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Which type of store performance analytics is right for you?

Data and analytics are taking a lead role in reassessing and improving store operations and performance, especially with this year’s sudden economic shift significantly impacting the retail industry. Store traffic is lower, traditional services like returns and the fitting room are being completely re-thought, and full omnichannel capabilities are the baseline expectations. Stores need to learn to become more efficient, smarter, and more agile in order to adapt to the new retail landscape and overcome the challenges of the current operating environment. 

To do this well, store leaders need to identify the right data, organize it, interpret it, find patterns, and make the best decisions to create the right labor budgets, schedules, and daily action plans needed for stores to operate at peak performance during each shift. 

Retailers can choose one of three analytic paths as they look to improve their store performance. 

Descriptive Analytics

Descriptive analytics provide static, backward-looking data and answer the question of “what happened?” Reports, dashboards (mobile or otherwise), and flash sheets are examples of descriptive analytics. The advantages of this approach are that the analysis is fairly simple to produce and can be standardized. In addition, they’re very familiar with leaders at Headquarters who are used to numbers, reports and analysis. As a result, this is how 90% of retailers operate. The core weakness, however, is that this approach requires the end user to interpret the data correctly, draw the right conclusions, and take appropriate actions. At the store level – where managers are balancing untold priorities and whose interest in retail does not come from a love of Excel – this breaks down completely.

Predictive Analytics

Predictive analytics is the next level of analytic maturity. Here, retailers take the output of descriptive analytics and utilize it to predict what they think will happen. Trend reports and re–forecasted sales targets are examples of this. Certainly, updating forecasts is a good thing to do and helps take some burden off of the end user to interpret past results. Predictive analytics is an important step in the right direction, but there are still major weaknesses. In most cases, this analysis is neither broad enough, nor frequent enough to impact actions. The analysis typically only looks at sales and sometimes traffic. Rarely does it look at all the other key metrics that drive the business (conversion, basket size, labor hours, sales productivity, etc.). As a result, the actionability of the new forecasts can fall short. This is true when the analysis is only refreshed quarterly or monthly. What a store operator needs is a real–time update on what is expected to happen today – specifically on their shift – and what they need to do about it. Only with this do you have real data-driven decision making at the store level.

Prescriptive Analytics

Prescriptive analytics offer a new way for retailers to look at data. This type of analytics takes the burden off the store operator to read, understand, interpret, and make the right conclusions from all the reports they’re being sent. Instead, this analysis is fundamentally forward-focused, offering a detailed view of what’s likely to happen. The big distinction from predictive analytics is that prescriptive analytics then identifies the implications of a forecast and specifically suggests the actions needed to best respond to what is likely to happen in the future. Essentially, prescriptive analytics standardizes what a great store operator already does – looks at the data, sees what it can tell you about the future, understands the implication to your business, and then devises a plan to avoid risk and capitalize on opportunities. There is a catch to this approach, however – it’s hard to do. You need sophisticated machine learning skills to model the data, and sophisticated IT skills to organize the data and have it operate at scale and in the timeframes you need (aka near real–time). More important, this is also not a one-time project. Machine learning needs constant attention to learn and improve, as does the supporting IT infrastructure. Retailers are not well-suited to either of these capabilities, and so the only viable option is to partner with companies that are.

Retailers can use data and analytics to enhance their retail operations and store performance while attracting, retaining and growing their most loyal customer base. At Tulip, we’ve expanded our retail mobile solutions to help retailers optimize store performance and drive smarter business decisions with machine learning. To learn more about Tulip’s Store Performance System or to schedule a demo, contact a Tulip Consultant today.