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December 2013 (12:4) | MIS Quarterly Executive 213

MISQUarterly Executive

Data Monetization in the Supply Chain1,2 Data is now being created and transferred at an unprecedented rate, fueling the growth

in business intelligence and analytics (BI&A)3 to discover opportunities for improving and innovating in supply chains and to enhance supply-chain collaboration.4 In retailing, new supplier/customer ecosystems are emerging in which BI&A services are offered through a supplier portal, which can be cloud-based. Cloud-based BI&A platforms allow retailers and their suppliers to share data and analytics, often for a price. Or a company may monetize its data by exchanging it for other benefits (e.g., merchandising benefits). These data-sharing ecosystems often involve new players (e.g., public cloud platform providers and/or third-party data coordinators, negotiators or analysts).

Many companies would like to monetize their data. Data monetization is when the intangible value of data is converted into real value, usually by selling it. Data may also be monetized by

1 Cynthia Beath, Jeanne Ross and Barbara Wixom are the accepting senior editors for this article. 2 An earlier version of this article was presented at the pre-ICIS SIM/MISQE workshop in Orlando, Florida, in December 2012. We are grateful to Omar El Sawy and other participants at the workshop for their insightful comments. We would also like to thank the anonymous retailer and big data analytics company that provided so much time and insight concerning their experiences with monetizing big data. 3 For background information on big data and BI&A, see Chen, H., Chiang, R. H. L. and Storey, V. C. “Business Intelligence and Analytics,” MIS Quarterly (36:4), 2012, pp. 1165-1188; Hopkins, M. S., LaValle, S., Lesser, E., Shockley, R. and Kruschwitz, N. “Big Data, Analytics and the Path from Insights to Value,” Sloan Management Review (52:2), 2011, pp. 21-32; and Wixom, B. H., Watson, H. J. and Werner, T. “Developing an Enterprise Business Intelligence Capability: The Norfolk Southern Journey,” MIS Quarterly Executive (10:2), 2011, pp. 61-71. 4 For a discussion on BI as an IT capability for supply-chain collaboration, see Rai, A., Im, G. and Hornyak, R. “How CIOs Can Align IT capabilities for Supply Chain Relationships,” MIS Quarterly Executive (8:1), 2009, pp. 9-18.

Data Monetization: Lessons from a Retailer’s Journey

In today’s era of big data, business intelligence and analytics, and cloud computing, the previously elusive goal of data monetization has become more achievable. Our analysis of a four-stage journey of a leading U.S. retailer identifies the potential benefits and drawbacks of data monetization. Based on this company’s experiences, we provide lessons that can help other companies considering data monetization initiatives.1,2

Mohammad S. Najjar University of Memphis (U.S.)

William J. Kettinger University of Memphis (U.S.)

Data Monetization

converting it into other tangible benefits (e.g., supplier funded advertising and discounts), or by avoiding costs (e.g., IT costs). Potential buyers of an organization’s data include a direct supplier, an upstream supply-chain partner, a data aggregator, an analytics service provider or even a competitor. Three current IT trends are enhancing the potential for data monetization: big data, BI&A and the cloud.

Retail firms, with their exacting merchandising strategies and tight supply-chain relationships, have taken the lead in demonstrating that monetizing data can provide a significant revenue stream and be an IT cost-sharing mechanism. Point-of-sale, consumer-loyalty and inventory data can be sold to suppliers, and some of the cost of analyzing a retailer’s data can be recovered from its suppliers.

Research has shown that data sharing in the supply chain improves supply-chain performance. Suppliers typically are interested in using a retailer’s point-of-sale data to enhance planning and better manage inventory, thus reducing the bullwhip effect5 (i.e., the phenomenon of demand variability amplification). Manufacturers can use downstream data about retail sales to improve product design, optimize operations and develop fact-based marketing and promotional campaigns. The availability of sales data to the supply chain means that demand can be more accurately forecast and, hence, inventory levels can be better predicted; in some cases, assemble- to-order can be achieved. Some suppliers may even use such data for strategic decisions by looking for product affinities to make merger or acquisition decisions.

Furthermore, data sharing can be a strategic tool in managing supply chains and channel relationships; sharing consumer or market data with supply-chain partners can influence their behavior.6 Nevertheless, a company must decide 5 See Lee, H. L., Padmanabhan, V. and Whang, S. “The Bullwhip Effect in Supply Chains,” Sloan Management Review (38:3), 1997, pp. 93-102. 6 For more discussion on the benefits of data sharing in the supply chain, see Zhou, H. and Benton Jr., W. C. “Supply Chain Practice and Information Sharing,” Journal of Operations Management (25:6), 2007, pp. 1348-1365; Eyuboglu, N. and Atac, O. A. “Information Power: A Means for Increased Control in Channels of Distribution,” Psychology & Marketing (8:3), 1991, pp. 197-213; Waller, M., Johnson, M. E. and Davis, T. “Vendor-Managed Inventory in the Retail Supply Chain,” Journal of Business Logistics (20:1), 1999, pp. 183-203; and Lee, H. L., Padmanabhan, V. and Whang, S., op. cit., 2004, pp. 1875-1886.

whether and when sharing its data with suppliers and other partners will pay off. The benefits a data-sharing strategy will have for the overall supply chain and distribution channel must be balanced against the benefits of holding data close to the chest.7 While the improvement in supply-chain performance might be a good reason for companies to share data with supply-chain partners, a more explicit direct dollar value of the data can be another tempting motivation.

