creating competitive products with analytics—summary of

WRAPPING

DATA MONETIZATION

ANALYTICS, BUSINESS INTELLIGENCE

DATA SCIENCE

COMPETITIVE ADVANTAGE

MACHINE LEARNING

ARTIFICIAL INTELLIGENCE

CUSTOMER ANALYTICS

BIG DATA

DATA TRANSFORMATION

CREATING COMPETITIVE PRODUCTS WITH ANALYTICS— SUMMARY OF SURVEY FINDINGS Ronny Schüritz, Killian Farrell, and Barbara H. Wixom

JUNE 2019 | CISR WP NO. 438 | 18 PAGES

In 2018, MIT CISR collected information from 511 product managers and other professionals responsible for the success of a product to learn how companies are creating analytics features and experiences, a phenomenon we call data wrapping. This report provides detailed survey results, and can be used by an organization to benchmark data wrapping practices and outcomes against those reported by the sample. Further, the report de-scribes three key activities that distinguish top data wrapping performers: they (1) please customers with useful and engaging wraps; (2) design fea-tures and experiences to anticipate, advise, adapt and act; and (3) quantify and monitor financial returns.

PARTICIPANT REPORT a report for research project participants with detailed methodology, analysis, findings, and references

MIT MANAGEMENT

SLOAN SCHOOL

CENTER FOR INFORMATION SYSTEMS RESEARCH (CISR) © 2019 Massachusetts Institute of Technology. All rights reserved.

Schüritz, Farrell, and Wixom | CISR Working Paper No. 438 | 2

CONTENTS

The 2018 Data Wrapping Survey: Findings............................................................ 4

The Survey Sample............................................................................................................................ 5 Survey Companies............................................................................................................................ 5 Survey Data Wrapping Projects ....................................................................................... 7

Company Resources ........................................................................................................................ 9

Wrap Design Characteristics ...............................................................................................10

Customer Value..................................................................................................................................11

Company Outcomes .....................................................................................................................12

Key Takeaways ....................................................................................................................................13

Appendix ...................................................................................................................................................14


CREATING COMPETITIVE PRODUCTS WITH ANALYTICS—SUMMARY OF SURVEY FINDINGS

In 2018, the MIT Sloan Center for Information Systems Research (CISR) investigated how companies use analytics to create competitive analytics-based product features and customer experiences—something we refer to as data wrapping. Between March and September 2018 we surveyed 511 product managers and other professionals who were responsible for the success of a product. This report summarizes the survey results and offers recommendations on how companies can generate business value from data wrapping. A key finding from the research is that companies achieve higher financial outcomes from data wrapping when they purposefully design their wraps to anticipate, advise, adapt, and act. Additionally, top-performing data wrapping projects produce features and experiences that are useful and engaging and measure value created for customers and for the company.

The MIT Sloan Center for Information Systems Research (CISR) defines data monetization as the conversion of data and analytics (directly or indirectly) into financial capital. Companies can monetize their data in three ways: by (1) selling information offerings, (2) improving business processes and decisions, and (3) wrapping data around products (i.e., data wrapping).1 In the 2018 study “Creating Competitive Products with Analytics,” MIT CISR investigated data wrapping to better understand how companies can maximize their returns from the practice.

1 For an explanation of data monetization, see Barbara H. Wixom and Jeanne W. Ross, “How to Monetize Your Data,” MIT Sloan Management Review, Spring 2017 issue, January 9, 2017, https://sloanreview.mit.edu/article/how-to-monetize-your-data/.

This participant report was prepared by Ronny Schüritz of the Karlsruhe Institute of Technology, Killian Farrell of the Karlsruhe Institute of Technology, and Barbara H. Wixom of the MIT Sloan Center for Information Systems Research (CISR). The authors would like to thank survey respondents for their participation in the research. © 2019 MIT Sloan Center for Information Systems Research. All rights reserved to the authors.

https://sloanreview.mit.edu/article/how-to-monetize-your-data/


We consider a product to be “the overall experience provided by the combination of goods and services to sat-isfy the customers’ needs.”2 A wrap is an analytics-based feature or experience—such as a dashboard, a report, an alert, a benchmark, an API, guidance, or an automated action—that is combined with a core product. In application, a tractor may be wrapped with a dashboard that communicates operational performance, a bank account may be wrapped with a chart that communicates a breakdown of the account owner’s spending, and a transportation service may be wrapped with notifications of predicted delivery times. Some wraps, such as automatic parallel parking with a car or automatic replenishment purchasing for a retail website, actually act on behalf of a customer.

