{"id":510,"date":"2020-04-07T08:32:17","date_gmt":"2020-04-07T06:32:17","guid":{"rendered":"https:\/\/digitaleconomy.org\/?p=510"},"modified":"2020-04-10T08:57:01","modified_gmt":"2020-04-10T06:57:01","slug":"managing-customer-experience","status":"publish","type":"post","link":"https:\/\/fromdatatoimpact.com\/index.php\/2020\/04\/07\/managing-customer-experience\/","title":{"rendered":"Managing Customer Experience"},"content":{"rendered":"\n

How to Measure Customers\u2019 Journey?<\/em><\/p>\n\n\n\n

by Christin Beer & Florian Stahl<\/a><\/p>\n\n\n\n

<\/div>\n\n\n\n

Lower prices and novel products or services are said to be no longer sufficient for firms to sustain their competitive edge. Instead, the creation of superior customer experience plays an increasingly crucial role in setting the business apart (Grewal, Levy, and Kumar 2009). Thus, understanding and managing customer experience has become one of the top priorities of both practitioners and scholars in the last decade. Essential prerequisite for this is a deep knowledge of the customer journey (Berry, Carbone, and Haeckel 2002). Advancements in digital and mobile technologies and the resulting emergence of big data enable the obtainment of granular insights into the behavior of individuals (Chen, Chiang, and Storey 2012). On the other hand, this technological proliferation has evoked a complex environment consisting of a plethora of channels, media, and touch points (Day 2011), making it difficult to seamlessly trace respective movements, especially in cross-channel and offline settings (e.g. Ailawadi and Farris 2017). Although many reports (e.g. Gartner 2018) show that the majority of firms are interested in customer experience and customer journey tracking, other sources also emphasize that most of these companies have not yet managed to transform their aspirations into tangible initiatives (e.g. Forrester 2018). Yet, fractions of the customer journey are generally already assessable. The key to such insights often lies in data sets that are readily at management\u2019s disposal. By leveraging and sharing their firm-specific information as well as their key performance indicators (KPIs) from the domains of digital marketing and customer relationship management across company departments, managers could track some of their customers\u2019 footprints, thereby laying the foundation for actively enhancing customer experience. <\/p>\n\n\n\n

\nBut\nwhat do the terms \u2018customer experience\u2019 and \u2018customer journey\u2019\nactually mean? <\/strong>\n<\/h2>\n\n\n\n

Though\n\u2018customer journey\u2019 has only recently emerged as buzzword, the\nunderlying idea can be traced back to Howard and Sheth (1969) who\nintroduced the concept of purchase stages: \n<\/p>\n\n\n\n

  1. \n\tPre-purchase\n\ti.e. need recognition, search, consideration\n\t<\/li>
  2. \n\tPurchase\n\t<\/li>
  3. \n\tPost-purchase\n\ti.e. consumption resp. usage, service requests, and engagement\n<\/li><\/ol>\n\n\n\n

    While a notable fraction of scholars asserts that \u2013 due to today\u2019s abundance of channels, media, and touch points \u2013 an individual\u2019s behavior may not always be as linear as implied by this structured model (e.g. Baker 2016), it is still accepted as fundamental conceptualization of the customer journey. Also, it is widely acknowledged that the customer journey encompasses a variety of touch points across several channels. Thereby, a channel is \u201ca customer contact point, or a medium through which the firm and the customer interact\u201d (Neslin et al. 2006), whereas touch points are \u201cepisode[s] of direct or indirect contact with a brand or firm\u201d (Verhoef, Kannan, and Inman 2015) with channels providing the environment in which these episodes occur. Eventually, the customer experience accrues along these touch points and is often described as multi-dimensional construct. For example, Lemon and Verhoef (2016) define the term as \u201ccognitive, emotional, behavioral, sensorial, and social responses to a firm\u2019s offerings during the customer\u2019s entire purchase journey\u201d. The following figure schematically illustrates the fundamental conceptualization of the customer journey and customer experience.<\/p>\n\n\n\n

    \"\"
    Figure 1:<\/strong> Fundamental conceptualization of the customer journey and customer experience <\/em><\/figcaption><\/figure><\/div>\n\n\n\n

    \nHow\ncan managers derive insights about customers\u2019 experience and\ncustomers\u2019 journey from primary data?<\/strong><\/h2>\n\n\n\n

