STUCK ONLINE: WHEN ONLINE ENGAGEMENT GETS IN THE

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STUCK ONLINE: WHEN ONLINE ENGAGEMENT GETS IN THE WAYOF OFFLINE SALESSagit Bar-GillϮ and Shachar Reichman AbstractIn recent years, billions of marketing dollars are spent, by both online and offline retailers, onwebsite design aimed at increasing consumers’ online engagement. We study the relationshipbetween online engagement and offline sales, utilizing a quasi-experimental setting whereby aleading luxury automobile brand launched a new interactive website gradually across markets,allowing for a treatment-control comparison. The paper finds surprising evidence that increasedonline engagement reduces (offline) car sales. Comparing markets where the website waslaunched to control markets, we find that the high-engagement website led to a decline ofapproximately 12% in car sales. This negative effect is due to substitution between online andoffline engagement, as the high-engagement website decreased users’ tendency to submit onlinerequests that lead to personal contact with a car dealer. We further show that the result is not dueto decreased website usability or efficiency, and perform several robustness tests to establish ourmain result. For pure offline products, hands-on engagement is a necessary step toward purchase,and thus increasing consumer engagement online may halt progression down the sales funnel andmay not be an optimal strategy.Keywords: online engagement, online-to-offline, sales funnel, e-commerce, natural experiment.ϮMIT Sloan School of Management, [email protected] Tel Aviv University and MIT Sloan School of Management, [email protected]

IntroductionCould a highly engaging and informative brand website be bad for business? Studying therelationship between online user engagement at a brand website and its offline sales, we find thatthe answer is a surprising ‘yes.’ We exploit a quasi-experimental setting, examining the impactof the launch of a new interactive website by a leading luxury automaker in four (out of 12)markets, on the brand’s offline car sales. We find a negative impact of the high-engagementwebsite on sales, with an average decline in sales of approximately 12% in markets where thewebsite was launched compared to control markets. Further examining the Online-to-Offline(O2O1) sales funnel, we identify the mechanism leading to the decrease in offline sales. Namely,higher online engagement at the automobile brand website decreased users’ tendency to submitrequests for personal contact with car dealers, resulting in a loss of opportunities to persuadepotential buyers.This work relates to ongoing efforts to understand the interaction between online and offlineretail channels (1–6). Over a decade ago, when e-commerce was in its infancy, it sparkedquestions and concerns as to the future of brick-and-mortar stores. Many wondered whetherphysical stores would become showrooms for e-retail, and whether and to what extent consumerswould shift from offline to online shopping (7–9). While about half of consumers do use physicalstores as showrooms, the reverse, online product research followed by an offline purchase isactually more common (10). Online-to-offline purchase journeys have become the norm for pureoffline products such as cars, real estate, and healthcare services, that are (largely) not availableonline. Specifically, in the automobile market, consumers shopping for a new car have -china-leads-the-online-to-offline-revolution/2

substituting dealership visits with online information gathering, leading to a decline in theiraverage number of dealership visits from 5 to 1.6 in just ten years2 (11, 12). In this newlandscape, it is increasingly important to understand and measure the end effect of web presenceon offline sales, and how it is mediated by online consumer behavior. Studying this question forthe online-to-offline sales funnel, we fill a gap in the existing literature that has examinedsettings where an actual purchase was not limited to the offline channel (1–6).We focus on the impact of increased online engagement on offline sales. Online engagementmetrics represent consumers’ level of web activity using different features of website visit,including bounce rate, number of pages viewed, number of events per page-view, sessionduration, average page-view duration, and return rate (13–15). Consumers’ online engagementhas been shown to increase website efficiency (16), by inducing more positive consumeropinions, reviews and comments. Moreover, engagement stimulates online word of mouth,which, in turn, increases online sales (e.g., 17–19). Relating to the online-offline purchase funnelframework, online engagement moves consumers from the initial consideration level down to thedecision-making level, and eventually leads to purchase (1).With this in mind, both online and offline retailers have been focusing their efforts onimproving their online presence and enhancing firm websites, and website spending iscommanding the lion share of marketing budgets (20). These efforts are aimed at increasingtraffic and consumer engagement on brand websites, and ultimately at increasing purchaseprobability (21–23). Our natural experiment setup, coupled with the context of a pure offline2The dealership visit remains a necessary step towards purchase, with 90% of American consumers surveyed reporthaving conducted at least one test drive prior to purchase.3

