue per user, LTV is a golden metric to determine the total prospective revenue or value of your users. In the free-to-install app economy, it’s an essential way to measure business health.

Here are some more reasons why it’s so important to measure LTV:

1. You can’t improve what you don’t measure. Once you begin measuring LTV and breaking down the various components, you’ll be able to employ more targeted strategies around pricing, advertising and user retention. This will help you achieve your goals of continuously improving your UX and increasing profit.
2. Make better user acquisition decisions. When you know what to expect in terms of average earnings per user, you can increase or decrease spending to ensure you maximize profitability and continue to attract the right audience.
3. Improve forecasting. LTV predictions can help you make forward-looking decisions around ad spend. LTV forecasting minimizes the risk of underspending and missing out on potential business, or overspending and wasting your money in the wrong places.
4. Boost customer loyalty and retention. When a company consistently provides value — in the form of a great, intuitive app, outstanding customer support, or an excellent loyalty program — customer loyalty and retention tend to soar. Focusing your efforts on users with higher LTV will enable you to drive retention. More loyal users means lower churn rate, as well as more referrals and positive reviews.
5. Drive recurring purchases. LTV allows you to measure web visits or app usage per year or over your users’ lifetime. You can then use that data to implement strategies that increase repeat business.
6. Charge up profitability. Overall, higher LTV leads to bigger profits. By keeping users for longer stretches of time and building a model that encourages them to spend more, you should see the benefit show up on your bottom line.

LTV for SaaS businesses

LTV is particularly relevant for SaaS (software as a service) businesses, where success depends on building lasting customer relationships. 

In a competitive marketplace, you’ll need to spend some money convincing customers to choose your product. For your business to be profitable, you need those customers to stay with you and increase their spend, for example by upgrading to premium features. 

It’s vital to understand not just how much value customers bring you over time, but how much you spent to acquire them. That way, you can identify the most effective acquisition strategies and the most lucrative relationships to focus on. Below, we help you figure out the perfect balance.

LTV and CAC: the magic number

Your LTV:CAC ratio allows you to compare the lifetime value of your customers with your customer acquisition cost (CAC).  

To work out your CAC, divide your total marketing expenses by the number of customers you’ve acquired. 

Your LTV:CAC ratio is calculated as LTV / CAC. This can be expressed as a ratio, for example 4:1. 

If your result is below one, you have a problem: your customers are effectively losing you money. Generally speaking, a good LTV:CAC ratio is at least 3:1 (in other words, your LTV is at least 3x your CAC). Too low, and you could struggle to recoup your acquisition costs over the customer lifetime. A very high number, on the other hand, suggests you’re great at retaining customers — but could be missing opportunities to attract more of the right ones. 

LTV reports vs. activity reports

Data reports sit at the heart of the mobile marketing operation. Without them, it’s impossible to make data-informed decisions that are vital to the success of your business. 

That said, and much like other elements of the mobile landscape, data is multi-faceted. When it comes to LTV, data reports come in two main forms.

Lifetime vs. activity data

Let’s explore the two main methods for analyzing your users’ events data. By this, we mean any actions performed by users post-installation, such as in-app purchases, registrations, or level completions etc. Install data, on the other hand, is considered to be neither activity nor LTV data.

• Lifetime data includes all events performed throughout the lifetime of users who installed during a specific date range. Good campaign optimization depends on LTV data, as it allows you to keep track of the quality of users coming from different media sources. 
• Activity data includes all events performed by all active app users during a specific date range, and shows an accurate breakdown of chronological events. 

It’s a solid business metric that allows you to keep a finger on the pulse of trends — for example, how many app sales took place on Black Friday 2022 vs. Black Friday 2021. But marketers usually look at LTV data because they can isolate specific cohorts. 

The train analogy can help explain the difference between the two:

Sam is standing next to a railway watching a train go by. During a single moment in time,Sam sees only the current actions performed by ALL of the passengers. This is activity data.

Now take David, who’s standing inside one of the railroad cars. David sees ALL the actions performed only by the passengers who boarded the train with him, from their arrival (i.e. install) until departure (i.e. uninstalling). He can’t see any action performed by passengers on other railroad cars, since they boarded either before or after him. This is LTV data.

LTV in the age of consumer privacy

While we can all agree that privacy is a blessing for both consumers and the mobile ecosystem, the changes that came in with iOS14 do bring their unique set of challenges. 

Fortunately, SKAdNetwork 4.0 (SKAN 4.0), released in October 2022, offers marketers some improvements on the previous version. Before, you could only receive one postback, which was based on data signals from early on in the funnel like installs. There was little or no postback from in-app events, which are useful indicators in measuring LTV. This lack of data proved a real challenge for campaign measurement. 

Now, SKAN 4.0 gives advertisers up to three postbacks, each based on a specific data window. There’s also a feature called LockWindow, which enables you to get postbacks sooner.  

Apple’s crowd anonymity technology means the data you get from postbacks is limited: it shows the conversion value of the campaign, and can’t be linked to specific users. But you can overcome the data limitations by using predictive modeling tools. Below, we look at how these can help you optimize your campaigns effectively and safely. 

Predicting the future with pLTV

It’s useful to see how much value your customers have brought in the past — but wouldn’t it be more powerful to know how they’ll behave in the future? With predicted lifetime value (pLTV), you can do just that, without the need for a crystal ball. 

pLTV is a form of modeling that uses machine learning and artificial intelligence (AI) to predict future actions based on past data. It enables you to segment your customers based on how they’ve interacted with your business — so you can see early on which campaigns, channels and strategies are the most profitable, and where to focus your efforts going forward. 

A big benefit of this approach is that it keeps you on the right side of the heightened privacy guidelines: you only need to know what customers do, not who they are.

How can you improve LTV?

There are loads of different strategies that can help you boost your LTV. We’ve gathered a lucky 13 for you to consider:

1. CX – customer experience is everything

Your app, website, customer care and other touchpoints are all part of your unique CX. If your customers enjoy a smooth, low-stress digital experience every time, they’re much more likely to come back again, spread the good word about your brand, ramp up your app store ranking, and boost your organic growth.

2. Improve your onboarding

Some consumers buy a product or service but then have no idea what to do next. Successful brands chart a path for their customers’ journey, which starts by investing in an intuitive, smooth, and interactive onboarding experience. 

This helps to ensure users come back frequently and use your app effectively, improving the likelihood of future upgrades or higher in-app spend.

3. Connect LTV to your attribution data 

This joined-up approach will allow you to pinpoint the best channels, media sources, campaigns, and creatives to focus on.

4. Offer better value

By focusing on value and giving customers something they can’t get elsewhere, you’re much more likely to increase your LTV — and even pricing — while minimizing churn. Just be sure to consider your competitors’ pricing before determining your own. 

5. Invest in customer loyalty or rewards programs

Rewarding your customers’ frequent purchases or long-lasting subscription will keep them feeling valued and engaged. Look for creative ways to incentivize your customers to return, increase their purchase frequency, and share the love with their friends.

6. Offer outstanding customer care

Poor customer service is a quick way to see your LTV drop and churn rates soar. Focusing on making every customer care interaction a delightful one will further enhance your customer loyalty and help you ramp up your LTV. 

Also, don’t forget to loop in your customer feedback. In addition to relying on customer care to fix the problem, brands need to continuously gather feedback to be able to link it into their regular product or service iterations, and enhance their CX. 

7. Design a better experience

Is there anything more frustrating than a website or app where you can’t immediately find the button you need?  

If your website or app is cluttered, hard to navigate, or slow to load, users are unlikely to stick around. Keep your design clean and on-brand for a consistent user experience. Test it on different devices, and follow best practices for accessibility — for example, avoid lengthy scrolling and ensure links and calls to action stand out clearly. 

8. Ensure your purchasing experience is smooth

Cart or checkout abandonment is a real and painful problem for most businesses. Building a short and simple purchase experience will help you capture every possible sale. Where purchases are abandoned, consider a follow-up email to nudge your potential customer to the checkout. 

9. Encourage upsells and cross-sells

It’s often easier (and cheaper) to re-engage or upsell an existing customer than bring in a new one. Find creative ways to highlight the added value of higher-end or additional products, encouraging your customers to spend more.

10. Be present on social media

One of the best places to get your customers’ attention is to reach them in places where they’re already spending most of their time. Whether it’s TikTok, Instagram, LinkedIn, Snapchat, Twitter or Facebook, social media channels are impactful for both advertising and interacting with your customers.

