Customer Data & Analytics Blog

Successful marketing campaigns begin with customer data and machine learning

Abhi Yadav | 2 minute read

If you’re a data scientist, you’re familiar with the many processes data must go through before it can be used for machine learning models and analytics. If you’re a marketer, you may not be aware of the lengthy journey customer data takes before it reaches you. This post highlights some of the processes customer data goes through before it is made available to marketers for customer analytics and marketing campaigns.

Topics: Customer Analytics

Predictive modeling techniques used in marketing

Janet Wagner | 3 minute read

The concept of predictive modeling has been around for decades, and it involves collecting data, formulating a statistical model, making predictions, and then revising the model as more data becomes available. It is only in recent years that the use of predictive modeling techniques in marketing has taken off- thanks to the abundance of customer data available. There is a wealth of internal and external data that data scientists and marketers can leverage to make predictions about customers such as the propensity to engage, convert, buy, and churn. This post highlights two common predictive modeling techniques used in marketing.

Topics: Customer Analytics

Leveraging customer analytics to reduce churn rates and grow marketing ROI

Chuck Leddy | 3 minute read

Customer retention, often measured by “churn” rate (the percentage of existing customers who leave in a specified period of time), is the most important success factor/KPI for any business. When customers stay, your business can build long-term profitability through repeat purchases, as well as cross-selling and up-selling opportunities. When you retain customers and optimize their lifetime value, you also create brand ambassadors who give you priceless word-of-mouth marketing and referrals. “Churn,” on the other hand, is a revenue killer.

Topics: Customer Analytics

Empowering the citizen data scientist: The democratization of customer analytics

Chuck Leddy | 3 minute read

Diffusion and democratization is a natural part of the life cycle of any technology, as cutting-edge technologies typically move from the laboratory, into the hands of a few early adopters, get commercialized by industry, and later become widely available to all. For example, the 1980s and ‘90s witnessed the arrival of accessible desktop computing, facilitated by easy-to-use Internet browsers. Computing technology thus moved from big server rooms in corporate headquarters/IT departments and onto the desktops of every single employee. That same computing power is now fully democratized and in everybody’s pocket.

Topics: Customer Analytics

How to go from bad data to good quality data in your business

Christina Tramontozzi | 2 minute read

Every marketer already knows that data is their lifeblood. It’s our sauce to making effective marketing happen. Yet as data quality issues persist in B2B marketing, how to achieve good data quality doesn’t need to be a secret. With technology advances today, every B2B marketer can have good quality data.

Topics: Customer Analytics