With the rise of “big data” and associated technologies, analytics solutions are mushrooming. Many organizations face an increasing amount of available data, but are still lacking support on how to systematically transform this data into business value. Although there has been progress to apply advanced analytics to improve internal efficiency and effectiveness, the adaptation or creation of more-valuable services – called “Analytics 3.0” by Davenport - is still in its infancy. In this paper, we try to shed light on the status of the industry and to identify the changes in value propositions that are contributed by analytics. We systematically analyze 700 advanced analytics projects across the industry worldwide via an open coding approach. First, we derive a series of distinct patterns of how organizations actually create business value – and find that in only 10% of our cases the value proposition is infused by data and analytics. Second, we reveal a set of eleven patterns that illustrate how data and analytics can enrich service offerings – resulting in added value for the customer. Through these patterns we explain how the utilization is altering the service experience, how added value is created for the customer and what data is needed. Thus, we contribute to the fundamental understanding of how the use of data and the application of analytics may spark service innovation. The identified patterns should give guidance for practitioners on how to utilize (big) data and analytics, not just internally but also for changed value propositions.

1. KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI)
www.kit.edu
www.ksri.kit.edu
KIT – The Research University in the Helmholtz Association
Data-Enriched Products and Services – Options to Apply Advanced
Analytics to Infuse Value Propositions
Ronny Schüritz, Gerhard Satzger, Lukas Eiermann
Frontiers in Services Conference, June 2017

2. 2 Ronny Schüritz / Gerhard Satzger
Research Group „Digital Service Innovation“
Karlsruhe Service Research Institute
www.ksri.kit.edu
Big Data and Advanced Analytics hold great potential for
existing businesses
Survey of 600 Chief Operating Officers
(IBM 2016):
32% already adopted advanced
analytics and modelling tools
63% plan to invest within next 2-5
years
Main challenges (Kart et al. 2013):
Understanding how analytics should
be used
Lack of management bandwidth
Lack in analytics skills within
business units
Academic focus so far (Lavalle et al.
2011): largely on technical questions
round the collection, storage and mining
of big data.Source: GE (2014)
Datavolumeinexabyte
Source: Turner et al. (2016)

3. 3 Ronny Schüritz / Gerhard Satzger
Research Group „Digital Service Innovation“
Karlsruhe Service Research Institute
www.ksri.kit.edu
The business model (BM) is a concept consisting of
three main components
Value
Proposition
Value Creation Value Capture
The business model is
• a “heuristic logic” (Chesbrough & Rosenbloom 2002),
• an “abstraction” (Betz 2002),
• an “architecture” (Osterwalder & Pigneur 2002),
• a “representation” (Shafer et al. 2005), or
• a “model” (Morris et al. 2005)
that articulates what businesses are doing and how they are doing it (Zott et al. 2011).
Value
Proposition

4. 4 Ronny Schüritz / Gerhard Satzger
Research Group „Digital Service Innovation“
Karlsruhe Service Research Institute
www.ksri.kit.edu
How do organizations use data and analytics to advance their business model
and what are specific options to drive enhanced value propositions?
RQ
New Data-Driven
Business Models
(Hartmann 2016, Chen 2011)
Existing
Business Models
(Schüritz & Satzger, 2016)
Data as a Service (DaaS) Data-Enabled
Improvements
(internal focus)
Data-Enriched
Products &
Services
(external focus)Analytics as a Service (AaaS)
Organizations can not only build new data-driven business
models, but also enhance their existing ones

5. 5 Ronny Schüritz / Gerhard Satzger
Research Group „Digital Service Innovation“
Karlsruhe Service Research Institute
www.ksri.kit.edu
Our research method: We analyze 361 cases and apply
inductive category building
 Large, heterogeneous dataset needed
 Software vendors as a proxy to identify
relevant organizations
 Collection of cases by keyword search
from vendor’s websites:
 Long list: 645 cases
 Short list: 361 cases
1 Source of Data
Application of hybrid coding approach
(Glaser & Strauss 1967; Patton 2002):
 Application of open and elementary
method to capture the outcome of
projects (internal focus and external
focus) (Saldaña 2009)
 Use of provisional codes that include
more detailed information about the
kind of data and the used methods
(Saldaña 2009)
 All coding is reviewed by a second
researcher.
Analysis2

6. 6 Ronny Schüritz / Gerhard Satzger
Research Group „Digital Service Innovation“
Karlsruhe Service Research Institute
www.ksri.kit.edu
Result 1 – Data-Enabled Improvements: 15 patterns in
3 maturity levels to raise internal efficiency
Get Data (122)
Generate Insight (71)
Use Insight (112)
Data consolidation (43)
Easier data exploration (27)
Faster data access (27)
Reduce data processing time (25)
Detect relationships to support
decision (27)
Analyze customer behavior (24)
Analyze customer feedback (12)
Predict demand and supply (8)
Target marketing activities (34)
Improve process efficiency (23)
Minimize risk and fraud (17)
Minimize inventory (11)
Enable internal predictive
maintenance (11)
Improve production quality (9)
Improve pricing precision (7)
(n) number of cases
 Data-enabled improvements change value creation and value capturing, but do not
directly impact the value proposition.
 A maturity model of data-enabled improvements was constructed out of the data – with
different focus and objectives:
305 out of 361
cases (84%)
Data-Enabled Improvements