There are several challenges in involving suppliers in monetizing data. Selling data to suppliers may eliminate the competitive advantage that can be gained from asymmetric8 information. Contracts have to be carefully prepared to ensure the data sold or shared is used for the mutual benefit of the firm and its partners. Trust has to be nurtured. The privacy and security of a company’s data may be at risk if appropriate assurance practices are not established. Data packaging has to be considered to identify what data can be made available for sale and in what format and at what price. Pricing models need to be developed to take account of the associated cost of making data available and its value to the buyer. A company must identify a suitable marketing model for its data. Although data monetization best practices have yet to be identified, this article describes how a major U.S. retailer tackled these challenges. (The research we conducted to create this case study is described in the Appendix.)

Pathways to Data Monetization Data monetization requires a strategic

choice on which of several pathways to follow. It is important to assess the technical (data infrastructure) and analytical (human) capabilities of the company to determine which strategic pathway a company should choose for monetizing its data. The technical capability includes the hardware, software and network capabilities that enable the company to collect,

7 For more discussion on the benefits of data sharing in the supply chain, see: Zhou, H. and Benton Jr., W. C., op. cit., 2007; Eyuboglu, N. and Atac, O. A., op. cit., 1991; Waller, M., Johnson, M. E., and Davis, T., op. cit., 1999; and Lee, H. L., Padmanabhan, V., and Whang, S., op. cit., 2004. 8 Information asymmetries occur when two people have different information about the same thing. If one has additional inside information, he or she can leverage or take advantage of that information.

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store and retrieve its data. The analytical capability is the mathematical and business analytical knowledge and skills of the employees in the company or in supplier firms. A company that has the data and the know-how (i.e., people and BI&A) to use the data properly will have an advantage in the era of big data. If both capabilities are low, then the company has three potential pathways to transition to the high capabilities that will enable it to monetize its data (see Figure 1).

Pathway 1: Move Direct to Higher Risk and High Reward

This direct pathway can be a riskier path to data monetization, as it requires simultaneously building both technical and analytical capabilities. As such, it requires the largest initial investment of the three alternative pathways. To follow this pathway, a company must invest in developing its technical infrastructure while hiring and training employees with the required business, mathematical and analytical skills. While costly, following this pathway will quickly position a company to be ready for monetizing its data and collaborating with supply-chain partners.

Pathway 2: Build Analytical Capability First

Following this pathway, a company chooses to develop its analytical capability first. This requires training employees and/or hiring business analysts with the required set of business, mathematical and analytical skills. As its analytical capability grow, the company may leverage them by generating more data (from internal sources) or buying data (from external sources). But growing an in-house analytical capability may not be sufficient to reach the point where the company can demonstrate the value of its big data and thus pave the way to data monetization. It may also require the company’s technical capability to be expanded. This pathway requires a higher internal investment to develop the in-house analytical capability.

Pathway 3: Build Technical Data Infrastructure First

Instead of first developing its own analytical capability, a company may choose to extend or outsource its technical data infrastructure to produce an attractive collection of data that can be sold to suppliers. The creation of an appropriate digital platform is a prerequisite for a company and its suppliers to share data securely. A company can build this platform internally or use the expertise of a service provider; the use

Figure 1: Three Pathways to Data Monetization—Moving From Low-Low to High-High Capabilities

Build both capabilities internally or hire a third party

Acquire (buy) data to leverage your analytical capability

Exploit suppliers’ BI&A

Monetize and dig deeper collectively as partners

Technical Capability

Analytical Capability

Low

High

1 2

Data Monetization

of cloud-based infrastructure can increase the flexibility, scalability and speed of developing the platform. By building a platform that will enable it to market its saleable data, a company can more quickly monetize its data and possibly avoid some analytical costs by leveraging the analytical capabilities of its suppliers rather than developing the analytical capability in-house. This pathway maximizes the potential data monetization pay-off because it enables sales of data and reduces startup costs. However, it does make the company more reliant on its partners as major sources of analytics.

The Data Monetization Journey of “DrugCo”

The case of “DrugCo,” a U.S.-based Fortune 500 drug retailer with several thousand stores in more than half of U.S. states, illustrates a company that has followed Pathway 3. This company, which wishes to remain anonymous, is recognized as being relatively mature in BI and data use, and it has been monetizing its data for almost 10 years. The case shows how cost and the willingness to work with external parties and openly share data were important issues that motivated DrugCo to monetize its data.

