Putting Analytics Theory Into Action: Three Real World Examples to Inspire Your Business

Putting Analytics Theory Into Action: Three Real World Examples to Inspire Your Business
Jim Rushton

The following is adapted from Guaranteed Analytics.

Analytics can get pretty theoretical sometimes, and it’s easy to get caught up in hypotheticals and what-ifs. Staying stuck in theory only can keep your business from taking the action steps it needs to actually derive benefits from analytics.

You may constantly start projects but not finish them because you’re not sure if what you’re doing is right. Or you may have endless meetings but nothing ever gets off the ground. Maybe you collect reams of data and don’t know what to do with it.

If you’re looking for real-world inspiration for your business, you’ve come to the right place. Here are three success stories that show what’s possible with analytics.

These stories illustrate action steps your business could try today to go from theory to real results. You’ll notice that in each case, the goals were realistic and very reachable. The businesses identified and addressed errors, like redundancies in their information, and they reaped benefits by leveraging small changes over multiple locations—something any company can adapt and profit from.

The Sporting Goods Retailer

Game On is a large sporting goods retailer, that had already figured out the logistics of how to put product in their stores and which products to pick. They had their merchandising down, but they were interested in finding other ways to increase their revenue without adding much in additional costs.

Analysis revealed an opportunity around market basket analysis. They wanted to improve how they analyzed product mix by transactions over time, enable better merchandising and marketing decisions off of that knowledge, and drive up the average market basket size.

The goal was to add one item for every 20 or 30 transactions. It was believed this would translate to an increase in sales of two to four percent across the company.

It was discovered that Game On could be smarter on the upsale, such as encouraging customers to buy a sports drink with the purchase of baseball gear. Exact products were identified throughout the store that should be recommended for upsale.

The merchandise team sprang into action along with the marketing team. Promotions were sent out, and even the website participated in the upsale campaign.

Game On was ultimately able to persuade customers to add additional items to their basket. This resulted in tens of millions of dollars a year in increased sales.

The Multilocation Discount Chain

Discount All was also looking for ways to encourage customers to buy more items. They were sourcing plenty of data from their point-of-sale systems, but they hadn’t been able to turn that data into useful, actionable information.

After working through their data, opportunities were identified around product affinity. The goal was to determine the top one hundred items sold over the previous twelve

months, determine what items usually sold with those products, and incentivize customers who weren’t making those purchases together to begin making those affinity product purchases.

Initial reporting produced redundant information. For example, the most popular item purchased alongside cans of pork and beans was… other size cans of pork and beans.

But eventually different product classes, like chips and mac and cheese, were teased out. Stores that were underperforming were identified. Underperforming was defined as purchasing affinity items at a lower rate.

Knowledge workers were then made aware of sales opportunities, including:

  • Building awareness
  • Using different signage
  • Changing merchandising/displays
  • Placing local newspaper ads

The opportunity for improvement that was identified only amounted to roughly $1,000 in additional weekly revenue per location per week. However, because Discount All had more than 10,000 locations, a $1,000 increase per store added up to $10 million in additional revenue per week. All they had to do was bring the underperforming stores closer to average, and they would easily reach that goal.

The Lifestyle Product Manufacturer

Cold Container didn’t have insight into how their products were selling across different distribution channels.They were sourcing a good amount of data through their ERP system, but they weren’t able to work with that data in a way that would help them make actual decisions.

They were certain they were leaving money on the table, but unsure of where or how, they just kept up with the status quo, manufacturing and distributing their products without knowing how to make better investments or how to better manage their product offerings.

Their CEO badly wanted insights so she could get a better understanding of what their sales were like the previous day, week, and month. The Cold Container team had this data, and while the problem seemed simple enough, they spent months trying to develop a reliable way to access and synthesize it into useful information. Frustrated and confused, they eventually sought help.

Data were pulled from 9,000 different tables and organized in a way that made sense. This enabled the Cold Container team to begin to see what they had been looking for and find new opportunities for their products.

Next, a variety of crucial insights were generated. For instance, they realized they

could double down on certain SKUs that were performing well and heavily reduce their SKU counts elsewhere. The result was fewer products and more investments in the products with the most upside, leading to a sales increase of over $3 million. This insight—sell more by carrying less—was risky, but it worked.

Before implementing their analytics program, Cold Container didn’t even know how certain colors were performing relative to others. Blue, cerulean blue, light blue, and sky blue were all rolled up into the broad category of “blue,” allowing a comparison to other color groups and providing trend insights. Once their data were unlocked and synthesized into useful information, Cold Container were able to produce insights that transformed their understanding of their own product offerings.

Insights that lead to transformation is what analytics is all about no matter who is using it. To make these real-life models work for your business, you’ve got to find the insight in your own similar projects. If you identify a goal, develop strategies to gather information, and leverage the hidden opportunities you uncover, your analytics projects will get out of the theoretical stage with action that yields real results.

For more advice on turning your analytics into a success story, you can find Guaranteed Analytics on Amazon.

Jim Rushton began his career in analytics working with some of the biggest consulting companies in the world, including Accenture, Deloitte Consulting, and IBM Global Services. Jim then moved to an executive position with Verizon, where he oversaw the company’s customer and marketing information. Leveraging his experience across corporate America, he helped found Armeta Analytics, and in the past decade, his team has helped dozens of Fortune 1000 companies learn how to monetize their data.

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