Predictive Analytics for User Acquisition

What is user acquisition?

User acquisition (often referred to as UA) is a marketing function of gaining new users for an app, platform, or service business. Depending on the type of business, marketers can design a user acquisition strategy to drive mobile installs, new customers, and free trial sign-ups through marketing and advertising campaigns.

Why is user acquisition important?

User acquisition is a critical part of a marketing strategy. It’s how companies can reach ACV, ARR, NRR, and ARPU goals, as well as ROAS targets. Individuals specializing in user acquisition focus on finding new audiences online to convert into paying customers. 

User acquisition relies heavily on data analysis to create strategic and data-driven marketing strategies to drive top-line revenue goals. User acquisition entails both marketing science and creative skills, plus a keen eye for budget management. Predictive analytics is an invaluable addition to this toolkit.

By paying close attention to the data, analyzing trends, and optimizing campaigns, user acquisition strategists can drive new users and edge out competitive brands, apps, or other advertisers.

What are the common challenges in acquiring new users?

Creating strategic user acquisition plans can be a daunting and challenging task. Knowing where to start is always a challenging first hurdle to overcome. 

Once a target audience is defined, and you have a general understanding of where this audience engages, you can start putting an acquisition plan together. That plan will promote your product or service and entice this audience to respond. 

Executing these plans involves its own challenges. For starters, the cost to acquire new customers has increased 222% over the last eight years. Furthermore, communication and ad tech platforms like Apple, Meta, and Google have released updates that change how marketers can track and report on user acquisition initiatives. The biggest of these changes is SKAN.

What is SKAN?

SKAN (also known as SKAdNetwork or StoreKit Ad Network) is Apple’s privacy-friendly way to attribute impressions, clicks, and app installations. SKAN is a new way Apple shares conversion values via iOS without revealing user-level or device-level data.

From an advertiser’s perspective, this creates several challenges and limitations, such as:

  • ROI/LTV tracking SKAdNetwork has a 24-hour back/post-back window. As a result, it’s hard for advertisers to track customer lifetime value or return on investment. 
  • Conversion granularity – Data collected from SKAN is on an aggregate campaign level and is usually limited to 100 campaigns per network, per app.
  • Postback delay – There is at least a 24-hour delay between when installs occur and when they are reported, making it very challenging for advertisers to optimize on the go.
  • Data ownership – Data is owned and reported by ad networks, which creates mistrust as advertisers cannot look at and analyze results. 
  • Ad fraud risk – Data can be manipulated, potentially increasing the risk of fraud.
  • No re-engagement attribution support – SKAdNetwork does not currently support view-through attribution. 

How can predictive analytics support user acquisition initiatives?

Predictive analytics supports user acquisition initiatives in three core areas. 

1. Predictive Campaign ROAS

In a recent Gartner survey, 90% of marketing leaders agreed that the marketing function needs to be more adaptive to shifts in customer needs. Predictive analytics can empower marketers to respond to these shifts and change campaign strategies to align closer to customer needs and demands. 

Predictive analytics offers a method for identifying high-value conversions earlier in the campaign lifecycle. Marketers can predict a campaign’s future return on ad spend (ROAS) to make optimization decisions. 

With Pecan, you can make confident campaign decisions by predicting campaign customer lifetime value early, removing the guesswork from optimization decisions later down the road.

It may seem challenging to predict campaign performance, but it’s possible with predictive analytics — and the stakes are high. 

Check out this example: If you are a brand spending $10M a year on advertising campaigns, your daily budget is around $27K. Generally speaking, depending on the industry, optimizing an ad campaign takes around 7-14 days, depending on optimization strategies, campaign learning phase, etc. 

By the time you can make your first significant optimization, the campaign will have spent roughly $200K. That’s a lot of money! 

But now, with the help of AI and machine learning, campaign teams can optimize earlier in a campaign. Instead of a day 7 or day 14 optimization, they can optimize on day 2. With a day 2 optimization, the campaign could save around $150K in spend — not to mention the lift generated by better results through focusing resources on higher-performing campaigns.

graph of two campaigns' ROAS
Optimizing campaigns as early as possible leads to decreased spend and better results.

2. Campaign Optimization With Predictive Events

Looking at your customer or user base from a future-value perspective gives you a clearer picture of the lifetime value of your users. With this foresight, you can create longer-term revenue, more profitability, and a stickier application, game, or service.

Leveraging predictive analytics for campaign optimization with predictive events allows you to generate optimization schemas customized to your business, overcome SKAN issues, and get more insights from mobile. One of Pecan’s mobile app customers achieved a 279% lift in conversion and a 64% lift in sales when using Facebook’s custom event optimization with predictive events instead of value optimization.

value optimization vs pecan pltv predictive event optimization (ceo)

Predictive models built on your customer data can offer detailed, nuanced signals that you can share with the ad platforms for use in optimization. Those signals can include greater depth about customer behaviors you hope to see beyond what the platforms already know. That critical information boosts ad platforms’ ability to deliver your ads to the right audience, finding you more high-quality leads that will convert into high-quality customers.

3. Marketing Mix Models (MMM) 

A recent Gartner report indicates that adding four or more channels to an integrated campaign could increase results by 300%. Marketers today use a variety of media channels to drive branding and revenue KPIs. Media mix modeling (MMM), also known as marketing mix modeling, is an analytic technique that allows marketers to measure the impact of multi-channel marketing and advertising campaigns. 

Marketing teams struggle to determine accurately whether current media channels have met saturation points or to recognize when adding a channel would produce incremental results. 

New software options (including SaaS solutions like Pecan AI), access to computing power, and improved data capabilities among marketing teams have made MMM available to practically any company. It’s now possible to build new models every week or every month for more frequent budget optimization. And MMM is especially relevant in a world with less readily available consumer-level data since this method works at the level of the marketing channels, not at the individual consumer level. 

With MMM, marketers can achieve a unified measurement approach for offline and online channels and better understand channels’ characteristics. MMM also supports strategic budget management and long-term allocation. With MMM, teams can model their marketing channels, determine which channels drive the most business impact, and see how adding additional channels could add incremental revenue gains.

Pecan MMM dashboard showing saturation, carryover effects

MMM also allows user acquisition managers to develop what-if scenarios for different budget allocations. They can predict outcomes as new channels, partners, or campaigns are added to a marketing mix or when budgets are decreased due to recessionary planning, EBITA adjustments, etc. 

Pecan MMM dashboard

Start predicting for future-focused user acquisition strategies.

Ready to take the next step and add predictive analytics to your user acquisition strategy? Get in touch to learn more about how Pecan’s low-code predictive analytics platform offers accessible AI for real business impact.

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