Best Practices for Improving Retail Operations
For decades, retailers have been collecting and analyzing point-of-sale data to get a better understanding of how buying trends impact revenue. In many ways, they pioneered the concept of “big data” well before it was a widely used term.
It’s no surprise then that more progressive companies are beginning to consider big data as a strategy to evaluate equipment performance, measure ROI and make better decisions that drive higher operating efficiencies. There are a number of factors converging to create the perfect storm for retailers. Facility system designs are becoming more complex, thus increasing the number of variables that need to be managed for efficient operations. Industry consolidation has led to fewer but larger retailers with enterprises that often contain a multitude of equipment brands and control platforms to manage. And, with a smaller percentage of today’s workforce pursuing education in HVAC/R, there are fewer experienced technicians working on disparate, more complex systems. Retailers are in a constant battle for operational efficiency, and having additional insight can help them with these issues.
Here are some best practices to better leverage the data available to improve retail operations:
Extracting value from as many data points as possible. One key industry theme is that there is not a lack of data in retail operations—there is actually too much data. The challenge becomes using that data to create a more precise picture of the business, using information to extract and highlight factors to drive action within the store and across the enterprise.
Retailers have admitted that much of their time can be spent poring over data. But, once the value is extracted, data analytics allow them to improve energy efficiency, lower expenses to enhance ROI, reduce complexity and manage capacity. Often the value of data collected can only be determined in the future, according to one of our retail customers, which is why they err on the side of collecting as many data points as possible and then extracting the value later.
Using data to uncover scenarios that lead to savings. Big data in operations can expose scenarios where an initial investment can lead to savings, sometimes in ways that are counterintuitive. When analyzing data, retailers have to realize they often need to make tradeoffs to see the benefits. For example, retailers may want to cut maintenance costs, but doing so can have an adverse affect on food quality, customer comfort or energy efficiency. Conversely, if they are investing in maintenance, they can positively impact food quality as well as realize energy efficiency benefits. Targeting the “where” in these scenarios is the goal of utilizing the data.
Retailers utilize seasonal lighting schedules for lighting efficiency, but deviations can occur, with excessive deviations leading to increased energy usage and costs. By gathering data within its enterprise, we provided one retail customer with reports on which stores had overridden their lighting schedules and the costs of each deviation. Leveraging this data helped the retailer realize where problems were occurring within its enterprise and ultimately reduce its electricity bills.
Putting the right people in the right place. Analytics require a different skill set. What is the right strategy for talent acquisition if a retailer wants to get serious about big data?
With all the data surrounding retail operations, some companies have begun hiring people who are skilled in data analysis over those with backgrounds in facility maintenance. For others, turning to outside experts may be a better and more practical alternative. Outsourcing data analytics allows a retailer to invest more in the skills they need internally to take data insights and move them into specific actions to improve business operations.
Layering in additional data points for further benefits. Once a good model for collecting, analyzing and acting on operational data has been established, retailers can go even further by layering in other data points. This will allow them to correlate additional information, such as weather, store traffic and customer credit card information with their operations data and get a deeper understanding of the dynamics that are impacting their business.
Each retail business will define an approach for dig data that reflects the unique opportunities and challenges they face. Our industry has been successful with prescriptive programs and retrofits to drive operational savings. We are moving into an era where the adoption of data-driven best practices can have a significant impact. Starting down this path will ensure a sound strategy, allowing retailers to more effectively use operational data to improve their facilities.