What makes data wrapping a unique approach to data monetization?3

1. Data wrapping converts data indirectly into financial capital. Data wrapping generates value by influencing a lift in sales of a core product.

2. Product owners control the wrap. Data wrapping decision rights reside within the Product function rather than in IT. The role that “owns” the product manages data wrapping as a component of the product’s overall feature and experience portfolio.

3. Data wrapping is highly coupled with a core offering. Data wrapping must be delivered at service levels aligned with quality standards for the underlying offering so as to circumvent negative impact on the offering. Further, the costs and benefits of data wrapping must be evaluated within the context of the offering’s profit formula to ensure that wrapping activities are lucrative.

Past studies in this research included “Winning with the Internet of Data wrapping: Enhancing Things” (2016), “Data Wrapping” (2017), and “Datatization as the the customer value Next Frontier of Servitization” (2017). The studies examined surveys (combined N=240) and interviews (combined N=43) with business and proposition of a product IT managers, and a total of thirty-two publicly available use cases, to by adding analytics-based understand data wrapping as a distinct data monetization phenomenon. features and experiences The 2018 study consisted of a survey of 511 product managers responsi-ble for generating revenues from products. The survey respondents, representing a diverse set of global com-panies, described data wrapping projects that differed on a variety of dimensions, including design features and maturity.

THE 2018 DATA WRAPPING SURVEY: FINDINGS Key findings from the 2018 research suggest that wrapping products with analytics-based features and expe-riences is an important way for product managers to create competitive offerings in the digital economy and generate financial returns. The findings indicate that companies that are effective at data wrapping leverage two resources—analytics and customer knowledge—to inform their use cases. Analytics enables the viability of data wrapping use cases and guides use case choices, and customer knowledge helps product managers identify and develop use cases that are valued by customers. Combined, these two resources help top-performing projects produce data wrapping features and experiences with four key design characteristics: wraps that anticipate, advise, adapt, and act.

These four characteristics are distinct; however, they work together to make the product more useful and more engaging for customers, which generates value for the company. Figure 1 summarizes these findings.

2 Greg Geracie and Steven D. Eppinger, eds., The Guide to the Product Management and Marketing Body of Knowledge: ProdBOK(R) Guide, (Product Management Educational Institute, 2013), 31.

3 B. H. Wixom and R. Schüritz, “Making Money from Data Wrapping: Insights from Product Managers,” MIT Sloan CISR Research Briefing, Vol. XVIII, No. 12, December 2018.

http://cisr.mit.edu/blog/documents/2018_1201_WrappingValue_WixomSchuritz.pdf


EMMii♦ Iii• Company Resources

enable

Design Characteristics

create Help Acquire/Deliver

Usefulness ~..: e Ti-f Eff0<1

•••- Engagement generate Ease of Use Save Money

Customer Value

/ Company \

Company Value

Figure 1: How Companies Generate Company Value from Data Wrapping

The survey findings were based on statistical analyses, primarily correlations, regressions, and comparisons of means. We combined survey and interview analyses with findings from our prior data wrapping research to make conclusions about causality. In this report, we provide detail on the statistical results from the survey.

We recommend two MIT CISR publications as supplemental readings to this report, as they contribute complementary insights to the findings shared in this report:

• B. H. Wixom and R. Schüritz, “Making Money from Data Wrapping: Insights from Product Managers,” MIT Sloan CISR Research Briefing, Vol. XVIII, No. 12, December 2018.

• B. H. Wixom and R. Schüritz, “Creating Customer Value Using Analytics,” MIT Sloan CISR Research Briefing, Vol. XVII, No. 11, November 2017.

THE SURVEY SAMPLE Beginning in March 2018, MIT CISR distributed the survey “Creating Competitive Products with Analytics” to product managers at MIT CISR sponsor and patron companies; business leaders who had participat-ed in past MIT CISR research or events, or programs produced by MIT Sloan Executive Education; and LinkedIn contacts of the research project team. MIT CISR also reached product audiences through the Association of International Product Marketing and Management (AIPMM), which promoted the survey to its members4; and by emailing purchased prod-uct manager distribution lists. In total, we reached out to thousands of product managers, product owners, and other individuals responsible for product success. This produced a set of 511 usable survey responses by September 2018.5

Survey Companies The survey sample represented companies of all sizes; about half had less than $500 million6 in annual revenues in 2017. The companies operated across the globe, most with some operations in North America. The com-panies represented a range of industries, with fifty-nine percent falling in industry categories of technology, services, and manufacturing. Note

HEARING BETTER Core Offering: Hearing implant

Wrap: Sound classifier

Industry: Healthcare

Business Type: B2C

Product: A hearing implant has a sound processor that sits outside the ear and collects incoming sounds. A correctly adjusted sound processor will filter or tune down “noise” and transmit only relevant sounds to the brain, a function that ear structures perform unconsciously when hearing functions correctly. In the past, the implant required that the customer manually adjust their processor as they move through different sound contexts.