product, provides a unique opportunity to identify a causal effect of online engagement onoffline sales. We demonstrate that for pure offline products, high online engagement can be adouble-edged sword, as substituting the offline hands-on experience with increased onlineengagement has the potential to decrease sales.Evidence from a Quasi-experimentWe partnered with a leading luxury automobile manufacturer with a substantial globalpresence to estimate the effect of increased online engagement on local brand websites on thecompany’s offline car sales, between 2011 and 2014. Increased online engagement, in oursetting, is due to the manufacturer’s launch of new interactive brand websites, replacing theprevious less-interactive websites at the same URLs.The auto-maker’s stated goal for the new website was to increase consumers’ engagementwith the brand and their awareness of different car models and features. The main changecompared to the previous website is a substantially improved car customization experience underthe “Build Your Own” tab. This tab is designed to engage users as they test out differentconfigurations of car models, interactively displaying the full set of customizable options,accompanied by detailed information and price for each option.To evaluate the impact of high online engagement generated by the upgraded website, weutilize the quasi-experimental setting arising from its staggered launch, whereby the website wasupgraded only in some countries, and at different times. This gradual launch strategy waspossible since the brand’s websites are centrally designed and deployed, yet maintained at local,country-specific URLs, such that all traffic from a specific country, or market, is automaticallydirected to the local URL. Specifically, between 2011 and 2014, the auto-manufacturer launched4

the upgraded high engagement (HE) website in four markets in our data-set, labelled T1-T4 – inT1 in December 2011, and in T2-T4 in December 2012. Other markets were left unaffected bythe HE treatment in the above time period. These markets, where the website remained in itsprevious low engagement (LE) format3, serve as our control group. We obtained data for eightsuch control markets, labelled C1-C8.We estimate the effect of the HE treatment by comparing pre- and post- launch engagement,sales, and user activity, for treatment vs. control markets. This is the difference-in-differences(DID) empirical strategy, whereby the effect of the treatment is measured as the change in thedifferences between treatment and control groups that is due to the onset of treatment (see 24 forfurther details and discussion).Our main data set has been made available by the leading luxury auto-manufacturer. Weanalyze quarterly sales data for the four-year period from 2011 to 2014, for T1-T4 and C1-C8.Further analyses of the effects of launch on engagement and user activity utilize additional datasets, and are based on subsets of these treatment and control groups, as well as subsets of thefour-year period, due to constraints in data availability as detailed below.Manipulation Check: The HE Website Launch Increased Online EngagementThe starting point of our analysis is to confirm that the launch of the interactive brandwebsite indeed resulted in higher online user engagement, as planned. This manipulation checkis performed using data from Alexa.com, which tracks and measures global online activity.We study the impact of the upgraded website on two variables, Time-On-Site and TrafficRank. Time-On-Site is our proxy for user engagement, and is measured as time spent on the3At least in our period of analysis.5