    Current research illustrates that customer experience can be measured via well-established customer feedback metrics that are traditionally collected by means of after-sales surveys, i.e. primary data. In general, academia seems to agree that, though they do not capture the multidimensionality of customer experience, such KPIs serve as modest approximates of experience-related customer perceptions (e.g. Klaus and Maklan 2013). Hereby, customer experience is most commonly approached based on the evaluation of the Customer Satisfaction Score (CSAT). Other authors also consider the Net Promoter Score (NPS) and the Customer Effort Score (CES) as well as (re)purchase intentions as insightful. Yet, researchers perceive such metrics to be too holistic (Meyer and Schwager 2007) or to solely tackle \u201cthe sale or after-sale service situation\u201d (Frow and Payne 2007), claiming that they should be assessed more regularly along the customer journey to examine the overall and touch point specific experience. Thus, marketers consulting such KPIs to estimate their customers\u2019 experience should look at different aggregation levels and also combine metrics to generate the most reliable feedback. Although such primary data ergo do entail information on the customer experience, they are said to be \u201cnotoriously unreliable [and] skewed by low participation rates, biased questions, and unreliable responses\u201d (Baker 2016) and thus not always indicative of future customer behavior. Against this background, it is suggested to particularly track secondary data that record genuine customer sentiment and behavior. <\/p>\n\n\n\n

    \nWhich\nrole do secondary data play in assessing customers\u2019 experience and\ncustomers\u2019 journey?<\/strong><\/h2>\n\n\n\n

    The scope of secondary data potentially informing customer experience and customer journey initiatives nowadays reaches beyond customer transaction records, as the rise of clickstream and social media data presents modern marketers with the opportunity of monitoring customer traces in the digital environment in unprecedented detail (e.g. Brynjolfsson, Hu, and Rahman 2013). In general, the analysis of these newly available data streams does not only allow for holistically assessing customer perceptions and intentions resp. experience by leveraging unstructured content from customer review platforms, social networks, forums, and blogs by means of text analytics (e.g. Lemon and Verhoef 2016), thus adding to the approaches based on primary data. More importantly, it enables managers to measure in how far the sentiment expressed by their customers is reflected in their real behavior along the different stages, channels, and touch points of the customer journey (e.g. Srinivasan, Rutz, and Pauwels 2016). Recalling the three-staged conceptualization of the customer journey, the next paragraphs introduce how present-day KPIs can be used to acquire a better understanding of the sequence of touch points encountered by customers along the pre-purchase, purchase, and post-purchase stage. Still, taking into account the currently limited availability of fine-grained data in the offline environment (e.g. Ailawadi and Farris 2017) and data integration issues (Dewsnap and Jobber 2000), seamlessly tracing each individual is not yet possible. Thus, up until now, the focus lies on leveraging clickstreams while partially augmenting these vast sets of information with often aggregated, traditional transaction and advertising expenditure data. Furthermore, though they are traditionally residing in the after-sales domain alongside the CSAT and other customer feedback metrics (Bolton, Lemon, and Verhoef 2008), practically applied KPIs such as average resolution time might also be of value to evaluate a firm\u2019s efficiency in dealing with customer requests across all journey phases. Besides, since clickstream data entail records on the type of gadget used for accessing content on the Internet, i.e. smartphone, tablet, or computer, they allow for detecting device switching resp. online versus mobile channel choice along the customer journey (de Haan et al. 2018). In this context, drawing on KPIs used by firms to monitor their customers\u2019 utilization of mobile apps, e.g. the number of downloads, users, and time spent using the app (Edelman and Singer 2015), may also contribute to an improved understanding of customer behavior. While such device-related measures potentially concern the entire customer journey (Shankar et al. 2016), other data sets allow managers to delve into its individual stages. The following figure provides an overview of the data used to measure customers\u2019 experience and customers\u2019 journey.<\/p>\n\n\n\n

    \"\"
    Figure 2:<\/strong> Overview of primary and secondary data used to evaluate the customer journey and customer experience<\/em><\/figcaption><\/figure><\/div>\n\n\n\n