Wrap: The hearing implant manufacturer offers an analytics-based sound classifier product feature. The feature relies on statistical algorithms that predict changes to a customer context by analyzing the sound being received by the processor in real time. Then, analytics are used to identify the optimum processor settings to produce high hearing quality–and automatically adjust settings.

Customer Value: Customers experience better hearing from their implant.

4 We would like to thank the Association of International Product Marketing and Management for promoting the MIT CISR survey to its membership; we truly appreciate the organization’s support.

5 Sample sizes varied for different analyses as not all questions were answered by every respondent.

6 Monetary amounts are in US dollars.

https://cisr.mit.edu/blog/documents/2018/12/20/2018_1201_wrappingvalue_wixomschuritz.pdf/

https://cisr.mit.edu/blog/documents/2018/12/20/2018_1201_wrappingvalue_wixomschuritz.pdf/

https://cisr.mit.edu/blog/documents/2017/11/16/2017_1101_wrappingatcochlear_wixomschuritz.pdf/


North America

Europe

Asia

Latin America

Australia & New Zealand

Middle East

Africa

> $3 billion 27%

$500milllon$3 billion 22%

188

171

N=481

439

285

260

229

221

N=S04

that most sample companies were for-profit; only five percent of companies were characterized as non-profit or governmental organizations. (See figures 2–4 for associated survey respondent breakdowns.)

Figure 2: Survey Respondent Breakdown by Company Size

Figure 3: Respondent Company Breakdown of Operations by Geography7

7 Many respondent companies operate in multiple locations.


Technology

Services

Manufacturing

Finance, Insurance

Trade, Transportation

Health Care, Social Assistance

Construction, Mining, Utilities

Other

7%

3%

27%

18%

14%

12%

8%

11% N=502

Figure 4: Respondent Company Breakdown by Industry8

Survey Data Wrapping Projects Survey respondents invested in data wrapping projects to create value in contexts of business-to-business (B2B), business-to-consumer (B2C), and business-to-business-to-consumer (B2B2C) (see figure 5). The projects drew upon five variations of analytics, which ranged from low sophistication (such as reporting and dashboards, and visualization) to high sophistication (such as machine learning, and specialized analytics like streaming analytics and natural language processing) (see figure 6).

Figure 5: Respondent Company Breakdown by Business Type

8 Services includes Educational Services, Information and Media, IT Services, Professional and Scientific Services, Public Administration, and Telecommunications.


Specialized Analytics

Machine Learning

Statistical Techniques (e.g., regressions)

Visualization

Reporting, Dash boards 18% N=433

10

Figure 6: Respondent Company Breakdown by Analytics Sophistication9

The projects represented different levels of maturity, with forty-seven percent of projects fully launched into the marketplace. Of the launched wraps, seventy-five percent had been available to customers for four years or fewer, and a quarter of those for less than one year. Ten percent of the launched projects were highly mature, having been available to customers for seven years or more; fifteen percent of respondents reported that their company was not currently engaged in data wrapping. (See figures 7–8 for associated project breakdowns.)

Technology Companies FINDING FRAUD FASTER

Are Losing Their Lead Core Offering: Credit card

Wrap: Transaction review Technology companies typically have a longer Industry: Bankinghistory of working with data than companies in Business Type: B2Cother industries. Thus, we expected technology

firms to use more sophisticated analytics Product: A bank offers a credit card to customers who are concerned techniques in their data wrapping projects. about fraud. The product manager prioritizes card features that help the

Indeed, in the subset of fully launched wraps, customer feel comfortable that their technology companies were more likely to use transactions are legitimate.

sophisticated analytics such as edge analytics and Wrap: The bank offers an online statement of past transactions in machine learning to enhance products. However, which transactions are presented

other kinds of companies seemed to be catching along with the merchant logo and a map of where the transaction up: within the subset of wraps that were in the occurred. The visual nature of the

implementation phase, technology and non- logo and map cues help customers review and vet their transactions. technology companies were equally likely to use

advanced analytics techniques.10 Customer Value: Customers can more quickly and effectively identify fraudulent transactions.

