In a nutshell:
- Acquiring new users for your app or technology platform requires staying ahead with AI-driven strategies.
- AI can bridge the data gap left by the phasing-out of third-party cookies, providing insights into user behavior and preferences.
- AI-powered marketing mix modeling helps understand the effectiveness of online and offline channels for user acquisition.
- AI optimization enhances retargeting and retention efforts by analyzing user behaviors and tailoring recommendations.
- Predictive AI enables the calculation of customer lifetime value, allowing for targeted marketing and increased profits.
Acquiring new users for your app or technology platform is no easy feat. In a rapidly evolving marketplace with countless moving parts, staying ahead requires being at the forefront of technological innovation — today, that means embracing AI.
“64% of marketers say AI is very or critically important to success in the next 12 months.”
— “The 2023 State of Marketing AI Report,” Marketing Artificial Intelligence Institute
Falling even a year behind could mean missing out on critical advancements in strategies, data accessibility, and technology. Lagging in your team’s digital transformation initiatives may lead to a suboptimal customer experience, losing market share, and even failing to capture your business’s potential.
For marketers looking to acquire more customers, the phasing-out of third-party cookies coupled with emerging privacy regulations — like Apple's SKAdNetwork — has left teams struggling to track users effectively to optimize targeting and campaigns. This shift has made traditional methods of measurement and overall user acquisition (UA) strategies far less effective.
Thankfully, we come bearing good news. There's a wave of data-driven strategies involving AI that gives marketers a new and better way to track customer activity and drive user acquisition. And we’re not just talking about generative AI.
Photo by Jonas Leupe on Unsplash
A new technology, generative AI, is valuable for brainstorming and creating some content at scale, but the real game-changer lies in harnessing AI-driven predictive methods to make full use of your customer data. Compared to gen AI, predictive AI provides detailed insights at the user level — revealing the offers and content that will resonate with who and where.
In this blog, we’ll dive into how predictive AI and machine learning can transform your user acquisition strategy, making you wonder how you ever managed without it.
Bridging user-level data gaps in a cookie-less world with AI
As we plan a farewell party for our trusty companions, third-party cookies, it’s hard not to acknowledge the substantial challenges facing marketers who rely on this data for personalized marketing. Third-party cookies have been the go-to for tracking users across the internet, offering valuable insights into user behavior.
With the dissolution of cookies, obtaining comprehensive user-level data has taken a hit. This data gap is a hurdle to effective marketing attribution, restricting marketers’ understanding of user journeys and stifling their ability to craft a dynamic user acquisition strategy.
Here’s where AI swoops in to save the day. Automated predictive analytics platforms, powered by machine learning, take the cookie-less data you have and deliver nuanced insights about user behavior and preferences. It’s not actually magic, but it doesn’t feel far off from having a crystal ball for user actions and tendencies without the help of cookies.
Today, using these predictive AI-based solutions has given marketers the upper hand. (And guess what? Top mobile app marketers vouch for it too.) It's the science underpinning successful user acquisition strategies, propelling your campaigns to new heights despite the uncertain and chaotic landscape of data tracking and privacy concerns.
Optimize your channel spend with AI
Another critical marketing need: understanding channel effectiveness and how each marketing dollar impacts your user acquisition goals. It’s always been a bit of a riddle to figure out how offline campaigns, like TV ad spend, influenced user acquisition when compared to digital channels, like YouTube, with simpler and more straightforward attribution models.
By themselves, attribution models merely scratch the surface — models like first-touch or last-touch — as the modern customer journey is rarely linear or straightforward. However, with AI-powered marketing mix modeling (MMM), marketers can unleash the power of machine learning to gain a granular understanding of the effectiveness of online and offline channels. This capability comes at a critical time — as 61% of companies have expanded their multichannel marketing strategies, surpassing pre-pandemic norms.
Here’s how it works: MMM meticulously gauges the incremental impact of campaigns across channels, considering several factors, including user interactions across platforms, the time between exposure and conversion, and the combined ripple effect of multiple touchpoints. At the end of the day, these insights help your team shape user acquisition strategy and allocate marketing funds to the channels with the greatest influence on your user acquisition goals.
