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Posts Tagged ‘Statistics’

‘Experience’ has gained a prime status in the past decade — everything seems to revolve around experience in the universe of management, marketing, and even more specifically with respect to relationship marketing. It has become like a sine qua non of operating in this universe. There can be multiple contexts for framing experience — customer experience, brand experience, user (or product) experience, and also employee experience. Nevertheless, these concepts are inter-linked, and customer experience could be the central point-of-reference just because all other forms of experience eventually contribute to the customer’s experience. After all, this is the age of experience economy (cf. Pine and Gilmore).

This focus on the role of experience and primarily customer experience (CX) in contemporary marketing surely has not escaped the attention of companies involved with data-based marketing particularly on the service side (e.g., technology, research, consulting). In mid-November 2018 enterprise information technology company SAP announced a stark move of acquiring research technology firm Qualtrics for the sum of $8 billion in cash (deal expected to materialise during the first half of 2019). Qualtrics started in 2002 by specialising in survey technology for conducting consumer and customer surveys online, and has later on broadened the spectrum of its software products and tools to address a range of experience domains, put in a framework entitled Experience Management (XM).

However, less visible to the public, Qualtrics made an acquisition of its own of Temkin Group — an expert company specialising in customer experience research, training and consulting — about two weeks before announcing the SAP-Qualtrics deal. Qualtrics was reportedly engaged at the time of these deals in preparations for its IPO. Adding the knowledge and capabilities of Temkin Group to those of Qualtrics could fairly be viewed as a positive enforcement of the latter prior to its IPO, and eventually the selling of Qualtrics to SAP. Therefore, it would be right to say that Qualrtics + Temkin Group and SAP are effectively joining forces in domain knowledge, research capabilities and data technologies. Yet since the original three entities (i.e., as before November 2018) were so unequal in size and power, it raises some major questions about how their union under the umbrella of SAP will work out.

SAP specialises in enterprise software applications for organisational day-to-day functions across-the-board, and supporting software-related services (SAP was established in 1972, based in Germany). It operates today in 130 countries with 100+ innovation and development centres; its revenue in the 2017 financial year was $23.46 billion. Many of the company’s software applications can be deployed on premises, in the cloud, or hybrid (SAP reports 150 million subscribers in the cloud service user base). The two product areas of highest relevance to this story are CRM & Customer Experience solutions and the Enterprise Resource Planning (ERP) solutions & Digital Core (featuring its flagship platform HANA). The two areas of solutions correspond with each other.

The S4/HANA platform is described as an intelligent ERP software, a real-time solution suite . It enables, for example, delivering personally customised products ordered online (e.g., bicycles). For marketing activities and customer-facing services it should require data from the CRM and CX applications. The ERP platform supports, however, the financial planning and execution of overall activities of a client organisation. The CRM & Customer Experience suite of solutions includes five key components: Customer Data Cloud (enabled actually by Gigya, another acquisition by SAP in 2017); Marketing Cloud; Commerce Cloud; Sales Cloud; and Service Cloud. The suite covers a span of activities and functions: profiling and targeting at segment-level and individual level, applicable, for instance, in campaigns or tracking customer journeys (Marketing); product order and content management (Commerce); comprehensive self-service processes plus field service management and remote service operations by agents (Service). In all these sub-areas we may find potential links to the kinds of data that can be collected and analysed with the tools of Qualtrics while SAP’s applications are run on operational data gathered within its system apparatus. The key strengths offered in the Customer Data Cloud are integrating data, securing customer identity and access to digital interfaces across channels and devices, and data privacy protection. SAP highlights that its marketing and customer applications are empowered by artificial intelligence (AI) and machine learning (ML) capabilities to personalise and improve experiences.

  • At the technical and analytic level, SAP’s Digital Platform is in charge of the maintenance of solutions and databases (e.g., ERP HANA) and management of data processes, accompanied by the suite of Business Analytics that includes the Analytics Cloud, Business Analytics, Predictive Analytics and Collaborative Enterprise Planning. Across platforms SAP makes use of intelligent technologies and tools organised in its Leonardo suite.

Qualtrics arrives from quite a different territory, nestled much closer to the field of marketing and customer research as a provider of technologies for data collection through surveys of consumers and customers, and data analytic tools. The company has gained acknowledgement thanks to its survey software for collecting data online whose use has so expanded to make it one of the more popular among businesses for survey research. Qualtrics now focuses on four domains for research: Customer Experience, Brand Experience, Product Experience, and Employee Experience.

  • The revenue of Qualtrics in 2018 is expected to exceed $400 million (in first half of 2018 revenue grew 42% to $184m); the company forecast that revenue will continue to grow at an annual rate of 40% before counting its benefits from synergies with SAP (CNBC; TechCrunch on 11 November 2018).

Qualtrics organises its research methodologies and tools by context under the four experience domains aforementioned. The flagship survey software, PER, allows for data collection through multiple digital channels (e.g., e-mail, web, mobile app, SMS and more), and is accompanied by a collection of techniques and tools for data analysis and visualisation. The company emphasises that its tools are so designed that use of them does not require one to be a survey expert or a statistician.

Qualtrics provides a range of intelligent assistance and automation capabilities; they can aid, guide and support the work of users according to their level of proficiency. Qualtrics has developed a suite of intelligent tools, named iQ, among them Stats iQ for statistical analysis, Text iQ for text analytics and sentiment scoring, and Predict iQ + Driver iQ for advanced statistical analysis and modelling. Additionally, it offers ExpertReview for helping with questionnaire composition (e.g., by giving AI-expert ‘second opinion’). In a marketing context, the company offers techniques for ad testing, brand tracking, pricing research, market segmentation and more. Some of these research methodologies and tools would be of less relevance and interest to SAP unless they can be connected directly to customer experiences that SAP needs to understand and account for through the services it offers.