9 The chart depicts the most sophisticated analytics technique reported in wrapping use by each respondent. Seven percent of the survey respondents did not apply an analytics technique, and therefore are not included in this chart; in those cases, the wrap included data in its raw form.

10 Analytics maturity was coded from 1 (low, i.e., data-only) to 6 (high, i.e., specialized analytics); the highest maturity level for each project was recorded. For example, a company selecting visualization (code=3) and statistical techniques (code=4) were assigned an overall analytics maturity of 4. Comparing arithmetic means, technology companies had a significantly higher average maturity for fully launched wraps (p<.05 using t-tests). When we included companies at all levels of wrapping maturity, the mean difference was not significant (p>.05).

https://techniques.10


Fully Launched

Implementation Phase

Not Wrapp ing

Longer than 4 years _/ (25%)

15%

1 to 2 years (25%]

2 to 4 years (25%)

N=Sll

N=238

Figure 7: Respondent Company Breakdown by Project Maturity11

Figure 8: Respondent Company Fully Launched Projects by Years on the Market

Note: Subsequent findings in this report are based on analyses of fully launched wrap projects (N=242).

COMPANY RESOURCES As described in “Creating Customer Value Using Analytics,” our research identified that two key resources are required for data wrapping12: analytics and customer knowledge. Companies draw on these resources to design great wraps.

Both resources can be broken down into a set of key dimensions. For example, analytics represents the availabil-ity of real-time, multistructured, externally sourced, and product-based data; analytics-savvy employees; and sophisticated analytics techniques and approaches. In effect, companies draw upon analytics resources when they have analytics-savvy technologists and product managers who ideate innovative data wrapping use cases, formulate feasible execution plans, and deliver advanced customer-facing features and experiences.

Customer knowledge represents the knowledge that a company has regarding its customers’ product usage, core needs, and latent needs. Core needs are needs currently met by the product, whereas latent needs are customer desires and preferences that are not yet addressed. Typically, companies develop customer knowledge by estab-

11 The Implementation Phase category includes wrapping projects that were in process of being planned, prototyped, or rolled out.

12 Wixom and Schüritz, “Creating Customer Value Using Analytics.”


• Anticipate

Advise

~ Proactive

~ Provide insight

The wrap understands -in advance - the customer's need

The wrap supports evidence -based decision

making

lishing customer connections using customer touchpoints (e.g., call centers, the company’s salesforce); ecosys-tem partners (e.g., purchased customer demographics or psychographics, shared supply chain data); customer involvement (e.g., co-creation, pilots, advisory boards); digital technologies (e.g., Facebook pages, mobile apps); and the products themselves (via sensors).

The current research discovered that companies should invest in building complete, robust analytics and custom-er knowledge resources to produce lucrative data wrapping projects.

WRAP DESIGN CHARACTERISTICS As described in “Making Money from Data Wrapping: Insights from Product Managers,” our analysis identified that useful, engaging wraps are associated with four design characteristics:13

1) Anticipate: The wrap intuits the customer’s need. Wraps that anticipate are predictive and proactive.

2) Advise: The wrap supports evidence-based decision making. Wraps that advise provide data and insights that inform the customer.

3) Adapt: The wrap meets the customer’s need in a tailored manner. Wraps that adapt are specific to an individ-ual customer’s needs, environment, or context.

4) Act: The wrap performs an action that benefits the customer. Wraps that act are integrated into customer processes or behaviors, or they trigger behavior automatically on the customer’s behalf.

See appendix A for descriptive statistics of design characteristics.

13 Wixom and Schüritz, “Making Money from Data Wrapping: Insights from Product Managers.”


&!t\lJl.t.llL -- -- -••• Real-time data 0

Data variety 0 - --- ---

Product data 0 0 0

External data 0 0 0

Analytics mat urity 0 0 0 0 --- --- --- -

Analytics-savvy 0 0 0 peop le

Use of product 0 0

Core needs 0 0

Latent needs 0 0 0

It is worth noting that unique configurations of analytics and customer knowledge dimensions are associated with the anticipate, advise, adapt, and act design characteristics14 (see figure 9). Also, we found it interesting that advancing analytics maturity contributes across all wrap design characteristics.15

Figure 9: Company Resources Associated with Wrap Design Characteristics

CUSTOMER VALUE In regards to company outcomes, top-performing data wrapping projects generate significantly higher value for customers across all measures.16

This finding supports our understanding that companies must create value for customers in order to generate outcomes for the company and eventually generate financial returns.

