Customer Data, Machine Learning

Three Things Marketers Must Do With Customer Data in 2020

Customer Data, Machine Learning | December 13, 2019

Customer data analytics has completely changed how marketers do their jobs and has become critical in marketing's efforts to better connect with customers. Having actionable, parsable records of all cross-channel customer engagements and transactions is critical when building marketing campaigns that actually resonate with customers and ultimately get them to convert. When this behavioral information is combined with other valuable insights like demographic and web cookie data, marketers are armed with all the information they need to get the right offer in front of the proper audience.

The importance of customer data in marketing has never been more evident that it is today. Heading into 2020, any marketers not using their customer data as a means of improving their short- and long-term strategies are putting themselves at a severe disadvantage. But In order for customer data analytics to fully reveal the kinds of insights needed to succeed, there are several things that marketers must do first. Let's take a look at them.

1. Unify customer profiles

Customer profile unification is a critical business endeavor for a number of reasons. For starters, it ensures a clean database with as few disparate pieces of data as possible. Considering you could be handling thousands of customers records (or more) spread across multiple systems in your technology stack, multiple profiles of a single customer can cause unnecessary confusion and lead to errors or mismatches. It's no surprise that data hygiene has become a hot topic.

But the chief reason customer profile unification is so important is because it creates a unified view of the customer. Having a single, holistic record that contains the details of every engagement customers have with a business is absolutely essential for personalized marketing and customer experiences. Having this single record also ensures that customer contact information, like name, email address, postal address, etc. is up-to-date and easily accessible, which is especially important if multiple people are viewing the record.

2. Segment data for better analysis

If you're not using the customer data you collect to better understand your customers, why are you even collecting it at all? OK that might be a bit melodramatic, but consider the wide range of insights you gather on each customer and what they might allow you to uncover if analyzed together.

For example, say marketing recently ran a new product announcement campaign that was delivered to all customers via email. Rather than just looking into email open rates to judge the campaign's success, marketers should be digging deeper into the engagement data to learn what members of your audience the content resonated with best. Perhaps this new product was a hit with customers age 50 and older, demonstrated by the high click-through rate you saw from that group. Using customer data segmentation, you can then build and execute future campaigns that target that specific group of customers with similar products or in a similar manner.

Marketers not utilizing customer segmentation analysis are only scratching the surface of how they measure past campaign successes as well as how they plan future ones.

3. Embrace machine learning for better decision-making

Machine learning has been a cross-industry buzzword for many years now, but unlike New Coke or the Zune, its hype was real and has been recognized, particularly in regard to customer data and marketing. The thought of turning important business decisions over to a computer program might sound a bit scary (not to mention the inspiration for a number of major sci-fi blockbusters), but the reality is that machine learning algorithms do an excellent job of analyzing vast quantities of data quickly and efficiently and turning that analysis into actionable decision-making information.

One of the best uses for machine learning is predictive analytics. Machine learning algorithms are designed to identify patterns and relationships in data sets, even ones that seem completely unconnected, and customer data records offer a trove of information for these algorithms to work with. These patterns and relationships are invaluable for marketers when it comes to planning future campaigns, potentially arming them with otherwise untapped knowledge of connections that they may not have uncovered in their own analysis.

New years, especially ones kicking off new decades, are a great time to reflect on the successes your marketing program and business have had. It's also a great time to re-evaluate and set new goals. What new strategies are you planning on implementing in 2020? If you feel like the current state of your customer data is getting in the way, let's talk!

New call-to-action

Subscribe Here!