Forbes covered “the dark clouds hang low over multiple retailers threatening profitability” in an article entitled The Demand Latency Hangover.

The article highlights the disconnect, due to timing, between shifting consumer purchasing patterns and a retailer’s supply chain. “Using conventional demand processes, the time to translate market demand to replenishment can be weeks, even months. This is too long to drive a profitable response.”

The author suggests that this demand latency problem arises because traditional approaches remain very supply chain centric focused on historical demand patterns and this is “ineffective in translating shifts in markets at the speed of business.”

“The lack of demand sensing, or the use of market insights, to drive the retail response results in a demand latency hangover.” Forbes suggested that while most companies have demand management solutions very few leverage market drivers to proactively adjust demand flows to reduce demand latency. “This results in mistakes in investor forecasts, inventory holding strategies, and merchandising programs.”

One always changing and consequential demand driver retailers should consider to push back against latency is the weather. Think about it… what is more local than the weather? Translating those local meteorological conditions into measurable, easy-to-integrate metrics for demand forecasting and store replenishment or inventory management solutions produces real benefits by knowing sooner when demand shocks will occur and accelerating replenishment cycles. Fewer stockouts. Reduced markdowns. Improved customer experience. Less waste.

Planalytics’ Weather-Driven Demand (WDD) analytics are used by leading retailers, restaurant chains, and consumer goods/services companies to stay a step ahead of shifts in buying behavior. WDD metrics isolate and quantify how changes in the weather will lead to periods of increased or decreased demand for specific categories in specific markets.

Using predictive WDDs to manage the weather’s impact on local demand will not completely solve demand latency issues, but it is a proven way to bolster one’s demand sensing capabilities and mitigate some of the negative ramifications that surface when inventories are mismatched with consumer demand.

“We must rethink our approach to demand management and unlearn the bad habits of using latent data to forecast future sales,” concluded the author. “There is no substitute for a market-driven approach to demand management.”

A slowing economy, a more cautious customer, and post-pandemic spending shifts were all concerns raised by retailers in the last couple of weeks as earnings were released.  These trends have hurt some retailers more than others and another key element of uncertainty and sales volatility – the weather – played into disappointing Q1 results for those chains heavily dependent on seasonal sales.

“As the weather turns warm and sunnier, it can inspire shoppers to buy summer dresses, beach towels or gardening supplies,” CNBC wrote in a recent report. “Sam’s Club has noticed slower sales of patio sets, perhaps because of the later-to-hit spring weather… Walmart has seen a sharp drop in air conditioner sales at its big-box stores.”

Shoe Carnival noted the impact in its Q1 earnings press release reporting that the weather “impacted net sales, with spring seasonal product down approximately 23 percent compared to first quarter 2022.”

“We saw materially softer demand in our seasonal products due to a delay in the spring selling season across most of our markets, notably in the last few weeks of March,” said the president and chief executive officer of Tractor Supply (Source: Hardware Retailing).

It is true that, in general, the weather did no favors for clothing retailers and DIY chains during the critically important early spring period. For the U.S. overall, this March was the coolest in ten years, delaying the start of the spring selling season in many western and northern markets (see graphic). Planalytics quantifies the business impacts of the weather with a metric called WDD (Weather-Driven Demand). Many spring product WDDs confirmed the challenging environment retailers faced in March.

WDD calculations measuring the impact on overall transactions were negative compared to last March for the DIY/Home Center, Apparel, and QSR/Restaurant sectors. For example, DIY transactions saw a weather impact of -11% in Boise and -7% in Albany; Apparel transactions were -6% in Buffalo and -4% in New York City due to the weather; QSR receipts were -9% in Sacramento and -2% in Indianapolis on less favorable conditions.

As always, the weather and its sales impacts are continually shifting presenting both risks and opportunities and a market-by-market basis. As the Chairman, CEO & President of The Home Depot said on their Q1 earnings call (Source: The Motley Fool) “where weather was favorable, we saw strength in key spring-related categories such as live goods and other garden-related categories.”

The “weather” is going to happen, but retailers leveraging Weather-Driven Demand analytics have valuable visibility into the sales impacts. How changes in the weather affect performance does not have to be a surprise and retailers can use predictive WDD analytics to proactively manage this critical external purchasing influencer. Contact us to discuss how your business can benefit.