Like other companies in the small-box retailing sector, DrugCo has:

● Many retail locations with narrowly defined geographical boundaries

● Limited shelf space ● Many stock-keeping units (SKUs) across

the company ● A diverse customer base ● Differing inventories within each location

to satisfy the local customer needs.

For DrugCo, data analysis is crucial for accurately assessing marketing campaigns, analyzing sales patterns, examining on-shelf availability and inventory levels, and customizing SKUs for each store based on its unique local consumer demand.

We describe key events that took place in the company, and we present a four-stage model that illustrates the four key stages it went through on its data monetization journey (the stages are depicted in Figure 2). We also provide lessons learned from DrugCo’s journey for other managers as they grapple with their data monetization decisions.

In Stage 1, Building BI&A capabilities, DrugCo built its technical and analytical capabilities to address internal business needs.

In Stage 2, Connecting to and sharing information with suppliers, DrugCo connected to its supply-chain partners and started to share information with them through its cloud-based

Figure 2: DrugCo’s Four-Stage Data Monetization Journey

Stage

Benefits (Value) of

Data Stage 1: Building BI&A capabilities

Stage 2: Connecting to and sharing information with suppliers

Stage 3: Monetizing data by charging for it

Stage 4: Further monetizing data and avoiding analytical costs by leveraging suppliers’ resources

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supplier portal, hosted by 3PP (a third-party data analytics firm that works with DrugCo and which also wishes to remain anonymous).

In Stage 3, Monetizing data by charging for it, DrugCo started selling its data to suppliers via its supplier portal.

In Stage 4, Further monetizing data and avoiding analytical costs by leveraging suppliers’ resources, DrugCo leveraged its suppliers’ data analytical capabilities and avoided some of the costs of its analytical function. This stage continues to the present day.

The characteristics of the four stages are described in Table 1. The stages differ in the technical and analytical (especially in people) capabilities the company required, the type of trust9 built, the focus of DrugCo’s information strategy, governance mechanisms, and the costs incurred and benefits achieved by various stakeholders. While there has been ample discussion of the first two stages, we were surprised by the third stage and even more surprised by the fourth.

As DrugCo moved from one stage to the next, the benefits realized from its data increased. DrugCo’s data was monetized in the form of revenue generated directly from selling the data, as well as through a decrease in labor and infrastructure costs for analysis. The company also realized benefits from new business opportunities associated with new analytical insights and enhanced its collaboration with suppliers.

Stage 1: Building BI&A Capabilities The growth of DrugCo’s data sources

meant that its traditional databases, database management systems and analytical tools became slow and inefficient. DrugCo’s VP of Pharmacy Services described this environment:

“The database … probably had about 1.2 to 1.3 million transactions a day and those transactions were very long … there were literally hundreds of fields on one of these transactions that could be evaluated.”

In response, DrugCo improved its in-house technical data capability by developing a data warehouse and using basic data analytical tools

9 Trust is categorized into contractual, goodwill and competence; see Sako, M. Prices, Quality and Trust: Inter-firm Relations in Britain and Japan, Cambridge University Press, Cambridge, 1992.

(e.g., Microsoft Access and Excel). Limited, functionally based BI capability was used to analyze and understand the implications of DrugCo’s data. Business users would attempt to perform basic ad hoc queries and, when faced with more complex or time-consuming analyses, would ask the IT department for help. The main focus of this stage was to use data to meet business needs and solve internal problems. DrugCo’s CIO described how limited capabilities meant limited analyses:

“If it takes you 45 minutes or an hour to get an answer… you’re probably not going to do a lot with it. But if you can do it within 30 seconds or a minute or two, you are more likely to do more analytics and what- if cases.”

Because all data use was internal to DrugCo during Stage 1, inter-organizational trust was not an issue. Information was used to inform internal stakeholders and to run the business more efficiently. Data exploitation was judged to be going well since problems were being solved and new insights were being generated. Various policies were enforced to maintain the internal security and privacy of DrugCo’s data.

The data exploitation costs in this stage were the technical cost of building the data warehouse and connecting it to the reporting tools, and the analytical cost of analyzing the data.

Stage 2: Connecting to and Sharing Information with Suppliers

In Stage 2, DrugCo created a secure, cloud- based portal for communicating with its suppliers. The portal provided access to point- of-sale, customer-loyalty and transactional data (e.g., purchases from DrugCo’s suppliers) and various BI&A applications. As an analytical data warehouse platform, it allowed suppliers to work with and analyze DrugCo’s data so the company and suppliers could collaborate on mutual business goals. DrugCo’s Senior Director of Category Management Support (CMS) explained the importance of the supplier portal:

“The great thing about this portal and this information is [that DrugCo and its suppliers are] working on the same set of reports a lot of times and we’re using the same information.”

