All posts by Marilyn Bernardo

Retail Impacts to Watch for as Energy Costs & Inflation Pressure Europeans

From London to Berlin and capitals across Europe, governments and the media are sounding the alarm regarding the difficult winter ahead.  With inflation already elevated and the war in Ukraine raging on, consumers across the continent are preparing for much higher heating costs and potential limits to energy usage. With energy demanding a greater percentage of household wallets in the coming months, consumer spending will be impacted for retail, dining, and leisure.

Certain consumer businesses face more risk than others.  Travel, entertainment venues, discretionary retail categories (e.g., jewelry, electronics), and higher-cost restaurants are likely to lose sales. However – even with the challenging times ahead – opportunities will arise for certain retail segments. Off-price retail, low to moderately priced clothing, grocery stores, and economical restaurant options may all stand to benefit.

An article in The Guardian (July could be ‘lull before the storm’ for retailers and consumers) took a look at the economic impacts on the Europe’s near-term horizon.

Excerpts from The Guardian:

Helen Dickinson, the chief executive of the BRC said the summer was “an incredibly difficult trading period”. “Consumer confidence remains weak, and the rise in interest rates coupled with talk of recession will do little to improve the situation,” she said. “The Bank of England now expects inflation to reach over 13% in October when energy bills rise again, further tightening the screws on struggling households… Paul Martin, the UK head of retail at the advisory firm KPMG, said: “The summer could be the lull before the storm with conditions set to get tougher as consumers arrive back from summer breaks to holiday credit card bills, another energy price hike and rising interest rates. With stronger cost of living headwinds on the horizon, consumers will have to prioritise essentials, and discretionary product spending will come under pressure… Shoppers are already switching to discount stores, dropping brands in favour of supermarkets’ own-label goods and trimming spending on luxuries such as subscription services and gambling, according to data from the Nationwide building society.

Gloomy forecasts for late 2022 and into the new year aside, the combination of a difficult economic environment, geopolitical events, and cooling temperatures point to some unique opportunities for certain retail segments in the coming months.

At Planalytics we help businesses plan for and manage the ever-present financial impacts of weather and climate on consumers, quantifying how the conditions outside drive footfall/traffic and specific product purchases. For the winter ahead, the “conditions inside” will also be influencing weather-driven demand trends.

For many households (and places of business) the energy supply crisis will force building temperatures lower this winter, either out of financial necessity or due to government conservation programs.  In an article entitled “Colder offices and fewer Christmas lights … what Europe is doing to cut down on energy use “, TheJournal.ie highlights some of the measures different countries are taking to reduce energy consumption. Although some conservation is slated to come from turning off signs and lights, plenty of locales are eyeing thermostat settings as a way stretch energy supplies.

People will face chillier-than-normal conditions in more places and for more hours of their days and this means clothing categories such as knitwear, thermals, fleece, boots, and coats will be in demand.  Discounters, off-price retailers, and moderately price chains stand to gain (rather than higher-end brands and department stores) as shoppers looking to bundle up will be looking to get the most for their money.

When, as an example, Planalytics’ weather-driven demand metrics confirm that a few degrees drop in temperature in a certain location produces a 15% lift in knitwear demand, the analytics are basing calculations on the outdoor climate.  For the 2022-23 fall-winter season, such consumer purchasing decisions will further be influenced by everyone’s indoor environments.

Similarly, certain “warming” food categories – coffee and tea, soups and stews, pastas, roasts, etc. — will be higher demand. This will provide sales opportunities for grocery stores as more people opt to eat in-home but also value-priced quick-serve restaurants and takeaway or delivery options. People will still want to take a break from their own kitchens, but these out-of-home outlays will again target value and spending [wisely] due to tighter personal finances.

In Europe and elsewhere, consumers are indeed standing on the doorstep to a difficult winter period with inflationary and recessionary pressures in play. Retail and consumer businesses are rightly preparing for the risks but there is a “silver lining” emerging for those businesses selling essential products at attractive prices.

This winter, the physiological response to the weather will not be limited to the outdoors and will include “indoor” climatic conditions as well. Visit Planalytics to learn about predictive weather-driven demand analytics and how business can quantify and proactively manage the impacts of weather on consumer purchasing.