Product managers create customer value by using data wrapping to increase a product’s ease of use or usefulness, increase engagement between the customer and product/company, improve acquisition or delivery processes, and reduce time or effort associated with using the product; they can also create customer value by helping customers save or make money. Figure 10 illustrates the types of customer value pro-duced by top-performing data wrapping projects in our study, weighted for frequency of occurrence.

14 For each design feature item (for example, predictive and proactive are items associated with anticipate) regressions were run with all capabilities as independent variables. Signif-icant (p<.05) capabilities for items aggregated back to the factor level are shown in the table.

15 Analytics maturity was coded from 1 (low, i.e., data only) to 6 (high, i.e., specialized analytics), and the highest maturity level for each project was recorded. For example, a company selecting visualization (code=3) and statistical techniques (code=4) were assigned an overall analytics maturity of 4.

16 Launched wraps were divided into low-performing and high-performing groups on company outcome measures. Means for customer value measures were significantly higher in high-performing groups, p<.05.

KEEPING VEHICLES ON THE MOVE Core Offering: Trucks, Buses

Wrap: Predictive maintenance

Industry: Transportation

Business Type: B2B

Product: A vehicle manufacturer sells trucks and buses to transportation and distribution companies. The manufacturer is intent on delivering to their customer businesses vehicles that operate without disruption.

Wrap: The manufacturer builds into its vehicles sensors that monitor operations in real time. Analytics draw upon the operations data (combined with historical data regarding past performance of other vehicles) to predict when maintenance of a truck or a bus is required, and then automatically schedules service accordingly.

Customer Value: Customers avoid unplanned downtime in operations.

https://measures.16

https://characteristics.15


Help Acquire/Deliver u sef u In ess Reduce Time/Effort

Make Money Engagement Ease of Use Save Money

customer satisfaction 0

new revenue ( ' customer retention streams

Company Value

margins O • wallet share ;o ' 'ca, economic ((le"'-\

market share

Figure 10: Word Cloud of Customer Value

The size of each word in the word cloud represents the average mean of the types of customer value produced across the high-performing groups in our survey on company outcome measures.

COMPANY OUTCOMES Data wrapping pays off when it generates financial returns for a company through an increase in product sales. Our research identified six economic metrics that product managers use to measure data wrapping returns (see figure 11). Our survey showed that data wrapping projects that were top performing (in regards to company outcomes) were more likely to be measuring project results.17 To do this, the product managers drew upon a portfolio of techniques, such as usage tracking, AB testing and controlled experiments, customer surveys, and pilot studies. (See appendix A for descriptive statistics.)

Figure 11: Common Metrics to Measure Data Wrapping Returns

SAVING FUEL Core Offering: Aircraft engine

Wrap: Performance dashboard

Industry: Aerospace

Business Type: B2B

Product: An airline engine manufacturer produces engines with a focus on fuel efficiency because fuel consumption is a major cost driver for its airline customers.

Wrap: The manufacturer builds into its engines sensors that monitor fuel usage. The manufacturer provides its airline customers with dashboards that communicate the current fuel consumption of the fleet and a Twenty percent of product managers reported that they were not con-prediction of the fuel consumption for

cerned about capturing value from data wrapping. When we analyzed the planned flight schedule. these cases, the data wraps typically were fairly basic (e.g., using low-so- Customer Value: Customers have fuel phistication analytics) and likely not designed to solve big problems. consumption information to inform

decisions regarding flight operations. These product managers intended to generate company value from data wrapping by increasing customer satisfaction and customer retention.

Most product managers are interested in finding ways to generate financial returns from their projects. In our research, eighty percent of product managers were purposely capturing value from data wrapping either directly

17 Launched wraps were divided into low-performing and high-performing groups for charging directly, raising the price of the core product, and selling more. High-performing groups reported significantly higher scores in measuring customer and company value, p<.05.

https://results.17


or indirectly. Thirty-seven percent used the direct approach, generating fi-nancial returns from data wrapping by charging customers for the feature or experience. The products of product managers who charged directly were often highly distinguishable from the wraps (e.g., the product and wrap were delivered via different channels or at different times). The re-maining product managers indirectly captured value for the company by raising the price of the underlying product and/or selling more product. In many of these cases, the product and wrap were offered as a seam-less single offering. (See appendix A for descriptive statistics of company outcomes.)

