AI can give brands the opportunity to harness massive amounts of data and continuously learn what’s driving business impact in a fraction of the time that it would take a human. Discover what this means for customer loyalty and lifetime value when applying AI-led experimentation over traditional A/B testing.
According to a recent study; “65% of respondents would become a long-term customer of a brand that provides a positive experience throughout an entire customer journey”. The challenge is that a positive customer experience is highly subjective and means many different things to different customers since it is highly dependent on their current needs and preferences. But the one common thread is that personalization and the ability to offer increasingly tailored experiences are highly sought after. Achieving this level of personalization highlights the critical importance of being able to experiment with many different ideas to not only improve the customer experience (CX) but also increase loyalty and customer lifetime value (CLV) as a desired outcome.
Being able to create relevant experiences for individual customers is no easy task. A common mistake many companies make is to personalize based on similar demographic characteristics. The reality is the two people with identical demographics likely have very different goals and needs. As a result brands cannot assume a one-size-fits-all approach. A better strategy is to see how individual customers respond to ideas in real time. This is why experimentation is the key to personalization.
Experimentation driven by artificial intelligence (AI) gives brands the ability to personalize at speed and scale. It does this by taking a few key variables and ideas, generates thousands of possible experiences and then learns from how visitors and customers respond to those ideas. It’s an ongoing process of continual learning and insights that are used to adapt and improve ideas which in turn creates more engaging and relevant experiences over time which encourages loyalty.
Take for example an airline that is looking to increase signups to its loyalty program by leveraging the benefits offered by industry partners such as ride sharing services, rental car agencies, and hotel chains. Knowing the right touchpoint at which to introduce a partner offer is nothing but guesswork unless you can experiment with different ideas to see which resonates with customers during different parts of their customer journey. What makes AI-driven experience optimization even more effective is the ability to experiment with and personalize experiences beyond the average customer to focus on individual profiles.
Maintaining customer loyalty and increasing CLV hinges on delivering consistently memorable experiences across all touchpoints and on all channels. This takes personalization to a new level as customers who participate in loyalty programs expect brands to provide more tailored experiences based on past buying behavior and the data collected about them. This too can be achieved through experimentation by combining historical data and learning from current browsing behavior to serve experiences that are relevant to what customers are looking for at the moment.
AI-driven experimentation and personalization enables brands to programmatically explore a wide variety of ideas in combination, by identifying the most effective individual user interface (UI) elements and the best overall customer journeys.
Personalizing experiences achieves loyalty by delighting customers at every touchpoint in the customer journey, even as their preferences and intent change over time. By understanding what’s important to customers and consistently delivering it to them, it becomes much easier to upsell or cross sell, thereby increasing the average order value (AOV) and also the CLV.
Mobile commerce is growing at a significant pace. It’s estimated to be worth more than US$720 billion by 2025, accounting for 44% of retail e-commerce. As a result, many brands have developed mobile apps for their loyalty programs and this adds a new element of CX to optimize for. The benefit of using AI-driven experience optimization is that it’s effortless to optimize for mobile and desktop experiences simultaneously, drawing learnings from both to identify the most impactful UI elements that work to increase AOV and CLV.
Mobile is an ideal way to keep customers engaged and is well suited to creating targeted experiences. Reminding customers of an account that’s due or that they’re eligible for a special promotion are two simple examples. But as most people continually have their mobile phones on them at all times, careful consideration needs to be given to what types of experiences to serve. There is a real risk of annoying and alienating customers when not done properly. That risk can be lowered through AI-driven experimentation and personalization that learns from how customers respond to experiences. Additional mobile capabilities such as geolocation can enhance targeted experiences, engaging with customers in the right place and at the right time.
Consistently serving up relevant experiences makes it easy for customers to return and become loyal customers. Because trust has already been established and customers are engaged, it becomes easier to increase the AOV with complementary or upgrade offers. Research indicates that loyal customers spend 67% more than new customers. This highlights the potential to increase the customer lifetime value.
Statistics reflect that increasing customer retention by just 5% can generate an increase of 25% in revenue over time. AI-driven experience optimization can accelerate these gains by generating lift while experimenting with different ideas. In other words, brands don’t have to wait until after an optimization has been completed to implement strategies based on insights. It all happens as part of the process: serving multiple combinations of ideas as unique experiences, gaining lift from how customers respond and learning from those responses to generate even greater lift. More importantly, optimizations can be geared to achieve company or project specific outcomes. For some companies this will be an increase in loyalty program sign ups, for another it may be increasing the engagement on a mobile app, while growth oriented companies may have objectives to increase AOV and CLV.
The major advantage is that regardless of the targeted objectives, learnings are a result of live customer responses and the value of this first-party data only increases over time with continual optimization. AI-driven experimentation and personalization is the most accurate way to find out what experiences resonate with customers to keep them engaged and loyal even in a highly dynamic and competitive marketplace.
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