 

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Using Predictive Weather-Driven Demand Analytics to Improve On-Shelf Availability

NielsenIQ published an analysis (Fresh Insights About Grocery On-Shelf Availability in the Post-COVID Era) pointing to the importance to the challenge of out-of-stocks for retailers.

NielsenIQ, a Planalytics partner, wrote that “Gaps in On-Shelf Availability (OSA) result in lost volume, lost baskets, and lost faith among shoppers…  Even where upstream supply shortages and/or panic shopping are root causes, there are steps that can be taken to ensure better on-shelf availability (OSA). It begins with a true read of trending demand – not just in your own stores, but also at the market level.”

Product availability was also cited as a top concern for shoppers by the FMI (The Food Industry Association) in partnership with the Hartman Group in their 2022 U.S. Grocery Shopper Trends Series, noting that a survey of shoppers indicated that “… 45% are concerned about out-of-stocks.”

Planalytics’ work with major grocers over the years has shown that adjusting demand forecasts and in-store replenishment for changes in the weather can significantly improve availability and add up to 200 basis points back to toplines by reducing lost sales.

GroceryDive explored how predictive weather-driven demand analytics can benefit retailers in the following article:  Analyze this everyday driver of demand to improve availability.

The article highlighted that “…the weather affects consumers and their purchasing decisions on a daily basis; no other external variable shifts store-level sales trends as immediately, frequently, and meaningfully… Weather analytics help store managers, category managers, and/or replenishment systems better align inventories with expected demand trends, rather than restocking stores based on recent or historical sales levels.”

US maps depicting different replenishment quantities in different locations due to change in favorable weather locationsue to

Optimized demand forecasts mean happier customers, improved market share, and increased margins. Visit Planalytics to learn more.

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Markdowns Rise & Margins Fall

WWD published an article (Q3 Markdowns Rising, Margins Falling – 8/30/22; subscription required) highlighting how several retailers are facing significant pressure on margins due to high inventories.

“It’s markdown city,” said Dana Telsey, chief executive officer and chief research officer of the Telsey Advisory Group. “Deals are all around. Everyone’s inventory levels are high. There was an acceleration of promotions in the second quarter, and it will stay at the same rate and could get even more aggressive in the third quarter.” … The retail landscape is awash in markdowns that continue to build — and that will take a sharp toll on third-quarter profits and margins, especially at apparel specialty chains and department stores catering to low- and middle-income families. Spending is shifting more to essentials and away from nonessentials. [Excerpt from WWD]

Overplanning certainly played a big part with some retailers over-correcting for supply chain challenges that had led to empty shelves and lost sales.  

Another reason stores are over-inventoried and forced to offer deep markdowns comes from an eternal external variable that shifts – often quite significantly – the year-over-year demand trends for certain products: THE WEATHER. As you will see below, the unfavorable weather in Q2 directly contributed to the backup in inventory that forced many retailers to take aggressive markdowns.

You can mark this down. Retailers preserve margin with predictive demand analytics is an article in Retail Dive that looked at three ways to limit markdowns with weather-driven demand analytics.  The first approach discusses how improved planning accuracy ahead of the season can reduce the need for markdowns as a selling season winds down.

Pre-season planning & allocation.
When retailers plan the next season or year they must correct the weather bias embedded in past sales performance. This “deweatherization” process improves accuracy by accounting for when favorable weather conditions exaggerated sales or unfavorable conditions deflated sales. In situations where prior sales are inflated, the positive weather environment rarely materializes to the same degree again the next year. The result: retailers often end up with excess inventories that need to be marked down to clear stocks. So instead of unintentionally chasing weather-biased sales from the prior year, which are statistically unlikely to repeat, retailers can use weather-driven demand to improve plan accuracy and adjust inventories on a market-by-market basis.
[Excerpt from Retail Dive]

Planalytics helps retailers use weather-driven demand analytics to “deweatherize” sales histories and remove planning/forecasting error.  For a great example of why this process helps improve plans, and ultimately, margins, look no further than March & April 2022 versus prior year.  Together these two critical months provided a much more unfavorable early season demand environment in many key U.S. regions and markets.