    Pre-purchase stage. <\/strong><\/em>A wide range of academics who aim at gaining knowledge on how customers become aware of, search for, and consider a specific brand resp. product or service resorts to evaluating within-website browsing behavior. For this, granular KPIs, i.e. the individual pages of a website requested by a user during one visit as well as the respective duration of such page views, can be adduced, as Bucklin and Sismeiro (2003) show. Also, the authors accentuate that such clickstreams differ between new and repeat visitors, enabling managers to identify the idiosyncrasies in user behavior associated with new versus recurring touch points. The same KPIs are deployed to classify within-website behavior into different categories, i.e. visits serving the purpose of (1) browsing, (2) searching, (3) knowledge-building, or (4) buying (Moe 2003). Tracing such KPIs enables to better understand (potential) customers\u2019 clickstreams, browsing objectives, and purchasing propensities in the context of a company website. Similar metrics are also leveraged to scrutinize information search on third-party platforms like Amazon (e.g. Kim, Albuquerque, and Bronnenberg 2011). Yet, not every website visit is initiated by direct type-in, which is why the monitoring of within-website behavior does not always capture the user\u2019s entire digital footprint (Park and Fader 2004). Rather, many individuals are directed towards a website e.g. by clicking on a banner ad or an organic or paid search result (e.g. Anderl, Schumann, and Kunz 2016), thereby encountering additional touch points. To better grasp these variances in origin, the source of website visitors constitutes an essential KPI. Relatedly, a noteworthy fraction of scholars studies cross-website browsing behavior, mainly by inspecting website log files (e.g. Chan, Wu, and Xie 2011) and cookies (e.g. Kireyev, Pauwels, and Gupta 2016). In fact, academics like Dinner, Van Heerde, and Neslin (2014) contend that both customer-initiated keyword search and subsequent website visits are often preceded by firm-initiated display ads, as, along with traditional media, the latter often trigger initial need recognition and awareness, thus eliciting touch points attributable to the earliest phase of the decision process. Also, in the light of the social environment\u2019s rising influence on customer decision processes (e.g. Wang, Yu, and Wei 2012) and the progressing disentanglement of advocacy and purchase behavior, touch points created by social media should not only be monitored in the post-purchase stage, but also prior to a transaction. While customer experience tracking focusses on sentiment in user-generated content as suggested previously, KPIs in the focal context are impressions of firm-generated posts as well as resulting reactions, measured by the number of likes, shares, clicks, and comments (e.g. Lee, Hosanagar, and Nair 2018). Moreover, as users often resort to review websites to inform buying decisions (Farhadloo, Patterson, and Rolland 2016), respective impressions should also be gauged. However, considering that the customer journey often encompasses touch points across multiple channels (Ailawadi and Farris 2017; Konu\u015f, Verhoef, and Neslin 2008), looking at the digital environment is not always enough to capture the full scope of journey-related customer behavior. Therefore, the focus should be on detecting cross-channel spillover effects. As offline data are scarce, it is difficult to track customers in such settings. Initial attempts to assess the role of traditional advertising in the pre-purchase stage thus mainly draw on expenditure-based measures (e.g. Dinner, Van Heerde, and Neslin 2014). Accordingly, Joo et al. (2014) observe a positive relationship between ad spend and online search volume. Similarly, Fossen and Schweidel (2017) develop an approach that studies the number of brand mentions on social media, i.e. Twitter, within a time frame of two minutes before and after the airing of a television ad, identifying a positive correlation. Yet, the ability to pursue such spillovers is nascent in both research and business practice. <\/p>\n\n\n\n

    Purchase stage.<\/strong> <\/em>While a customer\u2019s online buying behavior can be fully captured (Chen, Chiang, and Storey 2012), offline purchase data is usually only accessible aggregately, as buyers often remain anonymous (e.g. Neslin et al. 2006). Academics such as Chan, Wu, and Xie (2011) indicate that B2B firms can more easily trace their clients across bricks-and-mortar channels, as they have close relations with them (Maechler et al. 2017). Yet, irrespective of the aggregation level, when seeking to understand the purchase stage, the absolute or monetary amount of purchases, transactions, or contracts is a prevalent KPI in both academia and practice (Chan, Wu, and Xie 2011). Additionally, it should be monitored which channel is used for the transaction. As research suggests that the type of purchase may differ with channel choice \u2013 for example, customers are found to primarily use the mobile channel for buying habitual (Shankar et al. 2016; Wang, Malthouse, and Krishnamurthi 2015) and cheaper products (Huang, Lu, and Ba 2016) \u2013 tracking this figure may contribute to an enhanced apprehension of customer behavior in the purchase stage. In this regard, initial research models additionally aim at shedding light not only on the purchase incident itself, but also on the transition between pre-purchase and purchase stage within and across channels. Exemplarily, transaction data can be merged with common advertising and website KPIs as already introduced for the pre-purchase stage, identifying how far customers\u2019 touch points with a paid search ad drive online and offline purchases (Chan, Wu, and Xie 2011). Similarly, ad spend, online search volume, and sales data can be augmented, modeling how advertising impacts consumer interest, i.e. keyword search behavior, website visits, and, subsequently, conversion (Hu, Du, and Damangir 2014). In this context, the phenomenon of \u2018research shopping\u2019 as identified by Verhoef, Neslin, and Vroomen (2007) can often be observed, emphasizing that customers may switch from online to offline channels and vice versa when transitioning from pre-purchase to purchase. <\/p>\n\n\n\n