brand website.4 Traffic Rank is a measure of website popularity, determined by Alexa.com basedon global internet traffic, such that a lower rank indicates greater popularity.Alexa.com measures engagement only for the 100,000 most popular websites worldwide, andtherefore Time-On-Site is available only for two treatment markets (T2-T3) and three controlmarkets (C3, C4, C7). Traffic Rank for the brand’s market-specific websites is available for alltreatment markets, and for seven control markets (all but C8). Both metrics are available at amonthly level for September 2012-December 2014, i.e., starting three months before the T2-T4launch.The launch of the interactive website was not accompanied by any related promotionsdesigned to attract more users to the brand’s market-specific websites. The launch was aimedonly at increasing engagement for website visitors, and not at increasing website traffic. Hence,we expect to find a positive impact of launch on Time-On-Site, with no effect on Traffic Rank.Figure 1 shows that indeed this is the case. Comparing the pre- and post-launch three-monthperiods, average monthly Time-On-Site did not significantly differ between treatment and controlmarkets pre-launch, substantially increased for the T2-T3 launch markets in the post-launchmonths, and slightly decreased for control markets (where this decrease is not statisticallysignificant). For Traffic Rank, we observe that both treatment and control markets suffer anincrease in rank (i.e., decreased traffic) in the post-launch months.(a) Time-On-Site4(b) Traffic RankNumber of pageviews per visit is also measured by Alexa.com, yet pageviews before and after the new websitelaunch are incomparable, due to changes in the definition of a pageview event resulting from the upgrade.6

Figure 1. The Effect of Launch on Online Engagement. (a) Average Time-On-Site three months before andafter launch: T2-T3 vs. three control markets; (b) Average Traffic Rank three months before andafter launch: T2-T4 vs. seven control markets.We estimate the effect of launch on engagement using the following DID regression 𝑦𝑚 𝛼𝑐 𝛽𝑦 𝛾𝑚 𝜹 𝐿𝑎𝑢𝑛𝑐ℎ𝑐𝑦𝑚 𝜖𝑐𝑦𝑚where 𝑐𝑦𝑚 represents 𝑐𝑜𝑢𝑛𝑡𝑟𝑦 𝑦𝑒𝑎𝑟 𝑚𝑜𝑛𝑡ℎ. The model thus has a full set of country,year, and month fixed effects represented by 𝛼𝑐 , 𝛽𝑦 , and 𝛾𝑚 , to control for differences betweencountries, and for the trend and seasonality in the car market. The idiosyncratic error term is𝜖𝑐𝑦𝑚 . We define the binary variable Launch to equal 0 until the new website is launched (in eachmarket), and 1 from the month of launch onwards. We are interested in estimating 𝛿, which isthe effect of 𝐿𝑎𝑢𝑛𝑐ℎ. Estimation results are reported in column (1) of Table 1, where column (2)presents estimation results for the effect of Launch on Traffic Rank (with the same modelspecification). Standard errors, clustered at the country level, are bootstrapped using the “wildbootstrap” method due to the small number of clusters (25).The results show a significant increase in consumers’ engagement in the upgraded local websitesand no significant change in the traffic to these websites. Specifically, launch of the interactivewebsite increased Time-On-Site for the average user by approximately 63 seconds (p 7

0.003***). These results are in line with the manufacturer’s stated goal for the website’sredesign, namely – higher engagement.Table 1: The Effect of HE Website Launch on Time-On-Site and TrafficRankDependent variable:Time-on-SiteTraffic ervations1393082Adjusted R0.430.80Note:Fixed effects for country, year and month included.Standard errors (shown in parentheses) are clustered at thecountry level, and estimated using the wild bootstrap method.*** p 0.01, ** p 0.05, * p 0.1The Negative Effect of the HE Website Launch on SalesWe turn to our main DID analysis - examining the impact of the HE website launch on sales,employing country-level quarterly panel data of the number of cars sold, available from themanufacturer for T1-T4 and C1-C8, in 2011-2014. The DID analysis hinges on the parallel trendassumption stating that treatment and control groups follow a similar pre-intervention trend, andthus any divergence in trend for the treatment group in the post-intervention period is due to thetreatment. We employ three tests to validate the parallel trend assumption, following thepresentation of our main results.As a first visual inspection, figure 2 plots the average quarterly sales, in terms of numbers ofcars sold, before and after the December 2012 launch for T2-T4 and all control markets. Thedashed light blue line represents the parallel trend assumption, by showing the hypotheticalchange in sales for treatment markets had they continued to follow the same trend as controlmarkets (i.e., absent treatment). The “Launch effect” marked in figure 2 is the change in the8