The methods and tools by Qualtrics are dedicated to bringing the subjective perspective of customers about their experiences. Under the topic of Customer Experience Qualtrics covers customer journey mapping, Net Promoter Score (NPS), voice of the customer, and digital customer experience; user experience is covered in the domain of Product Experience, and various forms of customer-brand interactions are addressed as part of Brand Experience. The interest of SAP especially in Qualtrics, as stated by the firm, is  complementing or enhancing its operational data (O-data) with customer-driven experience data (X-data) produced by Qualtrics (no mention is made of Temkin Group). The backing and wide business network of SAP should create new opportunities for Qualtrics to enlarge its customer base, as suggested by SAP. The functional benefits for Qualtrics are less clear; possible gains may be achieved by combining operational metrics in customer analyses as benchmarks or by making comparisons between objective and subjective evaluations of customer experiences, assuming clients will subscribe to some of the services provided by the new parent company SAP.

Temkin Group operated as an independent firm for eight years (2010-2018), headed by Bruce Temkin (with wife Karen), until its acquisition by Qualtrics in late October 2018. It provided consulting, research and training activities on customer experience (at its core was customer experience but it dealt with various dimensions of experience beyond and in relation to customers). A key asset of Temkin Group is its blog / website Experience Matters, a valued resource of knowledge; its content remains largely in place (viewed January 2018), and hopefully will stay on.

Bruce Temkin developed several strategic concepts and constructs of experience. The Temkin Experience Rating metric is based on a three-component construct of experience: Success, Effort and Emotion. The strategic model of experience includes four required competencies: (a) Purposeful Leadership; (b) Compelling Brand Values; (c) Employee Engagement; and (d) Customer Connectedness. He made important statements in emphasising the essence of employee engagement to deliver superior customer experience, and in including Emotion as one of the pillars of customer experience upon which it should be evaluated. The more prominent of the research reports published by Temkin Group were probably the annual series of Temkin Experience Rating reports, covering 20 industries or markets with a selection of companies competing in each.

Yet Temkin apparently has come to a realisation that he should not go it alone any longer. In a post blog on 24 October 2018, entitled “Great News: Temkin Group Joins Forces With Qualtrics“, Temkin explained as the motivation to his deal with Qualtrics a recognition he had reached during the last few years: “it’s become clear to me that Qualtrics has the strongest momentum in CX and XM“. Temkin will be leading the Qualtrics XM Institute, built on the foundations of Temkin CX Institute dedicated to training. The new institute will be sitting on top of Qualtrics XM platform. In his blog announcement Temkin states that the Qualtrics XM Institute will “help shape the future of experience management, establish and publish best practices, drive product innovation, and enable certification and training programs that further build the community of XM professionals” — a concise statement that can be viewed as the charter of the institute Temkin will be in charge of at Qualtrics. Temkin has not taken long to adopt the framework of Experience Management and support it in writing for the blog.

The teams of Temkin and Qualtrics (CEO and co-founder Ryan Smith) may co-operate more closely in developing research plans on experience for clients and initiating research reports similar to the ones Temkin Group produced so far. Bruce Temkin should have easy and immediate access to the full range of tools and technologies of Qualtrics to continue with research projects and improve on them. Qualtrics should have much to benefit from the knowledge and training experience of Temkin in the new XM institute at Qualtrics. It seems easier to foresee beneficial synergies between Temkin Group and Qualtrics than their expected synergies with SAP.

However, there is a great question arising now, how all this vision and plans for Temkin and Qualtrics working together, and particularly their project of Qualtrics XM Institute, will be sustained following the acquisition of Qualtrics by SAP. One cannot overlook the possibility that SAP will develop its own expectations and may require changes to plans only recently made or modifications to Qualtrics CX Platform and XM Solutions so as to satisfy the needs of SAP. According to TechCrunch (11 Nov. 2018) Qualtrics will continue to function as a subsidiary company and will retain its branding and personnel (note: it may be gradually assimilated into SAP while keeping Qualtrics associated names, as seems to be the case of Israel-based Gigya). Much indeed can depend on giving Qualtrics + Temkin Group autonomy to pursue with their specialisations and vision on XM while they share knowledge, data and technologies with SAP.

Bill McDermott, CEO of SAP, is looking high in the sky: as quoted in the company’s news release from 11 November 2018, he describes bringing together SAP and Qualtrics as “a new paradigm, similar to market-making shifts in personal operating systems, smart devices and social networks“. But it is also evident that SAP still sees the move through the prism of technology: “The combination of Qualtrics and SAP reaffirms experience management as the ground-breaking new frontier for the technology industry“.

Temkin’s viewpoint is much more customer-oriented and marketing-driven vis-à-vis the technology-driven view of McDermott and SAP, which may put them in greater conflict with time about priorities and future direction for XM. Qualtrics headed by Ryan Smith will have to decide how it prefers to balance between the marketing-driven view and technology-driven view on experience. Temkin, for example, has reservations about the orientation of the technology known as Enterprise Feedback Management (EFM), suggesting instead a different focus by naming this field “Customer Insight and Action (AIC) Platforms”. In his comments on the acquisition of Qualtrics by SAP (16 November 2018) he explains that organisations “succeed by taking action on insights that come from many sources, combining experience data (X-data) and operational data (O-data)“. In his arguments in favour of joining SAP with Qualtrics, Temkin recollects an observation he made in an award-winning report from 2002 while at Forrester Research: he argued then that “widespread disappointing results of CRM were a result of a pure technology-orientation and that companies needed to focus more on developing practices and perspectives that used the technology to better serve customers”; he claims that much has changed in the field since that time. Yet it is hard to be convinced that technology has much less influence now in shaping organisational, managerial and marketing processes, on both service side (e.g., SAP) and client side.

