Even before the Coronavirus reached the US, brick-and-mortar retail operators faced fierce challenges from many sides – declining traffic, higher wages, DTCs opening physical retail locations, and e-commerce. Although associates provide stores with the key differentiator over competitors and online platforms, many retailers treat labor as an expense rather than an investment, often cutting it to achieve margin goals.

A better approach is for retailers to treat their store labor expense like an asset strategically understanding when to pull key levers to ensure maximum return.

A retailer’s own data holds the insights on how to best optimize store labor. In order to do this well, store leaders will need to identify the right data, organize it, interpret it, find patterns, and then make the best decisions to create the right labor budgets, schedules, and daily action plans needed to drive sales.

Retailers can choose one of three analytic paths as they look to optimize their store labor spend.

Descriptive Analytics

Descriptive analytics provide static, backward-looking data and answer the question of “what happened.” Reports, dashboards (mobile or otherwise), flash sheets, etc., 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 HQ 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 look forward to predict what they think will happen. Trend reports and reforecasted 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 doubly true when the analysis is only refreshed quarterly or monthly. What a store operator needs is a realtime update on what is expected to happen today – specifically on my shift – and what I 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 doeslooks 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 this approach, howeverit’s hard to do! You need sophisticated AI 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 realtime). Importantly, this is also not a one-time project. The AI 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.

We’d love to hear from you. How are you addressing your analytics needs? We are always about sharing best practices with the store leadership community. Share your thoughts by emailing [email protected].