Combining Shopper Traffic Data with Weather Analytics to Improve Customer Experience
Retailers spend a significant amount of time planning. Planning to make sure the right products are on the shelf. Planning the optimal price and promotional cadence. Planning how the product will be merchandised in the stores. In addition to all of these detailed processes, businesses are also reliant on the store and operations teams to execute these plans to support the selling process.
When all is said and done, retailers determine the success (or lack thereof) by evaluating results against what was planned. While key performance metrics are the direct result of the aforementioned metrics, there are other external factors which have a significant impact on performance. The most direct, immediate, and measurable external impact of this performance is the weather.
As an example, let’s look at a scenario where a retailer is evaluating performance of their beer sales in the alcohol section of a grocery store over the March through May timeframe. The retailer implemented various strategies and promotions over the period to drive store traffic overall and to the beer aisle specifically. How does the retailer measure performance? Moreover, how does the business factor in the weather impacts when evaluating performance?
Using “ASK” – the comprehensive shopper behavior insights tool from Amoobi which employs automated 3D sensor technology deployed in-store, the retailer is able to understand “WHAT” happened during this spring period. What were the traffic patterns of customers moving through the store? Which categories were they engaging with? What parts of the store saw reduced visits? This is valuable information the retailer can use to validate actual performance.
To better understand the “WHY” behind the overall store traffic and specific area visits metrics, the retailer can layer in weather-driven demand insights from Planalytics. From industry analyses of the beer category, Planalytics can tell the retailer that on average approximately 3% of beer sales are directly attributable to changes in the weather. This is just the average weather sensitivity for the category over the three months and, of course, larger deviations from normal weather conditions can lead to larger weather-based sales swings in specific markets and time periods. Planalytics’ weather-driven demand metrics isolates and quantifies the specific sales impacts and this allows retailers to make sensible, “apples to apples” comparisons when evaluating results.
For example, let’s assume three markets both typically experience 1,000 customer visits to the beer aisle in one specific time period — the first week of March. This year, these three markets had the Amoobi technology to precisely measure foot traffic in the beer aisle. Market A had 1,100 unique shoppers visit the beer category while both Markets B and C had exactly 800 customer visits. Once weather-driven demand impacts are considered, a different and more complete picture on performance emerges.
• Market A performed best and also had a +5% weather impact for the week. So 50 of the additional 100 visits were due to the weather.
• Market B saw 200 fewer visits than normal. Cold and wintry conditions in the market resulted in a -15% weather impact for the week. The weather explains 75% of the drop in visits here.
• Market C also saw 200 fewer visits but had more favorable weather than Market B. The negative weather impact was only -5%, which means 150 of the 200 visit reduction was due to factors other than the weather.
By combining the weather impact from Planalytics with the specific automated traffic counts from Amoobi, the retailer is able to better gauge performance across locations. The business can now explore what Market A is doing right versus what Market C is not. Having this understanding enables the retailers to tune and adjust offerings which best meet the needs and preferences of the local customer
This is just one simple example of how predictive weather-driven demand analytics, combined with shopper traffic patterns provided valuable insights in consumer behavior and purchasing. These insights, once scaled through the power of technology including artificial intelligence, analytics, and predictive modeling, can be integrated into retailer systems and processes to drive actions that lead to profitable and sustainable results.
For additional detail and to learn more, please visit Amoobi and Planalytics at NRF 2023: Retail’s Big Show in New York City in January. Planalytics will be joined onstage with retail speakers from H-E-B, Albertsons, and Chipotle Mexican Grill on Tuesday, January 17. More details here >