  • As a note aside, if SAP gets the upper hand in setting the agenda and does not give sufficient autonomy to Qualtrics as suggested earlier, the first sector at risk of having most to lose from this deal would be ‘marketing and customer research’.

SAP and Qualtrics are both involved in development and implementation of technology, yet SAP is focused on information technology enabling overall day-to-day operations of an organisation, whereas Qualtrics is focused on technology enabling experience and marketing research. Qualtrics and Temkin Group are both engaged in domains of experience: Qualtrics specialises in the technology that enables the research, while Temkin Group brought strengths in conducting research plus strategic thinking and training (education) on customer experience. In order for their joint forces to succeed they all will have to find ways to bridge gaps between their viewpoints, to ‘live and let live’, and at the same time complement one another in areas of shared understanding and expertise.

Ron Ventura, Ph.D. (Marketing)

 

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The strength, impact and value of a brand are embodied, fairly concisely, in the concept of ‘brand equity’. However, there are different views on how to express and measure brand equity, whether from a consumer (customer) perspective or a firm perspective. Metrics based on a consumer viewpoint (measured in surveys) raise particular concern as to what actual effects they have in the marketplace. Datta, Ailawadi and van Heerde (2017) have answered to the challenge and investigated how well Consumer-Based metrics of Brand Equity (CBBE) align with Sales-Based estimates of Brand Equity (SBBE). The CBBE metrics were adopted from the model of Brand Asset Valuator (Y&R) whereas SBBE estimates were derived from modelling market data of actual purchases. They also examined the association of CBBE with behavioural response to marketing mix actions [1].

In essence, brand equity expresses an incremental value of a product (or service) that can be attributed to its brand name above and beyond physical (or functional) attributes. Alternately,  brand equity is conceived as the added value of a branded product compared with an identical version of that product if it were unbranded. David Aaker defined four main groups of assets linked to a brand that add to its value: awareness, perceived quality, loyalty, and associations beyond perceived quality. On the grounds of this conceptualization, Aaker subsequently proposed the Brand Equity Ten measures, grouped into five categories: brand loyalty, awareness, perceived quality / leadership, association / differentiation, and market behaviour. Kevin Keller broadened the scope of brand equity wherein greater and more positive knowledge of customers (consumers) about a brand would lead them to respond more favourably to marketing activities of the brand (e.g., pricing, advertising).

The impact of a brand may occur at three levels: customer market, product market and financial market. In accordance, academics have followed three distinct perspectives for measuring brand equity: (a) customer-based — an attraction of consumers to the “non-objective” part of the product offering (e.g., ‘mindset’  as in beliefs and attitudes, brand-specific ‘intercept’ in a choice model); (b) company-based — additional value accrued to the firm from a product because of a brand name versus an equivalent product but non-branded (e.g., discounted cash flow); financial-based — brand’s worth is the price it brings or could bring in the financial market (e.g., materialised via mergers and acquisitions, stock prices)[2]. This classification is not universal:  for example, discounted cash flows are sometimes described as ‘financial’; estimates of brand value derived from a choice-based conjoint model constitute a more implicit reflection of the consumers’ viewpoint. Furthermore, models based on stated-choice (conjoint) or purchase (market share) data may vary greatly in the effects they include whether in interaction with each competing brand or independent from the brand ‘main effect’ (e.g., product attributes, price, other marketing mix variables).

A class of attitudinal (‘mindset’) models of brand equity may encompass a number of aspects and layers: awareness –> perceptions and attitudes about product attributes and functional benefits (+ overall perceived quality), ‘soft’ image associations (e.g., emotions, personality, social benefits) –> attachment or affinity –> loyalty (commitment). Two noteworthy academic studies have built upon the conceptualizations of Aaker and Keller in constructing and testing consumer-based measures:

  • Yoo and Donthu (2001) constructed a three-dimension model of brand equity comprising brand loyalty, brand awareness / associations (combined), and perceived quality (strength of associations was adopted from Keller’s descriptors of brand image). The multidimensional scale (MBE) was tested and validated across multiple product categories and cultural communities [3].
  • Netemeyer and colleagues (2004) demonstrated across products and brands that perceived quality, perceived value (for the cost), and uniqueness of a given brand potentially contribute to willingness to pay a price premium for the brand which in turn acts as a direct antecedent of brand purchase behaviour [4]. Price premium, an aspect of brand loyalty, is a common metric used for assessing brand equity.

Datta, Ailawadi and van Heerde distinguish between two measurement approaches: the consumer-based brand equity (CBBE) approach measures what consumers think and feel about the brand, while the sales-based brand equity (SBBE) approach is based on choice or share of the brand in the marketplace.

The CBBE approach in their research is applied through data on metrics from the Brand Asset Valuator model developed originally by Young and Roubicam (Y&R) advertising agency (the brand research activity is now defined as a separate entity, BAV Group; both Y&R and BAV Group are part of WPP media group). The BAV model includes four dimensions: Relevance to the consumers (e.g., fits in their lifestyles); Esteem of the brand (i.e., how much consumers like the brand and hold it in high regard); Knowledge of the brand (i.e., consumers are aware of and understand what the brand stands for); and  Differentiation from the competition (e.g., uniqueness of the brand)[5].

The SBBE approach is operationalised through modelling of purchase data (weekly scanner data from IRI). The researchers derive estimates of brand value in a market share attraction model (with over 400 brands from 25 categories, though just 290 brands for which BAV data could be obtained were included in subsequent CBBE-SBBE analyses) over a span of ten years (2002-2011). Notably, brand-specific intercepts were estimated for each year; an annual level is sufficient and realistic to account for the pace of change in brand equity over time. The model allowed for variation between brands in the sensitivity to their marketing mix actions (regular prices, promotional prices, advertising spending, distribution {on-shelf availability} and promotional display in stores) — these measures are not taken as part of SBBE values but indicate nonetheless expected manifestation of higher brand equity (impact); after being converted into elasticities, they play a key role in examining the relation of CBBE to behavioural outcomes in the marketplace.


