Data Modeling: Do the Numbers Match the Theory?

Earlier in this series we looked at the importance of building a profile of your ideal customer. Creating the persona of the “perfect” customer gives the marketing team an idea of what type of client to target by identifying traits that are the most profitable for the business.

When Right On Interactive begins working with new clients we often ask them to describe their ideal customers or clients. They usually have someone in mind; the problem is many of them use an emotional process when building customer profiles.

What do we mean by emotion?

An emotional approach is one built around human biases and rarely paints an accurate picture. Often the marketing team will simply approach the sales force and ask “who is our perfect customer?”

The problem with this method is that a sales person might make suggestions based on any number of random factors. They may suggest a customer that is easy to work with, or one that always returns phone calls. They rarely use the right data sets to identify who is actually the most profitable for the business.

Building a customer profile is like putting together a puzzle. The picture takes shape with the more pieces you’re able to fit together. This is why data modeling is so important. It helps marketers to verify their assumptions.

At Right On Interactive, we use a data focused analysis to identify key data points and assign them a specific value through a process called data mining. By breaking the data down into clusters, we can begin to take an un-biased approach to building that customer profile.

The numbers don’t lie. Data helps to identify what type of interaction and engagement it takes to convince someone to buy. Additionally, data lets you see what type of sales and service support a customer requires after the sale. Data lets you see who is satisfied with your products and who comes back consistently with complaints.

There are a number of ways companies can determine where best to focus their marketing and sales energy. Then it comes down to the discipline to apply that strategy and focus on only your best customers. By looking at just a few key data clusters it quickly becomes clear what type of customer is the most profitable.

At some point we’ve all been warned about the dangers of making assumptions. It’s no different for businesses. The data is out there. Companies that take the time to collect and analyze information are able to develop a focused approach to marketing and customer service. Companies that ignore key data might as well be throwing darts at a list of prospects.