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Record W2411706149 · doi:10.15200/winn.146521.13799

How Businesses are Influencing the Relationship between the Weather and Consumer Demand

2016· dataset· en· W2411706149 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Winnower · 2016
Typedataset
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsProduct (mathematics)BusinessSnowPurchasingExtreme weatherClothingMarketingHot weatherAdvertisingMeteorologyGeographyEcologyClimate change

Abstract

fetched live from OpenAlex

When it comes to weather affecting consumer behaviour and purchase decisions, it has long been known that weather has an impact on consumer demand. The food we eat, clothes we wear, and how, where and how much we buy has all been scientifically proven to be influenced by the weather, it being second only to the economy in being the biggest single influencer on consumer behaviour. Indeed, every day people make purchasing decisions based on the weather, from buying ice cream, sandals and swimsuits in the summer, hot soups and snow tyres in the winter, and less of beer and bottled water as autumn approaches. In turn, the seasonal cycle of weather purchases are accounted for by supply chain managers in stocking store rooms and giving discounts to clear out product before the seasonal event- or the season itself- leaves stockpiles of unsellable wares in their hands. But what if everything we have known to be true about how the weather affects consumer behaviour and our ability to control this relationship was wrong? What if the seemingly uni-directional, unmanipulatable relationship between the weather and consumer behaviour was now being found to be being turned on its head? Specifically, what if a business could influence the relationship between the weather and consumers to its advantage? On the face of it, this sounds like something out of a sci-fi tale, like the fictional X-Men character Storm’s psionic ability to control everything from ocean currents to electromagnetic fields. Or perhaps a reference to the days of the mythological weather deities of Jupiter, Thor, or Zeus. Moreover, it has some rather frightening ramifications for the c-suite, as gone would be the days of businesses blaming profit losses on the weather. Of course, we’ve seen rudimentary versions of such weather risk management techniques: consumers have been prodded out of their living rooms and into storerooms under even the most unfavourable weather condition by discounts and sales promotions since the dawn of the modern economy. And, more recently, these promotions have become more cutting-edge, with businesses taking advantage of advances in weather data technology, weather sensitivity modeling, and new index weather hedging tools to create weather-related sales promotions. In this way, if a certain weather event occurs in a given period, consumer will receive a full or partial reimbursement on purchases made during the sales period, with retailers themselves protected by reinsurance treaties underwritten by weather risk management specialists such as Meteo Protect. Thus, an automobile parts retailer will stock those aforementioned snow tyres, even if the meteorologists are calling for a mild winter[1]See weatherandeconomics.com/2016/05/30/its-not-gambling-if-you-never-lose. But taking this one step further, and drawing now from advances in digital signage, remote weather sensing, and available real-time feedback between advertising mediums and weather stations, today we have the brave new world of an inter-directional relationship between the weather and consumers. Say goodbye to everything from advertising promotions and restaurant menus being set in advance, static, or fixed. Welcome to the age of digital signage and interactive advertising platforms which draw from dozens of smart components, including the weather, but also what’s trending on the news and social media. Take McDonald’s, for instance, which this past year started installing digital menu boards in restaurants around the world following a successful testing period in Canada. These menus make food recommendations based on the weather. If it’s cold outside, the menu might promote hot soups and heartier meals, whereas on a hot and sunny day, refreshing beverages and ice creams might be highlighted. Moving images further engage customers and draw their attention to these promoted items. McDonald’s has already reported success with these digital menus, stating that customers are spending more on every transaction in restaurants where the new menu boards were tested.[2]uk.businessinsider.com/mcdonalds-menus-will-recommend-food-based-on-the-weather-2015-1\n1 Similarly, Mark’s, a Canadian apparel and footwear retailer, recently won accolades for their innovation in brand activation, coming up with a novel way to meet the business objective of clearing out holiday inventory in anticipation of spring, in the face of a price-point war across industry, and amidst clearance clutter. Mark’s used digital signage on transit shelters in areas in proximity to their flagship stores to drive consumer demand by linking their advertisements to live weather feeds. As the temperature dropped, so did Mark’s discounts on winter apparel. They effectively customized their prices and advertising to fluctuations in temperatures, and influenced consumer behaviour by responding to the weather and consumer demand in real-time. The campaign was an immense success: Mark’s reported up to a 21% increase in sales year-over-year for the 3-week campaign period of this digital weather-driven marketing platform.[3]promoawards.strategyonline.ca/Winners/Winner/2015/?w=marks-readytowinter It’s not surprising that out of Canada we should see such innovation in mitigating the effects of the weather, but what businesses may find surprising is how quickly they can be left in the cold if they don’t decide to make their relationship with the weather a priority in 2016. That’s precisely why Meteo Protect, experts in weather risk financial management, have partnered with industry leaders such as MeteoGroup, a world leading weather data provider, in order to create products that leverage the data of Meteo Group with the bespoke hedging solutions and marketing and consulting services of Meteo Protect. Thanks to the scale and capability of Meteo Protect’s SAP cloud data services platform, it is able to provide clients significant competitive advantage as they link their business and sensor data with weather and other pertinent information in real-time. This may or may not be as alluring as teaming up with Professor Xavier’s team, but a manager who employs cutting-edge weather risk management techniques to increase profits and control losses in the face of any weather variable is bound to be considered a superhero at the office. References [ + ] 1. ↑ See weatherandeconomics.com/2016/05/30/its-not-gambling-if-you-never-lose 2. ↑ uk.businessinsider.com/mcdonalds-menus-will-recommend-food-based-on-the-weather-2015-1\n1 3. ↑ promoawards.strategyonline.ca/Winners/Winner/2015/?w=marks-readytowinter

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.449
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.084
GPT teacher head0.283
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it