Elevating retargeting and retention efforts through AI optimization
Predicting which mobile game players or end users need a nudge back into the swing of things is tricky, right? Honestly, it can be when relying on conventional business rules — but AI can entirely redefine how you retarget and retain valuable users. With AI, you can analyze user behaviors with precision, spot signs of disinterest, and tailor recommendations in real time.
For example, SciPlay, a prominent mobile entertainment provider, previously employed a broad retargeting strategy resembling Oprah's signature audience handouts. It was a case of “You get retargeted, and you get retargeted!” However, they soon realized that not every player required a targeted campaign.
With Pecan's help, SciPlay transformed their strategy. They swapped out their inefficient retargeting strategy for a streamlined process that pinpointed the perfect players for retargeting. Plus, it served up juicier offers and messages, elevating the gaming experience for selected players. With AI-powered analytics, it saved the company millions in retargeting spend — and kept its key users coming back for more.
Unveiling user value with AI's lifetime predictive capability
Marketers aspire to cultivate enduring relationships with their customers, navigating through thick and thin. Before committing time and resources to nurture these relationships, it's essential to determine if both the customer and the company are a match from the start.
That’s the essence of understanding customer lifetime value (LTV). At its core, LTV analysis is more than a metric; it's a compass guiding crucial business decisions by meticulously calculating the anticipated revenue a customer is expected to generate over their entire relationship with the company. Juxtaposed against the cost incurred in acquiring and retaining them, you can gain a comprehensive understanding of customer profitability across different segments. Knowing the LTV of customers can also enable cross-selling or up-selling additional products or services to existing customers who have a higher potential lifetime value.
Now that we've aligned on the importance of measuring LTV, let's dig into predicting LTV. Calculating LTV traditionally involves peering into the rearview mirror of historical data to identify potential high-spending customers. By leveraging various customer data points — purchase history, demographics, and behavior — predictive AI actually forecasts the potential value of each customer's future business. Knowing which of your prospective customers provide the most value — and which don’t — can increase profits by 25% to a staggering 95%.
AI’s lifetime predictive capability can take most of the guesswork out of the matching process by turning data into smart predictions. That’s how mobile game publisher KSG Mobile (KSGM) ramped up marketing spend on its social casino games — and verified marketing performance to stakeholders. With added budget came increased responsibility to ensure campaign decisions were data-backed and targeting their perfect matches.
This meant KSGM dropped their rule-based calculations for estimating LTV. Business rules weren’t going to cut it for major campaign calls — they lacked the user-level predictions that could be sliced and diced across geography, channels, and campaigns. By building out different predictive LTV models with Pecan, KSGM was able to predict future revenues from users.
How explainable AI reveals holistic user behavior insights
Peeling back the layers of AI's "black box" isn't just about building trust in machines. For user acquisition marketers, it's about getting a peek behind the curtains, ensuring they're not left in the dark about how machine learning draws its conclusions. It's like slipping on reading glasses for data, making it clear where the AI excels and where it might stumble, and knowing how that affects its results.
Explainable AI doesn't stop there. It dishes out insights to marketers, going beyond crunching numbers and predicting the future based on historical data to investigating and uncovering the reasons behind these forecasts.
When it comes to unraveling holistic user behavior insights, explainability takes the crown. AI reveals behavioral patterns, hinting at future-paying users or those at risk of churn. It also divulges the specific factors fueling these behaviors, from user clicks to engagement metrics and demographics. This puts marketers in the driver’s seat to spot conversion cues, forecast churn, and engage high-LTV customers.
Ultimately, explainable AI empowers marketers to make smart budget decisions and proactive adjustments, fine-tuning their user acquisition strategies. This transparency fuels confident decision-making, allowing businesses to prioritize actions based on actionable insights.
Gain an AI advantage for your user acquisition strategy
User acquisition has always been challenging. With so many moving parts and obstacles in today’s economy, these challenges become even more intricate. And if your tech stack and user acquisition strategy aren’t keeping up, the potential consequences are dire.
But here’s a prediction: Once you experience the transformative power of AI, there’s no turning back. It’s not just about tweaking and fine-tuning. It’s about completely redefining how you predict user behavior, spot valuable monetization chances, and gain insights into channel performance. And guess what? The more transparent the AI model, the easier it becomes for everyone to make informed decisions about their user acquisition strategies.
Ready to witness the AI advantage in UA? Get your demo today and start designing a more dynamic user acquisition strategy.