differences between control and treatment markets’ average quarterly sales, comparing the postto pre-launch period. We observe a negative effect, as the HE launch group exhibits a smallerincrease in average quarterly sales compared to control markets.Figure 2. The Effect of the HE Launch on Sales. Average quarterly salesfor T2-T4 launch markets and all control markets, one yearbefore and after the December 2012 launch.To formalize this result, we estimate the launch effect using the following model:(2)log(𝑆𝑎𝑙𝑒𝑠𝑐𝑦𝑞 ) 𝛼𝑐 𝛽𝑦 𝛾𝑞 𝜹 𝐿𝑎𝑢𝑛𝑐ℎ𝑐𝑦𝑞 𝜖𝑐𝑦𝑞Where log(𝑆𝑎𝑙𝑒𝑠𝑐𝑦𝑞 ) is the natural logarithm of quarterly number of cars sold in country 𝑐 inyear 𝑦 and quarter 𝑞. The variables 𝛼𝑐 , 𝛽𝑦 , and 𝛾𝑚 represent fixed effects for country, year andquarter, controlling for these sources of variation. As both launches occurred towards the end ofa quarter, we define the binary variable Launch to equal 0 until the quarter in which the newwebsite launched (in each market), and 1 from the quarter following launch onwards. Thisfurther accounts for the pace of the market for new cars, where typically 1-3 months pass frominitial inquiry to the supply of a new vehicle (26, 27). Due to this supply lag, we test a secondmodel specification where the dependent variable is the one-quarter lead of sales, considering thepossibility of a delayed impact. To these base specifications, we add the variable9

TotalRegistered, which provides the total quarterly number of non-commercial vehiclesregistered in each country, allowing for better control for country-level trends in automobilesales. Estimation results for these four specifications are reported in Table 2 below. As before,standard errors, clustered at the country level, are bootstrapped using the wild bootstrap methoddue to the small number of clusters (25). We focus our attention on 𝛿, the effect of 𝐿𝑎𝑢𝑛𝑐ℎ.Our results show a significant negative effect of the HE launch on sales, such that postlaunch quarterly sales were, on average, approximately 12-13% lower in treatment compared tocontrol markets, using same-quarter sales (models (1) and (3), 𝑝 0.05), and approximately11% lower using next-quarter sales (models (2) and (4), 𝑝 0.05).Table 2: The Effect of the HE Website Launch on SalesDependent variable:log(𝑆𝑎𝑙𝑒𝑠𝑞 )log(𝑆𝑎𝑙𝑒𝑠𝑞 1 𝑠𝑞 )log(𝑆𝑎𝑙𝑒𝑠𝑞 1 21921922Adjusted R0.980.980.980.98Note:Fixed effects for country, year and quarter included.Standard errors (shown in parentheses) are clustered at the country level, andestimated using the wild bootstrap method.*** p 0.01, ** p 0.05, * p 0.1Validity of the Control GroupThe manufacturer chose to deploy the HE website gradually, and continued its roll-out in thesame manner in other markets after our period of analysis. Reportedly, the order of launchmarkets was chosen based on internal considerations, and was not based on previous webactivity or sales in these markets. This supports the soundness of our treatment-control10

