California Management Review
California Management Review is a premier academic management journal published at UC Berkeley
by Carsten Lund Pedersen and Thomas Ritter
Image Credit | Thierry Fousse
The data train is running at full speed, but not all firms have bought a ticket and found their seats. These firms find themselves standing on the platform, watching their peers rolling quickly toward the distant horizon.
“Data-Driven, Data-Informed, Data-Augmented: How Ubisoft’s Ghost Recon Wildlands Live Unit Uses Data for Continuous Product Innovation” by Karl Werder, Stefan Seidel, Jan Recker, Nicholas Berente, John Gibbs, Nouredine Abboud, & Yossef Benzeghadi
“Digital Data Streams: Creating Value from the Real-Time Flow of Big Data” by Federico Pigni, Gabriele Piccoli, & Richard Watson
“How to Use Big Data to Drive Your Supply Chain” by Nada R. Sanders
With all the noise surrounding big data, why do so many firms arrive too late to board the data train? Even though firms have heavily invested in digitization, their reasons for hesitating may be manifold: no data, no cash, no talent, no project, no idea, or no direction.
Yet, most organizations have at least one type of data that can help them climb aboard—customer data. Many organizations must collect and handle customer data for practical reasons, such as logistics requirements, and for legal reasons, such as compliance with tax regulations. Thus, a great point of departure for a data-utilization journey is customer data. In this regard, our research on data-driven growth allows us to present a typology of customer-data preparedness.
We view all digitized information about customers in a firm as customer data. It includes structural customer data, such as information on location, size, and industry, as well as transactional customer data, such as items purchased, turnover, and price paid. An emerging part of customer data relates to customers’ activities—their use of the firm’s offerings. This type of data is enabled by sensors and Internet of Things solutions that report on when, where, and how customers interact with a firm’s products and services. Thus, our initial questions for executives concerning customer data include:
However, the possession of data on customers will only get you to the train station. Actually climbing aboard the train requires sufficient motivation.
All the customer data in the world will not lead to anything if you are not highly motivated to use it. Hence, you need to assess the extent to which you are motivated to work with customer data.
According to Vroom’s expectancy theory, motivation (including the motivation to use customer data) is driven by three factors: the outcome’s desirability (“valence”—how much the outcome is valued), the impact of the activity on the outcome (“instrumentality”—the extent to which the activity will ensure the outcome’s achievement), and the impact of one’s own efforts on the activity (“expectance”—the extent to which you believe you can perform the activity).
In other words, executives need to value potential outcomes, such as increased profitability, turnover growth, and thought-leadership status in relation to data utilization. More specifically, organizational members must appreciate the potential outcome to warrant investing in its achievement. As such, there has to be some “goal-oriented arousal” (Park & Mittal, 1985). Furthermore, executives need to believe that using customer data will enable them to achieve the desired outcomes. Put differently, is customer data a realistic option for successfully achieving your formulated goals? Finally, executives need to be confident that their firms have the ability to launch and implement successful data initiatives. Thus, our next set of questions for executives is the following:
As we have now defined the necessary questions, we can use them to highlight the various starting conditions for firms on their data-utilization journeys.
Taken together, the two dimensions outline four starting conditions for an organization’s journey towards customer-data utilization (see figure below). The two dimensions are consistent with Merton’s (1957) motivation-ability framework, which is often applied in marketing studies, including studies focused on consumer behavior (Maclnnis, Moorman, and Jaworski 1991), marketing strategy (Boulding and Staelin 1995), marketing channels (Grewal, Comer, and Mehta 2001), and sales-force management (Johnson and Bharadwaj, 2005; Sabnis et al., 2013). We expand on these four starting conditions, their barriers, and proposed next steps in the following.
We call companies without any motivation or data “Beginners.” These firms are at the very start of their journeys. In fact, they may not have even considered embarking on the journey. These firms are typically small and conservative with a stable business model. As changes in their business model and their business environment are rare, they do not feel a need to collect and digitize customer data. Similarly, they lack motivation. Their mantra seems to be: “If it isn’t broke, don’t fix it.”