The negative weather comp extracted $864 million in sales from the Specialty Apparel sector alone during these two early spring months versus 2021. The Home Center/DIY sector fared even worse, with a negative year-over-year weather-driven sales impact of $1.9 billion.

Visit Planalytics to learn more about how predictive weather-driven demand analytics can help retailers more optimally align inventories with consumer purchasing and improve sales, lower costs, and enhance profitability.

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Measuring What Matters


Retailers should focus on supply chain outcomes, not just forecast accuracy metrics, when gauging success of inventory optimisation efforts.

Retailers commonly track forecast accuracy improvement to measure performance of their demand forecasts. Whilst forecast accuracy is certainly a metric that should be tracked, retailers need to be wary of drawing incomplete conclusions if this is where their analysis begins and ends. Why? Biased forecasts can obscure significant gains of key performance measures such as out-of-stock rates or inventory turns… in other words, metrics that directly impact turnover and profitability.

Mean absolute percentage error (MAPE) or root mean square error (RMSE) are often used by retailers to judge the accuracy of forecasts. Though widely used, these measures can be flawed when evaluating the efficacy of point solution overrides, especially in the presence of biased forecasts.

In our work with retailers, Planalytics has typically found some level of positive bias in forecasts, often in the +5% to +20% range. Whether this is intentional to ensure high service levels or due to challenges from in intermittent demand, this bias has proven to be persistent across all types of retailers and demand forecasting applications.

At a macro forecasting level, this bias may not pose obvious problems. For example, when choosing the optimal time series forecasting methodology (e.g. AVS Graves vs. Exponential Smoothing), some combination of error and bias factors will allow a retailer to choose which method they believe will maximise supply chain outcomes. However, biased forecasts will mask the effectiveness of point solution overrides.

Consider the following hypothetical scenario in which a demand forecast has an average bias of 10%. Figure 1 illustrates how the override – although “perfect” in that the base forecast with the adjustment exactly match observed demand – will appear to have a negative effect if only measured by forecast accuracy.

This hypothetical forecast assumes a perfect unbiased demand forecast of 100 units, a perfect forecast override of 10% and a known +10% bias to the forecast. When calculating the error rates from this “perfect” forecast, we see that the biased forecast appears to have an error rate of 0% while the biased overridden forecast has a MAPE of 10%. This result is despite the fact that the override exactly captures the increased demand. It is easy to see how a retailer can come to incorrect conclusions about the improvements that are being provided by the override and the accuracy of biased baseline forecast.

To more accurately gauge the effectiveness of forecast overrides, retailers utilise a before and after A/B testing approach such as difference-in-differences.

The difference-in-differences approach compares performance of supply chain metrics like out-of-stock rate or turn rate before and after implementation of a new forecasting technique. The test can be set up either as a comparison between stores or between products. The economists who popularized the difference-in-differences methodology recently won the Nobel Prize in Economics, further validating the above measurement methodology (The Royal Swedish Academy of Sciences 2021).

An increasingly common demand forecast adjustment or override that retailers look to address is the weather’s impact on consumer purchasing. This is no surprise as the weather is constantly changing and it continually and significantly alters consumer demand. Projecting how much (% or unit volume) demand will increase or decrease due to the weather across products and stores via overrides to the base forecast is a proven way to limit both understocking and overstocking specific products and categories in different locations (that will experience different weather-influenced demand trends).

Using an example in which Planalytics’ Weather-Driven Demand (WDD) outputs are used as forecast overrides will show how real financial gains may get overlooked when forecast accuracy improvement is the only metric evaluated. In a comparison (Figure 2) of the baseline difference in out-of-stock rate pre- and post-WDD overrides over the course of several weeks one can see how this disconnect can occur. The difference between the baseline and post-implementation out-of-stock rates is the treatment effect from the methodology change.

Instead of looking at changes in MAPE which (as was shown earlier) can produce spurious conclusions, the difference-in-differences approach on out-of-stock (OSS) rate illustrates the exact effect WDD forecast overrides have on the relevant supply chain outcome. In the end, it is this measurement of benefit and its financial value that ultimately matters to a business.

