“We’re using Walsh’s background and data brain to figure out how to bring something to market that would be extremely useful to clients,” Beth Lawrence, a sales exec at The Weather Channel, said in an Ad Age interview. The benefit for retailers is that knowing who to target when could help them promote their products more effectively.
The article offers this illustration: “If it’s set to be unusually cold in Phoenix next week, The Weather Channel could use Walsh’s predictions to tell sweater makers when to start advertising.” But that example offers little beyond the idea of telling people to advertise ice melt and snow shovels a couple of days before a snowstorm is due to arrive. There is nothing new about using those types of forecasts in marketing. Stores have done this for as long as news outlets have provided weather forecasts.
Walsh’s approach incorporates much more lead time, is based on long-term predictions, and plans for a whole season. In a recent presentation he gave for the Global Interdependence Center titled “Rethinking the Weather,” Walsh explains how businesses can optimize their planning by taking advantage of the advances of weather forecasting based on technology and big data analytics. A segment of the presentation is available on YouTube:
Walsh says The Weather Channel brought him in to create ways to use long-term weather data for “better long-range planning decisions.” It concentrates on retailers that use data to “optimize the way they plan and then market and then distribute products.”
Weather knowledge really is power, Walsh says. He cites his Air Force background and says the military has long used weather information for tactical and strategic planning -- in the case of the D-Day invasion, the Allies had better weather information than the Germans did.
As for the private sector, “weather is important because it has a significant impact on the economy,” which amounts to 30 percent, or $485 billion, a year for “normal weather.” For our current state of weather, Walsh shows a map dated at the end of December 2011. It shows a three-month prediction of patterns that include some cold snaps but generally mild weather for January and a colder March in the North. For seasonal apparel retailers, this pattern is far from ideal. They like a colder winter and a warmer start to spring.
In the video, Walsh does not offer possible solutions. However, we can infer that retailers that know the season will not match what they normally anticipate will adjust their strategies accordingly. For example, in anticipation of a cool March, retailers in the North could put off promotions for patio furniture and barbecues, because people usually need the prompt of warmer weather to start considering outdoor entertaining. Instead, the retailers could push blankets or sweaters in new spring colors to offer something seasonal that corresponds to the weather.
Everyone can continue to talk about the weather, but analytics can give retailers the tools to do something about it and make better business decisions.