    Post-purchase stage<\/em>. <\/strong>The customer journey does not end with the transaction, but also recognizes mostly customer-initiated post-purchase touch points. As per Westbrook (1987), such episodes of contact entail responses to the product or service consumption, finding expression for example in word-of-mouth and firm-directed complaints. While the latter has been measured by the number of customer complaints uttered via online and offline channels all along (e.g. Bell, Meng\u00fc\u00e7, and Stefani 2004), the tangibility of word-of-mouth has only recently gained traction due to the surge of customer engagement on social media (Bughin, Doogan, and Vetvik 2010). Here, online customer reviews represent a prominent way of sharing former experiences regarding a company or brand with a virtual community (e.g. Voorhees et al. 2017). Hence, next to analyzing the content of these posts to infer customer sentiment as described above, the number of published reviews can be traced to detect which resp. how many customers have encountered this type of post-transaction touch point. In addition to uploading such one-shot contributions, customers also harness social media networks to stay in contact with a brand after the purchase (Edelman 2010). Hence, KPIs such as impressions of firm-generated posts as well as the number of likes, shares, clicks, and comments (Lee, Hosanagar, and Nair 2018) that were already deemed valuable in the pre-purchase phase should also be considered here. As such measures are relevant in more than one part of the customer journey, placing the observed touch point into the right journey stage may be difficult. Recognizing this issue, V\u00e1zquez et al. (2014) showcase an algorithm-based method to infer a customer\u2019s current journey position from social media comments. Eventually, customers in both B2C (Court et al. 2009) and B2B (Lingqvist, Plotkin, and Stanley 2013) might conclude the post-purchase stage by entering the so-called \u2018loyalty loop\u2019 (Court et al. 2009) or \u2018enjoy-advocate-buy loop\u2019 (Edelman 2010), repurchasing from and engaging with a company without passing through consideration and evaluation in the pre-purchase stage again. Relatedly, KPIs such as the churn rate and the number of repurchases can be adduced to detect in how far customers carry out multiple transactions (Lemon and Verhoef 2016) and thus continue their journey with a company or brand. <\/p>\n\n\n\n

    Conclusion<\/strong><\/h2>\n\n\n\n

    All in all, the insights presented in this article imply that both primary and secondary data from the domains of digital marketing and customer relationship management may indeed enable marketers to trace fractions of their customers\u2019 experience and journey across channels, touch points, and stages. The figure below summarizes the potential inherent in such proprietary data sets, adopting a customer experience perspective as well as a customer journey perspective. <\/p>\n\n\n\n

    \"\"
    Figure 3:<\/strong> Customer experience and customer journey fractions identifiable from primary and secondary data in the domains of digital marketing and customer relationship management (basic conceptualization of customer experience and customer journey relationship based on Lemon and Verhoef (2016))<\/em><\/figcaption><\/figure><\/div>\n\n\n\n

    However, many marketers have not yet translated their interest into this topic into tangible actions, indicating that they may not be ready to partake in the battle for creating superior customer experience yet. Related efforts will only be crowned with success if knowledge from marketing, sales, and after-sales is not managed in siloes. Otherwise, vital fractions of the customer journey such as transitions between the marketing-dominated pre-purchase and sales-oriented purchase phase may not be assessible. Ultimately, if they succeed in employing analytical talent, cross-functional collaboration, and a data-driven culture (e.g. Jain, Aagja, and Bagdare 2017), companies can use their existing data to progress their customer experience and customer journey tracking ambitions. Eventually, such customer insights may pave the way for tailoring marketing efforts to the individual journey stage, thereby providing more valuable interactions and thus a better experience to every customer. <\/p>\n\n\n