Data Monetization

Table 1: Characteristics of the Four Stages of Data Monetization

Stage 1: Building BI&A Capabilities

Stage 2: Connecting to and Sharing Information with Suppliers

Stage 3: Monetizing Data by Charging for It

Stage 4: Further Monetizing Data and Avoiding Analytical Costs by Leveraging Suppliers’ Resources

Technical Capability

Implementing data warehouse with basic analytical tools

Developing a supplier portal

Extending the supplier portal with data integration and customized reporting capabilities for data

Offering a scalable data platform to accommodate expanded use of the suppliers’ analytical capabilities

Analytical Capability

Internally focused, limited functional analytical capability

More fully developed internal and inter- organizational analytical capability

Matured internal and inter-organizational analytical capabilities; Learning what data is saleable

Exploiting analytical capabilities of suppliers

Type of Trust Not an issue, as BI&A is internally focused

Contractual trust Contractual trust; Goodwill trust

Contractual trust; Goodwill trust; Competence trust

Information Strategy

Informing internally Supply-chain optimization

Revenue generation Information transparency

Governance Mechanisms

Basic performance metrics; Information assurance

Information sharing contracts; Data presentation mechanisms and standards; Non-disclosure agreements (NDAs)

Pricing structure; Data purchase agreement; NDAs

Evaluation of supplier- provided analytics

Achieved Benefits/Associated Costs

Achieved Benefits (DrugCo)

Data is used to meet specific business needs and solve problems

Data is shared across boundaries for supply- chain efficiency

Data is sold to generate monetary value and/or share technical costs

Data is traded for analytics to gain new insights; Cost savings and revenue growth

Associated Costs (DrugCo)

Technical cost; Analytical cost

Technical cost; Analytical cost; Contracting cost; 3PP’s fee

Contracting cost; 3PP’s fee

3PP’s fee

Achieved Benefits (Suppliers)

Refined BI&A using the accessed data

Increased sales through better understanding of markets and DrugCo’s business

Enhanced collaboration with DrugCo; Increased sales by shelf monitoring

Associated Costs (Suppliers)

Analytical cost; Contracting cost

Data cost; Analytical cost; Contracting cost

Data cost; Analytical cost

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DrugCo owned the data it put on the supplier portal, while 3PP offered data-analytics, data- cleansing and consulting services, and owned the portal infrastructure. DrugCo sent its data to 3PP, which cleansed it and then uploaded it to the portal. Data security was enforced by preventing suppliers from copying or downloading data from the portal; they could only work with the data while it was still on the portal. Once it was connected with its suppliers, DrugCo had to further develop its analytical capability so it could respond to new inter-organizational analytical needs, which imposed additional analytical costs on DrugCo.

Trust is an important factor when external parties are involved with data monetization. In Stage 2, the data-sharing relationship between DrugCo and its suppliers was still somewhat immature. Non-disclosure agreements (NDAs) were used to specify what suppliers could and could not do with the data. These agreements created contractual trust—a mutual understanding between DrugCo and its suppliers based on the agreements. DrugCo’s Senior Director of CMS described the contracting approach:

“We’ve limited the use of the data. It’s specifically limited to the purpose of growing the business of our company.”

3PP acted as a liaison between DrugCo and its suppliers, providing value-adding activities by hosting DrugCo’s data on the supplier portal, and BI&A services, administrating the information- sharing contracts, contracting directly with some suppliers (e.g., alcohol suppliers, which legally are not allowed to contract directly with DrugCo to purchase its data) and managing different aspects of the relationship, such as negotiating pricing of DrugCo’s data.

During this stage, data was shared for supply- chain optimization. The suppliers accessed part of DrugCo’s data, analyzed it and were able to enhance their marketing campaigns, production planning, pricing and inventory management.

The governance of DrugCo’s supplier portal was designed to be collaborative. Major suppliers joined an advisory board that oversaw how the supplier portal was implemented. Voting was used to prioritize enhancements and to determine data presentation mechanisms and standards. The VP of Retail Solutions at 3PP

explained the structure and function of the advisory board:

“[At any time] there’s around 18 to 20 suppliers on [DrugCo’s] advisory board and there are eight that are on their senior council … the larger group meets twice a year and the senior group meets four times a year ... they prioritize the changes or enhancements they want to see in the program and pass them to DrugCo ... DrugCo is only a member … It’s a user- driven advisory board.”

DrugCo’s costs during Stage 2 were the technical cost of building the supplier portal, the analytical cost for the additional inter- organization analyses, and the contracting cost for preparing contracts and NDAs with suppliers and third parties. 3PP incurred the cost of hosting the portal and providing additional analytical services. Suppliers connected to the portal also incurred contracting costs for the NDAs and analytical costs for analyzing the data they accessed. With direct access to the portal, suppliers could dynamically manipulate vast amounts of DrugCo data to answer questions on the fly.

Stage 2 laid the technical foundation (i.e., in the supplier portal) for data monetization and showed that DrugCo’s data was valuable to its suppliers.