KEY TAKEAWAYS Based on the 2018 data wrapping research findings, we offer several recommendations for product managers who want to maximize data monetization returns from data wrapping:

• Recognize and appreciate data wrapping as a key source of your compa-ny’s data monetization returns.

• Design data wrapping characteristics that advise, anticipate, adapt, and act. These characteristics will best help you produce useful and engag-ing wraps.

• Invest in analytics and customer knowledge resources so that the com-

KEEPING THE SHELVES STOCKED Core Offering: Consumer goods

Wrap: Sales dashboard

Industry: Retail

Business Type: B2B

Product: A consumer goods company sells its products globally via a mix of retailers (e.g., online stores, department stores). Retailers maximize their returns when inventory turns are fast.

Wrap: The producer provides retailers with access to an online portal where they can review product fill rates and other delivery reliability metrics. The portal is populated using data from the producer’s ERP system and from POS data that the retailers share.

Customer Value: Customers have product data that can be used to inform decisions in their ordering and fulfillment processes.

pany can deliver data wrapping at the service levels required for customer-facing offerings.

• Make or save the customer money—and then purposefully measure and (directly or indirectly) capture firm value.

• Because data wrapping’s impact on sales lift is indirect, measuring wrapping returns can be tricky. Identify a credible measurement methodology or technique (e.g., controlled experiments, usage tracking) that works for your company.

This report represents the current findings from MIT CISR‘s research on Data Wrapping. We continue to study how companies create competitive products using analytics to generate company value and will distribute addi-tional research findings as they come available.


Appendix: Descriptive Statistics

Note: The following tables include survey item frequencies and means from all responses (N=511). Sample sizes varied across items as not all questions were answered by every respondent.

How well do the following characteristics describe the core product (without the wrap)?

(1=Not well at all, 2=Slightly well, 3=Moderately well, 4=Very well, 5=Extremely well)

Core product characteristics 1 2 3 4 5 Mean

Physical (tangible) (N=500) 207 32 52 54 155 2.8

Generic (the same for every customer) (N=502) 70 107 161 107 57 2.9

High margin (N=505) 53 78 176 137 61 3.1

High volume (N=505) 66 99 122 133 85 3.1

Regulated (by industry or government) (N=507) 124 80 96 96 111 3.0

Generates revenue through recurring fees (N=507) 133 64 81 90 139 3.1

Generates data about its usage (N=508) 61 46 89 107 205 3.7

How strongly do you agree with the statements about your company?

(1=Strongly disagree, 2=Disagree, 3=Neither agree nor disagree, 4=Agree, 5=Strongly agree)

Company characteristics 1 2 3 4 5 Mean

My company is flexible in making short-term sacrifices to maintain a stable relationship with 8 31 60 290 118 3.9 customers (N=507)

My company is digitally connected with the cus-tomers of the product (i.e., the company is techni- 42 76 52 176 162 3.7 cally connected via sensors, internet, etc.) (N=508)

Customers trust my company (N=507) 2 3 51 255 196 4.3

Customers are willing to exchange their data in return for benefits that we provide them (N=504)

19 40 123 204 118 3.7

Customers believe that my company has domain expertise that can help them solve their problems 3 7 37 214 245 4.4 (N=506)

The Product business unit (or equivalent) is analyt-ics savvy (N=502)

24 73 127 179 99 3.5

The Product business unit (or equivalent) works closely with data and analytics professionals 44 84 97 155 126 3.5 (N=506)


To what extent do you have the following knowledge about the customer of the core product?

(1=To a very small extent (or not at all), 2=To a small extent, 3=To a moderate extent, 4=To a large extent, 5=To a very large extent)

Customer knowledge 1 2 3 4 5 Mean

Their needs regarding the core product (N=507) 2 10 78 218 199 4.2

Their latent (hidden) needs regarding the product 9 67 176 175 80 3.5

(N=507)

Their sentiment, attitudes regarding the product 8 41 149 208 101 3.7

(N=507)

Their use of the core product (N=507) 2 16 113 193 183 4.1

How well do the following characteristics describe the nature of your wrap example?