    \n
    <\/span>References<\/div>
    \n
      \n
    • Ailawadi, Kusum L. and Paul W. Farris (2017), \u201cManaging Multi- and Omni-Channel Distribution: Metrics and Research Directions,\u201d Journal of Retailing, 93 (1), 120\u201335.<\/li>\n
    • Anderl, Eva, Jan Hendrik Schumann, and Werner Kunz (2016), \u201cHelping Firms Reduce Complexity in Multichannel Online Data: A New Taxonomy-Based Approach for Customer Journeys,\u201d Journal of Retailing, 92 (2), 185\u2013203.<\/li>\n
    • Baker, Deren (2016), \u201cGetting More-Granular Data on Customer Journeys,\u201d Harvard Business Review, Digital Articles, 2\u20135.<\/li>\n
    • Batra, Rajeev and Kevin Lane Keller (2016), \u201cIntegrating Marketing Communications: New Findings, New Lessons, and New Ideas,\u201d Journal of Marketing, 80 (6), 122\u201345.<\/li>\n
    • Bell, Simon J., B\u00fclent Meng\u00fc\u00e7, and Sara L. Stefani (2004), \u201cWhen Customers Disappoint: A Model of Relational Internal Marketing and Customer Complaints,\u201d Journal of the Academy of Marketing Science, 32 (2), 112\u201326.<\/li>\n
    • Bolton, Ruth N, Katherine N Lemon, and Peter C Verhoef (2008), \u201cExpanding Business-to-Business Customer Relationships: Modeling the Customer\u2019s Upgrade Decision,\u201d 72 (1), 46\u201364.<\/li>\n
    • Bonchek, Mark and Cara France (2014), \u201cMarketing Can No Longer Rely on the Funnel,\u201d Harvard Business Review, Digital Articles, 2\u20134.<\/li>\n
    • Brynjolfsson, Erik, Yu Jeffrey Hu, and Mohammad S Rahman (2013), \u201cCompeting in the Age of Omnichannel Retailing,\u201d MIT Sloan Management Review, 1\u20137.<\/li>\n
    • Bucklin, Randolph E. and Catarina Sismeiro (2003), \u201cA Model of Web Site Browsing Behavior Estimated on Clickstream Data,\u201d Journal of Marketing Research, 40 (3), 249\u201367.<\/li>\n
    • Bughin, Jacques, Jonathan Doogan, and Ole J\u00f8rgen Vetvik (2010), \u201cA New Way to Measure Word-of- Mouth Marketing,\u201d McKinsey Quarterly.<\/li>\n
    • Chan, Tat Y., Chunhua Wu, and Ying Xie (2011), \u201cMeasuring the Lifetime Value of Customers Acquired from Google Search Advertising,\u201d Marketing Science, 30 (5), 837\u201350.<\/li>\n
    • Chandon, Pierre, Vicki G. Morwitz, and Werner J. Reinartz (2005), \u201cDo Intentions Really Predict Behavior? Self-Generated Validity Effects in Survey Research,\u201d Journal of Marketing, 69 (2), 1\u201314.<\/li>\n
    • Chen, Hsinchun, Roger H. L. Chiang, and Veda C. Storey (2012), \u201cBusiness Intelligence and Analytics: From Big Data to Big Impact,\u201d MIS Quarterly, 4 (36), 1165\u201388.<\/li>\n
    • Chintagunta, Pradeep K. (2001), \u201cEndogeneity and Heterogeneity in a Probit Demand Model: Estimation Using Aggregate Data,\u201d Marketing Science, 20 (4), 442\u201356.<\/li>\n
    • Court, David, Dave Elzinga, Susan Mulder, and Ole J\u00f8rgen Vetvik (2009), \u201cThe Consumer Decision Journey,\u201d McKinsey Quarterly, (accessed June 2, 2019), [available at https:\/\/www.mckinsey.com\/business-functions\/marketing-and-sales\/our-insights\/the-consumer-decision-journey].<\/li>\n
    • Day, George S. (2011), \u201cClosing the Marketing Capabilities Gap,\u201d Journal of Marketing, 75 (4), 183\u201395.<\/li>\n
    • Dewsnap, Belinda and David Jobber (2000), \u201cThe Sales-Marketing Interface in Consumer Packaged-Goods Companies: A Conceptual Framework,\u201d Journal of Personal Selling & Sales Management, 20 (2), 109\u201319.<\/li>\n
    • Dinner, Isaac M., Harald J. Van Heerde, and Scott A. Neslin (2014), \u201cDriving Online and Offline Sales: The Cross-Channel Effects of Traditional, Online Display, and Paid Search Advertising,\u201d Journal of Marketing Research, 51 (5), 527\u201345.<\/li>\n
    • Edelman, David C (2010), \u201cBranding in the Digital Age,\u201d Harvard Business Review, 88 (12), 1\u20138.<\/li>\n
    • Edelman, David C. and Marc Singer (2015), \u201cCompeting on Customer Journeys,\u201d Harvard Business Review, 93 (11), 88\u2013100.<\/li>\n
    • Farhadloo, Mohsen, Raymond A. Patterson, and Erik Rolland (2016), \u201cModeling Customer Satisfaction from Unstructured Data Using a Bayesian Approach,\u201d Decision Support Systems, 90, 1\u201311.<\/li>\n
    • Fornell, Claes (1992), \u201cA National Customer Satisfaction Barometer: The Swedish Experience,\u201d Journal of Marketing, 56, 6\u201321.<\/li>\n