11. Offer relevant and engaging content on your owned media channels

Your owned media — which includes your website, blog posts, e-books, videos, podcasts and social media content — can help you connect with particular segments of your audience, introduce them to new products or encourage optimized usage.

12. Boost retention with push notifications

Used properly, push notifications can be a great way of reminding users of the benefits of your app and encouraging them to access it more often. These short, clickable messages are sent by the app directly to the user’s device.

But don’t become a pest, or your retention efforts will drive users away. Push notifications will only work if they’re relevant, useful and sent at the right time: for example, alerting users to a limited offer or reminding them to do a daily activity. Crucially, users also need to have opted in to receive them. 

13. Test, test, test

Improving your LTV is all about understanding what your customers want. You can use A/B testing at every stage of your user journey to find out what drives the most conversions. By comparing how users respond to different headlines, calls to action, layouts, images, and more, you’ll be able to optimize future campaigns for maximum revenue.

Key takeaways

• LTV is a critical metric in growing your mobile app business. It helps you properly optimize campaigns by pinpointing exactly how much money you can afford to spend on user acquisition while remaining profitable.
• Related metrics include CLV (customer lifetime value), pLTV (predicted lifetime value), and ARPU (average revenue per user).
• Measuring LTV can help you be more strategic around pricing, advertising, and user retention. It enables you to make better user acquisition decisions, improve forecasting, boost customer loyalty, drive recurring purchases, and charge up profitability.
• To ensure a profitable balance between acquisition costs and lifetime value, aim for an LTV:CAC ratio of at least 3:1. 
• By harnessing machine learning algorithms, digital marketers are able to overcome some of the privacy limitations posed by Apple’s SKAdNetwork, make more sense of user behavior trends, and use these to predict user value over time. 
• Ways to improve LTV include offering more value, acting on customer feedback, and providing outstanding customer care. You should also share relevant content through your owned media channels, use push notifications, and keep testing to optimize your design. 

Mobile marketing dictionary from A-Z

Thanks for your download!

The post Lifetime value (LTV) appeared first on AppsFlyer.

Lifetime value (LTV) forecasting is essential for mobile app developers trying to understand the total value of their user base in quantitative terms. 

Once calculated, predictive LTV has a plethora of use cases, including ROI-driven marketing automation, long term accounting forecasts, and user re-engagement (CRM).

This article discusses why predicting LTV can be challenging and provides some insight on how it can be done effectively.

Challenge 1: Standard machine learning doesn’t fully solve the problem

Popular machine learning (ML) algorithms such as random forests, gradient boosted trees, and neural networks are tried and true approaches for finding complex patterns in data. 

Practical LTV forecasting pushes the limitations of these approaches, which require extensive training, large test sets, and long historical records. To outright predict day 365 LTV, a model would require access to a large number of users with known (actual) day 365 LTVs available, i.e., users who began using a product at least a year ago. 

Due to market changes, app updates, and changing UA strategies, the fundamental behavior of these older users can be different from new ones. Thus, a “pure” machine learning model by definition is always training on stale (years old) data. 

This may work for very stable products. However, most modern products experience frequent app updates, pricing changes, and market fluctuations. Thus, out-of-the-box machine learning alone doesn’t solve the problem. 

Additionally, many products yield significant revenue from users who convert days or months after install (i.e. mobile games), or are “whale” driven with a small percentage of rare users driving the bulk of the cohort’s revenue.  

ML models, in the context of LTV, also require backtesting to validate (i.e. training a model on a historical cohort, using it to predict a future cohort, and then checking against that cohort’s actuals). This procedure can be a lot more time consuming than typical cross validation within a single training set, and also makes the assumption that past performance is indicative of current performance. 

The graphic below describes the multiple submodels of our in-app purchases (IAP) forecasting system, which predict LTV for paying vs. non-paying users, and for the short term vs. long term. The individual outputs are then combined to produce a single LTV number for a cohort at a given horizon (number of days since that cohort began using the app).

Each submodel can have multiple theoretical foundations based on their applications, accuracy requirements, and scope.

We’ve achieved success in LTV modeling with the following principles:

• ML: We use ML models when one can access a large quantity of relatively recent training data; for example, predicting subscription to a product at the end of a free trial. Recency is key because products undergo frequent updates and changes to monetization. Any model that relied on year old or older data to train would not be robust to this.

• In-app events: Measuring in-app engagements can help predict LTV, especially for non-paying users and young cohorts. Knowing how often someone is engaging with an app, or whether they completed a certain series of events, gives models otherwise unknown insight into their purchase conversion probability.

• Model training data: Larger data sets are usually good for ML. However, we also dynamically and automatically choose what data to ignore. For example, holidays and promotions are automatically removed from training sets if those cohorts are significantly different.

• Bayesian methods: Parametric models may not be as sophisticated as their ML counterparts, but provide more extrapolation power. Bayesian approaches explicitly model heterogeneous or evolving user behavior and better quantify uncertainty. Additionally, they allow the flexibility to supplement predictions for newer apps with “priors” from comparable products.

• Secondary models: We use cohort level LTV models that have a different set of inputs and assumptions to validate our aggregated user level projections. When both models line up, we can be more confident in the results; when they don’t, it’s indicative of possibly incorrect assumptions being made, or changes in dynamics that weren’t captured.

Under-scoping the data science (and data engineering) resources and skills sets required to build a functional LTV system seems to be one of the major pitfalls for companies trying to build this tool in-house.

If it is built, the challenge becomes validation and adoption by users, and the most common sticking point is forecast accuracy.

Challenge 2: Everyone wants 100% accuracy all the time

Accuracy is hard! It is useful to break down the problem into two parts: 

• Systematic bias: A systematic over or under prediction in a category (i.e. iOS vs Android users)
• Variance: Sample noise remedied by larger samples

It is hard to avoid variance, especially in behavioral data (i.e., whales, product updates, data issues). Bias, however, can be reduced by identifying and addressing modeling flaws that remain present over a large sample size.

To address bias, consider how your modeling system approaches: 

• Categorical differences: Overvaluing a country platform or source
• Temporal changes: Overvaluing older installs by ignoring changing monetization or conversion dynamics
• Age: Overvaluing younger users compared with older ones 

A good LTV forecasting system will have three key performance goals:

• Globally accurate: We aim for less than 10% error at day 365 when up to a month’s worth of users are aggregated to support confidence in high-level ROAS forecasts. These forecasts are used to make the most consequential business decisions (i.e. deciding next month’s marketing budget) and so require the highest level of accuracy to ensure the best decision is being made.

• Unbiased over country/platform/channel dimensions: Marketers run campaigns across these dimensions; therefore, it’s important that our models correctly capture any LTV differences between them. If not, it can lead to biased decision making, where UA spend is incorrectly distributed across platforms or countries because LTV predictions do not fully account for the heterogeneity across them.

• Directionally correct at the campaign level: We want projections at the campaign level to drive intelligent decision making in the long term.  Thus, accurate forecasts from early user behavior and low volume are necessary.

Figure 4: (Left) A histogram of LTV error for a hypothetical model with 10% global all accuracy. The orange slice represents a sub-set (for example tier 1 users only) which may have  17% average bias. (Right) Some models may predict a campaign score which increases with increasing LTV.

Posing the right question on accuracy in the right way (model bias and sample variance) is almost as hard as building a model to answer it.

When assessing accuracy, think about your goal:

• For Return on Ad Spend (ROAS) forecasting or marketing automation, low sample size will cause large margins of error for each campaign’s ROAS. Therefore, think about an aggregate portfolio of campaigns where the overall ROAS has some bias and risk. Are you making the right portfolio decision on average over your entire spend?

• CRM also leverages user level forecasts, but the absolute number is not as important as the relative rank. When working with this goal in mind, also consider relative or directional accuracy. Is the true stack rank of day 365 revenue well predicted by LTV?

• Corporate financial models consider entire geos or sources and thus have low sample variance due to high volume. Here, being unbiased toward a specific category is most important. Are you over predicting your Tier 1 users?

Knowing the internal client’s needs and risk tolerance is essential for correctly directing R&D efforts, model improvements, and validation. Poor collaboration between end-users and the data science team can leave a partially or fully developed data science product mired in “accuracy” land. The next section provides a foundation for starting the conversation.

Get the latest marketing news and expert insights delivered to your inbox

Challenge 3: Building models to satisfy many use cases

Once a user level LTV prediction is made, it can be used by a variety of corporate stakeholders. Each audience requires different levels of accuracy and granularity. It is critical to understand the final use case when scoping and designing an LTV model.

