Defining an insight-led personalization roadmap with mature customer data

March 10, 2022

Personalization. We may hear the term a lot in discussions surrounding customer experiences, but personalization is anything but a marketing buzzword. With 91% of customers preferring to shop with brands that put relevant products or offers in front of them, defining successful personalization strategies is a must. 

Of course, achieving true 1-to-1 personalization isn’t an overnight task. Your personalization goals must be part of a roadmap that focuses on maturing your customer data strategy and learning how to make rich data actionable. After all, if you’re not collecting, aggregating, and analyzing customer data successfully, you can’t deliver the contextually relevant communications that are needed to drive conversions 

We highlighted some recommended steps for defining an insight-led personalization roadmap based on always maturing customer data.  

Getting to know your customers

At the root of every successful personalization strategy is an in-depth understanding of your customers and their needs. Here, combining historical customer data with real-time insights is crucial to build a single customer view (SCV) and identify opportunities for interactions. 

When it comes to personalization, many businesses want to dive in headfirst – but you don’t want to overcomplicate your strategy. Begin by using very basic insights, such as whether a customer has created an account on your website or not. Something as simple as directing registered customers straight to your site’s login page can help to kickstart a journey based on relevance and convenience. This goes back to the basic concept of telling your customers you remember and value them through a very simple enhanced user experience.  

Once you’ve mastered personalization at the most basic level, you can begin to create hyper-targeted communications based on more mature customer insights.  

Automating trigger-based interactions 

We know that tailoring interactions is crucial to making customers feel seen and heard – but what about timeliness? Applying intelligent analytics to real-time data processing can help you deliver contextually relevant interactions when they’re likely to make the most impact.  

You can start by automating communications, such as app notifications or email alerts, based on triggers and changes in customer behavior. The key is to always put relevant information in front of your customers, based on an understanding of their needs and wants at that moment in time.   

For example, imagine that a customer only partially completes an insurance policy form before closing their browser. After a couple of days, sending an email with a clear route back to pick up the form exactly where they left off could be a helpful reminder for the customer while moving the insurance provider one step closer to conversion.  

When personalizing your messaging, it is important to monitor your customer’s individual preferences. As a starting point, marketers will draw a line in the sand for their customer base. But you should look into this at an individual level, to identify what style, frequency, and message is resonating best with the individual customer.  

Delivering the next best experience

Trigger-based messaging can help you re-engage your customers after an initial interaction but reacting to behavioral changes is only part of the personalization process. To truly cut through the noise, you need to be able to predict and meet your customers’ future needs by consistently providing the next best experience. 

At this stage, an advanced customer data platform (CDP), such as NGDATA’s Intelligent Engagement Platform (IEP), can provide you with the predictive modeling capabilities you need to put the right offer, product or communication in front of your customers. By feeding sophisticated algorithms with real-time data, you can determine the next most appropriate action using always-evolving propensity scores, eligibility scores, and other intelligent metrics.  

What’s important is having the ability to adapt your messaging quickly based on your customer’s profile and the current context. For this, the real-time interaction management functionalities of an advanced CDP are vital.  

When guiding customers through their mortgage journey, for instance, ensuring relevant, timely, personalized messaging, and information from the nascent stages of customer exploration will ultimately lead to greater mortgage account openings. This includes different messaging by journey stage (e.g. early interest vs. application started) with considerations for different financial and behavioral attributes. By leveraging the analytical journey capability within the IEP, banks can create compelling acquisition journeys that are based on real-time customer insights. 

Balancing machine learning with the human touch

We can thank machine learning for helping us understand our customers and automate engagements that make them feel valued. However, for true personalization, some level of human involvement is needed. Your chosen data processing technology cannot make all your decisions.  

A crucial part of achieving personalization is demonstrating that you understand your customers, without making them feel uncomfortable about the level of insight you have. Here is where you’ll need to regularly test, learn from, and update your personalization strategies, and keep them balanced so you can support the customer experience. 

That’s not to say that you shouldn’t integrate a CDP into your data ecosystem. From evaluating data sets to identifying the next best action based on sophisticated metrics, advanced CDPs like our Intelligent Engagement Platform put the power of data in your hands.  

Nevertheless, true personalization comes from balancing automation with intelligent decision-making – aka the human touch, but data-led.  

Achieving 1-to-1 personalization

At this stage, you’re close to achieving true personalization. This means you’re combining historical and real-time data to create actionable insights and using these to determine the next best experience for your customers at the right time, across the right touchpoints.  

This sounds complicated, but the benefit of using our Intelligent Engagement Platform is having the ability to take baby steps. You can start by applying one use case at a time before building out your personalization strategy for customer experiences that you both get value from.  

What’s more, our Intelligent Engagement Platform analyzes data to determine actionable insights from one centralized interface, enabling marketers to deliver data-driven personalization without multiple platforms and processes. 

To find out more about our Intelligent Engagement Platform and how it can help you mature your customer data strategy for personalization success, get in touch with us today

You can also watch our “5 Key Personalization Steps to Grow Your Customer Data Maturity” webinar here.