  • Datta et al. seem to include in a SBBE approach estimates derived from (a) actual brand choices and sales data as well as (b) self-reported choices in conjoint studies and surveys. But subjective responses and behavioural responses are not quite equivalent bases. The authors may have aimed reasonably to distinguish ‘choice-based’ measures of brand equity from ‘attitudinal’ measures, but it still does not justify to mix between brands and products consumers say they would choose and those they actually choose to purchase. Conjoint-based estimates are more closely consumer-based.
  • Take for instance a research by Ferjani, Jedidi and Jagpal (2009) who offer a different angle on levels of valuation of brand equity. They derived brand values through a choice-based conjoint model (Hierarchical Bayes estimation at the individual level), regarded as consumer-level valuation. Vis-à-vis the researchers constructed a measure of brand equity from a firm perspective based on expected profits (rather than discounted cash flows), presented as firm-level valuation. Nonetheless, in order to estimate sales volume they ‘imported’ predicted market shares from the conjoint study, thus linking the two levels [6].

 

Not all dimensions of BAV (CBBE) are the same in relation to SBBE: Three of the dimensions of BAV — relevance, esteem, and knowledge — are positively correlated with SBBE (0.35, 0.39, & 0.53), while differentiation is negatively although weakly correlated with SBBE (-0.14). The researchers reasoned in advance that differentiation could have a more nuanced and versatile market effect (a hypothesis confirmed) because differentiation could mean the brand is attractive to only some segments and not others, or that uniqueness may appeal to only some of the consumers (e.g., more open to novelty and distinction).

Datta et al. show that correlations of relevance (0.55) and esteem (0.56) with market shares of the brands are even higher, and the correlation of differentiation with market shares is less negative (-0.08), than their correlations with SBBE (correlations of knowledge are about the same). The SBBE values capture a portion of brand attraction to consumers. Market shares on the other hand factor in additional marketing efforts that dimensions of BAV seem to account for.

Some interesting brand cases can be detected in a mapping of brands in two categories (for 2011): beer and laundry detergents. For example, among beers, Corona is positioned on SBBE much higher than expected given its overall BAV score, which places the brand among those better valued on a consumer basis (only one brand is considerably higher — Budweiser). However, with respect to market share the position of Corona is much less flattering and quite as expected relative to its consumer-based BAV score, even a little lower. This could suggest that too much power is credited to the name and other symbols of Corona, while the backing from marketing efforts to support and sustain it is lacking (i.e., the market share of Corona is vulnerable).  As another example, in the category of laundry detergents, Tide (P&G) is truly at the top on both BAV (CBBE) and market share. Yet, the position of Tide on SBBE relative to BAV score is not exceptional or impressive, being lower than predicted for its consumer-based brand equity. The success of the brand and consumer appreciation for it may not be adequately attributed specifically to the brand in the marketplace but apparently more to other marketing activities in its name (i.e., marketing efforts do not help to enhance the brand).

The degree of correlation between CBBE and SBBE may be moderated by characteristics of product category. Following the salient difference cited above between dimensions of BAV in relation to SBBE, the researchers identify two separate factors of BAV: relevant stature (relevance + esteem + knowledge) and (energized) differentiation [7].

In more concentrated product categories (i.e., the four largest brands by market share hold a greater total share of the category), the positive effect of brand stature on SBBE is reduced. Relevance, esteem and knowledge may serve as particularly useful cues by consumers in fragmented markets, where it is more necessary for them to sort and screen among many smaller brands, thus to simplify the choice decision process. When concentration is greater, reliance on such cues is less required. On the other hand, when the category is more concentrated, controlled by a few big brands, it should be easier for consumers to compare between them and find aspects on which each brand is unique or superior. Indeed, Datta and colleagues find that in categories with increased concentration, differentiation has a stronger positive effect on SBBE.

For products characterised by greater social or symbolic value (e.g., more visible to others when used, shared with others), higher brand stature contributes to higher SBBE in the market. The researchers could not confirm, however, that differentiation manifests in higher SBBE for products of higher social value. The advantage of using brands better recognized and respected by others appears to be primarily associated with facets such as relevance and esteem of the brand.

Brand experience with hedonic products (e.g., leisure, entertainment, treats) builds on enjoyment, pleasure and additional positive emotions the brand succeeds in evoking in consumers. Sensory attributes of the product (look, sound, scent, taste, touch) and holistic image are vital in creating a desirable experience. Contrary to expectation of Datta and colleagues, however, it was not found that stature translates to higher SBBE for brands of hedonic products (even to the contrary). This is not so good news for experiential brands in these categories that rely on enhancing relevance and appeal to consumers, who also understand the brands and connect with them, to create sales-based brand equity in the marketplace. The authors suggest in their article that being personally enjoyable (inward-looking) may overshadow the importance of broad appeal and status (outward-looking) for SBBE. Nevertheless, fortunately enough, differentiation does matter for highlighting benefits of the experience of hedonic products, contributing to a raised sales-based brand equity (SBBE).

Datta, Ailawadi and van Heerde proceeded to examine how strongly CBBE corresponds with behavioural responses in the marketplace (elasticities) as manifestation of the anticipated impact of brand equity.

Results indicated that when relevant stature of a brand is higher consumers respond favourably even more strongly to price discounts or deals  (i.e.,  elasticity of response to promotional prices is further more negative or inverse). Yet, the expectation that consumers would be less sensitive (adverse) to increased regular prices by brands of greater stature was not substantiated (i.e., expected positive effect: less negative elasticity). (Differentiation was not found to have a positive effect on response to regular prices either, and could be counter-conducive for price promotions.)

