What does the word Fusion mean to you? You might identify that term with nuclear power, the holy grail of carbon-free energy creation. Or you might think of Jazz fusion, the blend of traditional jazz instrumentation with electronic rock instruments. In the market research industry, fusion refers to the powerful melding of data from various sources with analytical and segmentation intelligence to account for challenges and deficiencies in survey research. 

While there is no doubt that customer surveys will always play a key role in forming business strategies, the fact of the matter is, respondents are less likely to fill out long and complex questionnaires than they used to be. We live in a world of diminishing returns.

But this is only half the picture. When it comes to ancillary data from commercial sources, the harvest of quality data about consumers is a rich and bountiful yield. Companies now have the power to augment their internal customer data with supplemental external data. 

What kinds of data do we have in mind? Think about how data such as granular geodemographics, socioeconomic characteristics, attitudinal and behavioral indicators could augment existing surveys and records. This is where the fusion concept applies. By fusing some or all of these aspects, you are likely to get a fuller, more accurate picture of your customer base. 

MSG’s data scientists work with clients to pull data from various sources, apply nuanced analytical and segmentation techniques to it, then output a robust, empirical basis for business decision making. Fusing data, then applying advanced analytics, means businesses are less in the dark than before. By reaching beyond simple statistical analysis, better inferences and more nuanced decisions can be made. 

Client Case Study

To see how this works in the real world, read this case study about one of our clients is in the fresh-fruits and vegetables delivery business. They take fresh produce that isn’t pretty enough to go on the supermarket shelves, which might otherwise go to waste, and get it into the hands of customers. The problem was the company didn’t know enough about their current customer base to be able to formulate a plausible growth plan. They knew that new market opportunities were out there, but they didn’t know where to look for them, because data items for each customer were scant. They needed better answers to questions like 

• What are the key characteristics of profitable customers across multiple markets?
• What characteristics differentiate loyal customers from the rest?
• Which geo-demographic segments include higher concentrations of loyal customers?
• How can loyal customers be located in new markets?

To tackle these questions, MSG developed a fusion plan involving an array of techniques:

• Map creation and plotting of all current customer locations
• Individual and household demographic variables appended to each customer.
• Classification and regression analytics used to zero-in on key predictors. 
• A series of spatial analyses to identify geographic clusters in new markets similar to existing markets.

Check out the full case study to see how the company achieved a much better grasp of their good recurring customers, where they were clustered geographically, what they looked like demographically, and what areas in markets of interest had the highest likelihood of potential new customers (people with similar demographics as the known customers).

Fusion of internal and external data can help your company to fill in the knowledge gaps, make more accurate inferences, and seed growth opportunities for new products, services, and markets.