Navigating Retail Success with Prescriptive Analytics

Navigating Retail Success with Prescriptive Analytics

Retailers have always generated massive amounts of data, but haven’t always known what to do with it. Enter the recent analytics boom, with legacy retail tech companies claiming to be able to “save” retail with better insights. And the industry is noticing. MarketsandMarkets expects the retail analytics market to grow from USD 3.52 Billion in 2017 to USD 8.64 Billion by 2022.

While there are many solutions on the market to help make sense of this data, many still rely on human interpretation. A single dataset can be interpreted a thousand different ways by different people, depending on their roles, experience, personal biases, and political views. When you consider that, across a large retail enterprise it can cause problems, especially since retailers have armies of staff in supply chain, store operations, and merchandising. Some of these employees have limited data science experience and some have a lot of data science experience. No matter what the experience level, there always seems to be a lack of communication between different areas of the field.

Today, there is prescriptive analytics. This growing form of analytics leverages companies’ existing data to provide simple, actionable tasks across the enterprise, from stores to headquarters to distribution centers, to eliminate negative behaviors or encourage and duplicate positive ones.

But what is prescriptive analytics?

Let’s break it down further to show how prescriptive analytics can go above other analytic solutions, using Gartner’s Four Types of Analytics Capability and the evolution of navigation. Imagine that you wanted to drive from Boston to New York City…

Descriptive Analytics

Similar to going to a store and buying a road atlas, descriptive analytics relies on your own navigation skills to get yourself from point A to point B. Because it is subjective, a driver could come up with hundreds of different ways to get to New York, but it doesn’t mean every route will be the most efficient, the fastest, or the most scenic, depending on the desired drive.

Diagnostic Analytics

Realizing you need a little more help getting to New York, you decide to visit a AAA agent to proactively plan your route for you. The agent uses their own professional diagnostic ability to take a deeper look and identify a frequently traveled route. They then create a report you can (hopefully understand), and provide it to you to decipher while you drive.

Predictive Analytics

Let’s say you need to leave at rush hour on Thursday and worry about hitting a lot of traffic on Rt. 95 during your morning commute. You print out the turn-by-turn directions from Google Maps for a 7 a.m. departure. The system generates a suggested route, and forecasts estimated time of arrival based on the most common traffic patterns based on historical data. Now imagine that your plans change, and you decide to leave on Saturday afternoon. Because data is perishable, as noted by Forrester Vice President and Analyst Mike Gualtieri, the printout for rush hour directions is now useless.

Prescriptive Analytics

So now it’s Saturday but what if there’s an accident that’s backing the highway up for miles. None of the previous analytics can handle close to real-time data to get you to your destination in the quickest route based on time. However, Waze, the popular consumer app, provides a perfect example of prescriptive analytics. The mobile app provides turn-by-turn directions based on data generated by other drivers. While it will calculate an initial route upon launching, it focuses on the next action a user needs to take, adjusting the additional steps and updating the ETA based on the data so that you can take action accordingly.

Now imagine it wasn’t New York that you were driving to, but better on-shelf availability, or optimized merchandising for a marketing campaign, or smoother pick-pack-and-ship at a distribution center, or even better promotional compliance at an individual store.

Just as Waze also pivots based on data to provide better routes, Profitect’s prescriptive analytics looks to enhance or improve retailers’ processes to increase efficiency, accuracy, and ultimately more profitability for the business. While Profitect’s platform can identify problems and suggest solutions, it’s also great at identifying successes or best practices that can be replicated across the enterprise.

Due to the endless flow of data made available to organizations, businesses are turning to analytics solutions more often to draw conclusions and to help their decision-making processes. Profitect’s prescriptive analytics and step-by-step directions ensure that nothing falls through the cracks, and that retailers are being guided in plain language, rather than reports, based on their data. To learn more about how we do this for our growing global customer base, visit our website.