Stage 3: Monetizing Data by Charging for it

In Stage 3, with the supplier portal running successfully and suppliers having a good feel for DrugCo’s data and its value, DrugCo began to extract more value from its data by monetizing it:

“They [retailers in general] accumulate billions of records every year of point-of- sales transaction data and they are taking that huge amount of data and creating their own commercial data clouds for their suppliers to analyze … A consumer- packaged-goods brand can just log in and see not only how their own products are doing in those stores but also how a competitor’s products are doing in those stores.” VP of Marketing, 3PP

The supplier portal was enhanced by adding additional data sets (particularly loyalty data)

Data Monetization

and customized reporting capabilities to provide a wider range of reports to the data- buying suppliers. DrugCo’s internal and inter- organizational analytical capabilities matured, and it started to identify what data was saleable.

Data was offered in different packages, each of which had a different level of data granularity, reporting capability and price tag. By now, DrugCo had a dedicated executive on its merchandising team for selling its data, and this executive worked with 3PP to market these data packages directly to DrugCo’s suppliers. Prices were often negotiated. If a supplier chose a higher level of information access and granularity, the price increased. There were four levels of data packaging—Basic, Bronze, Silver and Gold—for point-of-sale data (see Table 2). Only a limited number of DrugCo’s major suppliers were allowed to purchase the highest Gold level package. As discussed later, a supplier had to invest resources in its relationship with DrugCo to become a candidate for the Gold level.

A data-purchase agreement and NDA were prepared for DrugCo and any supplier who wanted to buy data. Trust in Stage 3 included goodwill trust (based on beliefs) in addition to contractual trust (based on written agreements). When goodwill trust exists, partners are willing

to go beyond stipulated contractual agreements. Thus, DrugCo trusted that the supplier would not only adhere to the data-purchase agreement, but would also use the data for the benefit of both parties. In essence, DrugCo’s major suppliers learned to tell DrugCo when they saw a problem that needed to be addressed, regardless of whether doing so was of immediate benefit to the supplier.

Big suppliers (such Johnson & Johnson, Procter & Gamble, Coca Cola, PepsiCo, 3M, Novartis and Unilever) have been applying analytical tools for a long time to better predict demand and develop successful marketing campaigns; they are equipped with significant know-how in terms of BI&A:

“There are hundreds of CPG [consumer packaged goods] companies … analyzing detailed data from retailers … mixing it together with econometric and demographic data, weather data, various kinds of geographic data, and trying to better understand the markets and figure out how to better sell the products.” Cofounder and CEO, 3PP

Table 2: Four Levels of Data Packaging

Level Data Access and Analytics Provided Current No. of Suppliers

Percentage of Suppliers

Basic • Supplier items only at POS transaction level detail filtered by SKU • Information provided shows supplier inventory level status • Access provided only through prebuilt reports

358 55.3%

Bronze Basic Package plus: • Summaries for all approved classes/categories provided by a few

prebuilt reports

128 19.8%

Silver Bronze Package plus: • All items at POS transaction level detail for approved classes

filtered by class • Ability to upload up to 10 GB of DrugCo’s data for enhanced

analysis by supplier • Third-party analysis tool provided for ad-hoc analysis by supplier • (Limited) basket view of categories a supplier operates in

82 12.7%

Gold Silver Package plus: • (Full) basket view for all baskets, regardless of categories or

supplier • Custom reports built for individual supplier or built for a set

timeframe

79 12.2%

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With access to more granular data, suppliers were able to fine-tune their operations by predicting sales trends more accurately and thus better develop marketing and promotional campaigns:

“They [suppliers] can see a trial and repeat. They can see how a BOGO [Buy One Get One] type of promotional offer is performing, how our customers react to that differently than maybe a BOGO 50 [50% off ] or a price point.” Senior Director of CMS, DrugCo

During Stage 3, 3PP provided additional services to DrugCo, including training and supporting suppliers, negotiating and administering data-package contracts, BI&A services and marketing of DrugCo’s data.

The information strategy of DrugCo at this stage shifted toward revenue generation; data was being sold and was generating a revenue stream for DrugCo. This revenue offset some of the costs of the underlying infrastructure, such as the data warehouse, the supplier portal and reporting tools.

Although DrugCo did not need to make additional investments in technical and analytical capabilities during Stage 3, it was still bearing 3PP’s ongoing costs for hosting the cloud- based data and portal, and providing additional analytical services. It also incurred contracting costs for preparing the purchase agreements with data-buying suppliers. Suppliers were incurring the costs of buying DrugCo’s data, negotiating the contracts for the data and analyzing the data. The suppliers benefitted by understanding the markets and DrugCo’s business better. They were able to increase their sales by using DrugCo’s granular data to design promotions and to leverage product affinities for additional promotional effectiveness. The Chairman & CEO of Procter & Gamble stressed the value of real-time, granular data:

“For companies like ours who rely on external data partners, [getting the data] becomes part of the currency for the relationship. So as we deal with retailers, I may not be interested in getting that Tide ad this week, but if you give me your data in real time for the next four weeks,

that’s more valuable to me … It would be heretical in this company to say that data is more valuable than a brand, but it’s the data sources that help create the brand and keep it dynamic.”10