While it is hard to satisfy every business need, ensuring the system is human interpretable may help development and adoption of an LTV forecasting system:

• Stability: What is a reasonable forecast update frequency? Frequent changes may signal inaccuracy. However, ignoring real surprising behavior that strongly changes LTV is also undesirable.
• Accuracy: What is an acceptable level of variance (sample size) and bias (model sophistication)?
• Temporal granularity: How soon after install does the forecast come? Is a cohort a week or a month? Different teams care about different temporal granularity and responsiveness. 
• Cohort granularity: Does your end user care about individual users, all users, or a country? 

Table 1 illustrates some common use cases and requirements for LTV predictions as they relate to stability, accuracy, and granularity.  

Bottom line

Predicting user-level LTV is challenging and requires months, or even years, of dedicated data science and engineering efforts to set up robust, accurate production systems.

However, the business value provided by user-level LTV predictions is undeniably significant, offering solutions to a variety of use cases. Once an LTV forecasting system built upon solid methodology is put in place, it becomes a highly beneficial tool that can be used to quickly judge the success of marketing efforts, and to automate intelligent marketing decisions.

To read more about predictive modeling for apps, check out Predictive modeling for app marketers: The complete guide guide.

The post Pitfalls of modeling LTV and how to overcome them appeared first on AppsFlyer.

What is cost per sale?

Cost per sale (CPS) measures the amount spent to generate a single sale from an advertising campaign. It’s calculated by dividing the total cost of the campaign by the number of sales it produces.

This metric helps you understand the direct financial impact of your marketing campaigns in relation to revenue generation. In other words, you get a straightforward look at how efficiently your ad spend translates into revenue.

How to calculate CPS

Use this formula to calculate CPS:

Cost per sale = Total marketing and sales cost / Number of sales

“Total marketing and sales cost” covers everything you spend on those areas, including things like ad spend, salaries for your sales team, and costs for marketing materials. Meanwhile, “number of sales” is simply the total sales you make from these efforts within the same timeframe.

Suppose you run a mobile marketing campaign with the following details:

• Total marketing and sales cost: $5,000
• Number of sales: 200

Using the CPS formula:

This shows that your business spent $25 to acquire each sale. You can use this info to understand the cost-effectiveness of your campaign and set a baseline for optimizing future marketing strategies.

Advantages of using CPS as a metric

CPS is a straightforward metric that neatly connects spend with revenue. That brings a number of advantages for your business:

• Performance-based cost efficiency: Whereas other metrics like cost per impression or cost per click are useful when you’re building awareness, CPS focuses specifically on closing the deal. By tying your budget to actual sales in this way, you can not only justify ad spend to stakeholders but also take practical steps to optimize performance — by lowering your costs, increasing sales, or a combination.

• Enhanced targeting and personalization: CPS encourages marketers to get laser-focused with their advertising strategies. When you focus on specific audience segments most likely to convert, you make your campaigns more effective. Advanced data analytics and user behavior insights play a crucial role here — use them to identify the most promising customer profiles.

• Tailored customer experiences: CPS also pushes you to tailor ads to individual interests and behaviors, creating a more engaging, relevant experience that drives sales, satisfaction, and loyalty. For instance, a subscription-based app might advertise to users who have shown interest in similar services, highlighting features that these users value most.

• Higher ROI: CPS inherently ties your marketing spend to sales revenue, giving a clear measure of marketing ROI. This performance-based model ensures every marketing dollar is spent efficiently, with a direct impact on revenue generation. Plus, you can easily calculate the profitability of campaigns by comparing CPS with the average revenue per sale.

• Comprehensive campaign performance insights: CPS serves as a transparent and quantifiable metric for assessing campaign performance. You can use it to pinpoint which campaigns are cost-effective and which aren’t, helping you make data-driven decisions and optimize marketing strategies.
  
  Imagine you run multiple campaigns across different channels (like social media, search engines, and in-app ads). Comparing the CPS of each campaign, you can identify which channel delivers the best results and allocate more budget accordingly.

• Iterative campaign optimization and improvement: By regularly monitoring and analyzing CPS, you can experiment with different ad creatives, targeting approaches, and messaging to find the best combinations. This iterative process helps you steadily lower CPS and improve campaign efficiency.

• Enhanced performance accountability and collaboration: With costs directly tied to sales, it’s easier to evaluate the performance of individual campaigns and initiatives. This creates a sense of accountability and builds a connection between sales and marketing teams, ensuring all app marketing efforts focus on achieving measurable sales targets.

What are the factors that can influence CPS?

CPS in mobile app marketing hinges on various factors that shape the efficiency and success of your campaigns. Knowing these factors helps you fine-tune your strategy for best results:

1 — Target audience

Identifying and reaching the right audience is crucial for lowering CPS, because it boosts conversion rates. For mobile app marketers, this means zeroing in on users most likely to buy in-app items or subscribe to premium services. For instance, you can target users based on their app usage patterns or past purchasing behavior to execute more effective campaigns.

2 — Marketing channels

Channels like social media ads, in-app ads, and search ads — each with unique advantages and cost structures — yield different results. For mobile apps, user acquisition through social media platforms like Facebook or Instagram might be more cost-effective than other channels. Picking the most efficient channels for your audience can lower CPS.

3 — Lead conversion time and funnel efficiency

The efficiency of your conversion funnel is crucial. You need to ensure the journey from app discovery to purchase is seamless. Reducing friction points, such as complex registration processes or slow load times, can speed up conversions and lower CPS. Optimizing your app UX and streamlining the purchasing process can also make a positive difference.

4 — Implementing conversion rate optimization (CRO)

CRO involves tweaking various elements of your app to boost the percentage of users who make a purchase. This can include A/B testing different versions of app screens, optimizing onboarding flows, and enhancing call-to-action buttons. Effective CRO leads to higher conversion rates, meaning more sales from the same ad spend, which helps trim down your CPS.

5 — Customer service and support costs

While not always a direct factor in mobile app marketing, customer service quality can still impact CPS. Efficient customer service that resolves issues quickly and enhances user satisfaction can reduce churn rates and improve overall user retention. And it typically costs less to sell to existing customers than to acquire new ones. 

6 — Dissatisfied customers (returns and refunds)

High rates of refunds and returns can negatively impact CPS. For mobile apps, this might mean users uninstalling the app or canceling subscriptions shortly after purchase. Ensuring high app quality, delivering on promises made in marketing materials, and providing excellent customer support can minimize dissatisfaction and reduce the number of refunds, thus lowering CPS.

CPS vs other metrics

CPS is great for judging how well your marketing campaigns are doing, especially in mobile app marketing. However, it’s best not to rely on it alone.

Comparing CPS with other key metrics helps you get a fuller, more accurate picture of your campaign’s performance and overall business health. Let’s understand how CPS stacks up against other important app metrics:

Cost per click (CPC)

Definition: CPC measures the cost incurred for each click on your ad.

CPS focuses on the cost of an actual sale, while CPC only tracks the cost of attracting a click, regardless of whether that click leads to a sale. Additionally, CPS gives you a direct link between ad spend and revenue, whereas CPC is more about driving traffic to your app or site.

Cost per mille (CPM)

Definition: CPM measures the cost per thousand impressions of your ad.

CPM focuses on the exposure of an ad, regardless of clicks or sales, which makes it useful for brand awareness campaigns. On the other hand, CPS is concerned with the actual sales generated from the ad impressions, making it more suitable for performance-driven campaigns.

Cost per lead (CPL)

Definition: CPL measures the cost incurred to acquire a lead.

CPL is about the cost of acquiring potential customers (leads), while CPS measures the cost to acquire paying customers. Consequently, CPL comes in handy for campaigns aimed at generating interest or capturing contact info, and CPS serves as the final step in the conversion funnel.

Lifetime value (LTV)

Definition: LTV estimates the total revenue a customer generates over their relationship with your business.

CPS measures the cost of acquiring a sale, but LTV provides insight into the long-term value of that customer. A low CPS is great, but it’s more meaningful when paired with a high LTV, indicating that those customers will bring substantial long-term value.

Customer acquisition cost (CAC)

Definition: CAC measures the total cost of acquiring a new customer, including marketing and sales expenses.

CAC is broader than CPS, covering all costs associated with customer acquisition — not just marketing expenses. CPS is part of CAC, focusing specifically on the direct cost of generating sales through advertising.