7. 7 Ronny Schüritz / Gerhard Satzger
Research Group „Digital Service Innovation“
Karlsruhe Service Research Institute
www.ksri.kit.edu
Result 2 - Data-enriched products and services: 9 Patterns
to augment existing value propositions
 Data-enriched products and services do impact the value proposition.
 9 Distinct patterns emerge:
Personalize
Help
customers
to succeed
Reduce
downtime
Improve
service
quality
Transparency
Enhance
customer
experience
Raise
customer
efficiency
Individualize
pricing
Create
insights for
customer
Data-Enriched
Products & Services
56 out of 361
cases (16%)

8. 8 Ronny Schüritz / Gerhard Satzger
Research Group „Digital Service Innovation“
Karlsruhe Service Research Institute
www.ksri.kit.edu
Example - Pattern: “Personalize”
• Allows organizations to personalize at a larger scale and in higher precision in order to
better understand their users and adapt recommendations as well as service offerings.
• Relies on data provided by the customer leveraged for analytics
Source: http://www.flaticon.com/
 Health tracking system
tracks the physical,
mental and emotional
health of patients
 The system provides
personalized care that is
more accurate and
customized than
traditional offerings
 Analyzing each patient’s
data using machine
learning.
Dartmouth-Hitchcock
Hospital providing medical services in the area of New Hampshire.

9. 9 Ronny Schüritz / Gerhard Satzger
Research Group „Digital Service Innovation“
Karlsruhe Service Research Institute
www.ksri.kit.edu
Example - Pattern: “Increase transparency”
Source: http://www.flaticon.com/
 ProSiebenSat.1 allows
advertisers to better
understand the
effectiveness of their TV
ads.
 Correlating the immediate
traffic to the advertiser’s e-
commerce website
followed by TV
advertising.
 Results in an improved
transparency for the
advertiser.
$
• Improves the transparency for customers while they use the organization’s products or
services
• A better understanding for customers is created about their consumption giving them
higher visibility of their effectiveness
ProSiebenSat.1
ProSiebenSat. 1 is a TV station group in Germany that sells advertising
slots of commercial breaks to business-to-business customers.

10. 10 Ronny Schüritz / Gerhard Satzger
Research Group „Digital Service Innovation“
Karlsruhe Service Research Institute
www.ksri.kit.edu
Example: Pattern “Reduce downtime”
• Offering customers predictive maintenance to schedule repairs
• Overall customer downtime is reduced by taking machines offline before they fail and by
responding faster to failures.
• Uses existing sensor data from their customer’s machines to apply predictive analytics.
Source: http://www.flaticon.com/
ThyssenKrupp uses product
sensor data to detect maintenance
requirements before elevators are
out of use
The model constantly monitors sensor
data and, with machine learning, it can
now recommend the ideal time for a repair
which reduces the downtime.
ThyssenKrupp Elevator

11. 11 Ronny Schüritz / Gerhard Satzger
Research Group „Digital Service Innovation“
Karlsruhe Service Research Institute
www.ksri.kit.edu
Personaliz
e
Help
customers
to succeed
Reduce
downtime
Improve
service
quality
Transparency
Enhance
customer
experience
Raise
customer
efficiency
Individualize
pricing
Create
insights for
customer
Summary
Managerial implications:
• While many enterprises use data &
analytics to enable internal
improvements, only few
enterprises enrich their external
value proposition
• Clear patterns for data-enriched
products and services can guide
the systematic search for
innovation
Data-enabled
improvements
84%
Future Work:
• What are the key capabilities required to engage in data-enriched products
and services?
• What are the performance outcomes of such endeavors and how can we
capture the value of data-enriched products and services?
Data-enriched
products and services
16%

12. 12 Ronny Schüritz / Gerhard Satzger
Research Group „Digital Service Innovation“
Karlsruhe Service Research Institute
www.ksri.kit.edu
www.ksri.kit.edu
Ronny Schüritz
Karlsruhe Service Research Institute (KSRI)
Karlsruhe Institute of Technology (KIT)
Kaiserstraße 89, 76133 Karlsruhe, Germany
Phone: +49 (0) 721 608 – 45625
Email: ronny.schueritz@kit.edu
https://de.linkedin.com/in/ronnyschueritz
Thank you –
we are happy to engage in further discussions anytime!
Prof. Dr. Gerhard Satzger
Karlsruhe Service Research Institute (KSRI)
Karlsruhe Institute of Technology (KIT)
Kaiserstraße 89, 76133 Karlsruhe, Germany
Phone: +49 (0) 721 6084-3227 (KIT)
Email: gerhard.satzger@kit.edu

13. 13 Ronny Schüritz / Gerhard Satzger
Research Group „Digital Service Innovation“
Karlsruhe Service Research Institute
www.ksri.kit.edu
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Patton, M., 2002. Qualitative Research and Evaluation Methods 3rd ed., Thousand Oaks,
CA: SAGE.
Saldaña, J., 2009. The coding manual for qualitative researchers, London: SAGE.
Schüritz, R. & Satzger, G., 2016. Patterns of Data-Infused Business Model Innovation. In
IEEE 18thConference on Business Informatics (CBI). Paris.