Stage 4: Further Monetizing Data and Avoiding Analytical Costs by Leveraging Suppliers’ Resources

The final stage extended DrugCo’s data monetization journey to new horizons, which enabled it to take even greater advantage of the analytical capabilities of its suppliers:

“The purpose of that [suppliers having access to our data] is for them to be able to help us be smarter about how we run our business.” CIO, DrugCo

The technical platform for DrugCo’s data was expanded to meet new scale requirements arising from the suppliers’ use of the platform to perform advanced analyses on the data. Also, advanced human capabilities were required to use applications that incorporated advanced analytical techniques (such as optimization, predictive modeling, simulation, time series modeling and principal component analysis). However, DrugCo avoided these additional analytical costs by exploiting its suppliers’ analytical capabilities; it began to rely more on the business insights generated by suppliers’ analyses of the data they purchased from DrugCo. The Cofounder and CEO of 3PP elaborated on the symbiotic relationship between retailers and CPG suppliers:

“CPG companies are often quite sophisticated … The retailers look at the CPG companies for advice [on] how to stock their shelves, how to do promotions, what products to sell, to whom [and] under what circumstances … There’s a symbiotic relationship in the sense that the retailer gets advice from the CPG company, and the more information the CPG company has about what’s going on at the retailer and in the market, the better advice they would get, and of course there’s the money angle

10 Interview with Robert McDonald, Chairman & CEO of Procter & Gamble, downloaded from http://www.mckinsey.com/insights/ consumer_and_retail/inside_p_and_ampgs_digital_revolution.

Data Monetization

… Retailers, like anyone else, are always looking for revenue sources and retail is a tough market, [with] very tight margins, and the more revenue they can get the better.”

With access to DrugCo’s data, suppliers started to understand the markets and DrugCo’s business better; a supplier could get better insights into how it and DrugCo could together grow their businesses. This led, in turn, to DrugCo gaining a better understanding of its own promotions and its customers, and how they were buying products over time.

DrugCo’s suppliers can now develop affinity analysis reports—which show what products are usually sold together—faster and more accurately, and pass these reports to DrugCo. The reports enable DrugCo to run separate promotions and advertising campaigns for highly related products instead of promoting and advertising them at the same time. The shift of data analytics to the suppliers resulted in a reduction of analytical costs for DrugCo.

Major suppliers offer insights to DrugCo through direct interaction on a daily basis between DrugCo’s merchandising team and the suppliers’ sales agents, often supported by BI&A analysts. In addition, supplier and DrugCo representatives are both involved in meetings of the supplier portal advisory board, where entire sessions may focus on analytics insights of benefit to DrugCo. For example, one major supplier presented a co-merchandising affinity- analysis program it had recently implemented, which predicts what third product will be purchased when two other products are bought. After reviewing the program, the advisory board voted and approved that it should be made available to Gold members, and it was included in the Gold level of data access.

In Stage 4, suppliers enhanced their collaboration with DrugCo and increased their sales; for example, they could use a shelf-monitor program that looks at sales of their products and detects a potential out-of-stock, which may cause a consumer to switch and buy a competitor’s product. Some suppliers became trusted sources of data analysis. Based on these analyses, suppliers developed merchandising strategies and targeted promotional programs that DrugCo could implement:

“What we do with retailers [is] what we call Joint Business Planning or Joint Value Creation … For us, getting data becomes a big part of value whereas for the retailer they have the data, so that’s become a big part of our work together, and then how can we use this data to help them, because we have analytical capabilities that many retailers don’t have, so often times we can use the data to help them decide how to merchandise or market their business in a positive way.” Chairman & CEO, Procter & Gamble11

An additional form of trust, competence trust, was needed in Stage 4. DrugCo trusted that its partners had the superior managerial and technical capabilities needed to analyze its data. The company trusted that some suppliers had the capability and the willingness to use and analyze its data in a way that benefitted both parties while refraining from any misuse or misconduct regarding the data. 3PP’s VP of Retail Solutions described how DrugCo’s supplier portal enabled the formation of competence trust:

“[A retailer] would let their [suppliers] see the actual performance of the SKUs by day by store in a [market] basket level perspective because they were starting to trust the advice and counsel that their suppliers were giving them … DrugCo can watch how the analysis was done by the [supplier] and argue it. The [supplier] really can’t be sneaky because everything they do is wide open.”

As DrugCo reached the fourth stage of the journey to data monetization, it shifted to a transparency strategy.12 With this strategy, a company recognizes that the benefits of sharing data with external partners exceed those of withholding information from them. However, DrugCo realized the importance of limiting strategic information partnerships to the suppliers entitled to the highest Gold level 11 Interview with Robert McDonald, Chairman & CEO of Procter & Gamble, op. cit. 12 A transparency strategy is defined as one that selectively discloses information outside the boundaries of the firm to buyers, suppliers, competitors and other third parties like governments and local communities; see Granados, N. and Gupta, A. “Transparency Strategy: Competing with information in the Digital Age,” MIS Quarterly (37:2), 2013, pp. 637-641.

