Personalization in a Composable Stack
Personalization is one of the keys to optimizing the customer experience. According to a recent article by Jeff Hasen in Adweek, personalization can decrease customer acquisition costs (CAC) by up to 50%, increase revenues by up to 15%, and increase marketing spend efficiency by up to 30%. E-commerce businesses want to create a continuous and consistent experience throughout the customer journey. This, however, is a complex process due to the number of channels and customer touchpoints involved in today’s digital landscape. Furthermore, creating a personalized experience in a composable tech stack is very different from an all-in-one monolithic suite. Not only are we trying to personalize the experience on multiple touchpoints, we also have to deal with content and customer data spread across multiple applications.
In order to deal with this complexity, we need to empower marketing teams with a centralized experience platform that gives them control over what content the customer should see from what source, on what channel or context, and by what customer.
What you should look for in your composable personalization engine
When deciding what kind of personalization engine you need, you should think about the building blocks it offers; one that gives your marketing teams control. Here’s how you should approach selecting the right personalization engine for your business.
1:1 Personalization
Personalize for every customer, not just specific customer segments — Your personalization engine should be able to centrally manage, control, and orchestrate who sees what content, when, and where on every channel. This should not be limited to personalizing for customer segments but should be able to execute 1:1 personalization or hyper-personalization for every individual customer.
With the looming deprecation of the third party cookie, it has become ever more important to build a personal relationship with your customer. This can be accomplished through capturing first party data from owned channels. Your customer is leaving behind a digital footprint everytime they engage with your brand. These signals can help you understand the customer’s real-time intent and their purchase behaviour. A personalization engine should be able to track these signals and use this interaction data to create a digital identity graph for every anonymous and known customer.
Omnichannel Personalization
You want to deliver personalized experiences to all channels, not just the website — Personalization was historically limited only to the web. Today, the customer’s journey goes far beyond the web. They interact with your brand through their smartphone, connected TV, kiosks, and digital displays in-store, A/R, V/R, and even the metaverse — personalization has become omnichannel. With an API approach to personalization, any of your owned channels, including email, web, mobile, and kiosks can determine with an API call, the right experience for a customer within their current context.
Component Level Personalization
Most traditional personalization engines create page-level variants for different customer segments and contexts as they are focused on the web experience. In order to scale your personalization efforts across channels, you want to be able to reuse your personalization logic across different types of experiences that go beyond the web. A hero banner, product recommendations, and featured blogs are examples of reusable components and can be personalized based on the user’s real-time and historical context.
The approach of breaking down the experience into reusable components provides additional benefits, including consistency, scalability, and unique experiences for every customer at a very granular level.
AI-Powered Recommendations
Consumers are more likely to view items that are recommended based on the information they have shared with the brand, and engage with content customized to their specific interests. The conversion rate also tends to be higher when an offer has been personalized to reflect previous interactions that the consumer has had with the brand. Therefore, it’s important to offer content and product recommendations that are specific and relevant to the customer.
Brands can achieve great success by leveraging AI and machine learning to quickly analyze massive amounts of information to gain valuable business insights from past customer activity in order to serve up relevant recommendations and information to their customers.
Advanced Analytics
Marketing teams often rely on advanced analytics to build a deep understanding of how customers respond and engage with campaigns, understand customers’ evolving needs, and then use the data to create unique and dynamic experiences. Furthermore, campaign-level analytics helps to plan out campaigns, analyze, experiment with variations of the campaign, and optimize them. Your experience engine should be able to provide you with centralized analytics dashboards so that you can understand and optimize your campaign performance.
A composable personalization engine lets you draw on diverse personalization strategies, from rule-based to ML, to provide the best possible consumer experience. By choosing the right personalization engine, you empower your marketing teams to optimize every touchpoint along your customer’s journey and across your digital ecosystem.