For Beginners, the way forward is to find the motivation to use customer data. These firms need to develop a “why”—a purpose for the journey. Such motivation can be triggered by customers demanding new services, authorities introducing new regulations, or competitors establishing new standards in the market. Somehow, Beginners need to see the light in order to get moving.
Admirers can see the benefits of using customer data, as they have a strong belief in the valuable outcomes that data utilization can offer them. Consequently, they are highly motivated to embark on data-utilization journeys but fail to do so due to a lack of customer data. As such, they are forced to watch others instead of acting themselves. Admirers are typically outspoken about their frustrations regarding their own data limits and about their admiration for those firms that thrive in their data-utilization journeys.
The key imperative for Admirers is to find data. Notably, as pointed out above, most firms already have customer data. Thus, the limitations these firms feel they face might be more perceptual than real. Alternatively, given their motivation, investing in data collection is often a feasible step forward for Admirers.
Hoarders have plenty of data but typically lack the motivation to use it. They simply do not see a reason to utilize customer data. These organizations are often digitized firms in which customer data is “automatically” gathered by an IT system. Nothing triggers the firm to start thinking about that data’s potential use. The data piles up—unnoticed, unused, and underutilized.
Hoarders lack the motivation to utilize customer data and, thus, they need a business case. In other words, they need to see a valuable outcome that they can achieve by using their customer data and they need quantified, plausible arguments. In order to get started, they need to see a light at the end of the data-storage tunnel.
Movers have both motivation and data. They are ready to go and are equipped to embark on the data-utilization journey.
What Movers need is momentum—energy that can maintain their interest and journey development, and help them make progress after the journey has started. Specifically, Movers need concrete customer-data projects that can be successfully completed. They need to constantly see their ambitions realized, which gives them motivation in the first place. These successes then lead to new “goal-oriented arousal.”
As always, it is important to know where an organization and its members are in order to engage with the right tools and move in the right direction. The matrix covering the four starting conditions offers a tool for understanding not only where your firm is but also where and how to move. Specifically, we propose three actions:
Becoming data-driven can seem like an insurmountable task. However, starting with customer data is often an easy choice in terms of having the required data and seeing an impact. You can get onto the data train—just imagine where it can take you.
1. Boulding, W. and R. Staelin (1995). “Identifying Generalizable Effects of Strategic Actions on Firm Performance: The Case of Demand-Side Returns to R&D Spending,” Marketing Science, 14 (3): G222-G236.
2. Grewal, R., J. Comer, and R. Mehta (2001). “An Investigation Into the Antecedents of Organizational Participation in Business to Business Electronic Markets,” Journal of Marketing, 65:17-34.
3. Johnson, D. S. And S. Bharadwaj (2005). “Digitization of Selling Activity and Sales Force Performance: An Empirical Investigation,” Journal of the Academy of Marketing Science, 33 (1): 3-18.
4. MacInnis, D. J., C. Moorman, and B. J. Jaworski (1991), “Enhancing and Measuring Consumers’ Motivation, Opportunity, and Ability to Process Brand Information from Ads,” Journal of Marketing, 55 (October), 32–53.
5. Merton, Robert. 1957. Social Theory and Social Structure. Glencoe, IL: Free Press.
6. Park, C. and B. Mittal (1985), "A Theory of Involvement in Consumer Behavior: Problems and Issues," in Research in Consumer Behavior, Vol. 1, J. N. Sheth, ed. Greenwich, CT: JAI Press, Inc., 201-31.
7. Sabnis, G., S. C. Chatterjee, R. Grewal, and G. L. Lilien (2013), "„"The Sales Lead Black Hole: On Sales Reps’ Follow-Up of Marketing Leads," Journal of Marketing, 77 (January), 52–67.
8. Vroom, V. H. (1964), Work and Motivation, John Wiley and Sons, New York, NY.