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data package. Allowing a supplier to purchase the Gold level package is viewed as a strategic merchandising decision and is based on the volume of transactions with the supplier, the number of people (i.e., the supplier’s data analysts and salespeople) who are dedicated to work only with DrugCo and DrugCo’s recognition of the supplier as a trusted advisor. Suppliers now compete to be designated by DrugCo as a “category captain.” These suppliers review the performance of the entire category and recommend a store-level sales strategy, including assortment, shelf-space assignments, promotion, and pricing.13 Category captains have the closest and most regular contact with DrugCo and invest time, effort and resources into the strategic development of their categories within DrugCo. They deploy dedicated analysts who only work with DrugCo and thus become trusted partners. In return, category captains have some degree of decision-making authority and an influential voice at DrugCo. DrugCo evaluates its suppliers’ analytical performance based on the value of the analytics and recommendations provided by them and their track record of promoting DrugCo’s business.

Lessons Learned Several important lessons emerge from the

DrugCo case. We believe the following practices will contribute to the successful monetization of data.

1. Consider How Creating and Sharing Data Will Change Relationships and Business Models

It is important to consider the dynamics among supply-chain members and to think about how data monetization might change the traditional relationships in the supply chain. Retailers can expect their major suppliers to compete for a category captain role to become a trusted advisor and a source of valuable business recommendations. Companies need to carefully consider the trade-off between higher levels of information transparency with their supply-chain partners and the possible risk of

13 For an analysis and recommendations for choosing a category captain, see Subramanian, U., Raju, J. S., Dhar, S. K. and Wang, Y. “Competitive Consequences of Using a Category Captain,” Management Science (56:10), 2010, pp. 1739-1765.

losing information advantages over suppliers, customers and competitors.

Data monetization creates a new business model for the company, in which revenue generation, cost structure, value proposition and relationships change. The company’s data is not only used to run the business, but also becomes a digital product the company can use to generate revenue and cover the costs associated with creating and gathering data. Leveraging suppliers’ analytical capabilities introduces a new era of informational collaboration among partners and supply-chain members. Suppliers can add value to their relationships with retailers by offering business insights and new business- growth opportunities. Third parties can provide value-adding services to create and sustain a data monetization platform.

As the dynamics of competition and cooperation among companies continue to evolve, IT provides opportunities for value co-creation. A data monetization relationship is a good example of the co-creation of IT- based value between companies at the assets, complementary capabilities, knowledge-sharing and governance levels.14

2. Identify Where You Currently Are in the Data Monetization Journey and Where You Want to End Up

An ideal end state of a data monetization initiative will result in deeper insights from the associated ecosystem, a revenue stream, a reduction in infrastructure and analysis costs, and trusted use of data by supply-chain partners. The following are several aspects that concerned stakeholders have to pay attention to, prior to and during their data monetization journey.

Prepare Your Data for Sale. The integration of additional relevant data sets into the company’s data will increase the value of the data to data buyers. For example, DrugCo enhanced the value of its data to its suppliers by adding loyalty data. Companies should also package the data for sale to meet different needs, analytical capabilities and willingness to pay. Multiple levels of data packaging (see Table 2) is a useful technique.

14 For more discussion on co-creating IT value, see Grover, V. and Kohli, R. “Cocreating IT Value: New Capabilities and Metrics for Multifirm Environments,” MIS Quarterly (36:1), 2012, pp. 225-232.

Data Monetization

Assess the Need for Value-Adding Third Parties to Join the Data Monetization Ecosystem. Third parties can provide various value-adding activities in the data monetization ecosystem. Examples include orchestrating the relationship between the company and the data buyer by hosting the data, contracting with data buyers, offering training and support, and providing technical and analytical capabilities. A third party can also be instrumental in the company’s effort to obtain and build the required technical and analytical capabilities. Assessing what can be outsourced can be instrumental to building and sustaining a data monetization initiative.

Market Your Data and Challenge Your Suppliers to Get Onboard. A marketing strategy is needed to advertise and promote the value of the company’s data. The company has to approach potential data buyers and highlight how and why the data is useful, as suggested by DrugCo’s Senior Director of CMS:

“Challenge them saying: “Well, your competitors understand this better now. You know you’re falling behind.”

Even when third parties participate in the data monetization initiative, the company still has to be involved in selling its data:

“You have to be involved with pushing it and selling it. You don’t really outsource the selling of the data.” Senior Director of CMS, DrugCo

Avoid Some Analytical Costs by Leveraging Suppliers’ Analytical Resources. A data monetization initiative can create new opportunities for the company to exploit its suppliers’ ability to analyze data. It is not uncommon for there to be more analytical resources on the supplier’s side dedicated to working on and analyzing the company’s data, as highlighted by DrugCo’s CIO:

“More [analytical] people on the [supplier] side have access to [our data] than we do internally.”

