Most companies have a truly shocking number of interactions with customers today. Across multiple channels (online and off), even a single customer can generate hundreds or even thousands of touchpoints.
Research shows 80% of consumers are more likely to buy from a brand that provides a personalized experience. If you want to engage more customers using real-time personalization, you need to start with data generated by customer interactions.
Every customer engagement leaves a highly valuable trail of data. Ideally, marketers have tools that allow them to do four things.
To capture customer data, many brands are turning to the growing market of customer data platforms (CDPs). Experts project these tools will be a $10.3 billion market by 2025.
While CDPs might be serviceable for capturing customer data, these platforms also have their limitations. Marketers should be aware that, for a variety of reasons, CDPs can’t always deliver the sophisticated level of actionable insights necessary to generate greater engagement (and revenue) with customers who want and expect 1:1 personalization in their messaging and loyalty offers.
In this article you will learn:
A CDP is a tool companies use to store customer data in a centralized place. Marketing teams can cull behavioral, transactional and demographic data in their CDP from a variety of sources and correlate it to get a singular or in-depth view of any customer.
A CDP may offer the following features.
Many CDP vendors appear similar on the surface, but they’re not are not all the same. Depending on their features, CDPs exist as one of the following three varieties.
While individual features differ, all CDPs can store, clean, enrich and collect customer engagement data. As a CDP collects data on a customer, it links each element to that customer to improve profile accuracy. CDPs generally draw customer data from the following sources:
You might face several challenges when implementing a CDP. Here are a few common examples.
Lack of insight due to limited data sets.
CDPs primarily operate with first-party data sets. This data is collected directly from customers and is typically less abundant than anonymous data, so you may not have enough data available to derive useful insights.
Difficulty uploading images or certain kinds of documents.
CDPs don’t handle unstructured data (images or documents) well. It can be difficult to upload this type of data into a CDP. Even if you can, you won’t be able to use it easily unless you integrate your CDP with a business intelligence tool.
Requires integration expertise.
CDPs draw data from different sources, which requires systems integration. Depending on the data collection tools you’re using and the CDP you choose, this may be easy. More often, however, this requires custom integrations or complicated configuration work.
The original vision of CDPs was to help organizations combine customer data across sources and channels, driving applications like hyper-personalization across multiple marketing channels. In reality, however, CDPs fall far short of being able to offer these benefits. Here are some of the CDP limitations that often frustrate marketing teams.
One of the goals of a CDP is to segment customers in a way that enables you to work with distinct personas for campaigns and communications. However, many of the segments created by CDPs are based on templates and only focus on recency, frequency, monetary (RFM) analysis or purchase categories. When segments are limited in these ways, you miss out on many other valuable insights.
CDPs can’t create custom segmentation layers based on your unique data sets. With custom layers, you can use your unique data to emphasize your competitive advantages. (For example, if you sell luxury products, customers may not purchase frequently enough to provide the data needed for template segmentation layers.)
To address this lack, you could analyze other factors, such as brand awareness or engagement. Unfortunately, creating custom segmentation takes so much time and effort with most CDPs that it’s often infeasible for marketing teams to do so.
Lack of actionable insights and personalization.
Yes, CDPs are great at collecting data, and they also provide analytics. But they don’t give marketing teams the actionable insights they can use in marketing campaigns. That means marketing teams can’t really rely on CDPs to craft personalized messaging and offers (remember, 80% of consumers are more likely to buy from a brand that provides a personalized experience).
A CDP can store the history of a customer’s interaction with a brand, behavior on different marketing channels and valuable third-party data, but it fails to connect this data to actionable insights. It might collect data from brand channels, but a CDP can’t push 1:1 personalized messaging to these same channels. Marketing teams would need to work very hard to implement this based on CDP data alone.
Today’s consumers want relevance. Segmentation—and even microsegmentation—falls short. Many CDPs claim to offer 1:1 personalization, but in reality they offer a version of microsegmentation. To truly create individualized, personalized marketing to your customers, your CDP will need more sophisticated technology, like artificial intelligence (AI) and machine learning (ML). This means a CDP is not an all-in-one solution for marketers who want to offer personalized messaging, communications and loyalty offers to their customers.
Lack of integration and automation.
After you aggregate and analyze data to gain customer insights, you still need to access those insights and implement them in the form of campaigns, messaging and offers. Unfortunately, many CDPs lack automation and can’t do this without a number of labor-intensive, manual steps, including building segments, exporting and importing data sets, testing insights and creating reports.
Without automation, you might end up with a data swamp. This leads to a few key problems. One, data swamps are filled with information you cannot use. Why? It takes a while to manually turn big data in CDPs into actual insights, and customers tend to generate data at a faster rate than most marketing teams can analyze at scale. Second, because they’re not powered by automation, data swamps are also usually full of outdated information about customers. You definitely don’t want your data to gather cyber dust in a world where user behaviors, wants and needs often change rapidly and dramatically —think of how purchase habits have shifted during the response to the coronavirus pandemic.
To truly capitalize on your customer engagement, you need a tool that can help you transform data into actionable insights at scale. That way, you can achieve real-time customer personalization with your offers and messaging, across all your customer segments and marketing channels.
Customer data platforms have been promoted as a great solution for marketers to use customer data, but they fall short of creating the actionable customer insights marketers need for hyper-personalization.
The only way to achieve this is with a solution that leverages AI for marketing. Platforms powered by both AI and ML can help marketers continually analyze customer data to craft the personalized offers and messages that attract new and existing customers. The result will be highly individualized, unique interactions between you and each of your customers, at scale, thereby boosting overall customer loyalty, engagement and revenue.
Learn more about how the right level of personalized offers can help your marketing team deliver higher engagement and revenue in our white paper, “Why Personalization Isn’t Always Personal.” You can also contact us to see how Formation can help your business create a post-pandemic bounce back strategy centered on personalized customer experiences.
Learn more about the Formation hyper-personalization platform
Want to learn more about customer engagement?
Have a look at these articles