More favorable weather arrived in April in northeastern markets although California and the southwest were challenged by cooler conditions.  And, for the first month (May) of most retailers’ Q2, cooler year-over-year temperatures in the East, and chillier and wetter conditions compared to 2022 in the Southwest suggest difficult weather headwinds remained a challenge for many retailers.

How Analytics Around a Core Consumer Demand Variable Help Businesses Understand & Optimize Performance

Harvard Business Review (HRB) published a useful overview on how advanced analytics “can help companies solve a host of management problems, including those related to marketing, sales, and supply chain operations, which can lead to a sustainable competitive advantage”.

The article focuses on three different categories of analytics and what each approach can help businesses: Descriptive “business intelligence” to understand what happened, Predictive “predictive engines” to forecast what will happen, and Prescriptive “decision automation” to answer what should be done next. As companies increasingly look to incorporate artificial intelligence (AI) and machine learning (ML) capabilities into their everyday operations, the authors lay out how the different analytics approaches best fit in the spectrum ranging from human-driven decisions to automated AI-directed actions.

Planalytics has worked with hundreds of retailers, restaurants, consumer goods, and service companies to provide demand analytics that isolate and quantify the impacts of the weather. In providing Weather-Driven Demand (WDD) analytics to clients over many years, the ability to precisely calculate how the weather – which is unmatched in its significance and volatility as a daily external influencer of consumer purchasing decisions – is applicable in all three (descriptive, predictive, and prescriptive) analytics environments.

For the consumer-centric clients Planalytics partners with, WDD analytics offer both understanding and actionability around a core determinant of constantly changing demand for what can be thousands of a businesses’ products across thousands of locations on a day-to-day basis.

The HRB article describes descriptive analytics as aggregated observations often referred to as business intelligence. “Descriptive analytics is about making sense of the past to inform the future” and these insights tend to be “coarse in nature, and they require the nontrivial step of extrapolating past trends and projecting them into the future.”

Weather-Driven Demand analytics do fit in the descriptive bucket as the WDD models that determine the weather-to-sales relationships start with a company’s historical transactions data. One critical application of these descriptive analytics is the ability to quantify the weather impacts that are embedded in past sales performance and cleansing the history to create a planning baseline with the weather bias removed.

WDDs are also useful descriptive analytics on an ongoing basis. The ability to calculate the weather impacts across the business enables companies to evaluate last week’s (or month’s or quarter’s) sales from a weather-adjusted or weather-neutral perspective, providing a clearer read on performance that can inform reactions and decisions. For example, a retailer that understands that strong weather “headwinds” have slowed sales the last two weeks may resist implementing heavy promotions or price markdowns thinking that the lower demand levels are permanent.

One key advantage WDDs offer over typical descriptive analytics is the fact that the metrics are accessible at a very granular level (e.g., product by day by location) that can also be aggregated.

The article moves on to discuss predictive analytics and the “limited view of the future” this approach can provide. The authors describe predictive analytics as suitable for more frequent, partially automated decisions with application areas such as demand planning and promotion management.

Calling out structural limitations, the article notes that “…predicting individual input variables can be highly complicated: Weather, competition, and supplier performance, for example, require their own prediction models,” and that “There are also limits to the number of input variables that can be modeled and the level of granularity that can be achieved.”

By having Planalytics provide the specialized model for weather impacts, retailers and other businesses can address one of the more complicated parts of the demand picture. These WDD metrics become a key element for managers to consider when using predictive analytics to identify when and where additional staff or product may be needed to meet increased demand or how it may make to adjust digital advertising spend based on how responsive consumers in specific locations are likely to be due to the weather they will be experiencing.

Lastly, the article focuses on the opportunities of machine-driven prescriptive analytics. “Well-designed prescriptive models can deliver greater financial rewards and better business performance” but are often quite challenging and costly to set up.

One must match the tool to the job and AI/ML-driven prescriptive analytics are likely to disappoint in areas like strategic planning where “the initial definition of the question is actually more important than the formation of accurate answers. But when it comes to optimization of prices, inventories, or marketing investments, analytics offers companies substantial opportunities because accurate answers will better serve their customers’ needs.”

Weather-Driven Demand metrics are perfectly suited as an input into a company’s larger prescriptive analytics framework, especially in situations where a high volume of decisions need to be made frequently at the product/day/store level. For example, a grocery chain or mass merchant that needs daily demand adjustments for thousands of products across all its stores can feed WDD calculations into their demand forecasts (AI/ML-based platforms or packaged software solutions) to optimize store-level inventory replenishment. Financial benefits include increased sales from fewer out-of-stocks, lower inventory costs, and the ability to grow profit by 2-6%.

