Understanding the weather has always been one of the greatest enigmas of life on earth. Decoding its pervasive effects on businesses and consumers remains an even bigger challenge. In the highly dynamic business landscape of the modern day, any insights that help quantify business yields and combat complex problems are essential toward seizing a competitive advantage.
An inherent part of the psychological makeup of man is the environment in which he/she lives. Suffice it to say that everyday weather has for centuries influenced the human mindset across the globe. Diverse conditions have led to consumption and consumer behavior patterns that vary with weather. Products such as umbrellas and rain boots experience an increase in sales during the fall months, while sunglasses, suntan lotion and shorts experience heightened sales in the summer months in certain areas. Similarly, adverse weather conditions such as rain and snow have led to the customer preference of staying indoors and to higher levels of e-commerce sales as opposed to brick-and-mortar sales. Weather's pervasive influence has been dissected over the course of history, with today’s technological prowess and analytical initiatives carving the way for stark improvements in forecasting, predictive analytics, simulations and modelling aimed at demystifying weather’s relationship with business opportunity.
Some organizations are already taking the lead and leveraging weather analytics to optimize their businesses. Point Defiance Zoo & Aquarium located in Tacoma, Wash. (US) evaluated historical weather data and correlated it to zoo attendance and time of day helping it to accurately predict attendance within 113 people. It’s critical for the zoo to precisely predict attendance since admission fees account for 40 percent of its earned revenues. Additionally, it also helps the zoo manage its staffing optimally.
Vestas, a global energy company dedicated to wind energy, handles vast amount of data -- maps of the world, down to 3km by 3km squares on a grid -- and can retrieve wind, temperature, solar radiation and humidity data on an hourly basis going back 10-plus years. The data is used to generate "hindcasts," or models to predict the weather conditions at prospective turbine sites, and the different combinations of electricity that turbine locations can generate. It’s also used to optimize turbine locations, improving the output of the power plant by measurable amounts.
Let’s take another example of a routine daily life and see how weather impacts many aspects of it. For instance, John plans to do his shopping on Sunday, play golf, and then catch the local football game. Sunday however yields a day of rain and snow. Instead of driving on icy roads to the large grocery chain a town over, he decides to purchase his requirements from a smaller, local store but unfortunately its short stocked since it didn’t anticipate high demand. The weather makes for poor golfing conditions, so he decides to stay home and purchase the latest season of House of Cards’ from Netflix. Later, as the game kicks off, due to the rain, most of his hometown team’s passing play attempts result in incompletions owing to slippery conditions. The underdogs manage to adapt to the weather better, and emerge as victors.
Based on this hypothetical example of one consumer, we can clearly see how a simple day of normal or inclement weather can decide the nature of an organizations’ performance and hence revenues.
Institutionalizing Weather-based Analytics
A company’s overarching policy should be to institutionalize the use of weather-based analytics and data driven decision making to combat the adverse revenue effects of the weather. The first step should be to make sure they have the right man-machine ecosystem. Weather data can be cumbersome and complicated, and organizations need the right analytical mindset along with the right analytical tools to generate actionable insights. Secondly, to address the complications imposed by weather, organizations need to start adopting a multi-pronged approach of descriptive, inquisitive, predictive and real-time analytics.
1. Descriptive & Inquisitive Analytics
Enables organizations to understand the state of sales based on historical data, and what has led to the current state. The large grocery chain could have carried out inquisitive analytics on the reason behind its sales dip to discover that heavy rainfall in the last week resulted in lower customer visits and hence sales.
2. Predictive Analytics
Allows organizations to forecast the nature of weather-related sales and the impact it may have on the business. The small nearby retailer could have planned its inventory better and capitalized on the opportunity presented. The stronger football team could have strategized better before the kick-off and emerged victorious.
3. Real-time Analytics
Equips companies with the ability to consume real-time evolving data and take the right decisions pertaining to optimized sales, and stay ahead of competition. Netflix could have captured the increase in site traffic and could have sent out mailers with offers to its prospects or could have offered better deals on the site helping them to up-sell.
Lastly organizations need to ensure that the creation of analytics is supplemented by its consumption also. Creation of analytics will require organizations to combine their sales and weather data and using the right technologies they can decipher the huge amount of weather information.
This could involve:
1. Analyzing previous weather records and segmenting based on occurrence.
2. Combining sales and weather data in order to determine segments for location area and products or segment according to the sales.
3. Utilize regression analysis at a climate region level to create segments for extreme weather conditions highlighting the impact on sales.
The weather is intrinsic to the functioning of human life. Its effect on global business can therefore not be underestimated. In order to bridge the void to optimize sales and revenues, weather-based analytics initiatives will soon become driving forces in strategy and policy development. For businesses world over, adopting the right data-driven decision making and analytics initiatives which enable them to understand the clandestine nature of the weather is the silver lining on the dark cloud.
Sumit Prasad is analytics & marketing manager, Mu Sigma. He earned a degree in Statistics from Delhi University in 2007 and joined IT giant TCS for 2.5 years, where he worked in the pharma domain. He then went on to pursue a master’s degree in International Management from IE Business School. Since then, he has been a part of Mu Sigma, providing analytical solutions to Fortune 500 clients, leveraging cross-industry learning to ideate and innovate, and providing thought leadership. Follow him on Twitter: @sumit_pd.
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