Converting Insights Into Income

Insights are a hot topic, and everyone is talking about them.  If you Google “insights from analytics,” you’ll get more than 50 million results.  Scanning those results, you’ll notice an array of ideas, ranging from simplistic to complex.  Simplistically, many people confuse looking at reports with analytics. When they review data reports, they believe they are performing an analysis. You can get insights from looking at reports, but it won’t always lead to profits.

On the other hand, some companies get bogged down and focus on building huge data marts and the associated analytics tools, losing sight of the insights.  The starting point for converting insights into income is asking yourself: what unknowns do you want to eliminate?

Companies and executives often don’t understand that monetizing insights requires a disciplined process that asks – and answers – several important questions along the way.  Revenue Analytics has developed an analytical decision framework that, if followed correctly, can help you derive insights from the data that you have at your disposal. It starts with a series of questions that companies need to answer before they head down the path of driving organic revenue growth:

1. Why Are We Doing This? Companies need to determine what decisions they are trying to impact and what unknowns they are trying to eliminate. Is it the prices you’re charging? Is it the inventory you’re stocking? Or is it the products you’re bundling or promoting?
2. What Data Is Needed to Inform Decisions? To understand what your customers are doing and how your competitors are impacting your business, you need to leverage different types of data. For example, integrating customer transactional data, market share, and competitor data could help to generate insights you didn’t have at the onset.
3. What Insights Can We Glean from the Data? With the right analysis, your company can determine how different customers respond to different price changes; what regional preferences exist as well as which products can command a price premium. These insights – or retrospective interpretations of the facts – could yield fresh competitive strategies.
4. What Do We Do with the Insights? Insights give an historical perspective; knowledge provides foresight. Convert insights into knowledge through predictive analytics. Predictive analytics can tell you what happens when your company takes certain actions. So, if you raise prices 5 percent, what happens to demand? Is there a price point where you can gain market share while remaining profitable? How do customers respond differently by region or product over time?
5. What Do We Do with What We Learned? Create wisdom by optimizing what you do in the marketplace with prescriptive analytics. Informed by the results or strategy, you can determine where you want to grow share, what are your optimal prices, inventory and promotions, and which products are the best performing and lucrative to fund your business.
6. What’s Our Execution Strategy? This comes down to having well-defined metrics and real-time feedback. Placing a stake in the ground in terms of what you expect to deliver in terms of revenue and/or profit. You need to boldly determine how much your insights will drive profits, then measure against that promise. This approach will produce optimal results.

Asking and answering all of these questions is critical not just in theory, but also in practice. Companies that focus on “insights from analytics” stop at step three and don’t successfully generate dollars from data.

Applying this analytically driven framework, a big-box retailer eliminated the unknowns of how to compete against Amazon profitably. The Q&A went something like this:

Why are we doing this? To understand where and when to compete with Amazon without draining cash. What data do we need? Transaction data on 300,000 SKUs over the past three years, including all the price changes and changes in volume, as well as competitor prices. What insights can we glean? By looking at differences in customer response based on price, we can identify price/volume relationships, which proved to give insights on how different categories responded in different ways. What do we do with the insights? Predictive analytics showed we could get a 6-7 percent premium over Amazon on 20 percent of the SKUs–and price reductions were needed on another 20 percent. What do we do with what we learned? Prescriptive analytics targeted precise prices to charge on the 120,000 impacted SKUs. What’s our execution strategy? We monitored results as we captured immediate feedback from sales data. We tracked how customers responded to our price changes and competitors’ prices, fine-tuned prices then measured the revenue uplift.

Results for the big-box retailer were proof that this critical Q&A can convert insights into income. The retailer posted its best holiday shopping season ever, beating the previous year’s revenue uplift by a multiple of 5 and generated a $60 million gain in profits.