Top Tip: Use our metrics comparison tool to compare app marketing metrics and take informed marketing decisions.

How to reduce your CPS

Lowering your CPS is all about using your budget as efficiently as possible —  ensuring all your marketing efforts are geared towards maximizing conversions (sales), and nothing is wasted. Follow these best practices to keep your campaigns on track:

1 — Refine target audience

Zero in on your target audience to make sure your ads reach those most likely to convert. Use demographic data, user behavior, and psychographic profiles to tailor your ads to specific segments.

For instance, a fitness app could focus on users who’ve downloaded health-related apps or shown interest in fitness content on social media. By not not wasting ad spend on uninterested users, you’ll lower your CPS.

2 — Improve the performance of ads

Create compelling ad creatives and copy that resonate with your audience. Think: high-quality visuals, clear messaging, and strong CTAs. These high-impact elements can significantly boost engagement and conversions. A tool like AppsFlyer’s AI-powered Creative Optimization can help you find the winning formula.

3 — Focus on lead generation

Lead generation builds a pipeline of potential customers you can nurture over time. Capture leads through free trials, email sign-ups, or pre-registration to engage with users and convert them into paying customers eventually.

4 — Leverage search traffic

Search traffic is a goldmine because users actively looking for solutions are more likely to convert. With search engine advertising (like Google Ads), you can capture this intent-driven audience, leading to higher conversion rates and lower CPS.

For example, if you have a language-learning app, target keywords like “best app to learn Spanish.” Your ads will be shown to users already interested in finding a language-learning solution, boosting conversions.

5 — Use negative keywords

Negative keywords keep your ads from appearing in irrelevant searches, saving you money. By excluding terms that don’t align with your app, your budget only goes toward high-intent searches.

For example, using negative keywords like “free” or “download” helps avoid showing ads to users looking for free options, focusing instead on those ready to purchase.

6 — Use data analytics to test and optimize ads

Use data analytics to pinpoint which aspects of your campaigns are working and which need improvement. Creative testing and optimizing ad elements can further reduce CPS by enhancing ad effectiveness. Test various ad headlines and images to find the highest conversion rate, using tools like Google Analytics and Facebook Ads Manager to track performance.

7 — Optimize landing pages

A seamless user experience on your landing page can increase conversion rates and reduce CPS.

Make sure your mobile app landing pages are optimized for conversions, with fast load times, clear and compelling content, easy navigation, and a straightforward call to action. For example, a finance app should highlight key benefits, include user testimonials, and have a prominent download button to encourage immediate action.

8 — Use email marketing to nurture leads

Use email marketing to keep potential customers engaged, by sending personalized email campaigns that guide leads down the sales funnel.

For instance, if you have a music streaming app, target users who signed up for a free trial but haven’t yet subscribed with updates on new features, exclusive content, or limited-time offers to boost conversion rates.

Key takeaways

• Cost per sale (CPS) shows how much you spend to make a single sale from an ad campaign. You find it by dividing total marketing and sales costs by the number of sales.
• CPS helps you understand how effectively your ad spend translates into sales, which is key for measuring the profitability and success of your campaigns.
• In mobile app marketing, several factors can affect your CPS. These include honing your target audience, picking the right marketing channels, optimizing your conversion funnel, using conversion rate optimization (CRO), and cutting down on customer service costs and refunds. Getting these elements right will improve conversion rates and reduce CPS.
• While CPS is important, it works best when used with other metrics like CPC, CPM, CPL, LTV, and Customer Acquisition Cost (CAC). Together, they give a full picture of your campaign’s performance.
• Lowering your CPS is about using your budget efficiently to maximize conversions. Strategies include focusing on users most likely to convert, using keywords effectively, optimizing landing pages, and continually testing and optimizing your ads.

The post Cost per sale (CPS) appeared first on AppsFlyer.

• What are data clean rooms?
• A comparative overview of a new(ish) market
• The practicality of Data Clean Rooms – Harnessing everyday use cases to fire up campaign measurement

Introduction

If you’re a marketer, it’s unlikely you’ve managed to avoid a conversation in the past few months where “Data Clean Room“ was not brought up at least once, and usually in an excited yet slightly confused tone.

What is this strange, hygienic chamber of data everyone’s talking about? 

Some refer to data clean rooms as “the Switzerland of data”, and rightfully so, because it offers a neutral, safe space for 1st-party user data to be leveraged collaboratively. In a data clean room environment, two parties can securely share and analyze data with full control of how, where, and when that data can be used. 

In this way, brands are given access to much-needed data, but in a regulatory compliant space that doesn’t violate consumers’ privacy. While user level data goes into the data clean room, aggregated insights come out in a co-mingled audience group called a cohort. 

So, to get you well equipped for 2022, we’re going to take you on a journey through thick forests of unknowns and deep lakes of 1st-party data, in a guide entirely dedicated to the topic of data clean rooms.  

By the end of which you’re going to know all about what they are, how they work, why marketers need them, and how they’re going to dramatically affect our ability to measure campaigns in the years to come.

But before we do, let’s begin with the story that actually led us all to this point.

Chapter 1

What are data clean rooms?

It’s evolution, baby

Despite its resurgence in the past year, data clean rooms as an infrastructural concept have actually been around for a few years now. 

Google was not the first to coin the term, but it was the first company to commercialize a data clean room solution, launching its Ads Data Hub in 2017. The goal was to create a secure and private environment for enriching their 1st-party data (from CRMs, CDPs, event logs, etc.) with user level data contained within Google’s ecosystem, after which it could be leveraged for Google campaigns.

A mere month later, Facebook announced its own data clean room offering for the purpose of sharing data with its customers. A coincidence? Probably not. 

But it was 2018 that truly set off the starter pistol of the user privacy era, with legislation such as the GDPR and Apple’s Intelligent Tracking Prevention 2.0 becoming the new privacy sheriffs in town.

Following suit in 2019, Amazon launched a data clean room platform titled Amazon Marketing Cloud, the CCPA was brought into effect in early 2020, and in April 2020 – the entire mobile app ecosystem gasped as Apple dropped its opt-in mechanism bomb in iOS 14 – aka the ATT.

Amounting user privacy laws and stricter data privacy standards have transformed the way advertisers and brands can collect and share consumer data.

Facebook announced in October of 2021 that it will no longer send user level campaign data to advertisers, but to Mobile Measurement Partners (MMPs) only, with other networks expected to join the party soon.

Between Apple’s game-changing ATT framework, Facebook’s user level data decision, and the upcoming demise of Google’s 3rd-party cookies in 2023, the scale and breadth of data sharing is becoming increasingly limited, making campaign measurement and optimization more challenging than ever before.

So, brands are now scrambling to find new ways to gain meaningful marketing insights in a privacy-compliant way. 

Kicking off the data exchange alliance trend in 2019, Disney began collaborating with Target, Unilever joined forces with Facebook, Google and Twitter to create a cross-channel measurement mode, ITV entered a partnership with Infosum in 2020, and in 2021, TransUnion launched its data collaboration with BlockGraph.  

The binding element that enabled all these bountiful data collaborations that are only expected to increase? Why, Data Clean Rooms, of course.

What is a data clean room anyway?

Data clean rooms allow marketers to harness the power of the combined data set while adhering to privacy regulations. Personally identifying information (PII) or attribution restricted data of individual users is not exposed to any of the involved contributors, which makes it impossible for them to single out users with unique identifiers.

PII and user level data are processed so that it can be made available for a variety of measurement purposes, producing anonymized data that can then be cross-referenced and combined with data from different sources. 

In most cases, the only outputs from the data clean room are aggregate level insights, e.g. users (plural!) who have performed action X should be offered Y. That being said, user level output can take place given the full consent of all involved parties.

The key ingredient that makes data clean rooms a highly credible platform is the fact that access, availability, and usage of data are agreed upon by all data clean room parties, while data governance is enforced by the trusted data clean room provider. 

This framework ensures that one party can’t access the other’s data, which upholds the ground rule stating that individual or user level data can’t be shared between different companies without consent.

Let’s say a brand wants to share insights with Target. To facilitate that, each party needs to place its user level data into a data clean room – to see what the other already knows about audiences they have in common, e.g. reach and frequency, audience overlap, cross platform planning and distribution, purchasing behavior, and demographics.

Data clean rooms can also be used as an intermediary tool for measuring campaign performance. Instead of guesstimating audience insights, brands can actually look under Amazon or Google’s 1st-party data hood, all while being completely privacy-abiding.