Recognize and Reward Your Top- Performing Suppliers. Determining appropriate measures to identify top-performing suppliers

in your data monetization ecosystem and rewarding them will establish a collaborative relationship in which actions are guided by the principle of mutual benefit. A supplier can be rewarded by allowing it to have a higher level of data package and by nominating it as a category captain. Decisions to recognize top performance should not only be based on transaction volume, but also on the supplier’s provision of human capabilities and the quality of advice provided. The performance of existing category captains should be continuously monitored so that underperforming category captains can be replaced with new ones.

3. Develop Contracts to Ensure Adherence to Data Monetization Policies

Several contracts were developed between DrugCo, 3PP and DrugCo’s suppliers throughout the data monetization journey, notably NDAs and data-sharing and -purchase contracts. These contracts restricted the use of the shared or purchased data to specific purposes. Suppliers were obliged to use the data they purchased for the sole purpose of growing the mutual business of the suppliers and DrugCo.

4. Nurture Trust Between the Involved Parties

Different forms of inter-organizational trust exist between business partners. Trust can lower the contracting cost and conflict level required to reach a data-purchase agreement. The progression from trust based on written agreements to trust based on beliefs contributes to the formation of a collaborative relationship in which mutual benefits are considered by the parties involved. Inter-organizational trust can be built by communication of trustworthiness, inter-organizational coordination to establish governance mechanisms, and successful and repeated interactions that demonstrate each partner’s reliability. The transparency of the collaboration portal can also nurture trust between a company and its suppliers; suppliers can be held accountable for their use of the company’s data and the quality of the analysis and advice they provide.

December 2013 (12:4) | MIS Quarterly Executive 225

Data Monetization: Lessons from a Retailer’s Journey

Concluding Comments The DrugCo case demonstrates that getting

direct monetary value from a company’s data is no longer elusive. Data analysis tools and cloud computing have paved the way to monetizing a company’s data. We have described how DrugCo was able to monetize its data by going through four distinct stages and ultimately increased both tangible and intangible benefits. Building technical and analytical capabilities and connecting with the retailer’s suppliers facilitated the emergence of a digital ecosystem that enabled data monetization. DrugCo managed to cut its analytical costs by leveraging its suppliers’ well-established technical and analytical capabilities. Joint benefits emerged from this new relationship by generating a new revenue stream and providing a cost-sharing mechanism for the retailer, and offering suppliers real-time access to the retailer’s data.

Appendix: Research Approach

The topic of data monetization arose when one of the researchers interacted with an executive of 3PP, a company that provides cloud- based big data hosting as well as analytical and consulting services. This firm had considerable experience with building supplier portals and/or cloud-based data ecosystems so companies could monetize their data. At the researcher’s request, 3PP identified several of its clients that had monetized their data, and the researcher approached them about the possibility of in-depth cases concerning the “how and why” of data monetization. DrugCo was willing to discuss its journey on the condition that it remained anonymous.

First, we carried out numerous rounds of interviews at 3PP with the VP of Business Analytics, VP of Retail Solutions, Client Project Manager and Client Relationship Manager to more fully understand data monetization in general and 3PP’s experiences with DrugCo in its role as a catalyst and facilitator of DrugCo’s data monetization journey. The data provided by these interviews was analyzed and formed the initial picture of DrugCo’s journey.

Next, data gathered from the interviews with 3PP was used to develop the interview

guide to be used at DrugCo. Executives at DrugCo who were knowledgeable about and had participated in DrugCo’s data monetization journey were identified with the help of 3PP. In- depth interviews were conducted with DrugCo’s CIO, the Director of Category Management Services and the VP of Pharmacy, who provided details about DrugCo’s journey. Email follow-up questioning also occurred.

Finally, follow-up corroborating interviews were conducted with 3PP’s VP of Retail Solutions, Client Project Manager and Client Relationship Manager to triangulate accounts. Secondary sources, including some additional interviews at 3PP and public sources, complemented our primary sources and allowed us to form an overall view of data monetization.

About the Authors

Mohammad S. Najjar Mohammad Najjar ([email protected]) is a Ph.D. candidate at the Fogelman College of Business and Economics at the University of Memphis. He received his M.B.A. and B.Sc. from the University of Jordan. His research interests include IS services, business intelligence, information assurance and information management. He has published in and reviewed for several international conferences.

William J. Kettinger William Kettinger ([email protected]) is Professor and FedEx Endowed Chair in MIS at the Fogelman College of Business and Economics at the University of Memphis. Kettinger’s focus is practical, rigorous research appearing in leading journals. He has received such honors as a Society of Information Management’s Best Paper Award and directed a SIM APC study of the business drivers of IT value. He has served on the editorial boards of MIS Quarterly, Information Systems Research, Journal of the Association of Information Systems and MIS Quarterly Executive. He consults with global companies such as enterpriseIQ®, AT&T and IBM.