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COVID-19 has accelerated the shift to online in 2020 and we expect further disruption goingÌý forward.ÌýÌýÌý ToÌý answerÌý howÌý muchÌý thisÌý canÌý affectÌý hundredsÌý ofÌý USÌý stocksÌý worthÌý ~$8T, this report leverages our previous Q-Series and this year's US eCommerce survey.Ìý ÃÛ¶¹ÊÓƵ built an interactive eCommerce penetration model based on 7 years of proprietary surveyÌý dataÌý fromÌý ÃÛ¶¹ÊÓƵÌý EvidenceÌý Lab.ÌýÌýÌý BasedÌý onÌý thisÌý analysis,Ìý weÌý nowÌý forecastÌý onlineÌý share will reach 31% of US Retail sales by 2024 vs. our prior Q-Series forecast of 20% by 2022 and up from 14% in 2019.Ìý This ongoing shift implies eCommerce gains 340 bps of share annually.Ìý Our new forecast entails eCommerce sales rising at a 12% 5-yr. CAGR versus our previous Q-Series estimate of 11%.

COVID-19 accelerated eCommerce penetration, but stores still have a place:

Our work suggests brick and mortar sales can marginally grow (less than 1% annually), assuming overall US retail sales rise at 3%.Ìý Thus, we think there could be more retail store closures.Ìý One of the enduring outcomes of COVID-19 is that it inspired broader digitalÌý adoption,Ìý especiallyÌý inÌý olderÌý ageÌý cohorts.ÌýÌýÌý However,Ìý consumersÌý indicatedÌý theyÌý still shop in stores (i) to see and try products and (ii) because they enjoy the experience.

ÃÛ¶¹ÊÓƵ Evidence Lab market research and data science analysis power our views:

ÃÛ¶¹ÊÓƵÌý EvidenceÌý LabÌý surveyedÌý 2,500Ìý USÌý adultsÌý ageÌý 18+Ìý regardingÌý onlineÌý andÌý offlineÌý shoppingÌý habitsÌý byÌý category.ÌýÌýÌý TheÌý surveyÌý hasÌý beenÌý administeredÌý annuallyÌý sinceÌý 2014,Ìý with questions kept consistent in order to provide y/y comparisons.Ìý We then leveraged data science techniques to build detailed penetration models for each retail category. WeÌý acknowledgeÌý theÌý mathÌý forÌý howÌý weÌý formulateÌý eCommerce penetration heavily relies on the most recent survey data.Ìý This data point was attained duringÌý aÌý pandemic,Ìý whenÌý behaviorsÌý mayÌý differÌý fromÌý aÌý steadyÌý stateÌý orÌý normalizedÌý behavior, potentially influencing the long run forecast from the data science team.

What sectors and stocks are best positioned? Who has most to lose?

OurÌý interactiveÌý modelÌý suggestsÌý grocery,Ìý apparelÌý &Ìý footwear,Ìý andÌý homeÌý improvementÌý areÌý likelyÌý theÌý threeÌý biggestÌý contributorsÌý toÌý theÌý shiftÌý byÌý dollarÌý volume.ÌýÌýÌý WeÌý forecastÌý fasterÌý onlineÌý adoption inÌý under-penetratedÌý categoriesÌý (groceries,Ìý householdÌý productsÌý andÌý personalÌý care).