    • Forrester (2018), \u201cThe Business Impact of Investing in Experience.\u201d Forrester Research, 1-12.<\/li>\n
    • Fossen, Beth L. and David A. Schweidel (2017), \u201cTelevision Advertising and Online Word-of-Mouth: An Empirical Investigation of Social TV Activity,\u201d Marketing Science, 36 (1), 105\u201323.<\/li>\n
    • Frow, Pennie and Adrian Payne (2007), \u201cTowards the \u2018Perfect\u2019 Customer Experience,\u201d Journal of Brand Management, 15 (2), 89\u2013101.<\/li>\n
    • Gartner (2018), \u201cKey Findings from the Gartner Customer Experience Survey,\u201d Gartner, (accessed April 30, 2019), [available at https:\/\/www.gartner.com\/en\/marketing\/insights\/articles\/key-findings-from-the-gartner-customer-experience-survey].<\/li>\n
    • Grewal, Dhruv, Michael Levy, and V. Kumar (2009), \u201cCustomer Experience Management in Retailing: An Organizing Framework,\u201d Journal of Retailing, 85 (1), 1\u201314.<\/li>\n
    • Guenzi, Paolo and Gabriele Troilo (2006), \u201cDeveloping Marketing Capabilities for Customer Value Creation Through Marketing\u2013Sales Integration,\u201d Industrial Marketing Management, 35 (8), 974\u201388.<\/li>\n
    • de Haan, Evert, P.K. Kannan, Peter C. Verhoef, and Thorsten Wiesel (2018), \u201cDevice Switching in Online Purchasing: Examining the Strategic Contingencies,\u201d Journal of Marketing, 82 (5), 1\u201319.<\/li>\n
    • Han, Sang Pil, Sungho Park, and Wonseok Oh (2016), \u201cMobile App Analytics: A Multiple Discrete-Continuous Choice Framework,\u201d MIS Quarterly, 40 (4), 983\u20131008.<\/li>\n
    • Homburg, Christian, Laura Ehm, and Martin Artz (2015), \u201cMeasuring and Managing Consumer Sentiment in an Online Community Environment,\u201d Journal of Marketing Research, 52 (5), 629\u201341.<\/li>\n
    • \u2014\u2014\u2014, Danijel Jozi\u0107, and Christina Kuehnl (2017), \u201cCustomer Experience Management: Toward Implementing an Evolving Marketing Concept,\u201d Journal of the Academy of Marketing Science, 45 (3), 377\u2013401.<\/li>\n
    • Howard, John A. and Jagdish N. Sheth (1969), \u201cA Theory of Buyer Behavior,\u201d Journal of the American Statistical Association, 65 (331), 467\u201387.<\/li>\n
    • Hu, Ye, Rex Yuxing Du, and Sina Damangir (2014), \u201cDecomposing the Impact of Advertising: Augmenting Sales with Online Search Data,\u201d Journal of Marketing Research, 51 (3), 300\u2013319.<\/li>\n
    • Huang, Lei, Xianghua Lu, and Sulin Ba (2016), \u201cAn Empirical Study of the Cross-channel Effects Between Web and Mobile Shopping Channels,\u201d Information & Management, 53 (2), 265\u201378.<\/li>\n
    • Jain, Rajnish, Jayesh Aagja, and Shilpa Bagdare (2017), \u201cCustomer Experience \u2013 A Review and Research Agenda,\u201d Journal of Service Theory and Practice, 27 (3), 642\u201362.<\/li>\n
    • Jansen, Bernard J and Simone Schuster (2011), \u201cBidding on the Buying Funnel for Sponsored Search and Keyword Advertising,\u201d Journal of Electronic Commerce Research, 12 (1), 1\u201318.<\/li>\n
    • Joo, Mingyu, Kenneth C. Wilbur, Bo Cowgill, and Yi Zhu (2014), \u201cTelevision Advertising and Online Search,\u201d Management Science, 60 (1), 56\u201373.<\/li>\n
    • Kannan, P.K., Werner Reinartz, and Peter C. Verhoef (2016), \u201cThe Path to Purchase and Attribution Modeling: Introduction to Special Section,\u201d International Journal of Research in Marketing, 33 (3), 449\u201356.<\/li>\n
    • Keiningham, Timothy, Joan Ball, Sabine Benoit (n\u00e9e Moeller), Helen L. Bruce, Alexander Buoye, Julija Dzenkovska, Linda Nasr, Yi-Chun Ou, and Mohamed Zaki (2017), \u201cThe Interplay of Customer Experience and Commitment,\u201d Journal of Services Marketing, 31 (2),148\u201360.<\/li>\n
    • Keiningham, Timothy L., Bruce Cooil, Lerzan Aksoy, Tor W. Andreassen, and Jay Weiner (2007), \u201cThe Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Customer Retention, Recommendation, and Share\u2010of\u2010wallet,\u201d Managing Service Quality: An<\/li>\n
    • International Journal, 17 (4), 361\u201384.<\/li>\n
    • Keller, Thorben (2016), \u201cA Conceptual Framework for Governance of Customer Experience in the Digital Age,\u201d Conference Paper, Amsterdam.<\/li>\n
    • Kim, Jun B., Paulo Albuquerque, and Bart J. Bronnenberg (2011), \u201cMapping Online Consumer Search,\u201d Journal of Marketing Research, 48 (1), 13\u201327.<\/li>\n