In return, advertisers can get an aggregated output without individual identifiers, including segmentation and look-alike audiences, which can then be shared with a publisher, a DSP, or an ad network to inform a campaign. Alternatively, if you’re a retailer with an ad network, for example, you will be able to leverage this output when buying ads.

Making sense of it all – How does a Data Clean Room work?

A data clean room operation involves four parts: 

1 – Data ingestion

In the very beginning, 1st-party data (from CRMs, site/app, attribution, etc.) or 2nd-party data from collaborating parties (i.e. brands, partners, ad networks, publishers) is funneled into the data clean room. 

2 – Connection and enrichment

Data sets are then matched at the user level, and are made to complement one another using tools such as 3rd-party data enrichment.

3 – Analytics

At this stage, the data is analyzed for: 

• Intersections or overlaps
• Measurement and attribution
• Propensity scoring

4 – Marketing applications

At the very end of the data clean room journey, aggregated data outputs enable marketers to: 

• Build more relevant audiences
• Optimize their customer experience and A/B testing
• Execute cross platform planning and attribution
• Perform reach and frequency measurement
• Run deeper campaign analysis

Now that we’ve covered the how, what about how the data is actually matched? 

When working with a data clean room, identifiers such as email, address, name, or mobile ID are similar on both the advertiser and publisher side, which enables successful matching of both data sources.

If such identifiers do not exist, advanced tools such as machine learning and probabilistic modeling could be applied to enhance matching capabilities.

Why do marketers need a data clean room?

First and foremost – rising scrutiny around data privacy. 

Driven by privacy regulations and walled garden privacy initiatives (more on that in a bit), it’s becoming increasingly complex for advertisers and publishers to collect, store, analyze, and share data.

Second reason would be lack of commercial trust between parties. As we all know well, handing over valuable 1st-party data outside of a data clean room is risky from both a legal and commercial perspective. 

Lastly, inefficient data synthesis processes, where data correlation across separate data sets requires heavy lifting by data scientists, which is a costly and time-consuming endeavor. 

Data clean rooms to the rescue!

When it comes to data privacy, all parties within a data clean room maintain full control over their data, which is usually fully encrypted throughout the process. A data clean room includes strict governance and permissions, where each party defines what and how their data is accessed and put to use.  

Another important aspect that addresses the challenges mentioned above is differential privacy, which makes it impossible to tie back a specific impression, click or activity to a specific user. 

Last but certainly not least, data clean rooms offer privacy-centric computing, querying, and aggregated reporting fit for purpose integrations so data sets can be stitched together. 

Chapter 2

A comparative overview of a new(ish) market

User-level data used to be what mobile marketers relied on wholeheartedly. In recent years, however, the surge in privacy-centric regulation and the fact that this data was rendered more elusive than a snow leopard – means that advertisers are now struggling to make data-driven decisions.

And if you thought this is just a phase, well – think again. These ecosystem-sweeping changes are only predicted to accelerate, which would further restrict access to this  data, making business optimization even more challenging than it already is.

But this is not a sad story, and these changes could very much serve as a valuable opportunity for brands to cultivate their competitive edge. Forrester articulated it well when they stated that “ethical privacy practices will be the next consumer-driven, values-based source of differentiation.”

Data clean rooms stem from this very consumer privacy-centric mindset. And driven by the need for privacy compliance and cross-media measurement and optimization, data clean rooms are becoming an essential tool in marketers’ tech stacks.

According to Gartner, 80% of advertisers with substantial media budgets will utilize data clean rooms by 2023, estimating that there are currently between 250 to 500 data clean room deployments that are either active or in various development stages.

What kind of data clean room creatures are out there, you ask? Let’s break it down.

Types of data clean rooms – Introducing the cast and crew

All data clean rooms help to hide consumers in a crowd by de-identifying their user-level data and clustering them based on common attributes. But in what ways do they differ from each other?

To help you make sense of the rapidly developing data clean room landscape, let’s break down the actual breeds out there, assess the relative performance of each across the value chain, and examine their unique pros and cons:

Walled Gardens – Big Tech platforms

This group consists of closed ecosystems where the tech provider has significant control over the hardware, applications, or content.

Walled gardens were first introduced by Google, Amazon, and Meta (Facebook) to safely commercialize their 1st-party data, and also capture ad spend from rivals while they’re at it. 

Needless to say, nearly 70% of all ad media spend sits with these three giants — each of which allows advertisers to work within their walled garden data clean rooms: Google Ads Data Hub (ADH), Facebook Advanced Analytics (FAA), and Amazon Marketing Cloud (AMC). 

These security-stringent environments are where the mega SRNs make the event-level data accessible for marketers to be able to make informed campaign decisions, without jeopardizing consumer privacy or the ecosystems’ defense moats.

Pros 

• Supporting 1st-party data set enrichment with event-level data

Cons

• Offering raw materials for analysis – making this data readable to the common marketer will require a team of data scientists, analysts, and engineers 
• Rigid architecture
• Lack of cross platform ability to generate actionable data (i.e. multi-touch attribution)
• Lack of intercompany data collaboration
• Strict query functionality

Multi platform or neutral players

This type of data clean rooms consists of two sub-groups, each with their unique set of strengths and drawbacks:

Diversified

These are primarily legacy businesses operating in adjacent industries like marketing applications or cloud data storage, offering data collaboration mechanisms for gathering signals in a regulatory compliant way. This group includes providers such as Epsilon, Measured, BlueConic, and Merkle.

Pros  

• Architectural flexibility
• Bespoke governance controls over type of data and level of analysis

Cons 

• Limited access to walled garden data
• Narrow partner ecosystem 
• Limited downstream integrations 
• Leverages existing Customer Data Platform (CDP) / Complex Event Processing (CEP) functionality, which could lead to potential data issues

Pure players

These are your young, small- to mid-scale data clean room providers, among which are Habu, Harbr, InfoSum, and Decentriq, as well as more enterprise-focused tools such as SnowFlake.

Pros 

• Architectural flexibility
• Leverages existing data piping and storage infrastructure (SnowFlake)
• Access to an ecosystem of integrated partners (SnowFlake) 

Cons

• Limited 1st-party data granularity
• Often relies on 3rd-party infrastructure for data ingestion
• Narrow pool of downstream integration options

Mobile Measurement Partners (MMPs)

Ideally, an MMP is a trusted and unbiased player that enables all available user-level data to be leveraged using customers’ own business logic, and then consumed via aggregated and actionable insights.

Pros 

• Cornered resource – user-level and cross channel data granularity
• Real-time conversion data
• Comprehensive analytics built for mobile apps’ business logic
• Flexible integration options
• Top-quality aggregated reporting 

Cons

• Some limitations around data granularity and query-related actions could be imposed by SRNs
• Lack of existing CDP architecture

To assess the best data clean room provider for you, be sure to factor in your main channel (mobile, app, or web), business size, marketing needs, data structure, and internal resources.

Where is the market heading?

1st-party data collection has already become a highly strategic mission, and this trajectory will continue to pick up speed in the years to come. Driven by this trend, the growing interest in privacy-preserving data collaboration beyond walled gardens has resulted in a proliferation of neutral data clean room providers. 

In fact, Gartner predicts that 80% of marketers with media budgets in excess of $1B will adopt data clean rooms by 2023. 

This is good news for our entire data-starving ecosystem, because the more diverse the options are, the easier it would be for businesses to adopt the most suitable data clean room platform for their unique needs. 

And the more businesses collaborate over regulated intermediary data grounds such as data clean rooms, the easier it would be for marketers to measure, attribute, and optimize their campaigns.

Let us show you to your room – How to choose the right data clean room for your business?

Advertisers who spend meaningful dollars on data ecosystems – need to make a data clean room investment now. But whether you’re implementing a brand new data clean room or looking to ramp up an existing one – how do you make an informed decision on the best-fit solution for your business?

To help you decide, let’s shed more light on the competitive landscape of data clean rooms, where two main factors are considered:

• The volume and quality of the data – referred to as depth
• And the variety of received data – referred to as breadth

The walled garden group has the advantage of data depth – but lacks variety. The pure-play group usually offers the data clean room technology alone with very little data depth or breadth. And then there are your MMPs – providing both the data clean room technology, depth and breadth of data, and a variety of partner integrations.