An important implication of brand equity should be that consumers are more willing to pay higher regular prices for a brand of higher stature (i.e., a larger price premium) relative to competing brands, and more forgiving when such a brand sees it necessary to update and raise its regular price. The brand may benefit from being more personally relevant to the consumer, better understood and more highly appreciated. A brand more clearly differentiated from competitors with respect to its advantages could also benefit from a protected status. All these properties are presumed to enhance attachment to a brand, and subsequently lead to greater loyalty, making consumers more ready to stick with the brand even as it becomes more expensive. This research disproves such expectations. Better responsiveness to price promotions can help to increase sales and revenue, but it testifies to the heightened level of competition in many categories (e.g., FMCG or packaged goods) and propensity of consumers to be more opportunistic rather than to the strength of the brands. This result, actually a warning signal, cannot be brushed away easily.

  • Towards the end of the article, the researchers suggest as explanation that they ignored possible differences in response to increases and decreases in regular prices (i.e., asymmetric elasticity). Even so, increases in regular prices by stronger brands are more likely to happen than price decreases, and the latter already are more realistically accounted for in response to promotional prices.

Relevant stature is positively related to responsiveness to feature or promotional display (i.e., consumers are more inclined to purchase from a higher stature brand when in an advantaged display). Consumers also are more strongly receptive to larger volume of advertising by brands of higher stature and better differentiation in their eyes (this analysis could not refer to actual advertising messages and hence perhaps the weaker positive effects). Another interesting finding indicates that sensitivity to degree of distribution (on-shelf availability) is inversely associated with stature — the higher the brand stature from consumer viewpoint, larger distribution is less attractive to the consumers. As the researchers suggest, consumers are more willing to look harder and farther (e.g., in other stores) for those brands regarded more important for them to have. So here is a positive evidence for the impact of stronger brands or higher brand equity.

The research gives rise to some methodological questions on measurement of brand equity that remain open for further deliberation:

  1. Should the measure of brand equity in choice models rely only on a brand-specific intercept (expressing intrinsic assets or value of the brand) or should it include also a reflection of the impact of brand equity as in response to marketing mix activities?
  2. Are attitudinal measures of brand equity (CBBE) too gross and not sensitive enough to capture the incremental value added by the brand or is the measure of brand equity based only on a brand-intercept term in a model of actual purchase data too specific and narrow?  (unless it accounts for some of the impact of brand equity)
  3. How should measures of brand equity based on stated-choice (conjoint) data and actual purchase data be classified with respect to a consumer perspective? (both pertain really to consumers: either their cognition or overt behaviour).

Datta, Ailawadi and van Heerde throw light in their extensive research on the relation of consumer-based equity (CBBE) to behavioural outcomes, manifested in brand equity based on actual purchases (SBBE) and in effects on response to marketing mix actions as an impact of brand equity. Attention should be awarded to positive implications of this research for practice but nonetheless also to the warning alerts it may signal.

Ron Ventura, Ph.D. (Marketing)

Notes:

[1] How Well Does Consumer-Based Brand Equity Align with Sales-Based Brand Equity and Marketing-Mix Response?; Hannes Datta, Kusum L. Ailawadi, & Harald J. van Heerde, 2017; Journal of Marketing, 81 (May), pp. 1-20. (DOI: 10.1509/jm.15.0340)

[2] Brands and Branding: Research Findings and Future Priorities; Kevin L. Keller and Donald R. Lehmann, 2006; Marketing Science, 25 (6), pp. 740-759. (DOI: 10.1287/mksc.1050.0153)

[3] Developing and Validating a Multidimensional Consumer-Based Brand Equity Scale; Boonghee Yoo and Naveen Donthu, 2001; Journal of Business Research, 52, pp. 1-14.

[4]  Developing and Validating Measures of Facets of Customer-Based Brand Equity; Richard G. Netemeyer, Balaji Krishnan, Chris Pullig, Guangping Wang,  Mahmet Yageci, Dwane Dean, Joe Ricks, & Ferdinand Wirth, 2004; Journal of Business Research, 57, pp. 209-224.

[5] The authors name this dimension ‘energised differentiation’ in reference to an article in which researchers Mizik and Jacobson identified a fifth pillar of energy, and suggest that differentiation and energy have since been merged. However, this change is not mentioned or revealed on the website of BAV Group.

[6] A Conjoint Approach for Consumer- and Firm-Level Brand Valuation; Madiha Ferjani, Kamel Jedidi, & Sharan Jagpal, 2009; Journal of Marketing Research, 46 (December), pp. 846-862.

[7] These two factors (principal components) extracted by Datta et al. are different from two higher dimensions defined by BAV Group (stature = esteem and knowledge, strength = relevance and differentiation). However, the distinction made by the researchers as corroborated by their data is more meaningful  and relevant in the context of this study.

 

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Big Data, Big Science, Data Science — This triad of concepts exemplifies the new age of utilisation of data in large Volume by companies to produce information and insights for guiding their operations, such as in marketing, to perform more effectively and profitably. Yet Big Data also means that data exhibit great Variety (e.g., types and structures), and are generated and transformed in high Velocity. The data may be retrieved from internal or external sources. To be sure, non-business organisations also utilise Big Data and Data Science methods and strategies for a range of purposes (e.g., medical research, fraud detection), though our interest is focused here on marketing, inter-linked with sales and customer service, as well as retailing.

It is not quite easy to separate or draw the line between the concepts above because they are strongly connected and cover similar ideas. Big Data may seem to emphasise the properties of the data but it is tied-in with specialised technologies and techniques needed to store, process and analyse it. Likewise, Data Science (and Big Science) may imply greater emphasis on research strategies, scientific thinking, and analytic methods, but they are directed towards handling large and complex pools of data, namely Big Data. Nonetheless, we may distinguish Data Science by reference to occupation or position: Professionals recognized as “data scientists” are identified as distinct from all other business analysts or data analysts in this field – data scientists are considered the superior analysts, experts, and mostly, the strategists who also connect between the analytic domain and the business domain.