Wrap nature 1 2 3 4 5 Mean

Predictive (N=429) 141 81 98 69 40 2.5

Proactive (N=428) 82 100 97 97 52 2.9

Automated (N=431) 38 57 90 149 97 3.5

Tailored to individual customer needs (N=433) 52 86 124 110 61 3.1

Tailored to an individual customer environment/ context (N=430)

54 72 114 122 68 3.2

Distinguishable from the core product (N=433) 47 53 124 137 72 3.3

Can be used as a standalone solution without the core product (N=432)

255 56 55 45 21 1.9

Delivered via the same channel or in the same way as the product (N=432)

54 56 68 119 135 3.5

Delivered at the same time frequency as the prod-uct (N=429)

66 45 85 109 124 3.4


How well do the following characteristics describe your wrap example?


Wrap characteristics 1 2 3 4 5 Mean

Provides data to the customer (N=434)

Provides insight to the customer (N=434)

Acts automatically on behalf of the customer (N=433)

Integrated into customer processes (B2B) or be-haviors (B2C) (N=431)

Supports current uses of the product (N=429)

Creates new uses for the product (N=429)

Relies on data that is produced by the product (N=430)

26

26

131

77

11

104

26

34

46

63

74

27

72

31

65

74

90

122

92

117

56

134

119

78

91

166

83

102

175

169

71

67

133

53

215

3.9

3.8

2.8

3.0

3.9

2.8

4.0

How well do the following characteristics describe the data used for your wrap example?


Data characteristics 1 2 3 4 5 Mean

Large volume (e.g., petabytes or greater) (N=425) 152 87 72 59 55 2.5

Real-time (N=431) 50 69 86 126 100 3.4

Multi-structured (e.g., images, audio, documents) (N=429)

171 91 79 51 37 2.3

Requires integration of publicly available or purchased data sets (e.g., from a data aggregator) (N=428)

254 52 60 38 24 1.9

Requires integration of external data sources from partners (e.g. the customer, customer systems, ecosystem partner systems) (N=430)

156 60 73 82 59 2.6


Note: The following tables include survey item frequencies and means from fully launched wrap projects (N=242). Sample sizes varied across items as not all questions were answered by every respondent.

To what extent does the wrap add value for customers?


Customer value 1 2 3 4 5 Mean

Improves ease of use of the core product (N=240) 26 27 54 71 62 3.5

Improves the usefulness of the core product (N=240)

14 19 38 82 87 3.9

Increases engagement with the core product (N=240)

12 28 48 71 81 3.8

Improves acquisition/delivery of the core product (N=240)

40 37 42 66 55 3.2

Reduces time/effort to use the core product (N=239)

41 50 34 58 56 3.2

Helps the customer make money (N=240) 71 41 40 47 41 2.8

Helps the customer save money (N=239) 32 29 52 62 64 3.4

To what extent does the wrap add value for your company in the following ways?


Company outcomes 1 2 3 4 5 Mean

Provides access to new data (N=240) 41 26 54 70 49 3.2

Creates new capabilities (N=240) 31 42 47 65 55 3.3

Leapfrogs competitors (N=239) 36 40 60 58 45 3.2

Increases customer satisfaction (N=240) 11 20 56 87 66 3.7

Increases customer retention (N=237) 6 27 74 71 59 3.6

Increases sales from existing customers (N=238) 28 29 60 81 40 3.3

Creates new sales from new customers (N=237) 33 40 48 72 44 3.2

Lowers cost to serve (N=235) 58 53 52 41 31 2.7

Increases revenues from higher prices (N=237) 74 51 51 35 26 2.5

Increases revenues from new revenue streams (N=238)

71 47 52 38 30 2.6


How well do the following statements describe your wrap example?


Competitive advantage 1 2 3 4 5 Mean

My wrap example is hard for competitors to repli-cate (N=237)

My wrap example is hard for customers to get elsewhere (N=238)

My wrap example creates customer stickiness/ lock-in (N=236)

47

35

31

59

45

43

64

48

64

47

69

68

20

41

30

2.7

3.2

3.1

How well do the following statements describe how you capture value from your wrap example?


Value capture 1 2 3 4 5 Mean

We directly charge for the wrap (N=239) 136 15 30 19 39 2.2

We raise the price of the core product or service (N=239)

126 48 35 16 14 1.9

We sell more of the core product or service (N=239)

49 45 60 60 25 2.9

We are not concerned about capturing value from the wrap (N=239)

110 45 37 26 21 2.2

We measure (and can report back) how much val-ue the wrap generates for customers (N=238)

85 58 41 39 15 2.3

We measure (and can report back) how much val-ue the wrap generates for the company (N=238)

79 46 45 39 29 2.6

0 0

MIT SLOAN CENTER FOR INFORMATION SYSTEMS RESEARCH

Founded in 1974 and grounded in the MIT tradition of rigorous field-based research, MIT CISR helps executives meet the challenge of leading dynamic, global, and information-intensive organizations. We provide the CIO and other digital leaders with insights on topics such as business complexity, data monetization, and the digital workplace. Through research, teaching, and events, the center stimulates interaction among scholars, students, and practitioners. More than ninety firms sponsor our work and participate in our consortium.

