Retail stores today pulsate with activity and generate streams of data about customer behaviors, customer traffic, employee performance, and inventory placement and sales.
This creates incredible opportunities for retailers, their regional managers and their store managers to leverage analytics to better understand shifts in customer preferences, employee productivity, and merchandising strategies that can help them gain a competitive advantage.
Point-of-sales (POS) systems offer retailers a font of information about customer behaviors. Store managers and other retail executives can analyze POS data to extrapolate insights about hot-selling products as well as other customer buying trends that are emerging. Syndicated data in tandem with POS data create even greater insight.
For example, a regional manager for a retail pharmacy store chain can use POS data and data discovery tools to identify consumer purchasing trends that otherwise might not be apparent.
Data discovery techniques may reveal to the regional manager that a high percentage of female shoppers who purchased over-the-counter seasonal medications such as allergy relief medicines were also likely to purchase hosiery and other items when the over-the-counter medicines were offered at a certain discount.
The regional manager can then use analytics to determine the best prices to offer for certain commercial medicines as well as how much incremental revenue and profit will likely be generated from such promotions.
Retailers can also use different types of data with analytics to shape merchandising strategies. For instance, a category manager for a regional supermarket can analyze sell-through rates, inventory, margin, and other sales data to identify the most effective product positioning strategies.
Analytics can reveal insights about trade promotions such as the value of placing a certain brand of cookies at the end of an aisle to entice certain shoppers to grab a box or two, especially if they are promoted a certain way (e.g., “Buy two for $5; Save $2.50”).
The category manager can then use analytics to compare the results of different product placement strategies by day of the week, product type, price, and demographic information that’s known about the typical shopper who converts.
Additionally, analytics can also provide store managers and others in operational leadership roles with rich insights into employee performance that they can then act on.
For example, POS data that’s collected at registers in a big box discount outlet can be used to compare the total sales and the number of items that are rung up by clerks on an hourly basis.
A deeper dive into the data may reveal that a high percentage of clerks who are given two 15-minute breaks every four hours ring up 12% more sales, on average, than clerks who receive just a single 15-minute break per four-hour period.
Next Steps:
- We invite you to watch the first part of a three-part complimentary, on-demand webcast series, “Optimizing Store Performance with Analytics.” In the first webcast with Spotfire partner and CPG&R expert, InfomatiX, you will discover why having more insight through ad hoc analytics is something your business shouldn’t go without.
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