When considering a data clean room, keep in mind there are several best practices you can follow to ensure you get the most value possible:

• First, be sure to factor in your main channel (be it mobile, app, or web), business size, marketing needs, data structure, and internal resources. 
• Then, begin designing your data clean room with your consumers in mind. Not just for the present, but for the future. The best data clean rooms are set up to anticipate shifts in consumer behavior. 
• Finally, start testing with a live audience. Analyzing consumer behavior in real time and getting actionable insights is nothing short of invaluable.

Here’s a head scratcher for you – Why haven’t data clean rooms been more widely adopted (yet)?

• Let’s get this one out of the way, folks – data clean rooms aren’t cheap. The mega-sized walled garden providers offer alternatives, but the logistical and operational hurdles of working with these platforms can put a strain on all parties. 

• The success of data clean rooms is rooted in data being shared, and not all advertisers are quick to divulge detailed transactional data, mainly due to the misconception of potential privacy risks. And when half-baked data goes in – half-baked data comes out, resulting in rough measurement at best.
• Universal standards for implementation are yet to be determined. That means that pooling data that exists in multiple formats and the prep work that goes into aggregating it – could be time intensive.
• Lastly, we need to remember that user-level data is still available in some instances (e.g. Android devices and consenting iOS users), which could alleviate at least some of the urgency to implement a data clean room solution.

Can these hurdles be overcome given the right technology partner, resources, and data preparation? Of course. But more on that in our next chapter.

Chapter 3

The practicality of Data Clean Rooms – Harnessing everyday use cases to fire up campaign measurement

By now we know that Data Clean Rooms offer advertisers and publishers secure, closed-loop measurement that is fully privacy-compliant. 

But in which instances should you put it to use? Which scenarios could benefit from analysis in a Data Clean Room environment?

Buckle up, people. Because in this section we’re going to learn how Data Clean Rooms empower marketers to: 

1. Build more relevant audiences
2. Continuously improve their customer experience
3. Fuel cross platform planning and attribution
4. Optimize reach and frequency measurement
5. Perform deeper campaign analysis

So, let’s get practical.

1 – Performance measurement

Keeping track of retention, ARPU, LTV, and ROAS are flagged as key use cases for Data Clean Rooms, and rightfully so. A Data Clean Room offers a neutral environment to analyze both the advertiser’s CRM data and the ad exposure data provided by the relevant marketing partners.

In this use case, advertisers can upload their 1st-party data into a Data Clean Room following a campaign, match up identical key identifiers, and conduct analysis across their customer data and the ad exposure data made available by the Data Clean Room provider. 

Let’s say you’d like to compare your recent purchase data against Google’s ad exposure data. Google’s walled garden data clean rooms — Ads Data Hub — will allow you to attribute the percentage of new customers to the marketing activity that took place across Google’s advertising channels.  

If you’re in eCommerce, simply feed the Data Clean Room with your CRM data, unique identifiers (emails, postal addresses, mobile IDs etc.), and purchase date. Then, each media owner will include their ad exposure data and unique identifiers used to create the campaign audience. 

At this point, you’ll be able to accurately measure the intersection between new customers and those exposed to the campaign across each media avenue, and then determine what percentage of new customers can be attributed to each channel.

2 – Building more granular audiences

After Apple dropped its ATT bomb, which dramatically hampered access to user-level data — granularity became marketers’ most saught-after holy grail over the past year.

A Data Clean Room enables granularity to a degree that up until recently was simply not possible. It collects data from authorized 3rd-party sources that are ingested and segmented into a range of behavioral, demographic, and location buckets, and then leveraged to enhance your internal database for more granular data enrichment and analysis. 

The beauty of it all — is that rather than requiring users’ personal data to be shared in order to conduct analysis, a Data Clean Room enables multiple data sources to be virtually connected through anonymized cohorts. 

This enables marketers to measure the intersection that exists between their target audience and the various media audiences. Finally, they’re able to understand the optimal route to reach their audience, plan more effective campaigns, and unlock omni-channel measurement.

How can granular audience insights supercharge your marketing efforts? Glad you asked: 

Honing audience targeting

Segmenting your audiences based on fine-tuned data such as consumer behavior and shopping habits — can have a dramatic effect on your campaign strategy. 

Let’s say your brand has recently solidified a new partnership with another brand that shares an audience overlap with yours. Using Clean Room-enabled audience insights, you can identify overlay points and shared characteristics that can then be leveraged into further strategic analysis.

Crafting tailored content and curating engagements

When you understand the interests of each market segment, you can create more relevant content, promotional recommendations, and new ad formats specifically tailored to those interests.

Refining your messaging, formats, ad types and channels to be able to address each segment individually, speak their unique language and address their specific pain points — is so much easier when utilizing a Data Clean Room environment.

Granular segmentation use case

Say you own an eCommerce brand and your 1st-party data includes customer attributes and associated product stock keeping units (SKUs). You’d like to run a campaign targeting a prospective audience that exhibits similar attributes, and then follow up with a relevant remarketing campaign based on shopping history and frequency. 

First, create your target segments. Then, upload the relevant data sets into a Data Clean Room, where your team can work with ad partners to cross analyze your 1st-party data with their 3rd-party data. This results in aggregated, actionable outputs that can help you craft targeted campaigns — without jeopardizing your users’ privacy.

3 – Optimizing reach and frequency measurement

Once you have PII-level impression data from partnered ad networks, you can understand exactly what ads are being served to which customers and how often, which — in turn — can be used to deduplicate campaign reach and frequency, minimize ad fatigue, and improve your media planning. 

Data clean rooms can also validate the assumption you’re reaching out to the right audience, which will help you tweak and hone your segmentation criteria. And, Data Clean Rooms allow you to optimize your customer journey, engaging users based on where they are in the funnel and how they interact with your ad. 

4 – Incrementality measurement

Impression data from publishers, audiences, 1st-party response and conversion data can all be tied together at the user level to help you understand the incremental impact of your marketing efforts.

Think about the ability to compare between your test and mediating groups through A/B testing, or more importantly — between your exposed and unexposed groups. Pretty powerful stuff, huh?

5 – Showcasing user quality to prospective advertisers

Publishers can inject user level data into a Clean Room’s secure environment and allow advertisers to gauge customer overlap — and even users’ quality — based on various characteristics.

On the flip side, advertisers can build an audience and then test it against publisher X to assess results. It’s an ideal sandbox for both publishers and advertisers to weigh in and demonstrate the value of their acquired users.

6 – Forging 1st-party data partnerships

On the strategic side of things, two entities can agree to join and match datasets in a safeguarded and permission-only environment, cultivating new partnerships within the media ecosystem.  

This secured cross-analysis can also help propel product development, and enable marketers to improve their strategic planning.

7 – Training, inference, and propensity scoring

Lastly, a Data Clean Room environment enables you to regain access to restricted granular user level data — required to successfully run training and inference models, and even propensity models, by which you can get an estimate of the likelihood that a customer will perform a specific action.

The post It’s time to come clean – the complete data clean rooms guide appeared first on AppsFlyer.

The privacy era has led to a loss of data signals that has significantly hampered marketers’ ability to target, measure, and optimize their campaigns. According to Mckinsey, up to $10 billion is at risk because of signal loss — in the US alone!

But thanks to innovation and adaptation, overcoming the challenge is more than possible by building a new data reality based on filling gaps in existing data, as well as creating new data signals.

In this blog, we’ll explain what led to this reality, the impact of signal loss on marketing, and how to overcome it. Let’s dive in!  

What is signal loss

The loss in data signals as the result of diminishing levels of user level data identifiers. These identifiers have been, and in some cases still are, the backbone on which performance-driven marketing measurement is based on. Signal loss is therefore a massive change and challenge in the digital marketing landscape.   

Background: What led to this reality

In recent years, there’s been increased scrutiny from legislators and consumers to protect user privacy through various regulations (GDPR, CCPA, DMA, to name a few major recent ones).

At its core, it’s about controlling how user level data is being shared across different companies and how consent to share this data is requested and presented.

In parallel (whether reactive or proactive), Google and Apple have developed mechanisms to limit/deprecate user level data via: 

• iOS – App Tracking Transparency (ATT): Led to the rate of installs with an IDFA dropping from 80% (due to Limit Ad Tracking) to only 27% (still very important for modelling).

• Android – Google Advertising ID (GAID) deprecation: It is assumed that Google will eventually fully deprecate Android’s user level identifier, but not before 2025.