The example above using Weather-Driven Demand forecasts generate a measurable 100-200 bps improvement in out-of-stock rates, 5-10% reductions in on-hand inventory, and 7-15% reductions in shrink for perishable items. In calculating bottom line financial return that results from improvements in these key business metrics, retailers typically capture EBITDA gains between 2-5 million for every 1 billion in revenue exactly captures the increased demand. It is easy to see how a retailer can come to incorrect conclusions about the improvements that are being provided by the override and the accuracy of biased baseline forecast.


David Frieberg, VP Marketing
dfrieberg@planalytics.com

Read the original article here.

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Why the Same Temperature Can Feel Different Somewhere Else

FiveThirtyEight published an interesting article (“Why The Same Temperature Can Feel Different Somewhere Else”) discussing how reactions to the same weather conditions are all “relative” based on a person’s location and related cultural norms.

“In much of the United States, the high 80s in Fahrenheit is hot, but it’s not hot-hot. It could even be a day of sweet relief in the South, maybe time for a family picnic. But last month, across the United Kingdom, headlines warned of temperatures that could hit 31 degrees Celsius. When Americans found out that translated to 88 degrees Fahrenheit, they quickly concluded: “Europeans are weak.” And while temperatures in Europe kept going up, eventually hitting levels even Texans would find daunting and killing thousands of people across the continent, the question of how a temperature could mean serious danger in one place while being an average summer Saturday in another remained.

The temperature may be an objective number, but how we experience it is not. Culture influences the biology and psychology of thermal comfort, shaping what our bodies are used to dealing with and how our homes and businesses are set up to adapt.”

At Planalytics, where we help retailers and other consumer-based businesses quantify and forecast how much demand for a product or service will increase / decrease due to the weather, the points made in this article are certainly confirmed by the metrics we produce for clients daily.

The below example for a very basic commodity product – bottled water – illustrates the subjective nature of temperature on different populaces.  Weather-Driven Demand (WDD) analytics isolate and measure the impact that the weather alone has on demand. The graphic below shows the large variance in WDDs for different U.S. cities that all experienced high temperatures in the low 90s during a mid-July period. The WDDs indicated the increase / decrease this temperature level had on bottled water demand compared to each location’s normal for that time of year.

July-Weather-Driven-Demand-for-Bottled-Water

In addition to looking at different responses to identical absolute metrics such as a temperature or a heat index, the changes in weather measures for people in a particular location and a particular time of year has a significant impact on the activities people pursue and the products they purchase.  Whether it is a change from what’s normal or what the recent weather situation has been, consumer reactions are again localized in nature. The below example, shows how differently demand for three products will change with an 8 degree drop in temperature in Mid-April:

Mid-April-Weather-Driven-Demand-Map

And while the FiveThirtyEight article highlights temperature, other weather components like rain or snow and the combination of those factors add further complexity in terms of behavior and consumer purchasing.  Planalytics applies advanced statistical methods and machine learning, mountains of sales data, years of unmatched retail demand expertise to the most omnipresent external influencer of purchasing — the weather — to help businesses understand, quantify, and proactively manage the never-ending impacts.  Learn more at www.planalytics.com.

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Week Ending August 13, 2022 / Retail Week 28 / August Week 2

Cooler in the East and Along the West Coast, Summer Heat in the Middle. Showers for Many. West-East Split in Canada. Showery West and East.

 

Weather-Driven Demand (WDD) Category Notables


Weather-Driven Demand LegendWDDs represent the estimated % change in demand of the product / category based purely on the year-over-year changes in weather. 

 

Weekly Summary

 

A Welcome Cool-Down. Cooler air provided welcome relief from the recent sizzling conditions in the central and eastern U.S. The West Coast had cool onshore flow and the interior West stayed hotter than LY. The Eastern North Central region was coolest since 2014, the Southeast since 2015. New Orleans was coolest since 2008, Las Vegas since 2010, Atlanta and Chicago since 2014. Contrarily, the Northwest region was hottest since 1971. Denver was hottest in the past 60+ years, Salt Lake City since 2013, and Oklahoma City & Philadelphia since 2016.