comparison, in our quasi-experimental setting. However, to ensure the validity of our DIDempirical strategy, we directly test the parallel trend assumption, and examine whether acommon sales trend exists for treatment and control markets prior to the launch of the HEwebsite (if these groups follow different pre-launch trends then the estimated effect may simplybe due to the difference in trends). Two additional robustness tests follow, offering furthersupport of the validity of our empirical strategy.Analyzing pre-launch trends. Figure 3 allows us to visually inspect the sales trend, andshows parallel pre-launch trends for T2-T4 and all control markets. Post-launch, we observe asmall downward vertical shift in treatment markets’ sales, and a difference in trends, as thetreatment group’s growth rate is now slower compared to that of the control group.Figure 3. Linear Trend of 𝐿𝑜𝑔(𝑆𝑎𝑙𝑒𝑠) for Treatment (T2-T4) vs. Control Markets (C1-C8), Pre- andPost-Launch.Formally, we estimate the following model as a direct test for differences in pre-launch trends:(3)log(𝑆𝑎𝑙𝑒𝑠𝑐𝑦𝑞 ) 𝛼𝑐 𝛽1 𝑇𝑟𝑒𝑛𝑑𝑦𝑞 𝜷𝟐 𝑇𝑟𝑒𝑛𝑑𝑦𝑞 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑐 𝜖𝑐𝑦𝑞Where 𝑇𝑟𝑒𝑛𝑑𝑦𝑞 is the index of quarter 𝑞 in year 𝑦, and 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡𝑐 is an indicator variablethat equals 1 if country 𝑐 is in T1-T4, and 0 otherwise. Other variables are defined as in equation11

(2). We also test a second specification that includes 𝑒𝑑𝑐𝑦𝑞 as an additionalcontrol. In both specifications, 𝛽2 is not statistically significant (𝑝 0.1; see Table S1 in the SI),implying that there is no difference in pre-launch trends between the treatment and controlgroups.Granger-causality test. We conduct a second robustness test, as in (28). This test of Grangercausality (29), checks that HE launch status predicts sales only after- and not before- launch,where a finding of no pre-treatment effect provides further evidence of no pre-launch differencesin trends for treatment and control markets. For this test, we create a set of dummy variables,indicating the quarter relative to the HE launch. Specifically, we use indicator variables for 1-3quarters before launch, and 0-4 quarters after launch, labelled 𝑅𝑒𝑙𝐿𝑎𝑢𝑛𝑐ℎ𝑐,𝑡 , where 𝑡 { 3, 2, . . , 4}; and an indicator for the 5th quarter and onwards after launch, labelled𝑅𝑒𝑙𝐿𝑎𝑢𝑛𝑐ℎ𝑐, 5𝑜𝑛𝑤𝑎𝑟𝑑𝑠 . These variables allow for a possible effect of launch before and after theactual HE launch, and further allow us to examine the dynamics of the HE impact – whether theeffect increases over time or remains stable.The following model (4) incorporates these variables, that replace 𝐿𝑎𝑢𝑛𝑐ℎ in (2), and furtherincludes country and quarter fixed effects (𝛼𝑐 and 𝛾𝑞 ) as well as control for market specifictrends in car sales, represented by 𝑒𝑑.(4)log(𝑆𝑎𝑙𝑒𝑠𝑐𝑦𝑞 ) 𝛼𝑐 𝛾𝑞 𝑡 { 3,. 4} 𝛿𝑡 𝑅𝑒𝑙𝐿𝑎𝑢𝑛𝑐ℎ𝑐,𝑡 𝛿 5𝑜𝑛𝑤𝑎𝑟𝑑𝑠 𝑅𝑒𝑙𝐿𝑎𝑢𝑛𝑐ℎ𝑐, 5𝑜𝑛𝑤𝑎𝑟𝑑𝑠 𝑒𝑑𝑐𝑦𝑞 𝜖𝑐𝑦𝑞Estimation results are reported in Table S2. The results confirm that there are no anticipatoryeffects, that is, the differences between treatment and control markets do not appear prior to theHE launch, in support of the parallel trends assumption. Furthermore, we observe the negativeimpact of the HE website increasing in magnitude in the periods following launch.12