The Trend Lab VINT (Vision – Inspiration – Navigation – Trends), part of Sogeti network of experts (Netherlands), published an instructive e-book on Big Data. In the e-book titled “No More Secrets With Big Data Analytics” (2013), the team of researchers propose a logical linkage between these concepts while relating them to Big Business. Big Science was conceived already in the early 1960s (attributed to atomic scientist Alvin Weinberg) to describe the predicted rise of large-scale scientific projects. It was not associated necessarily with amount of data (typical contexts have been physics and life sciences). Big Data as a concept emerged nearly ten years ago and turned the spotlight on data. Data Science is introduced by VINT as the toolbox of strategies and methods that allows Big Data to bring us from Big Science to Big Business. Data Science is “the art of transforming existing data to new insights by means of which an organizsation can or will take action” (p. 33). Originally, Big Science emphasised a requirement of scientific projects that is true today with regard to Big Data projects: collaboration between researchers with different areas of expertise to successfully accomplish the research task.

  • The researchers of VINT note that some scientists disapprove of connotations of the word “big” and prefer to use instead the term “extreme” which is in accordance with statistical theory.

The VINT e-book cites a profile for the position of data scientist suggested by Chirag Metha (a former technology, design and innovation strategist at SAP). In the headline Metha stated that the role of a data scientist is not to replace any existing BI people but to complement them (p. 34; BI=Business Intelligence). He defined requirements from a data scientist in four areas: (a) deep understanding of data, their sources and patterns; (b) theoretical and practical knowledge of advanced statistical algorithms and machine learning; (c) strategically connecting business challenges with appropriate data-driven solutions; and (d) devise an enterprise-wide data strategy that will accommodate patterns and events in the environment and foresee future data needs of the organisation. Therefore, primary higher-level contributions expected from a data scientist include the capacity to bridge between the domains of business and data/analytics (i.e., translate business needs to analytic models and solutions and back to [marketing] action plans), and an overview of data sources and types of data, structured and unstructured, and how to combine them properly and productively.

The pressure on companies to implement data-driven marketing programmes is growing all the time. As one company becomes publicly commended for successfully using, for instance, feedback on its website and in social media to create better-tailored product offerings, it gains an advantage that puts its competitors under pressure to follow suit. It may also inspire and incentivize companies in other industries to take similar measures. Such published examples are increasing in number in recent years. Furthermore, companies are encouraged to apply individual-level data of customer interactions with them (e.g., personal information submitted online, stated preferences and tracking page visits and item choices made on viewed pages) in order to devise customized product offerings or recommendations for each customer. Already in the late 1990s the grocery retailer Tesco leveraged its business in the UK and gained a leading position by utilising the purchase and personal data of customers gathered through their loyalty Clubcard to generate offerings of greater relevance to specific customer segments they identified. Amazon developed its e-commerce business by recommending to individual customers books related to those they view or purchase based on similar books purchased by other customers and on customers’ own history of behaviour.

A key challenge facing many companies is to implement an integrative approach that enforces a single view of the customer across organisational functions and channels. Thus, marketing programmes and operations must be coordinated and share data with sales and customer service activities. Moreover, data of interactions with customers, and consumers overall (as prospects), need to be examined and incorporated across multiple channels — offline, online, and mobile. This is a mission of utmost importance for companies these days; ignoring or lagging behind on this mission could mean losing ground in a market and relevance to customers. This is because customers’ experience extends over different stages of a journey in their relationship with a company and across multiple alternative channels or touchpoints they may use to fulfill their objectives. They expect that data that become available to companies be employed to improve in some way their customer experience anywhere and anytime they interact with the company. For companies, it definitely requires that they not only gather but also analyse the data in meaningful and productive ways. Whether the interactions occur in-store, over the phone, on a company’s website, in social media networks, or through mobile apps, customers consequently expect those interactions in and between channels to be smooth and frictionless. As for companies, they need to be able to share and join data from the different channels to obtain a comprehensive view of customers and co-ordinate between channels.

  • The American leading pharmacy retailer Walgreens established a platform for monitoring, analysing and managing its inventory jointly across all of its outlets, over 8,000 physical stores and four online stores, so as to allow shoppers to find, purchase and collect products they need in as a seamless manner as possible. They integrate point-of-sale data for customers with data from additional sources (e.g., social media, third-party healthcare organisations) in order to improve patient care.
  • Procter & Gamble, which does not have direct access to sales data as retailers, created an independent channel of communication with consumers; with the help of Teradata, they use personal data provided by consumers online and other data (e.g., social media) to put forward more personalised product offerings for them.

An additional important aspect is the need to join different types of data, both structured (e.g., from relational customer databases) and unstructured (e.g., open-end text in blog posts and social media posts and discussions). Data that companies may utilise become ever more heterogeneous in type, structure and form, posing greater technical and analytical challenges to companies, but also offering better opportunities. Companies may also consider using digital images, voice tracks (i.e., not only for verbal content but also tone and pitch), and all sorts of traffic data (e.g., electronic, digital-online and mobile, and even human-physical traffic in-store). For example, suppose that a company identifies photo images posted by its customers online and recognizes that the images include objects of product items; it then may complement that information with personal data of those customers and various interactions or activities they perform online (e.g., company’s websites, social media) to learn more about their interests, perceptions, and preferences as reflected through images.

  • The US airliner JetBlue uses the Net Promoter Score (NPS) metric to trace suspected problems of customer satisfaction, and then utilise survey data and content from social media networks, blogs and other consumer-passenger communications to identify the possible source and nature of a problem and devise an appropriate fix (an award-winning initiative by Peppers & Rogers).