CISR RESEARCH PATRONS AlixPartners LLP Avanade BT Cognizant Huawei Technologies Co., Ltd. (China) ISACA LTI (India) The Ogilvy Group, LLC Pegasystems Inc. PricewaterhouseCoopers Advisory Services LLC

CISR SPONSORS Aetna, Inc. Air Canada

Allergan, Inc. Allstate Insurance Company

ANZ Banking Group Ltd.(Australia) Australia Post Australian Securities and Investments Commission

Australian Taxation Office

AustralianSuper Banco Azteca (Mexico) Banco Bradesco S.A. (Brazil) Banco do Brasil S.A.

Bank of Queensland (Australia) Barclays (UK) Bayer AG

BBVA (Spain) Biogen, Inc. BMW Group

BNP Paribas (France) BNY Mellon

Canadian Imperial Bank ofCommerce Caterpillar, Inc. CEMEX (Mexico) Chevron Corporation

CHRISTUS Health

Cochlear Limited (Australia) Commonwealth Superannuation Corp. CPPIB (Canada) Credit Suisse (Switzerland) CSBS

DBS Bank Ltd. (Singapore) Equifax

ExxonMobil Global Services Company

Ferrovial Corporacion, S.A. (Spain) Fidelity Investments

Fortum (Finland) FrieslandCampina

General Electric

Genworth Financial GlaxoSmithKline (UK) Grupo Santander/Santander UK

The Hanover Insurance Group

Heineken International B.V. (The Netherlands) Insurance Australia Group

Iron Mountain

Johnson & Johnson

Kaiser Permanente King & Wood Mallesons

Marathon Oil Corp. Markel Corporation Mater Private Hospital (Ireland) MLC Life Insurance, a Nippon Life Group Company (Australia) National Australia Bank Ltd. New Zealand Government— GCIO Office

Nomura Holdings, Inc. (Japan) Nomura Research Institute, Ltd. (Japan) Nordea Bank

Northwestern Mutual OCP S.A. Org. for Economic Co-operation and

Development (OECD) PepsiCo Inc. Pioneer Natural Resources USA Inc. Posten Norge AS

Principal Financial Group

QBE Raytheon Company

Reserve Bank of Australia

Royal Bank of Canada

Royal Philips (The Netherlands) Scentre Group (Australia) Schneider Electric Industries SAS (France) Standard Bank Group (South Africa) State Street Corp. Stockland (Australia) Suncorp Group (Australia) Swinburne University of Technology (Australia) Teck Resources Ltd. (Canada) Tetra Pak (Sweden) Trinity Health

USAA

Westpac Banking Corp. (Australia) WestRock Company

World Bank

Info

rmati

on a

s of J

une

2019

MIT CISR is funded by Research Patrons and Sponsors, and we gratefully acknowledge their financial support and their many contributions to our work. Sponsorship and benefits: cisr.mit.edu/community/sponsor-and-patron-benefits

Stay on top of all new releases of MIT CISR content: cisr.mit.edu/feeds

MIT Sloan School of Management Team | Kristine Dery, Christine G. Foglia Associate Director, Center for Information Systems Research Nils O. Fonstad, Amber Franey, Dorothea Gray-Papastathis,

Cheryl A. Miller, Leslie Owens Executive Director, 245 First Street, E94-15th Floor Joe Peppard, Jeanne W. Ross, Ina M. Sebastian, Aman Shah, Cambridge, MA 02142 Nick van der Meulen, Peter Weill Chairman,

Barbara H. Wixom, Stephanie L. Woerner t 617-253-2348 | e [email protected]

cisr.mit.edu |

http://cisr.mit.edu/

https://www.linkedin.com/company/mit-center-for-information-systems-research/

https://twitter.com/mit_cisr?lang=en

http://cisr.mit.edu/feeds/

http://cisr.mit.edu/community/sponsor-and-patron-benefits/

mailto:[email protected]

creating competitive products with analytics—summary of

Documents