• February 28, 2017

What’s the Story? 2 Tips on Using Big Data to Innovate

Analytics can be used to identify innovations and quickly deploy them. 

Big Data may not possess the human creativity needed to innovate, but it can provide the critical fuel needed to power game-changing ideas, a potential differentiator at a time when innovation must be fast and furious for companies to survive and thrive. Here are two tips to get started using Big Data to identify and quickly deploy innovations.

Big Data analytics, especially when powered by machine learning, have already produced a wide array of incredible insights in every industry. Most of the progress so far has been in marketing, operations, and other Big Data projects where the queries and tasks are generally straightforward. Indeed, this is the year of the machine when Big Data-driven automation, artificial intelligence, and the Internet of Things are set to take over most of those tasks entirely.

1. Get the rest of the story.

Typically, analytics are used to answer a specific question. For example, in retail, an enterprise might look to the data to find out which products are selling well. That’s good to know for many reasons—managing inventory and guiding merchandise buyers on future stock selections, for starters.

However, there’s more you need to know to innovate. For example, you might want to use analytics to determine which stores are not selling that same product well and why. Maybe the answer is that there’s road construction blocking the store’s roadway entrance. Maybe it’s bad weather. Maybe it’s the wrong size mix or price point for that market.

Traditionally, retailers simply cut the price of products that don’t sell well in that store and lose profits in the process. But if you have a more granular understanding of why products are not performing well, you can innovate the means to move more of that product at a higher price. For example, you may discover that enabling store managers to see product sales and inventory data on other stores in the chain is a profitable answer. That way the store managers can move slow-selling merchandise to stores that are selling it well, avoiding discounting the price.

Dig for the rest of the story in whatever you are using analytics for now, and odds are that innovations will come to light.

2. Look for the story arcs in algorithm outputs.

A story arc is simply an extended or continuing story line throughout multiple episodes. In a television series, each episode tells a story, but each episode is also tied to other episodes in story arcs thereby telling a bigger story at the same time. Algorithm outputs (answers from analytics) can be viewed as event “episodes,” and you can often find big picture story arcs among them that will lead you to new innovations.

Begin by combining the outputs in various ways and look for relationships between them. Look at them with both an artistic and a logical eye. What story are these new combinations telling that the individual elements can’t tell alone?

One example of this can be found in grocery retailing. For decades it was thought that apple was America’s favorite pie. After all, Americans buy more apple pies every year than any other flavor. If grocers used just these analytics for inventory and marketing purposes, they would still think that. But, as it turns out, apple actually isn’t America’s favorite pie.

“You look at supermarket sales of 30-centimeter pies that are frozen, and apple wins, no contest. The majority of the sales are apple,” said Kenneth Cukier, data editor at The Economist, in his TED Talk.

“But then supermarkets started selling smaller, 11-centimeter pies, and suddenly, apple fell to fourth or fifth place. Why? When you buy a 30-centimeter pie, the whole family has to agree, and apple is everyone’s second favorite. But when you buy an individual 11-centimeter pie, you can buy the one that you want. You can get your first choice. You have more data.”

By comparing flavor rankings in large pies to flavor rankings in small pies or individual slices of pie, you can find a story arc that reveals not only what is happening at kitchen tables everywhere, but why it is happening. That in turn leads to profitable innovations.

One of the innovations in this case was the bundling of slices of several different pie flavors in a single container. These enabled customers to serve their family and guests their preferred pie—at a slightly higher cost of course, but also with a pre-sliced convenience for easier serving. The grocer or the producer incurs little to no extra cost, as they are actually producing the same number of pies; they’re just configuring them in a container differently.  

It doesn’t matter what industry you are in. Looking for those story arcs between outputs almost always leads to insights you wouldn’t have thought to ask of your analytics. 

The examples above are simple innovations that could have easily been implemented at any time had the retailers been able to see the need for them. By using these methods, you too are likely to see opportunities that were invisible until now.

Remember, analytics are telling a story, and once you see that, you can write a happy, profitable ending!

Like this story? Learn more about accelerating better business outcomes with Big Data analytics.