But there is reason for some concern. In a report titled “Big Data: The Next Frontier for Innovation, Competition, and Productivity” (2011), McKinsey & Co. Consulting Group cautioned of an expected shortage in highly advanced analytic professionals and data-proficient managers. They estimated that by 2018  organisations in the US alone could face a shortage of 140,000 to 190,000 people with “deep analytical skills”. Nonetheless, the report also predicts a shortage of 1.5 million managers and analysts “with the know-how to use the analysis” of Big Data and its effective application for decision-making.  The first part seems to refer to the professional-technical level whereas the second part points to utilisation of Big Data at the business level. Thus, McKinsey & Co. appear to be even more concerned by inadequate ability of companies at a managerial level to benefit from the business advantages, such as with marketing-related objectives, that Big Data can produce. Data Scientists may be counted in both categories of this forecast, but because they need to be simultaneously expert analysts and business-savvy they could belong more closely with managers.

However, the situation may not improve as quickly as sought. The problem may be that young people are not attracted, not encouraged, and are not educated and trained enough to obtain high proficiency and skills in the exact sciences of mathematics and statistics, at least not at a growing pace that the industry may require. This problem seems to be imminent particularly in Western countries. Popular areas of studies such as technology, computer sciences and business administration can not compensate for lack of sound knowledge and skills in mathematics and statistics as far as utilisation of Big Data in marketing in particular and management in general is concerned. Yet business students, MBAs included, are more inclined to stay away rather than embark on their courses and tasks in statistics and data analysis; and the number of graduates in exact sciences is not increasing fast enough (in some cases even decreasing).  Here are some figures indicative of the problem that may help to illuminate it:

  • In the latest PISA exams carried out by the OECD in 2012 for school students aged 15-16, seven out the ten top ranking countries (or economies) in math are from the Far East, including Shanghai and Hong-Kong of China, Singapore, Republic of Korea, and Japan. Three European countries close the top list: Switzerland, adjacent Lichtenstein, and the Netherlands. Their scores are above the mean OECD score (494), ranging between 523 and 613.
  • Western countries are nevertheless among the next ten countries that still obtain a score in math above the OECD mean score, including Germany, Finland, Canada, Australia, Belgium and Ireland. But the United Kingdom is in 26th place (score 494) and the United States is even lower, in the 36th place (481). Israel is positioned a bit further down the list (at 41st, score 466). [34 OECD members and 31 partner countries participated].

  • In Israel, the rate of high school students taking their matriculation exam in math at an enhanced level (4 or 5 units) has changed negatively in recent years. It ranged in the years 1998-2006 from 52% and up to 57% but since 2009 and until 2012 it dropped dramatically to 46% of those eligible to a matriculation certificate, according to a press release of the Israeli Central Bureau of Statistics (CBS). It is noted by CBS that this decrease occurs in parallel with an increase in the total number of students who obtain the certificate, but this suggests that effort was not made to train and prepare the additional students to a high level in mathematics.

  • In statistics published by UNESCO on the proportion of academic  graduates (ISCED levels 5 or 6 — equivalents of bachelor to PhD) in Science fields of study, we find that this proportion decreased from 2001 to 2012 in countries like Australia (14.2% to 9%), Switzerland (11.5% to 9%), Republic of Korea (9.8% to 8.5%), UK (17.4% to 13.7%), and Israel (11.7% to 8.5% in 2011).
  • This rate is stable in the US (8.6%) and Japan (though low at 2.9%), while in Finland it has been relatively stable (10%-11%) but shifting down lately. Nice rises are observed in Poland (5% to 8%), Germany (13% to 14.5%), and the Netherlands (5.7% to 6.5%); Italy is also improving (up from 7.5% to 8%). [Levels of ISCED scheme of 1997; a new scheme enters this year].

The notion received is that supply of math and science-oriented graduates may not get closer to meet market demand by companies and non-business organisations in the coming years; it could even get worse in some countries. Companies can expect to encounter continued difficulties to recruit well-qualified analysts with potential to become high-qualified data scientists, and managers with good data and analytics proficiency. Managers and data scientists may have to work harder to train analysts to a satisfying level. They may need to consider recruiting analysts from different areas of specialisation (e.g., computer programming, math and statistics, marketing), each with a partial skill set in one or two areas, continue to train them in complementary areas, and foremost oversee the work and performance of mixed-qualification teams of analysts.

Big Data and Data Science offer a range of new possibilities and business opportunities to companies for better meeting consumer needs and providing better customer experiences, functionally and emotionally. They are set to change the way marketing, customer service, and retailing are managed and executed. However, reaching the higher level of marketing effectiveness and profitability will continue to command large investments, not only in technology but also in human capital. This will be a challenge for qualified managers and data scientists to work together in the future to harvest the promised potential of Big Data.

 

Ron Ventura, Ph.D. (Marketing)

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Dr. Diederik A. Stapel was a respected psychology scientist at Tilburg University in the Netherlands, specialising in social psychology. Until lately he held the position of dean of social and behavioural sciences at the university. Among his areas of research, he studied interactions between self-image and the perceptions people hold of stimuli in the world surrounding them (e.g., other people, objects, advertisements). Or so he wanted us to believe. He was recently expelled from his university because it was revealled this month by an enquiry committee that over more than ten years he had manipulated and fabricated data in his studies, putting into doubt at least 30 published papers. Although it remains unclear whether he falsified data for his doctoral thesis at Amsterdam University (the data have been destroyed), he voluntarily returned his title to the university following the interim report and his own admission of committing fraud and deception in his research work.

