Customer Data & Analytics Blog

Customer Segmentation: The Key to Targeted Marketing

Josh Fayer

Today’s marketing teams need to be tactical and precise to efficiently use their budget and gain the highest return on investment. This means finding the right customer to target at the right time, and in turn saving resources on wasted outreach and increasing the likelihood that a given campaign will translate into revenue. Customers in a database can be divided into segments which help to define the audience. There are tons of ways to achieve this. Below are just a few of the ways consumers can be classified.

Topics: customer segmentation analysis big data analysis marketing segmentation customer data personalization

The Mechanics of Predicting Customer Churn

Andrew Malinow, PhD and Mimoza Marko


When The Business Follows A Subscription Model

Customer Churn is a typical dynamic in any business – for one reason or another, a customer who has previously purchased from a company, no longer purchases. However, to surface potential causes for churn that can inform mitigation activities, we need a more operational definition.

Churn can be defined in several different ways. If a business uses a subscription model (e.g., Netflix, Amazon Prime membership), Churn can be defined as those customers who have cancelled their subscription. A subscription cancellation typically exists as an explicit field in a database (cancelled_subscription=True), or it may need to be derived in some way. In either case, there is a specific event, that is either explicitly captured, or can easily be derived from existing data points, that provides the definition for Churn.

Topics: machine learning research Innovation customer insights churn

Data and Customer Loyalty: Part 3

Ed Wolf

“Bottom-up” Analytics—Data as a strong foundation for customer loyalty

Continued from part 2 of this series that ran last week on challenges marketers can face when they turn to data for guidance.
So the key, then, is to leverage all available data, while making sure that the data is clean, unified, and gives a true “360” view of the customer. Only then can marketers extract the real behavioral insight necessary for true customer engagement. While existing programs may be able to provide some general insight into customer behavior and inform marketing efforts designed to influence that behavior, we favor a “bottom up” approach. In other words, just as you would not want to build a house on a weak foundation, a strong loyalty program will leverage all available data sets to determine the most effective offers, tone, and cadence to deliver to customers and provide the framework to engage and reward them with relevance and personalization.
Topics: Customer Data Management customer analytics data driven marketing customer experience strategy customer centric marketing customer insights loyalty

How AI Solutions Can Benefit Your Business

Josh Fayer


Implementing new technologies inside of an organization can present tough obstacles to climb. Businesses need to be sure that those technologies are worth the investment. In the case of AI-based solutions, they often are. According to Capgemini Consulting, 3 out of 4 businesses that implement AI-based solutions saw both sales and customer satisfaction increase by over 10 percent; 78 percent also saw operational efficiency increase by over 10 percent. What’s behind these numbers? What do AI solutions offer that companies couldn’t do before?

Topics: artificial intelligence marketing analytics AI MarTech customer data Marketing customer insights

Data and Customer Loyalty: Part 2

Ed Wolf

Data Challenges

In theory, brands have more data than ever before on their customers—their preferences, purchase history, spending habits, demographics, intent, and other attributes. In theory, that large amount of data should make it easy for companies to truly understand their customers and deliver the best, most personalized experience, whether online, in store, via email, or on a customer support call. We say “in theory” because the reality is that shockingly few are able to take advantage of the benefits that “big data” provides.