Placebo treatment model. As a final robustness test of the DID results, we estimate aplacebo treatment model to demonstrate that the observed effect on sales cannot be attributed tochance. For this exercise, we use pre-launch data for T2-T4 and control markets, and estimate theeffect of a placebo (fake) launch starting December 2011, using the same specifications as inTable 2. The effect of the placebo treatment is not statistically significant, as expected (𝑝 0.1;estimation results are reported in Table S3).Mechanism: Higher Online Engagement Decreased Personal Contact with DealersThe automobile brand’s website is designed to affect sales via sales leads – online requestsfor information, requests for dealership offers, and test drives — following which the interestedconsumer is contacted by a car dealer. This is the case for the pre- and post-launch website. If theHE launch is indeed the cause of the decline in sales in treatment markets, then we expect to finda negative effect of launch on online sales leads, to establish the mechanism through whichhigher online engagement led to lower offline sales.We study the effect of launch on sales leads using a panel of monthly sales leads for T3 andfour control markets, between January 2012 and December 2014.5 Online sales leads arecaptured by the following three variables: (1) BD – brochure downloads, where a brochureincludes all possible options for model configuration along with their price (for a single chosenmodel); (2) RFO - requests for an offer; and (3) TDA - test drive applications.Requests for test drives (TDA) are performed by customers with a strong purchase intent, asthey demonstrate a commitment to arrive at a dealership. On the other hand, brochure downloads(BD) represent an earlier stage in the car purchase journey, when the customer is still gathering5The automaker provided limited data for online sales leads.13

information and deliberating, and a request for offer (RFO) is an intermediate stage, in which thedeliberating customer seeks out personal contact with a dealer.Figure 4 illustrates the effect of launch on these three variables. Comparing the differencebetween control markets to T3 in BD, RFO, and TDA in the year before T3’s HE launch to theyear after, we observe a positive effect of launch on BD and TDA, and a large negative effect onRFO.(a) BD(b) RFO(c) TDAFigure 4. The Effect of HE Launch on Online Sales Leads: (a) BD; (b) RFO; (c) TDA. Comparing T3to control markets, a year before and a year after the T3 launch, we observe a positive effectof launch on BD and TDA, and a negative effect on RFO.The effect is estimated in the following DID 𝑦𝑚 ) 𝛼𝑐 𝛽𝑦 𝛾𝑚 𝜹 𝐿𝑎𝑢𝑛𝑐ℎ𝑐𝑦𝑚 𝜖𝑐𝑦𝑚where 𝑆𝑎𝑙𝑒𝑠𝐿𝑒𝑎𝑑 is one of {𝐵𝐷, 𝑅𝐹𝑂, 𝑇𝐷𝐴} and the remaining variables are the same as inspecification (2). Estimation results are reported in Table 3.14

Table 3: HE Launch effect on Online Sales LeadsDependent 𝐴)(1)(2)(3)*********Launch0.23 (0.05)-0.89 (0.09)0.95 (0.20)Observations2424322Adjusted R0.980.750.77Note:Fixed effects for country, year and quarter included.Cluster robust standard errors shown in parentheses (Clustered on country).*** p 0.01, ** p 0.05, * p 0.1The results indicate that the launch of the HE website increased the number of brochuredownloads and test drive requests, while reducing the number of requests for offers.6 The resultscontinue to holds when we further control for total car registration in each market (Table S4).7Our results suggest that higher online engagement led to increased information gatheringonline, as represented by the increase in brochure downloads. Furthermore, HE helped movecustomers with a strong purchase intent down the purchase funnel, by increasing test driveapplications. Yet, higher online engagement also resulted in fewer, pre-test drive, dealershipcontacts, represented by the decrease in requests for offers (likely due to improved availability ofcomprehensive pricing information). This reduction in personal offline contacts with customerswho are still in the deliberation stage, is the driver of the decrease in sales.The mechanism by which online engagement impacts offline sales is further discussed in theModel section in the SI, where we present a formal model of the online to offline purchasefunnel, for purely offline products. The model highlights two roles of online engagement, in the6We refrain from comparing the magnitude of these effects to the magnitude of the effect on sales, as this analysis isbased on a more limited dataset.7The DID parallel trends assumption holds, as for the three type of sales leads, there was no difference in pre-launchtrends between the treatment and control groups (see Table S5 in the SI).15