    • Kireyev, Pavel, Koen Pauwels, and Sunil Gupta (2016), \u201cDo Display Ads Influence Search? Attribution and Dynamics in Online Advertising,\u201d International Journal of Research in Marketing, 33 (3), 475\u201390.<\/li>\n
    • Klaus, Philipp \u2018Phil\u2019 and Stan Maklan (2013), \u201cTowards a Better Measure of Customer Experience,\u201d International Journal of Market Research, 55 (2), 227\u201346.<\/li>\n
    • Konu\u015f, Umut, Peter C. Verhoef, and Scott A. Neslin (2008), \u201cMultichannel Shopper Segments and Their Covariates,\u201d Journal of Retailing, 84 (4), 398\u2013413.<\/li>\n
    • Kranzb\u00fchler, Anne-Madeleine, Mirella H. P. Kleijnen, and Peeter W. J. Verlegh (2018), \u201cOutsourcing the Pain, Keeping the Pleasure: Effects of Outsourced Touchpoints in the Customer Journey,\u201d Journal of the Academy of Marketing Science, published online, 1\u201320.<\/li>\n
    • Krohmer, Harley, Christian Homburg, and John P. Workman (2002), \u201cShould Marketing be Cross-functional? Conceptual Development and International Empirical Evidence,\u201d Journal of Business Research, 55 (6), 451\u201365.<\/li>\n
    • Lee, Dokyun, Kartik Hosanagar, and Harikesh S. Nair (2017), \u201cAdvertising Content and Consumer Engagement on Social Media: Evidence from Facebook,\u201d Management Science, accepted and forthcoming, 1\u201357.<\/li>\n
    • Lemon, Katherine N. and Peter C. Verhoef (2016), \u201cUnderstanding Customer Experience Throughout the Customer Journey,\u201d Journal of Marketing, 80 (6), 69\u201396.<\/li>\n
    • Lewis, Randall A. and David H. Reiley (2014), \u201cOnline Ads and Offline Sales: Measuring the Effect of Retail Advertising via a Controlled Experiment on Yahoo!,\u201d Quantitative Marketing and Economics, 12 (3), 235\u201366.<\/li>\n
    • Li, Hongshuang (Alice) and P.K. Kannan (2014), \u201cAttributing Conversions in a Multichannel Online Marketing Environment: An Empirical Model and a Field Experiment,\u201d Journal of Marketing Research, 51 (1), 40\u201356.<\/li>\n
    • Liaukonyte, Jura, Thales Teixeira, and Kenneth C. Wilbur (2015), \u201cTelevision Advertising and Online Shopping,\u201d Marketing Science, 34 (3), 311\u201330.<\/li>\n
    • Lingqvist, Oskar, Candace Lun Plotkin, and Jennifer Stanley (2013), \u201cFollow the Customer Decision Journey if You Want B2B Sales to Grow,\u201d (accessed June 12, 2019), [available at https:\/\/www.mckinsey.com\/business-functions\/marketing-and-sales\/our-insights\/<\/li>\n
    • follow-the-customer-decision-journey-if-you-want-b2b-sales-to-grow].<\/li>\n
    • Maechler, Nicolas, Adina Poenaru, Thilo R\u00fcdt von Collenberg, and Patrick Schulze (2017), \u201cFinding the Right Digital Balance in B2B Customer Experience,\u201d McKinsey & Company, (accessed June 12, 2019), [available at https:\/\/www.mckinsey.com\/business-functions\/marketing-and-sales\/our-insights\/finding-the-right-digital-balance-in-b2b-customer-experience].<\/li>\n
    • Malthouse, Edward C., Michael Haenlein, Bernd Skiera, Egbert Wege, and Michael Zhang (2013), \u201cManaging Customer Relationships in the Social Media Era: Introducing the Social CRM House,\u201d Journal of Interactive Marketing, 27 (4), 270\u201380.<\/li>\n
    • Meyer, Christopher and Andre Schwager (2007), \u201cUnderstanding Customer Experience,\u201d Harvard Business Review, 85 (2), 1\u201311.<\/li>\n
    • Moe, Wendy W. (2003), \u201cBuying, Searching, or Browsing: Differentiating Between Online Shoppers Using In-Store Navigational Clickstream,\u201d Journal of Consumer Psychology, 13 (1&2), 29\u201339.<\/li>\n
    • Neslin, Scott A., Dhruv Grewal, Robert Leghorn, Venkatesh Shankar, Marije L. Teerling, Jacquelyn S. Thomas, and Peter C. Verhoef (2006), \u201cChallenges and Opportunities in Multichannel Customer Management,\u201d Journal of Service Research, 9 (2), 95\u2013112.<\/li>\n
    • Nottorf, F. (2014), \u201cModeling the Clickstream Across Multiple Online Advertising Channels Using a Binary Logit With Bayesian Mixture of Normals,\u201d Electronic Commerce Research and Applications, 13 (1), 45\u201355.<\/li>\n
    • Oracle (2018), \u201cSmarter CX Insights Report,\u201d Oracle.<\/li>\n
    • Park, Young-Hoon and Peter S. Fader (2004), \u201cModeling Browsing Behavior at Multiple Websites,\u201d Marketing Science, 23 (3), 280\u2013303.<\/li>\n
