Understanding Customer Data Modeling

Understanding Customer Data Modeling

Whoever said “Ignorance is bliss” probably wasn’t standing at the helm of a business empire and responsible for revenue growth. Sure, there are things in life we’re better off not knowing, but the ticks and tangles that influence buyers’ decisions to do business with us isn’t one of them.

With ever-encroaching competition, it’s more important than ever to learn what turns candidates into customers, and then leverage that intellect for higher conversions. And that’s exactly the cue taken by Customer Data Modeling.

I. What You Need to Understand About Customer Data Modeling

The name itself sounds complicated and intimidating, but don’t let what you don’t understand scare you. Customer Data Modeling is actually a useful tool that, when analyzed correctly, provides fundamental particulars about your customers that might otherwise be overlooked.

If you’ve been in business long, then you know the importance of maintaining “rigid flexibility.” That is, you know the end result you want, but you may have to adjust your plans on how to get there. Customer Data Modeling gives you clear visual representation of pertinent information about your customer base that you can use to redirect your marketing efforts to reach your goals.

What You Can Learn From Customer Data Models

You’ve got mounds of valuable insights sitting in front of you already: your CRM, testimonials, surveys, referrals, loyalty programs, email analytics, and so on. But if you don’t take the time to sculpt that information into something useful, you’re selling yourself short of golden opportunities to engage with your customers and discover how you can make your products more relevant to them.

Your data collections can tell you where your business is coming from, who your best customers are, which products and services are generating the most revenue, and how much churn to expect in a given year. But through combing several pieces of information into a model and seeing how they relate to each other, you can make sharper connections and bolder predictions to build a healthier bottom line.

II. How to Construct Customer Data Models

There are countless ways to model customer data and transform it into something of value. Data is everywhere these days, but given its size and continual growth, you can’t rely on insight to simply happen. Oftentimes we become so involved seeking gems in the data mine we neglect to chisel those gems into usable insight.

Ask any market research expert, and they’ll preach about the importance of using frameworks to sift through the noise and hone conclusions based solely on what’s relevant. Frameworks provide a sound foundation to construct your data models, but also provide boundaries to prevent data overload from spiraling out of control. Let’s take a look at some examples:

SWOT-TOWS Analysis

A classic framework used by market researchers, SWOT stands for Strengths, Weaknesses, Opportunities, and Threats. Using this simple framework gives you specific targets to look for in your customer data repository: Discover what customers like about you, what they don’t like, what they want that you can potentially provide, and what might prevent you from providing it.

TOWS, on the other hand, combines two of the elements from SWOT to help develop a more distinguished strategy. Ask yourself:

  • How can my strengths help me to capitalize on my opportunities?
  • How do my weaknesses prevent me from cashing in on my opportunities?
  • How can my strengths negate my threats?
  • How do my threats and weaknesses combine to limit my potential?

Scenario Planning

When George Santayana said, “Those who don’t learn history are doomed to repeat it,” he probably wasn’t talking about a sales environment, but the shoe fits. Every interaction you have with  a prospect is unique – and ultimately should be treated as such – but that doesn’t mean you won’t encounter similar situations in the sales cycle.

Scenario planning helps you predict customer behavior in future interactions. Make sure you document any and all information regarding the sales cycle for every prospect. Learn what challenges the salesperson encountered, and how they overcame those challenges. Specify how the prospect learned about your company, what piqued their interest, how they first contacted you, what questions they asked, what makes them need your product, and any other information that may rear its head in a future situation. Most importantly, establish a way to make this information available to anyone who needs it, and make it easily searchable.

Word Clouds

Easier to create than a complex framework, a word cloud is a visual representation of words or phrases of varying sizes grouped together, where larger words are the ones used more frequently. When using a word cloud to model customer data, you can visualize words and phrases customers use to describe an aspect of your business, keywords used to search for your product online, or customers’ potential grievances with your product. From there, you can easily apply this knowledge to one of the previously mentioned frameworks, or develop your own strategy.

If you’re in a time crunch, or you are artistically challenged, this free tool will generate a word cloud for you.

Buyer Personas

A buyer persona is a unique set of information that places you at the customer’s eye level throughout the buying cycle. Using buyer personas helps  you discover a customer’s sales triggers, pain points, potential setbacks during the buying process, and other relevant information that can influence whether they give you their business. Leveraging the deeper aspects of customer data to create buyer personas delivers valuable insight that can help navigate your marketing efforts into a more successful sales stream.

III. Using Customer Data Modeling To Get Ahead

Finding ways to model customer data is only half the battle – you also need to know how to use those models to make predictions. Take a look at your data scheme and start connecting the dots, even if you aren’t sure how those dots relate to each other. Do your customers in New York buy more of Product A in December than the rest of the year combined? Do you close more deals at the end of the month when you offer a certain incentive via email?

The objective is to find common denominators in your endless data collection, and use those characteristics to redirect your marketing efforts. Establish metrics along the way to help ensure you’re heading in the right direction. Continue to update your customer data models to keep them relevant and valuable as new information comes along. And last but not least, don’t feel like you have to include every known piece of information into your data models. It can take some experimenting to determine what’s most important, but once you do, it will be well worth the investment.

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