• Web – 3rd party cookies: After years of planned deprecation of cookies on its Chrome browser, Google decided in July 2024 to keep them after all, and adopt what appears to be a similar approach to ATT. “Instead of deprecating third-party cookies, we would introduce a new experience in Chrome that lets people make an informed choice… We’re discussing this new path with regulators, and will engage with the industry as we roll this out,” the company said in a statement. 
  
  Although Google did not provide any details about this “new experience in Chrome that lets people make an informed choice”, it appears that signal loss will be mitigated, going from full to partial deprecation from non-consenting users only. But just how much is impossible to tell as there are still many unknowns: will Google use an opt-out or opt-in mechanism? Single or dual consent (advertiser and publisher side)? Strict/fixed or flexible use of language/UX functionality? Time will tell.

But while the loss of user-level data is currently an iOS reality because of ATT, it will soon expand to Android and later to the Web, affecting all digital marketers. And while each scenario is different, the underlying story remains the same.

The impact of signal loss on advertisers

While this ecosystem shift is a much-needed step to combat prevalent abuse in user data, it has been a painful adaptation for marketers who rely on user level data for targeting, measurement, and optimization. 

 1) Measurement

Signal loss has a significant impact on measurement, creating two main challenges for marketers:

a) Fragmented data has been a challenge for years but has increased significantly since iOS14.5 was released. To name just the important data sources:

• MMP attribution (deterministic and probabilistic) and consequent in-app data
• OS and stores (SKAN and later Sandbox) data and consequent in-app data
• SRN attribution data
• iOS consenting user data
• Apple Search Ads data
• Top down measurement data (incrementality and MMM)
• Beyond mobile data: CTV, Web, PC&C, offline

And that’s just in iOS.

How does one reconcile data from so many sources and frameworks to understand performance?

b) Limited and delayed data:

SKAN and AdAttributionKit (SKAN’s successor) offer intentionally delayed signals and only up to 64 values (and only 3 in later postbacks). And despite improvements in AdAttributionKit and Sandbox data, which is said to reach a 30-day LTV, OS data will still come in delayed and limited. As marketers rely on real time, post-install signals for optimization, the fact that OS data is delayed and limited is a significant hurdle. 

 2) Targeting

When user level data is not available:

• Advertisers cannot create remarketing segments, and as a result they cannot engage effectively with their existing engaged customers, which leads to increased cost per action
• Advertisers cannot create suppression lists so they end up targeting the same users, which leads to increased cost per new acquisition
• Ad networks’ ability to optimize delivery is significantly hurt, which leads to higher costs across the board

In short, signal loss resulting from privacy measures has compromised the completeness of data, posing a risk of misinterpretation and, consequently, misleading insights and the wrong decisions. As a result, a marketer’s job in the signal loss era is 10x harder and so is measurement!

How to overcome signal loss to maintain confidence in measurement

Before ATT, marketers had a rich view with full user level data granularity, like an ultra HD 8k resolution image:

However, the new data reality where user level data is diminishing is a fact and there’s no going back. In this reality, there are a lot of holes, making it difficult to see the full picture.

Therefore, marketers must leverage every available data signal and consolidate the different data sources into a single source of truth to measure their campaigns with confidence.

To create a new “data picture”, gaps must be filled wherever possible and new data signals must be added to further compensate. Let’s explore this.

 1) Filling in the gaps in the existing data set with modeling

Modeling has been around for years but it wasn’t truly needed in the past since most data was deterministic. However, in a reality of signal loss, it becomes a must, and it can be done with models that do NOT infringe on user privacy. And with AI at our side, the accuracy of models is dramatically improving model accuracy.

Modeling is used to fill in the gaps in:

1. OS attribution: Because SKAN data comes in late and limited (and so will data from Sandbox), gaps in the data are created. With modelling, real time signals, geo-level data (not available in SKAN), and LTV data for day 7, day 30, and beyond can be provided.

• Modelling on the media side: SKAN limitations make measurement difficult for Self-Reporting Networks, and since they’ve always relied solely on deterministic attribution, they miss out. By leveraging probabilistic modelling on top of deterministic, they gain lost credit. According to our analysis, Snap increased its share in the iOS install pie by 138%, and its eCPI dropped 46%. 

Moreover, large media companies like Google and Meta have also been placing more weight on modeling to navigate and overcome data signal loss challenges in their advertising and marketing strategies.

For instance, Google Ads utilizes machine learning models to refine bidding strategies, while Meta’s Ads Manager platform employs predictive modeling to tailor ad delivery and targeting. Additionally, both companies leverage data modeling to create personalized experiences, reach relevant audiences, and optimize campaign performance.

• Bottom-up (last touch) measurement: Incrementality and Media Mix Modelling (MMM) provide a complementary perspective on campaign performance without having to rely solely on last touch bottom-up signals.

To continue with the high res image analogy, modelling helped fill up some holes, but it’s still not enough. 

 2) Utilizing and creating new signals

1. Top of funnel creative and campaign signals: With fewer bottom funnel signals, more focus is now placed on the top of the funnel where there is an abundance of data tied to creatives and campaigns. This covers two areas:

• Data from creatives: Rich and highly granular signals can be obtained, for example, by identifying, within a single ad, scene types (e.g. user generated content, animation/real-life footage, opening/closing) and specific elements (anything from color, scenery, objects, texts etc).

• Enriched-engagement types (EET): The current attribution standard that only looks at clicks or views is flawed. There are many signals in between that can provide valuable performance data and redefine attribution logic.

2) [More] 1st party data: With data sharing between different companies limited, the value of 3rd party data sinks. Instead, marketers must step up their use of 1st party data and prioritize its collection and utilization. This includes:

• How to properly collect it, ask users for their info (UX and legal included)
• How to make sure the data is clean and actionable
• How to use it in owned media
• How to use it paid media (remarketing, commerce media)

This data need not remain only within a company’s own environment. A data collaboration platform can then serve as a trusted environment for 1st-party data monetization, audience activation, and measurement. By allowing brands to share this data with different companies in a privacy-compliant way, marketers can truly maximize the potential of their own highly valuable data.

3) Omni-channel signals: Apps, Web, CTV, PC & Console, as well as commerce media networks and out-of-home all present unique opportunities to digitally acquire and engage with a user base. By connecting attribution across channels, marketers can tie new data points and user behavior across their various platforms together to provide a holistic understanding of how cross platform marketing drives business outcomes.

4) Single source of truth (SSOT): As mentioned above, data fragmentation has led to an extremely convoluted reality that’s become largely non-actionable for marketers.

Enter SSOT which creates a single reality by combining data from multiple sources and applies robust tech solutions to identify and remove duplicate installs, fill missing data/gaps in geos, web-to-app, longer LTV, null CV modeling, organic, remarketing etc.

According to our analysis, by consolidating the data into a single view, the average app sees a 29% lift in attributed installs, a 40% drop in eCPI, and a 62% surge in revenue attributed to marketing.

At present, SSOT is centered on iOS but the solution for Web and Android will be similar.

When we add new data signals, we gain information that wasn’t there before for a wider picture of our marketing.

The bottom line

Signal loss has created a challenging environment for performance marketers. With diminishing user level data, driving growth is much harder. Innovative solutions are needed to fill in the gaps in the existing data and create new data signals to allow performance marketers regain control and measure their campaigns with confidence. 

The post How to overcome signal loss by building a new data reality appeared first on AppsFlyer.

Nothing is linear anymore. We live our lives on the go, with apps and devices to manage everything from our home heating to our grocery shopping and favorite TV shows. The way consumers shop today is even more nonlinear. It might take a series of ads on TV, Hulu, billboard, Instagram feed, and at the movie theater before they even consider buying a product. 

In today’s hypercompetitive world, every touchpoint matters. And it’s up to the brand to build a customer experience that is not only non-intrusive, but seamless and engaging. 

That’s what cross-channel marketing is all about — using multiple marketing channels to reach, engage, and convert customers in a seamlessly integrated experience.

In this guide, we’ll cover everything you need to know about cross-channel marketing, with expert strategies on how to execute it in today’s complex digital landscape. No more “spray and pray” — it’s time to start designing joined-up customer experiences that get results. 

What you need to know about cross-channel marketing 

From digital billboards at the bus stop to personalized TikTok feeds, people are consuming content in a myriad of new ways. This presents an opportunity for you to engage with your audience at the right place and at the right time, wherever they go.

But it’s not only about distributing your message across different channels. You also need each channel to communicate with each other to ultimately build a unified, and great, customer experience. 

Solving the cross-channel marketing equation also helps you measure the effectiveness of your campaigns, while building more meaningful relationships throughout the customer journey.