spirit of those attributed to traditional advertising: providing product information and persuasion(30). The first is modelled as uncertainty reduction regarding consumers’ fit with the product,and the second as the introduction of a non-negative product bias. These effects counteract whenconsumers’ uncertainty regarding product fit is relatively high and their match probability withthe product is low. Within this framework, we derive conditions for which online engagementwill have a negative impact on offline sales.We find that the overall effect of high online engagement on offline sales will be negativewhen the share of consumers who match with the product is relatively low8 and uncertaintylevels regarding product fit are high, on average. In this case, lower online engagement, whichmaintains high uncertainty levels, is a stronger driver of movement down the sales funnel thanhigh online engagement, which biases toward purchase, yet reduces uncertainty (furtherdiscussed in the SI ).Comparison to Major CompetitorsWe now compare sales for our luxury brand to two close competitors in the treatmentmarkets, before and after launch, as another robustness test of the negative effect of HE launchon sales identified in the market-level DID analysis. This comparison will rule out the possibilitythat the negative effect we find is due to some exogenous negative shock to the luxury segmentin the treatment countries, which is not related to the launch of the HE website.The brand’s two closest competitors were identified by the company. We use new vehicleregistration data as a close proxy for sales, as we do not have access to internal sales data for thecompeting brands. We thus analyze a brand-level panel of monthly vehicle registrations, for three8Quite likely for luxury cars, with the segment comprising approximately 13% of total car sales,http://www.thecarconnection.com/news/1104264 6

brands — the focal brand and its two main rival brands — focusing on the two largest marketsT2 and T3, in which registration data is publicly available.We estimate a DID model similar to specification (2), where the control groups arecompeting brands. The soundness of this comparison is ensured, as we find no difference in prelaunch trends between the treatment and control groups (Table S6). We study the effect of launchon both a one- and two-month lead for sales, to account for the pace of the car market as well asa possible lag between purchase and registration.The results reported in Table 4 and Figure 5 show a significant decrease of approximately 910% (𝑝 0.01***) in sales following the HE website launch, compared to the control brands.We therefore reaffirm our main finding that high online engagement led to a decrease in carsales.Table 4: The Effect of HE Launch on Sales – Comparison to Competing BrandsDependent variable:log(𝑆𝑎𝑙𝑒𝑠𝑡 1 )log(𝑆𝑎𝑙𝑒𝑠𝑡 2 )Control Brand1 Control Brand2 Both Control Brand1 Control Brand2 usted R0.780.830.800.780.830.80Note:Fixed effects for brand, country, year and month included.Cluster robust standard errors shown in parentheses (Clustered on brand).*** p .01,** p .05,* p .117

Figure 5. The Effect of HE Launch on Sales – Comparison to competing luxurybrands, two years before, and two years after the HE launch in T2 andT3.The Effect of Website Launch on Online Engagement – Online Lab ExperimentAn alternative explanation for the negative impact of the HE website could be faulty webdesign that influenced key user-experience parameters, such as site usability, information quality,and interactivity features (31–34). To test this alternative explanation, we conducted an onlinelab experiment where 335 participants were randomly assigned to either the HE or LE version ofthe brand website to complete three tasks, and then answered a survey reporting on differentaspects of their online experience. Specifically, participants were asked to browse themanufacturer’s website and perform the following three tasks, associated with purchaseintention: (a) design their own car using the “Build your own car” feature of the website (BYO);(b) locate and download a brochure of their selected model (BD); and (c) locate and complete thetest drive application form (TDA). The experiment was carried out on the brand’s live localwebsites (in one treatment market and one control market) that are in the same language.18

Following task completion, participants answered a ten-item survey on website perceivedusefulness and ease of use (35, 36), with responses on a seven-point Likert scale.The experiment tasks were submitted as human intelligence tasks (HITs) to AmazonMechanical Turk (MTurk). Each pa

which, in turn, increases online sales (e.g., 17–19). Relating to the online-offline purchase funnel framework, online engagement moves consumers from the initial consideration level down to the decision-making level, and eventually leads to purchase (1). With this in mind, both online and offline retailers have been focusing their efforts on