Big data: it's highly valued, but little understood. In fact, more than half of executives today downplay the importance of data analysis because they just don’t get it.
If that’s you, your business is missing out on some major opportunities to engage your most valuable customers how and when they want.
Instead of relying on instinct, you could be efficiently and accurately calculating the outcomes of your business strategies through a process called data modeling. A model is a framework that will help you understand and predict any number of scenarios, from customer churn to seasonal sales trends — depending on the data you put into it and the analysis you’re using.
Consequently, if you’re not modeling, there’s a lot of information you just won’t have. Below are four ways you might be falling behind if you aren’t leveraging your big data effectively.
1. You don’t understand how your customers’ behavior influences your profit margins
You’re probably already collecting data about where your customers click on your website, what emails they open, how much they spend or whether or not they’ve signed up for your loyalty program.
But are you putting all of that data together to understand the types of activities your customers engage in before they buy (or before they defect from your service altogether)?
A well-built model can help you tackle this tricky equation. For instance, if you want to know why your customers hit unsubscribe, content modeling could show you what content features (for example, frequency, length or ratio of text to images) make your customers more receptive to your emails. Use this analysis to alter your content strategy so that your customers don’t hit unsubscribe as often.
2. You don’t know what characteristics make up your best customers
Gone are the days of conversion marketing, in which businesses haphazardly targeted as many potential customers as possible in the hopes that a certain percentage would convert. Now, using data like the age, location, and income of your customers, the devices they used to buy, or some combination of other attributes, you can figure out what your most desirable customer has looked like in the past — and then strive to acquire similar customers in the future.
The process you’ll likely employ is called customer segmentation, in which you group similar customers together in a way that’s relevant to your marketing efforts through a modeling technique like clustering. You could even take things a step further and forecast how these groups might shift over time.
3. You don’t know which customer acquisition channels deliver the best ROI
Well, hopefully you have some idea of what channels have the highest return on investment.
But in order to optimize your marketing spend, you’ll need a clearer picture of where your best customers are coming from and how much it costs to acquire them. Even if you’re getting great customers from a particular channel, with the acquisition costs factored in, you might be experiencing a loss. The best way to avoid this pitfall is to model your customer acquisition and retention efforts.
This can be a complicated endeavor, since you’ll want to take into account all types of marketing — from discount emails to television ads — to model acquisition, and any expenditures you made trying to retain the customers you brought in with those efforts. The model you build will make sense of all this data in a way you can’t, giving you insight into how your customer acquisition efforts are really panning out.
4. You don’t know what your customers will do in the future
Curious about the lifetime value of your customers? The most important dollars are the ones you haven’t made yet, so if you’ve only calculating how much your customers are worth to you in the moment, you’re being shortsighted. A lifetime value model, on the other hand, will account for future sales.
Lifetime value modeling is the backbone of many of the analyses we’ve already talked about. It gives you an idea of the profit a customer might bring in over the entirety of their interactions with your business.
A lifetime value model might use customer demographic information, purchasing trends, acquisition channels, acquisition costs or other information to give you an expansive view of how valuable any given customer — or a whole segment of customers — might be.
Still on the fence? Forrester has found that marketers who use predictive analytics are nearly three times as likely to see higher-than-average revenue growth rates. And the top benefit they see is better customer engagement.
So yes, the data modeling process is a technical one that usually requires input from a machine learning expert or data scientist. But if you develop a framework that accurately reflects your business, you’ll bridge the gap between your analytics team and management — as well as ultimately target your customers more effectively.