Summertime Showers. Showers and storms were scattered across the central and eastern states while monsoon rains persisted in the West. The Northwest was wettest since 2019. Nashville was wettest in the past 60+ years, Las Vegas since 1984, Memphis since 2002, Atlanta since 2005, and Houston since 2007. There were around 360 reports of severe weather but only 2 tornadoes.

A Shift in Canada. Temperatures slipped below LY for much of eastern Canada while the West (apart from the immediate coast) turned hotter. Montreal and Ottawa were coolest since 2014, Toronto and Winnipeg since 2019. Conversely, Calgary was hottest in the past 60+ years, Edmonton since 2014. Rain was most prevalent in the East and West. Ottawa was wettest in the past 60+ years, Montreal, Saint John, and Quebec City since 2015.

Last Year (week-ending 08/14/21), the U.S. was hottest in the past 60+ years and wettest since 2018. Canada was hottest in the past 60+ years and wettest since 2017.

 

Weekend Review

The weekend trended hotter than LY for much of the central U.S. but the West and East trended cooler. Rain and storms moved from the North Central region to the Ohio Valley. Showers and storms were also strung along the Gulf and Southeast Coasts and the interior West. Western and eastern Canadian markets were cooler than LY while the Prairies remained hotter. Showers and storms crossed the Prairies and light rain fell in the Maritimes.

 

Contact us to learn how predictive weather-driven demand analytics can help your organization.

 

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Week Ending August 6, 2022 / Retail Week 27 / August Week 1

Hotter Week for Many in the U.S. Rain and Storms Coast to Coast. Split Temperature Pattern in Canada With Rain and Storms.

 

Weather-Driven Demand (WDD) Category Notables


WDDs represent the estimated % change in demand of the product / category based purely on the year-over-year changes in weather. 

 

Weekly Summary

 

Summer Sizzle. Summer heat spread across much of the country although pockets of the Northwest and Southwest trended below LY. The Northeast region was hottest since 2001, the Western North Central since 2011, and the Southwest since 2012. Denver and Tampa were both hottest in the past 60+ years. Boston was hottest since 1980, Philadelphia since 2005. Conversely, New Orleans was coolest since 2005, Las Vegas since 2014, and Phoenix since 2016.

A Stormy Week. Heavy rain and storms occurred across much of the eastern U.S. while monsoonal showers continued in the West. There were around 650 reports of severe weather but only 4 tornadoes. The Southwest was wettest since 2017, the Eastern North Central and Southwest Coast since 2018. Cincinnati, SLC, and St. Louis were all wettest in the past 60+ years, Atlanta since 2017.

Cooling in Western Canada. Temperatures slipped below LY for much of western Canada while the East stayed hotter than LY. Saint John’s was hottest since1970, Saint John since 1990, and Toronto since 2016. Winnipeg was coolest since 2017, Calgary and Edmonton since 2019. Most markets saw light showers. Winnipeg was wettest since 2016.

Last Year (week-ending 08/07/21), the U.S. was coolest since 2017 and driest since 2006. Canada was coolest since 2019 and driest since 2009.

 

Weekend Review

The weekend trended hotter than LY for much of the central and eastern U.S. but the West trended cooler. Rain and storms moved across the North Central region and Great Lakes. Showers and storms were also scattered across the Northwest and Southeast. Western Canada was cooler than LY while the East remained hotter. A few showers or storms dotted portions of the East.

 

Contact us to learn how predictive weather-driven demand analytics can help your organization.

 

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Week Ending July 30, 2022 / Retail Week 26 / July Week 4

Cooler Across the Middle, Hotter Around the Edges. Rain, Storms, and Flooding. Three-Way Split in Canada. Stormy Prairies, Drier West and East.

 

Weather-Driven Demand (WDD) Category Notables

2022-08-01-Weather-Driven-Demand-Products
Weather-Driven Demand Legend

WDDs represent the estimated % change in demand of the product / category based purely on the year-over-year changes in weather. 