    • Pauwels, Koen, Peter S.H. Leeflang, Marije L. Teerling, and K.R. Eelko Huizingh (2011), \u201cDoes Online Information Drive Offline Revenues?,\u201d Journal of Retailing, 87 (1), 1\u201317.<\/li>\n
    • Polo, Yolanda and F. Javier Sese (2016), \u201cDoes the Nature of the Interaction Matter? Understanding Customer Channel Choice for Purchases and Communications,\u201d Journal of Service Research, 19 (3), 276\u201390.<\/li>\n
    • Pournarakis, Demitrios E., Dionisios N. Sotiropoulos, and George M. Giaglis (2017), \u201cA Computational Model for Mining Consumer Perceptions in Social Media,\u201d Decision Support Systems, 93, 98\u2013110.<\/li>\n
    • Rawson, Alex, Ewan Duncan, and Conor Jones (2013), \u201cThe Truth About Customer Experience,\u201d Harvard Business Review, 91 (9), 1\u201310.<\/li>\n
    • Richardson, Adam (2010), \u201cUsing Customer Journey Maps to Improve Customer Experience,\u201d Harvard Business Review, 15 (1), 1\u20134.<\/li>\n
    • Shankar, Venkatesh, Mirella Kleijnen, Suresh Ramanathan, Ross Rizley, Steve Holland, and Shawn Morrissey (2016), \u201cMobile Shopper Marketing: Key Issues, Current Insights, and Future Research Avenues,\u201d Journal of Interactive Marketing, 34, 37\u201348.<\/li>\n
    • Skinner, Christopher (2010), \u201cThe Complete Customer Journey: Avoiding Technology and Business Barriers to Measure the Total Value of Media,\u201d Business Strategy Series, 11 (4), 223\u201326.<\/li>\n
    • Spiess, Jeffrey, Yves T\u2019Joens, Raluca Dragnea, Peter Spencer, and Laurent Philippart (2014), \u201cUsing Big Data to Improve Customer Experience and Business Performance,\u201d Bell Labs Technical Journal, 18 (4), 3\u201317.<\/li>\n
    • Srinivasan, Shuba, Oliver J. Rutz, and Koen Pauwels (2016), \u201cPaths to and off Purchase: Quantifying the Impact of Traditional Marketing and Online Consumer Activity,\u201d Journal of the Academy of Marketing Science, 44 (4), 440\u201353.<\/li>\n
    • Stein, Alisha and B. Ramaseshan (2016), \u201cTowards the Identification of Customer Experience Touch Point Elements,\u201d Journal of Retailing and Consumer Services, 30, 8\u201319.<\/li>\n
    • Sweetwood, Adele K. (2016), \u201cHow One Company Used Data to Rethink the Customer Journey,\u201d Harvard Business Review, Digital Articles, 2\u20135.<\/li>\n
    • Verhoef, Peter C., P.K. Kannan, and J. Jeffrey Inman (2015), \u201cFrom Multi-Channel Retailing to Omni-Channel Retailing,\u201d Journal of Retailing, 91 (2), 174\u201381.<\/li>\n
    • \u2014\u2014\u2014, Scott A. Neslin, and Bj\u00f6rn Vroomen (2007), \u201cMultichannel Customer Management: Understanding the Research-shopper Phenomenon,\u201d International Journal of Research in Marketing, 24 (2), 129\u201348.<\/li>\n
    • Voorhees, Clay M., Paul W. Fombelle, Yany Gregoire, Sterling Bone, Anders Gustafsson, Rui Sousa, and Travis Walkowiak (2017), \u201cService Encounters, Experiences and the Customer Journey: Defining the Field and a Call to Expand our Lens,\u201d Journal of Business Research, 79, 269\u201380.<\/li>\n
    • Wang, Rebecca Jen-Hui, Edward C. Malthouse, and Lakshman Krishnamurthi (2015), \u201cOn the Go: How Mobile Shopping Affects Customer Purchase Behavior,\u201d Journal of Retailing, 91 (2), 217\u201334.<\/li>\n
    • Wang, Xia, Chunling Yu, and Yujie Wei (2012), \u201cSocial Media Peer Communication and Impacts on Purchase Intentions: A Consumer Socialization Framework,\u201d Journal of Interactive Marketing, 26 (4), 198\u2013208.<\/li>\n
    • Wedel, Michel and P.K. Kannan (2016), \u201cMarketing Analytics for Data-Rich Environments,\u201d Journal of Marketing, 80 (6), 97\u2013121.<\/li>\n
    • Westbrook, Robert A. (1987), \u201cProduct\/Consumption-Based Affective Responses and Postpurchase Processes,\u201d Journal of Marketing Research, 24 (3), 258\u201370.<\/li>\n
    • Woolridge, Jeffrey M. (2002), Econometric Analysis of Cross Section and Panel Data, Cambridge, MA: MIT Press.<\/li>\n
    • Zaefarian, Ghasem, Vita Kadile, Stephan C. Henneberg, and Alexander Leischnig (2017), \u201cEndogeneity Bias in Marketing Research: Problem, Causes and Remedies,\u201d Industrial Marketing Management, 65, 39\u201346.<\/li>\n
    • Zhao, Yi, Sha Yang, Vishal Narayan, and Ying Zhao (2013), \u201cModeling Consumer Learning from Online Product Reviews,\u201d Marketing Science, 32 (1), 153\u201369.<\/li>\n<\/ul>\n<\/div><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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