Effective targeted marketing is built on knowledge of the customer. Attaining deep knowledge of the customer depends on knowing more than just the interactions with your brand; it entails knowledge of their behavior in different digital and physical contexts. The goal for marketers is to attain a unified, holistic customer profile known as the Single Customer View (SCV) or 360-degree view. Yet, even with the emergence of CDPs, attaining this goal has remained elusive. Today, with the help of AI and machine learning this is now possible.
Top Down, Bottom Up
As Arkle Advisors Partner, and pioneer in digital media testing, targeting, and optimization
Jonathan Mendez wrote in his blog, CDPs have the potential to be truly transformative. He notes that they can combine essential functions that need to be in place for the complete view of the customer: One is to “unify customer identity in a first-party business-controlled environment.” The other is to “create a platform for that identity that can be activated, tested and optimized” for actionable insight.
The first part depends on data unification and verification. As Mendez notes, “CDPs take the raw data from anywhere and process it by cleaning it, validating it, de-duping it, and merging it into a single table of customers with their dimensions and attributes.” Ideally, it would merge the “bottoms-up” form of data gathering with a “’top-down’ understanding.”
However, CDPs that aren’t equipped with AI can’t implement the full range of machine learning that can drive orchestration across the consumer journey. That is why they fall short of the promise for a 360-degree view. While CDPs do effectively pick on the data through the “bottoms-up” approach, they fall short of “top-down” implementation of insight. A CDP solution can only achieve its true transformative potential if it achieves agility to work in both directions.
Achieving agility with AI
Agility is one of the primary gains of AI application. This is why marketing is one of the fields with the most to gain from the technology. According to a recent McKinsey Global Institute report, AI makes it possible to gain continuous updates on customer visits and clicks and then to respond with personalized promotions, prices, and products for each customer dynamically and in real time.
The McKinsey Report offers a number of real-world use cases of how AI contributes to not only achieving the 360-degree view but acting on it effectively. One example they call out is a travel company that applied a recommender system algorithm trained on product and customer data. This algorithm enabled suggestions for additional services, including lodging and travel options that were relevant to the customer. As a result, the company saw a 10 to 15 percent increase in ancillary revenue.
Discoveries made by Machine Learning
Going a step further, machine learning can identify behavioral patterns that help inform what marketing communication to deliver and when. For example, it can pick up on how long a particular customer takes to buy. Some customers take the approach of, “I came, I saw, I clicked, and checked out.” Others do a kind of protracted dance around the item in question, looking at it and possibly even, putting in the cart, but not checking out with it in one visit. While the first type of customer doesn’t need any coaxing because they make their decisions quickly, the second one does.
If the individual has a history of abandoning the cart until nudged with a special promotion, that indicates they will register interest but not commit without some extra incentive to press that buy button. This can be a free shipping upgrade or bonus bucks toward their next purchase. Knowing what got them to buy in the past can help you make them move forward in the future.
Building the 360-degree customer view
With an AI-powered 360-degree customer view you can gain additional insights into your customer including:
• Their life stage, i.e., student, young couple, family with children, retired, etc.
• Their form of employment and income level, which informs what kind of price points would likely appeal to them and if they are a big spender.
• Where they live identified not just by physical address with city and state but geo-cluster.
• At what time of day they tend to browse and buy; these may be two different times as some may browse during the day between work activities and only put through purchases after work.
• Which social networks they use and favor, and whether they tend to share or respond to what their network shares with them.
• Data on their behavior offline in physical stores, which categories tend to draw them there and what they’ve purchased.
On this basis, it’s possible to more accurately predict an individual’s likelihood to respond to a particular type of marketing communication and tailor it accordingly. Just as one size does not fit all for buying patterns, it does not fit all for communication preferences. This is where Machine Learning picks up on the pattern of how customers prefer to reach out to businesses, as well as which form of communication from businesses they respond to best.
Meeting your customers on their terms
Effectively driving sales through marketing depends on meeting customers on their terms. Today’s always-on customers have a digital device within reach at all times, with a world of choices at their fingertips. They don’t want to spend time searching for what they want. Catering to these customers means coming up with choices that are custom-curated to their needs and tastes. The optimal way to achieve that in near-real time is through the capabilities of AI.
Having an accurate holistic picture of what makes customers tick, how frequently they reach out to or respond to brand communication, and where their interest lies, means you can address them when and where they are receptive to your messaging. When it comes to marketing, knowledge really is power, and an AI-driven 360-degree customer view is what can empower marketers to achieve the best possible results.