Tag Archives: predictive analytics

Predictive Weather-Driven Demand Analytics Is the Secret Ingredient Enabling Retailers to Succeed in Uncertain Times


There are many external variables influencing businesses and consumers today.

Recently, we’ve heard a lot about volatile supply chains, delayed product shipments, fluctuating inventory levels, inflationary pressures, and rising prices. In addition, retailers continue to be understaffed. Each of these individual variables denote business disruptions, distractions, and challenges for businesses who are looking to meet customer demand and achieve service level targets.

These are just some of the macro-environmental ‘unknowns’ that are impacting retailers and consumers.

Yet one of the most volatile external variables that impacts consumer purchasing decisions and overall retail sales is also one of the least analyzed: the weather.

No other external variable can influence your sales as frequently, immediately, or meaningfully as the weather. Because weather is always changing, retailers are tasked with addressing constant shifts in shopping patterns, as well as demand for specific products and services. Fortunately, for retailers, the impact of the weather on consumer purchasing is NOT an unknown — it is known, and can be unlocked through predictive demand analytics. Understanding and measuring the impact of weather is the first step. Once measured, it can quickly be implemented at scale across the entire business to drive improved results across systems integration, people, and processes.

SGG Associates LogoIn today’s environment, successful retailers are consistently evaluating activities that can improve customer loyalty and enhance the shopping experience. Predictive weather driven demand analytics are an enabler to quickly realize these benefits at scale. Planalytics and SGG + Associates partner together to leverage the power of predictive demand analytics to help retailers and brands proactively manage weather volatility and improve profitability.

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Deweatherization and the DIY Retail Market


The beginning of spring is the most important time of year for DIY retailers. This is the period where homeowners start new renovation projects, revive dormant ones, and repair any damage caused by winter weather.  It is also when new construction begins on homes and other major projects that drive new business. However, a poorly predicted demand plan could have a damaging impact on a DIY’s “Christmas” season and cause a surplus or shortage of inventory.  But how can a DIY retailer better predict sales and customer behavior pre-season and in-season to adjust accordingly?


Deweatherization Greatly Reduces the Guessing Game



Did you know that the weather only repeats itself year to year about 20% of the time? If you use last year’s metrics to plan for this year’s sales, you will unintentionally build error into your plans. For example, let’s take a look at spring 2015. The 2015 spring season for DIY retailers was better than 2014 because there was warmer weather early-on, providing a strong start. Warmer weather in March was favorable and drove consumer demand. DIY sales leveled off in early summer when wetter weather drowned out many outdoor projects.

By analyzing historical weather and sales, Planalytics estimated a $1 billion favorable weather-driven sales impact for DIY retailers in spring 2015. If those retailers had used 2014’s weather data to make decisions about 2015 spring sales, they would have missed out on excellent opportunities to leverage the warmer early spring weather and may have experienced losses due to the poorer weather later in the season.

Deweatherizing sales to plan from a weather-neutral baseline can greatly enhance a retailer’s ability to mitigate weather-related costs and capitalize on the opportunities. Reviewing last year’s weather data is simply not good enough for predicting what will happen in the upcoming season. While there is no way to control the weather, a retailer can certainly control how it forecasts sales based off accurate predictive analytics.

Planalytics allows DIY retailers to see exactly how customers in a particular region are affected by changes in the weather during a certain time of the year. Weather analytics goes beyond just looking at the data and instead measures how weather volatility affects sales.  This is certainly the case when it comes to the DIY retail market. A warm or cold start to the season or ongoing precipitation could have a significant impact on sales with ramifications that extend throughout the season.

Planalytics can help DIY retailers deweatherize their business, so they are prepared for whatever the weather may bring, especially during the incredibly important early spring retail period. Deweatherizing takes the guessing game out of weather’s impacts.

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