As a modern marketer, you aim to collect a significant amount of customer data, on everything from digital clicks to in-store foot traffic, and parse it through an ever-increasing list of technologies. Chances are you’re using upwards of 10 different tools, including web analytics, marketing automation, social platforms, a data management platform, and a host of others.
And yet, even though you have all this data somewhere in the organization, I can guarantee you have difficulty accessing the appropriate data and generating insights to optimize customer experiences. Because here’s the naked truth: brands often miss the mark on customer expectations by either underwhelming them with poor personalization efforts or overwhelming them with too many messages or offers.
In fact, this was what Rusty Warner, principal analyst at Forrester Research, and I discussed on a recent webinar hosted by Direct Marketing News—along with what it takes to truly optimize the customer experience in a marketplace where consumers are always addressable and “always on.”
So how can marketers bridge the gap between brand strategy and execution? It’s all about taking a data-driven approach to customer engagement and, mostly, comes down to a handful of factors:
Although marketers collect extensive customer data, the systems of insight used to analyze this information aren’t always in sync with the systems of engagement used to interact with customers. According to Warner, closing this gap between systems of insight (customer data and real-time analytics) and systems of engagement (marketing automation and marketing content) is crucial to creating the kind of value exchange that will increase customer engagement.
Aligning these two systems means you can more effectively leverage learnings from customer data to create the kind of personalized microsegments that can improve brand engagement and increase revenue from each interaction. This also allows you to better understand changing customer behaviors and preferences, which can drive further increases in customer lifetime value and benefit your company.
Part of the value in data-driven personalization lies internally as well as externally. Role-based insights—the internal version of personalization—can help your team members by serving up only the information they need in their day-to-day activities.
Consider the marketing campaign manager, who only needs to see snapshots of campaigns and doesn’t need full view into the entire breadth of marketing analytics. A creative designer might only want to work with content and offers, and not have to deal with the complexity of the targeting and campaign segmentation strategies. Serving up insights specific to your team’s roles and day-to-day functions can streamline operations tremendously.
Personalization can also help you meet customer expectations more effectively, which is substantially beneficial in regards to driving the most value from your messaging. Without using data in this regard, you risk creating messaging that falls flat and doesn’t perform because you have not met customer expectations effectively.
Perhaps the most crucial facet of data-driven personalization is having high-quality data in the first place. Lack of data quality could stem from a number of issues—such as integration issues, data aging, irregular data structure, etc.—any one of which could derail even your most thoughtful plans.
Because of this, the ability to cleanse and structure customer data is hugely important. This is why you need to consider a platform that can manage data from any source through the cleansing and structuring process so you can access the insights necessary to craft focused and powerful messaging.
Conquering the customer engagement “gap” can be a significant challenge, requiring you to gain alignment between various internal stakeholders on strategy and also get the right kinds of tools in place. That said, a focus on data-driven personalization can pay dividends for your marketing program’s success and make an impact that reaches far beyond improving marketing metrics.