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• Minimizing lost sales – and potentially lost customers – due to stockouts is more critical than ever as store traffic trends evolve and competitive alternatives expand.
• The weather affects consumers and their purchasing decisions on a daily basis; no other external variable shifts store-level sales trends as immediately, frequently, and meaningfully.
• Weather analytics improve forecast accuracy in planning, allocation, and replenishment processes and enable retailers to better align inventories with consumer demand.
High levels of customer satisfaction are critical to protecting and growing market share, and nothing puts this in jeopardy more than empty shelves or clothes racks. Retailers can’t afford to miss opportunities to capture sales and to earn shopper loyalty. It has never been easier for customers to go elsewhere to get what they need.
Improving availability needs to be a top priority, but this goal must be pursued in a cost-effective way.
When allocating product ahead of the selling season, store-based retailers want each location to have enough stock to meet demand over time but also want to avoid sitting on costly, unproductive inventory. This is no easy task and when dealing with seasonal merchandise, using the prior year’s sales as a guide often leads to mismatches between market-level demand and available inventories. Some locations miss sales while others sit on excess stocks that eventually may need to be marked down or moved.
In businesses where replenishment decisions are made regularly, demand forecasts need to account for not just what recent sales trends have been but also how purchasing patterns will be shifting going forward. Retailers need to smartly increase stocks ahead of demand spikes, while safely trimming inventories where demand is falling (especially in sectors like grocery where limited shelf life perishables can drive up waste costs).
Don’t overlook a key piece to the ever-changing demand puzzle
Many factors go into what a shopper wants to buy and when they want to buy it. But there is one very influential external variable that too many retailers are still ignoring – the weather.
Demand for all sorts of consumer products – from food and beverages to clothing to items for our homes, gardens, cars and more – varies based on the conditions outside. And the weather never stops changing! This brings challenges to planning because year-to-year the conditions that influence sales rarely repeat. So retailers –those with stores and purely online players – often find that plans that rely heavily on the prior year’s sales often miss the mark. This mistake of “chasing the weather” also affects nearer-term replenishment forecasts, as the favorable conditions supporting sales or the unfavorable conditions suppressing sales, change daily.
Retailers that do not account for the impact of weather are unintentionally building error into plans and demand forecasts. The good news is that precise weather-based demand analytics can be operationalized at scale and integrate with a retailer’s existing technology solutions and/or processes. However, companies should not fall into the trap of assuming that weather data (e.g. temperatures, rainfall totals, etc.) and forecasts will bring the demand forecast accuracy improvements needed to broadly grow sales and profit. The weather’s impacts need to be translated into category- or product-level units in order to make adjustments to plans or automatically modify replenishment system outputs in a systematic, scalable, and repeatable way.
Weather-driven demand metrics, developed through multi-year analyses of actual sales by product/location/time period and corresponding weather conditions, transform weather data into something quantifiable and actionable. With data-driven visibility into how the weather affects purchases, retailers have a powerful way to optimize inventories and improve availability which leads to increased sales and happier shoppers.
Improve forecast accuracy and capture more sales
Whether it is pre-season planning and allocation or in-season replenishment, weather analytics can highlight both when and where sales opportunities and risks for product categories are likely to arise. Quantifying those weather-driven opportunities and risks in unit-based volumes improves a retailer’s forecast accuracy, and for certain products and time periods, those accuracy gains can be as high as 30%.
Reducing forecasting error means inventories are better aligned to consumer demand and this leads to improved availability and fewer lost sales. By applying weather analytics across the business, retailers can add up to a 2% to total topline sales, with much larger gains for specific products and locations. The additional sales and better optimized inventories can also boost profits by 3-5%.
Retailers must capitalize on sales opportunities whenever possible or risk losing customers and market share. Below are a couple of product-specific examples where companies were able to see and take advantage of opportunities highlighted by weather-driven demand analytics:
• An apparel chain reduced winter boot and accessories inventory by 60% after pushing products into markets where demand spiked due to favorable late-season weather.
• A DIY retailer reduced out-of-stocks at distribution centers by 25% without increasing inventory levels.
• A supermarket chain captured over $50k in incremental sales in a fresh category over just a 3-day period by increasing replenishment volumes into markets where the weather would be positive for sales.
For many retailers, optimizing inventories to account for weather impacts remains a great untapped opportunity to improve performance. Retailers would be hard-pressed to find an alternative approach to revenue growth which allows them to extract more from existing technology investments with minimal technical effort to implement. The value return materializes quickly, usually within weeks.