How AI and CLV help app marketers drive business growth
Contributor Brian Solis says marketers need to move away from traditional vanity metrics and look toward AI and machine learning to identify the most valuable customers and deliver targeted experiences.
Did you know that 80 percent of users churn within three months of downloading an app? That’s because most apps are marketed to the masses and not necessarily to the right customers.
Oftentimes, the goal of app marketing is to reach as many consumers as possible with the hopes of recruiting en masse and converting at better-than-average ratios. But part of the challenge for marketers is that many of today’s strategies are driven by metrics that don’t link to advanced user targeting and growth.
More specifically, app marketers aren’t using available data strategically to deliver productive user experiences that ultimately drive greater business profitability.
Now more than ever, marketers must shift from tracking traditional vanity metrics to measuring the very things that contribute to retention and growth. More and more, successful companies are investing in customer-centric metrics such as CLV (customer lifetime value) to gain intelligent, consumer-centered insights that not only identify the most valuable customers but also key behaviors and preferences to continually improve consumers’ experiences and journey.
Next-generation marketing and CX are about identifying and engaging valuable consumers
CLV is more important than apps in isolation. It helps apps and other touch points work together to deliver value-added, cohesive experiences.
CLV measures the value a consumer represents to the business across all interactions over their lifetime, not just a single transaction or touch point. That is ultimately the definition of customer experience. It is the sum of all moments a customer has with your brand throughout their life cycle. Marketing and customer engagement is now a cross-functional mandate.
Not all app users are the right users. If you use the Pareto Principle, you can assume that 80 percent of business value is attributed to 20 percent of your active consumers. While these percentages aren’t by any means a standard, they do emphasize the need to identify and cultivate the important customers who drive your business.
Instead of casting a wide net and attracting as many users as possible in the hopes of retaining a reasonably active base, CLV tied to artificial intelligence (AI) and machine learning focuses marketers and also developers on targeted engagement and growth. The idea is to drive profit by investing in more value-added user experiences and personalized offers. Doing so intentionally cultivates meaningful relationships with key customers.
Next-generation customer engagement is about cross-functional collaboration and data sharing
Unfortunately, customer experience today is largely siloed. Marketing, mobile, in-store, ecommerce, digital and so on are not collaborating nor operating against the same customer and market data. But that’s all about to change with the proliferation of AI and machine learning tied to smart CLV initiatives.
When the goal is to deliver targeted and integrated experiences, not just in-app, but across each touch point and the life cycle overall, companies create a truly customer-centric approach. AI then helps brands get a more complete, shared view and understanding of customer behaviors and expectations.
Additionally, AI-driven customer-centricity fosters cross-functional collaboration and data sharing that, by design, boosts customer experiences, along with CLV and business growth.
Identify highest-value customers and deliver targeted experiences
AI/machine learning platforms offer intelligent insights when pointed in the right direction. Successful brands study how much revenue highest-value customers drive over their lifetime and how much it costs to manage those relationships. And they examine CLV across all channels to get a holistic view of high-value behavior in all interactions. When the system can analyze important traits of high-value users, it can learn how to optimize CLV.
For example, to reach potential high-value customers, AI/machine learning uses data from existing high-value customers to optimize campaigns and touch points. In a study by Bain aimed at retail banking, it was found that it costs banks $4 every time a customer calls or visits. However, if consumers can complete the transaction via an app, it costs only 10 cents.
The key is to deliver capabilities in ways that consumers prefer and appreciate. Imagine how much AI and machine learning could additionally uncover when tasked with identifying friction points and new opportunities.
AI and CLV call for a new customer-centric playbook
You’ve probably heard time and time again that it costs more to acquire a new customer than to retain one. Brands that are winning prioritize CLV and AI and are drafting the playbook as they go. They:
- develop a customer-centric mindset.
- open doors between silos around in-store, digital and mobile so teams can focus on one clear business goal, rather than individual metrics (such as engagement or clicks).
- align customer-facing groups to a business outcome such as CLV and promote cross-functional collaboration and data sharing to assemble a holistic view of the customer across all touch points.
- understand who their highest-value customers are, how much revenue they drive over their lifetime and how much it costs to manage the relationship — across all channels.
- focus on measuring and communicating clear business goals rather than individual or vanity metrics.
AI and machine learning improve both by using existing data without cognitive bias. The more the system learns, the more it optimizes.
In the end, not all customers are created equal. By identifying those who drive value, how and why, you can learn how to design and deliver personalized value to them and enhance customer engagement and experiences to grow your business now and over time.
Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.