Read HRB’s “Analytics for Marketers” article for more detail and explore Planalytics’ website to learn more about Weather-Driven Demand analytics.

Retailers and other consumer-focused businesses need to consider many factors when forecasting consumer demand. Everything from broader macroeconomic conditions (like employment levels and inflation) to buying trends (is a product’s sales growth accelerating or turning negative) to the type of product (is it a staple item or discretionary purchase) play a role in determining a core demand profile.

While the above-mentioned items are important factors behind demand variations, they tend to change gradually (usually months or years) over time. Other variables, such as pricing and promotions, will drive more immediate demand responses adding an additional level of complexity to forecasts.

Like pricing and promotions, the ever-present external variable of weather will also generate rapid and significant demand responses. One major difference – the weather is not a demand lever a retailer can control. However, with the right analytics, companies can proactively manage the weather’s impacts.

A Forbes article entitled Retailers Have Always Faced Unpredictable Changes Impacting Supply And Demand – Weather Shouldn’t Be One suggested “that rapidly changing environmental conditions, like weather, need to be factored into their supply and demand forecasts.” The article went on to reference a study by UBC Sauder School of Business that concluded “that retailers take into account weather changes on a seasonal basis, but are leaving a lot of money on the table by not factoring in the daily weather in the places were consumers live.”

Remember video games that had cheat codes that gave a player extra powers or the ability to advance to the next level in the game?  In the world of retail, “WDD” turns out there is a consumer demand cheat code businesses can use to stay a step ahead of weather-based sales volatility. Getting to the next level of sales and profitability means reducing error in demand forecasts, minimizing out-of-stocks, and lowering inventory costs. WDD, or Weather-Driven Demand, helps businesses quickly address these objectives.

No other external variable shifts consumer buying as frequently, immediately, or directly as the weather. Planalytics’ Weather-Driven Demand (WDD) precisely calculates when, where, and how much demand for specific products increases or decreases due to changes in the weather. Incorporating WDD helps retailers improve both pre-season planning and in-season inventory management.

How does one apply the WDD cheat code and begin capturing the benefits? Integration into ERPs, planning and demand forecasting software, and other solutions is critical to scale WDD analytics (unit- or percent-based adjustments) to thousands or tens of thousands of product, location, and time intersections.

Planalytics’ partnership with Groupsoft enables retailers and other businesses to systematically address weather volatility in solutions such as SAP Ariba, SAP S/4HANA, and other platforms. Groupsoft’s expertise in integrating and implementing these solutions ensures that businesses can fully leverage the power of Weather-Driven Demand (WDD) to enhance their demand forecasting and inventory management processes.

By combining Groupsoft’s technical know-how with Planalytics’ WDD analytics, businesses can gain the following benefits:

Improved demand forecasting accuracy: Integrating WDD analytics into demand forecasting software allows retailers to more accurately predict sales and inventory requirements, taking into account the impact of weather on consumer behavior.

Better inventory management: With more precise demand forecasts, retailers can optimize their inventory levels, minimizing stockouts and reducing excess inventory costs.

Enhanced pricing and promotions: Incorporating WDD analytics enables retailers to more effectively plan pricing strategies and promotions in response to weather-driven changes in consumer demand.

More informed decision-making: Retailers can use WDD insights to make data-driven decisions in areas such as product assortment, store layout, and marketing campaigns, all tailored to the specific needs of their target consumers.

Increased agility: By anticipating and reacting to weather-driven fluctuations in demand, retailers can adapt more quickly to changing market conditions and stay ahead of their competitors.

Once WDD metrics are integrated, the weather-informed demand adjustments help retailers improve planning and replenishment processes.

• Proactively address the built-in, weather bias error in plans. When planning the season or year ahead, businesses all too often fail to factor in the impact of weather on sales. When left unaddressed, retailers end up unknowingly “chasing last year’s weather” immediately adding error to plans since the weather’s impact on sales rarely repeats itself from one year to the next. WDD analytics are used by retailers to deweatherize sales history or remove the weather bias embedded in performance results. From a total business perspective, plan accuracy improvements are typically around 5% with 20% or higher gains common for specific products, locations, and time periods.
• Weather “smart” store-level replenishment. For in-season activities like replenishment, WDD adjustments based on near-term weather forecasts offer significant benefits. Instead of basing replenishment volumes on historical averages or recent sales trends, WDD analytics keep retailers a step ahead of those abrupt weather-based demand swings that are all too common. A store that saw sales spike on warm and dry conditions the last two weeks can end up with too much inventory when the weather pivots and becomes unfavorable for sales. Alternatively, retailers can miss sales due to out-of-stocks when the opposite scenario arises.