 

Weekly Summary

2022-08-01_WDD Temp Precip Notables

 

Cooler Center, Hotter Coasts. A most welcome shot of cooler air broke the heat across the middle of the country , but hotter temperatures covered both coasts and the South Central region. Portland, OR topped 100° and Seattle reaching the 90s for 6 straight days. Both cities were hottest since 2009. The Northwest region was hottest in the past 60+ years, the South Central since 2012, and the Southeast since 2016. Salt Lake City and Tampa were also hottest in the past 60+ years. Conversely, the Southwest Coast was coolest since 2013, and the North Central regions and Southwest since 2017. Phoenix was coolest since 1999, Las Vegas since 2003.

Storms and Flooding Rain. Heavy rain broke out from the South Central to Mid-Atlantic causing severe flash flooding from St. Louis to eastern Kentucky. Storms also tracked across the North and monsoon rains expanded across the West. The Eastern North Central region was wettest since 2011, The Southwest since 2014, and the Northwest 2015. St. Louis was wettest in the past 60+ years. There were around 400 reports of severe weather, including around 10 tornados.

Cooling for the Prairies, Hotter for the Rest. Western and eastern Canada experienced a hotter vs. LY trend but cooler air settled across the Prairies. Calgary was hottest in the past 60+ years, Vancouver since 2009. Conversely, Winnipeg was coolest since 2013. Showers and storms were scattered across central and eastern Canada. Winnipeg was wettest since 2012, Toronto since 2017.

Last Year (week-ending 07/31/21), the U.S. was coolest and driest since 2019. Canada was coolest since 2013 and driest since 2020.

 

Weekend Review

The weekend trended hotter than LY for much of the Northern Tier but southern areas were near to below LY. Rain and storms moved across the North Central region and extended from the Southwest Coast to the Mid Atlantic region. Most of Canada was hotter than LY with a cool pocket in the Prairies. Storms tracked across the center of the country.

 

Contact us to learn how predictive weather-driven demand analytics can help your organization.

 

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Week Ending July 23, 2022 / Retail Week 25 / July Week 3

Scorching Heat for Most. Stormy North and East, Drier Pockets in the South. Hotter for Much of Canada with Showers from Coast to Coast.

2022-07-28-Maps-vs-LY

 

Weather-Driven Demand (WDD) Category Notables

2022-07-28_Weather-Driven-Demand-Product-Categories
Weather-Driven Demand Legend

WDDs represent the estimated % change in demand of the product / category based purely on the year-over-year changes in weather. 

 

Weekly Summary

2022-07-28_Temperature and Precipitation Notables

Summer Sizzle. Most of the country was hotter than LY with temperatures soaring into the century mark for many major markets. The South Central and Southwest were hottest in the past 60+ years, the Northeast since 2011, and the Eastern North Central since 2012. Most Texas markets were hottest in the past 60+ years. Nashville was hottest since 1986, Sacramento since 2006. Conversely, the Northern Plains, Florida, and the immediate West Coast was a bit cooler, with San Francisco coolest since 2009 and Seattle since 2014.

Stormy North and East. Showers and storms tracked across the central and eastern U.S. but most regions averaged drier than LY. The Northwest region was driest since 1988, the Southwest since 2000. Miami was driest since 1983, Philadelphia since 2001. Las Vegas was wettest since 2009, Tampa since 2015, and Chicago since 2016. There were nearly 1100 reports of severe weather, including 17 tornados, 2 in the Chicago suburbs on Friday.

Hotter in Canada. Most of Canada experienced a hotter vs. LY trend but a cooler trend developed in the Prairies late in the week. Saint John was hottest since 2007, Montreal, Ottawa, and Toronto since 2011. Vancouver was coolest since 2014. Rain and storms occurred in most markets. Quebec City was wettest since 2003, Winnipeg since 2013.

Last Year (week-ending 07/24/21), the U.S. was coolest since 2019 and driest since 2016. Canada was coolest since 2017 and driest since 2019.

Weekend Review

2022-07-28_Weekend-Temp-and-Precip-MapsThe weekend trended hotter than LY for much of the U.S. but slightly cooler air pushed into the Northern Plains. Rain and storms tracked across the northern U.S., with additional showers in the Mid-Atlantic and Southeast regions. Monsoon showers continued in the Interior West. Most of Canada was hotter than LY with a cool pocket in the southern Prairies. Storms spanned across the southern border.

 

Contact us to learn how predictive weather-driven demand analytics can help your organization.

 

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