B2E Data Blog

Who Are Your Most Profitable Customers?

Apr 12, 2022 3:13:43 PM / by Keith Snow

When looking at your customer base, who are the “vital few?” 

If you are familiar with Pareto’s Principle – or the 80/20 rule – the concept says a small segment of your customers are likely responsible for a majority of your results. This is the Law of the Vital Few – or in more common marketing terms, your “ideal customers.”

Understanding your ideal customers – and finding more of them – should be a goal for every growing business. Without this understanding, you risk spending time and money where ROI won’t be the greatest. 

Here’s what a data-driven approach can tell you about who matters most to your business.

Important and Unique Customer Characteristics

Though businesses are accumulating more data than ever before, not every organization will have access to the detailed customer information that enables effective targeting and personalization. Many customer databases contain mostly contact information and transaction details. If this sounds familiar – don’t get discouraged. This is actually a perfect starting point to begin the customer discovery process. 

Data appending services enrich basic customer information with behavioral, financial, and demographic information. In fact, B2E has access to up to 900 informational attributes that bring to life rich and detailed pictures about who your customers are.

Family meeting real-estate agent for house investment

For example, which customer could you build a more effective relationship with? 

“Jane Doe at 123 Main Street,” or: 

“Jane Doe, a 52-year-old affluent city-dwelling homeowner who is married with children, highly educated, and financially savvy. She is a ‘quality matters’ consumer who minds the details and is willing to pay more for durable materials and quality experiences.”  

Achieving a deeper level of understanding about who your customers are can also reveal a lot about why. Use this information to communicate with them in more meaningful ways.

How They Might Act in the Future

A descriptive customer profile like the one above is truly valuable information for every organization. But when you’re ready to go to the next level, it’s time to predict their future. (No crystal ball required.) 

Predictive analytics gives brands the ability to derive powerful insights about which actions a customer is likely to take by synthesizing an array of data. It opens a window into customer behavior that simply hasn’t been available before. This form of personalized forecasting helps anticipate future customer buying behaviors and trends, lighting a path for which products, messages, offers, and channels will strike the right chord for each customer.

Unless your marketing budget is unlimited, having insights into behavioral and purchase propensities can transform your marketing with smartly optimized strategies. The power to predict maximizes every dollar and result.

Who Else You Should Target

Equipped with an understanding of the collective traits that define your best customers, marketing to prospects immediately becomes more efficient and effective. When you’ve done the legwork of understanding your customers, it can be put to work over and over again. 

Data modeling can help to identify “lookalike” or similar audiences that share characteristics with your ideal customers, based on the premise that they will have a higher purchase propensity. You can reach these prospects with the messages and offers that have already proven effective with your current best customers. 

It’s been reported that Facebook’s lookalike audience advertising achieves a higher click-through rate 90% of the time, compared to other audiences.  That’s right – more engagement, better performance, and less work. Music to every hard-working marketer’s ears! 

It’s time to get to know your most valuable customers. Understand, nurture – and find more of them! When you’re ready to take the guesswork out of your marketing campaigns, here’s how to schedule a conversation. 

Tags: ideal customers, customer profile, Predictive analytics, personalized forecasting, lookalike audience

Keith Snow

Written by Keith Snow

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