A Retail Dive article estimated that out-of-stocks could be costing retailers a trillion dollars annually. On the other hand, overstocking ties up capital and can drive up related costs, including waste and markdowns. Using weather analytics in plans and forecasts can help retailers better align inventories with consumer demand on a localized basis throughout the year.  

In a three-part series, Grocery Dive explored how supermarkets can increase profitability while pursuing sustainability initiatives.

“As sustainability work becomes more prominent across the grocery industry, food retailers are faced with the questions of if, how and when to have their eco-friendly measures tie back to their bottom line.  In this three-part series, Grocery Dive explores how sustainability initiatives connect to grocers’ financial planning and reporting.”

As grocers spotlight sustainability, financial analysts don’t see the connection with the bottom line

In the first article, Grocery Dive looks at how grocers are finding they are pleasing shoppers by addressing sustainability but how it remains more challenging to win over investors.

“Top managers of other publicly traded food retailers that also highlight their sustainability programs…  don’t often field questions during earnings calls about their Environmental, Social and Governance (ESG) initiatives, even as metrics like same-store sales and earnings per share are top topics of discussion. That disconnect stems from the fact that while paying attention to the planet might be laudable, demonstrating the connection between sustainability and the bottom line remains a substantial challenge for the supermarket industry, according to analysts.”

Planalytics works with leading retailers to improve demand forecasting with Weather-Driven Demand analytics. Enhancing store-level replenishment in produce, dairy, meat, seafood, and other fresh food departments offers the double benefit of optimizing inventories and increasing sustainability by reducing waste. Less perishable food waste also reduces the company’s carbon footprint.

Better demand forecasts are a great place to start for a grocer pursuing sustainability gains and, because the business is improving a core, everyday process, there are significant profit gains to be had. Planalytics’ experience with grocers shows that a business can reduce perishable waste by an average of 12%, which corresponds to more than a 4% in decreased carbon emissions. Learn more here.

5 expensive energy mistakes grocers are making

The next topic in the series focused on five energy inefficiencies that hurt grocers’ profits.

“Food retailers can save roughly $15,000 annually per store by improving five overlooked practices, according to Ratio Institute, a nonprofit specializing in grocery sustainability.”

Where can grocers look for gains? Improving appliance efficiencies, unblocking return air vents, reducing air infiltration, keeping walk-ins closed, and maintaining walk-in doors.

How a regional grocer balances sustainable efforts with its bottom line

In the final article, Grocery Dive highlights how Harmons Neighborhood Grocer made its green initiatives a priority for top management.

The interview with Harmons’ director of sustainability, Kate Whitbeck, “shared how she advances Harmons sustainable practices and balances those projects with consideration for the company’s bottom line.”

“I understand some individuals are obviously swayed by a financial argument more so than an environmental argument” said Whiteck. “So I try to spell out if there’s an ROI on a particular investment. Obviously, not everything we do related to sustainability can show a cost savings but, fortunately, with most interventions related to energy or waste, there are cost savings to be had.”

In one question, Grocery Dive asked Whitbeck about the typical timeframe for sustainability initiatives to begin generating a profit. “I don’t think there’s a typical number, it’s very specific to the initiative and to the location,” said Whitbeck. “I think it’s obviously anything that’s five years and under, it’s going to be an easier sell.”

In another question, the interviewer asks about carbon footprint calculations and measuring the performance of sustainability efforts. Whitbeck offers an example of waste diversion. “What percentage of the waste are they diverting from the landfill at each store? We’re looking at tonnage rates.”

Using Weather-Driven Demand (WDD) metrics in demand forecasting and replenishment helps grocers balance on-shelf availability and limiting waste in perishable categories. Moreover, sustainability gains and profit gains can be measured in this inventory replenishment use case.  “Five years and under” is a benchmark that is easily met as grocers typically begin seeing the financial return to the business within 90 days and a full ROI within 180 days.  And as the graphic above illustrates, waste reduction is an area that allows a company to directly measure and report sustainability gains.

For more information, check out this article posted by Planalytics’ partner SAP or download our Leveraging Demand Analytics to Reach Sustainability Goals white paper.