So how do you do it well? Your advertising success hinges on a few key factors, including your channel mix, messaging, and attribution. But before we dive into that, let’s talk about what the landscape looks like for advertisers today.

Cross-channel marketing landscape

Cross-channel marketing is becoming the norm as businesses grow more comfortable using different marketing tools and platforms. Around three quarters of small businesses use at least two marketing channels to promote their business, while 20% of them use five channels. 

Here are some of the themes and trends shaping the current landscape. 

Personalization and AI

While artificial intelligence (AI) and machine learning have recently entered the public consciousness, such tools are nothing new in advertising (especially in programmatic). However, generative AI is making it easier than ever to personalize messaging and creatives at scale — especially with ad platforms baking in generative AI tools into the workflows themselves. This helps marketers craft personalized ad experiences based on timing and customer needs.

Data privacy

Customers are also becoming more wary of data privacy issues. Apple’s massive ATT changes have spurred big changes in online advertising, including pushing Google into accelerating the deprecation of cookies and introducing more data privacy protocols. This has affected the way marketers can collect customer data and handle personally identifiable information. 

Probabilistic modeling

Probabilistic modeling is the product of the two points above. With increasing data privacy changes making it more difficult to target individual customers, AI and machine learning are helping marketers move from deterministic to probabilistic modeling to accurately predict campaign performance. AI can connect the dots on many more data points to identify demographics and behaviors, funneling the right customers down the appropriate path.

How to develop a cross-channel marketing strategy

Great change requires a great plan, and you want to make sure you’re covering all your bases before going all-in. Here’s a general framework to help you get started with your cross-channel strategy.

Be a practical visionary

The very first step is to build a business case with real data and examples. “Because everyone else is doing it” isn’t a good enough reason to invest in a completely new strategy. 

Shifting to a cross-channel marketing strategy is neither quick nor cheap, and will require organizational buy-in, especially if you need to get your expanded budget approved. Identify the issues with your current marketing initiatives, while presenting the upsides of shifting to cross-channel marketing. Focus on the financials for maximum impact. 

Understand where your customers are

No matter what channels you’re using, marketing is always about your customer. Surveys, questionnaires, interviews, focus groups, historical data, and social listening are all excellent ways to learn more about them. Then, create personas incorporating their demographics, content consumption habits, and preferred channels.

Figure out your modeling mix

On top of getting your customer data, analyze your competitors, find industry benchmarks, and analyze historical channel performance to help determine which channels to invest in. 

The right channel mix will depend on your business. Before you decide on a model, you’ll need to gather, clean, and prepare the following data:

• Sales data
• Marketing spend per channel
• Control and seasonality variables

With a workable dataset, you can also determine the best model for you by considering the following factors:

• Changing ROI over time: Does your model identify the factors of changing performance (algorithm changes, new competitors, competitor increasing budgets, keyword competition)?
• Timelines per channel: Does your model take into consideration how long it takes to see the impact of your campaigns based on the channel? What about time delays?
• Declining marginal efficiency of spend: Does the model consider declining effectiveness of ad spend?
• Model seasonality: Does your model consider the changes in spending behaviors throughout the year?

Measure the right numbers

A flawless cross-channel marketing initiative hinges on understanding the numbers behind it. And unlike traditional linear campaigns, the metrics need to paint the whole picture of how every ad dollar contributes to the bottom line. Here are some metrics to consider:

• Sales metrics: revenue, customer acquisition cost (CAC), return on ad spend (ROAS), lifetime value (LTV)
• Brand metrics: brand awareness, brand lift, brand mentions, net promoter score (NPS), sentiment analysis
• Traffic metrics: traffic by source, new customers, website traffic
• Channel metrics: conversion rates by channel, reach by channel, impressions, bounce rates

Above all this, the data you gather must be able to tell a story of how each campaign contributes to the success of your marketing campaigns. Since cross-channel marketing is all about how the various channels communicate and interact with each other, you need the metrics that prove it.

Gather your data sources

One of the biggest challenges in running multiple campaigns across different channels is breaking down data silos. You need to get a firm grasp of where all your data is flowing and where you need it to go. Gathering your data sources here will help you understand what tools you’ll need to execute on your plan.

Identify the right technology

There’s no perfect, one-size-fits all marketing tech stack that will flawlessly execute your cross-channel campaigns. But once you have a firm understanding of the channels you want to use, you can identify the right tools that work best for your budget.

Here are some questions to help you determine your exact needs:

• What are your growth goals to inform data volume and scalability needs?
• What does your customer journey look like?
• What are the primary KPIs for each step of the journey?
• What additional events would be helpful to measure, in order to optimize the full customer journey?
• What channels and functionalities will you need to acquire and retain users?
• What media partners are you working with (or planning to work with)?
• What infrastructure will you need to store and manage data?
• Who owns your data, and what security protocols should you consider to ensure your data is safe?
• How will you visualize marketing and product performance?
• Do you need a full-funnel view of marketing/product activity across channels and platforms?

For a full in-depth rundown, read our guide on how to build an app-centric MarTech stack here.

Nail down your messaging

Brand consistency – a uniform message, voice, tone, color palette, and imagery across all touchpoints – makes it easier for your brand to be remembered and stand out. Scattered messaging can easily turn into white noise. 

Here are the most important aspects of brand messaging to get in writing:

• Brand promise: What do people want from you, and how can you deliver?
• Positioning statement: What makes you different?
• Target audience: Who do you serve?
• Mission: How are you changing the world?
• Tone of voice: How do you speak to your audience?
• Brand pillars / core values: What do you stand for?
• Customer benefits: How do you actually help your customers and what supporting examples do you have?

Determine your internal capabilities

To allocate your resources effectively, you first need to define your team’s skills and capacity to execute your cross-channel marketing strategy. This will help you determine whether you need to upskill current team members, shift roles horizontally, or increase your budget to bring in new hires. 

This isn’t just about your team’s strengths and weaknesses — ask them what they want to learn and what they’re passionate about. Capacity planning is understanding how much bandwidth each team member has and what they’ll be able to take on in the new initiative. 

Solve attribution

Now that you have a good understanding of what you need to achieve, it’s time to figure out how to measure the success of your campaigns. Without trustworthy data, you won’t be able to make informed decisions on how to optimize your campaigns.

According to Statista, the leading approach to achieving cross-media measurement is a combination of proprietary and third-party measurement solutions for each platform (29%). Only 15% of marketers use one third-party measurement platform to measure all platforms.

So, before you shop around for an attribution partner or a combination of different tools, identify your attribution needs by answering the following:

• What is this provider doing to protect customer privacy?
• How are they using AI?
• What are their insight tools?
• What platforms and channels do they measure? 
• Do they have an effective solution to measure iOS campaigns?
• How are they combating ad fraud?

Leading change and garnering organizational buy-in

As a leader, proposing a solution to a key business challenge is only one small piece of the puzzle—particularly if you’re in a mature organization that may be stuck in its ways. 

If cross-channel marketing isn’t a priority, you need to build a business case to change the shift of this being a “difficult, costly, and weird” challenge to being “doable, rewarding, and normal.”

Start by asking yourself the following questions:

1. Who are the stakeholders involved in gaining organizational buy-in?
2. What are the current challenges of our marketing operations? How is it affecting our business? Why do we need to change?
3. What are the costs associated with running this program? What’s the profit potential?
4. What specific aspects of cross-channel marketing seem difficult?
5. What terms, phrases, or concepts are confusing to the stakeholders?
6. What does a future with cross-channel marketing look like? 

Answering each of these questions by providing concrete examples, evidence, and numbers will help you successfully communicate your new initiative. Nervousness, hesitation, and resistance are normal. It’s your job to ensure everyone feels heard and supported, while presenting this approach as a win for everyone involved.

Key takeaways 

• Cross-channel marketing is the marketing strategy that uses multiple marketing channels to reach, engage, and convert customers. These channels should be integrated and coordinated for a unified customer experience.
• The key elements of a cross-channel marketing strategy include: setting a clear vision, building customer profiles, solving media modeling, setting clear metrics, unifying data sources, building a tech stack, nailing down messaging, determining internal capabilities, and solving attribution.
• Building a great strategy is only one part of the equation. To get buy-in and drive organizational change, you need to present the benefits of a cross-channel marketing strategy with facts and benefits for each individual stakeholder.

The post Cross-channel marketing in 2024: Perfecting the channel mix formula appeared first on AppsFlyer.