What a shame for Stapel, what an embarrassment for a whole field of theory and research in psychology. But foremost Stapel betrayed colleagues who researched and published papers with him as well as the doctoral students he supposedly “guided” towards their degrees. Imagine how it feels to find out that research work you have done and published with a colleague you trusted could be tainted by his tempering with data and become unworthy of citation. It should also be uncomfortable for researchers who cited those papers in their own work, planned experiments based on his findings, or relied on these findings to develop theory and support their own arguments. The dismay for fresh doctorates could be even more disturbing. When a researcher deceives in the way Stapel has done he destroys not only his or her career but adversely affects the life and work of many others around him.

Some of the work in which Stapel was involved concern themes in consumer behaviour, marketing and advertising. Thus the deception carried out by Stapel is also suspect of inflicting on those fields. His work alone and with colleages directly investigated issues associated with consumer judgement and thinking; Assimilation-Contrast effects and information processing; advertising and self-image; beauty in advertising; product comparison and evaluation, and more.

Investigation into Stapel’s fraud so far suggests that already in early stages of his career he manipulated data he collected so as to “adapt” the data to hypotheses he wanted to support. Thereafter he has become more brazen and effectively fabricated data for his experiments altogether. Stapel may have been dishonest but certainly not stupid or incompetent. It does require numeric skills and good understanding of data in order to simulate datasets that seem as though they were innocently and genuinely collected. Yet, he may have not been so skilled or confident in his ability to conceal the fabrication as he reportedly almost always refused to submit raw data to the inspection of colleagues and graduate students. There is no guarantee that even if other researchers analysed data Stapel did provide they would succeed in uncovering anomalies or regularities indicative of fraud. Still it is baffling how this charade could have lasted for so many years: Have colleagues with whom Stapel co-operated not been part in the process of collecting, processing and analysing the data? Were they not in position to raise questions about the findings he brought in? Nonetheless, where criticism and doubts did come up towards him, Stapel allegedly made different excuses for his refusal, often arrogantly, and thus was able to fend them off .

Researchers are not immune from faults in human judgement such as using evidence selectively to support their prior beliefs or hypotheses. The blame is likely to be in the increasingly competitive culture of the academic system. In a response to International Herald Tribune (New-York Times), psychologist Jonathan Schooler (University of California) suggested that the problem is in a culture in academia that allows researchers to “spin their work” in a way that portrays a nicer picture of their findings than it really is. In an honest perspective contributed by  psychology professor Joseph P. Simmons (Wharton School) he conjures: “We know the general tendency of humans to draw the conclusions they want to draw… With findings we want to see, we ask, Can I believe this? With those we don’t, we ask: Must I believe this?” (IHT, 3 Nov. 2011).

The IHT tells of critics of current practices in psychological research, particularly the practice of keeping data in near secrecy, who claim that there should be greater demand from researchers to share data with other researchers in the field, allowing them to analyse the data and come to their own conclusions. I doubt that this will fix methodological malpractices in this field and may instead increase animosity among researchers. Researchers can and should raise questions and criticism about design and analytical methods their colleagues apply and the conclusions they draw; that is part of academic life. But if researchers do not trust the authenticity of data collected, then confidence in relying on published papers and books might be seriously damaged. Vigilance is always advised but is should be confined to circles within the academic institute and its departments and among colleagues working together. In the competitive climate that prevails in academia, perpetuated by the race for publishing papers (“publish or perish”), I do not think it is fair to blame researchers who are reluctant to submit datasets willingly to colleagues they are not truly familiar with. No one really wants to help others who might publish a paper that contradicts their own work. It is important, however, that researchers working together in a group or a research programme, not necessarily in the same research institute, exchange data and let more than one person analyse the data in parallel.

A particularly disturbing finding of the enquiry committee claims that Stapel may have jeopardised the theses of his doctoral students. It is assessed that between 12 and 14 of the 20 theses he supervised could be flawed because they relied on fabricated data he provided his students for analysis. Stapel gained the dubious title of “lord of the data” because of his insistence not to disclose how and where exactly the data were obtained and not to provide raw data (which raises the question, what kind of data did he provide students for analysis). Even if any of his research students suspected something was wrong with data they received, one would need to have clear evidence and the confidence to make allegations of fraud against one’s supervisor; it can put a doctoral student in a very difficult dilemma. Making things worse, Stapel used to threaten and insult students who dared to raise doubts about the data — one such student talking as a witness to the committee, its report says, was accused by Stapel of putting into doubt the latter’s well-established reputation as a top researcher. The student also complained of receiving hints from Stapel that the student’s aspirations as a young researcher could be compromised (Dutch News.nl).

Stapel apparently intimidated also his colleagues who questioned his methods and his superior investigative talents. But eventually he came across three young researchers who either were smarter than he was or were fed up with his behaviour, probably both. They investigated one of his datasets and exposed his tactics to the head of Tilburg University (Dutch News.nl). As Stapel continued to get away with his deception, he became more bold, and possibly too complacent at the end. Was he alert to the risks involved in his conduct or did he overlook them? Did he get tired of hiding his tracks? And did he expect or even want to get caught at some point in time? Stapel will have much time now to analyse his behaviour by introspection. And perhaps we will be able to find answers to such questions in a book that Staple will write himself.

Ron Ventura, Ph.D., (Marketing)

Sources:

“New scandal is latest to taint psychology field”, Benedict Carey, The International Herald Tribune, 3 Nov., 2011, p.1+4.

“Five questions about: The Diederik Stapel affair”, DutchNews.nl, 2 Nov. 2011,

http://www.dutchnews.nl/features/2011/11/five_questions_about_the_diede.php

“Diederik Stapel: The lying Dutchman”, The Washington Post Online (Opinion), The Achenblog by Joel Achenbach, 1 Nov. 2011,

http://www.washingtonpost.com/blogs/achenblog/post/diederik-stapel-the-lying-dutchman/2011/11/01/gIQA86XOdM_blog.html

An entry on Diederik Stapel in Wikipedia (with a link to the original report of the enquiry committee in Dutch).

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