A Footwear News article cited data from Circana (formerly IRI and The NPD Group) that sales “slowed in the first quarter of 2023 as demand softened and warmer weather impacted boot sales,” although Beth Goldstein, Circana’s footwear and accessories analyst said that “steady price increases managed to keep the overall revenue losses in the low single digits”.

Circana cited performance footwear as a bright spot with sales rising 2% in the quarter to $1.8 billion, driven by the sports segment.  However, for the leisure footwear category, wholesale sales slipped 3% and fashion footwear fell 4% in the first quarter. “These declines can be felt mostly in the boot segment, which experienced a challenging season due to the mild winter weather in the U.S. this year.”

Planalytics’ Weather-Driven Demand (WDD) metrics for the boot category confirm Circana’s findings and show that most regions struggled against strong headwinds due to warmer temperatures. For the U.S. overall, there was a negative 4.4% weather impact on the category.

Circana noted that the Pacific states of California, Oregon, and Washington showed strong sales gains, and that due to “the atypical winter weather, the top five (and nine of the top 10) fastest-growing cities for cold-weather boot sales in the period were in California.”  The map above shows Planalytics’ WDD calculations for the quarter and confirm Circana’s findings. For retail Q1 (February through April), Planalytics reports +21% WDD (vs. LY) for Los Angeles, +9% for San Francisco, and +4% for Portland.

On the other hand, unfavorable weather conditions hurt sales in the Midwest and Northeast.  Negative year-over-year WDD metrics for Q1 include Pittsburgh -24%, Indianapolis -19%, Boston -16%, Chicago -12%, and New York City -10%.

Check out the “Profiting from Predictive Weather-Driven Demand Analytics” white paper to learn more about WDD analytics and the benefits businesses can capture by measuring and proactively planning for an ever-present and constantly changing driver of consumer demand.

In a recent report – Cooking up extraordinary growth for restaurants during a downturn – McKinsey & Company discussed the challenging landscape facing the restaurant industry and opportunities that strategic and innovative companies can pursue to achieve sustainable growth and profitability.

The report offers a lot of great information including what McKinsey highlights as “eight ingredients” for growth.

Front-of-house ingredients:

• data-driven marketing
• revenue growth management
• store development, footprints, and formats
• new revenue streams

Back-of-house ingredients:

• sourcing
• restaurant operations
• enterprise organization and operating model
• technology and analytics

Planalytics has worked with many leading quick service restaurant (QSR) chains over the years, providing weather-driven demand (WDD) analytics that help companies address some of the key areas listed (bolded above). WDD analytics isolate and quantify the impact that changes in the weather have on consumer buying. These metrics can be calculated at the total transactions or traffic level (by channel – dine-in, drive-through, delivery, etc.) and for specific products (salads, cold beverages, etc.). Weather-driven demand analytics are available on a market-by-market basis on a daily or weekly basis.

Will unfavorable weather keep customers from visiting restaurants or ordering specific menu items? Will traffic shift to online orders and delivery? Is there an opportunity to capture more sales due to an upcoming change in the weather? Since the weather impacts customers, their activities, and their purchases each day, it is an important factor to monitor and act on. Below are examples of how businesses can leverage Planalytics weather impact analytics.

WDD for data-driven marketing: Improve ROAS (return on advertising spend) and other key performance metrics by optimizing when to spend in certain markets and what products/content to feature based on the weather. Can you save advertising budget (or shift to more favorable markets) when traffic and sales will be down due to poor weather?

WDD for restaurant operations: McKinsey’s report mentions opportunities for reducing food waste and optimizing staffing as two areas of opportunity. WDD analytics can help with both. Reducing perishable inventories in locations expected to have lower traffic and sales due to negative weather conditions is a great way to minimize waste and costs. For labor costs, hours can be trimmed when unfavorable weather slows sales, or staff can be added proactively ahead of expected high-demand periods to ensure high customer service levels are maintained.

WDD for technology and analytics: WDD analytics help businesses put a number on the most consistently impactful external driver of consumer purchasing. As businesses build out machine learning (ML) platforms, planning and forecasting technologies, and customer engagement solutions, integration-ready WDD analytics are an optimization layer that restaurants can rapidly systemize and scale for key business processes. From understanding sales performance or determining if a promotion was successful to looking ahead and identifying opportunities to drive and capture additional sales, weather-driven demand offers a valuable perspective for restaurants.

Learn more by reading 4 ways restaurants leverage weather analytics.