Vortrag von Raj Venkatesan und Kim Whitler an der HWZ-Darden Konferenz vom 8. Juni 2017 an der HWZ Hochschule für Wirtschaft Zürich. https://fh-hwz.ch/conference

1. Building a Sustainable Analytics Orientation
Raj Venkatesan
Kim Whitler

2. Buzz About Big Data and Analytics
• Companies in the top 3rd of their industry in the use of data driven
decision making are on average 6% more profitable than their
competitors.
- The Big Data Management Revolution, Harvard Business Review, 2012

4. What is analytics?
Source: Thomas Davenport, Competing on Analytics, 2007
Descriptive
Analytics
(the “what”)
Degree
of
Intelligence
Predictive and
Prescriptive
Analytics
(the “so what”)
Metrics, Then Analytics!
Optimization
Predictive Modeling
Randomized Testing
Statistical Analysis
Alerts
Query/drill down
Ad hoc reports
Standard Reports
“What’s the best that can happen?”
“What will happen next?”
“What if we try this?”
“Why is this happening?”
“What actions are needed?”
“What exactly is the problem?”
“How many, how often, where?”
“What happened?”

6. CarMax
6

7. Strategic Insights From Big Data are RARE
• MSI conference hosted in Darden in 2014 identified senior management
adoption of marketing analytics as a major challenge.
• https://www.msi.org /conferences/marketing-resource-allocation-moving-from-analytics-to-
action/
• Duke CMO survey shows companies are however not using analytics, not
evaluating the quality of analytics etc.
• http://cmosurvey.org/files/2015/02/The_CMO_Survey-Highlights_and_Insights-Feb-
2015_Compressed.pdf
• 86% of respondents say their organizations have been at best only
somewhat effective at meeting the primary objective of their data and
analytics programs
• McKinsey Quarterly, November 2013

8. Research Objectives
• Can we identify organizational factors that enable a data analytics
orientation?
• What is the process adopted by firms that harness predictive
analytics, machine learning, and artificial intelligence in their
(marketing) strategy, i.e., a data analytics orientation?

9. Research sponsors
• Sample:
• Input from over 200 executives in a variety of
firms across retail, media, financial services,
industrial goods and technology.

10. Customer Centricity and Top Management Emphasis
“…I can get silos to work together and get
me data across organizational boundaries
if I talk about the customer experience,
and how going this extra mile could make
a difference in the customer’s life…”
Senior Vice President
Strategic Analysis
Financial Services Firm

11. “…We need to understand the end consumer needs
to develop better products for our customers. Text
data from salesperson reports and consumer blogs
have to be harnessed to understand and anticipate
end consumer needs…”
Chief Innovation Officer
Manufacturing Firm
Customer Centricity and Top Management Emphasis

12. 1. Cognitive Inertia
2. Uncertain Outcomes
3. Problem Complexity
Analytics Adoption Obstacles

13. I. Cognitive Inertia

14. “We are very good at using data analytics in
[product design] but have challenges in
using the same capabilities to develop our
brand story…. and brand is very
imperative for us in the going forward …”
- Chief Marketing Officer
Financial Services Industry
I. Cognitive Intertia

15. II. Uncertain Outcomes
• Market disruptions and new business ventures
generate data possibilities but also create a
need to
• revise KPMs
• develop capabilities to glean insights from new
kinds of consumer data

16. II. Uncertain Outcomes
“ We want to know where the customers will be ten years from now….we
have a c-level strategic priority setting process every year that is very fact
based and involves us taking a stance on the uncertain outcome, learning
from the past, and planning for the future….this then informs the
analytics, marketing, and other strategic functions across the enterprise….”
- Vice President, Branding
Global Entertainment Conglomerate

17. III. Problem Complexity

18. III. Problem Complexity
• Break complex problems into small parts
and know to combine data and heuristics
• Develop rapid prototypes, and then test,
and learn
• Synchronize decision cycles

19. 0.9 0.9
0.7
1.4
Short Term Long Term
Trade Advertising
III. Problem Complexity

20. “We want analytics to ask the
questions, not just fulfill requests
from [the line] managers…”
- Chief Executive Officer
Banking Industry

21. A Systems Approach to Analytics
Mental Model
Transformation
Field Tested Mental Model
Data +
Insights

22. Strategic Challenges and Metrics
• A data-driven process begins with the identification of an important
business problem or opportunity that could be addressed using big data
and analytics.
• Next step is to identify relevant strategic options (or choices) to address
the business challenge, and the key performance metrics (KPMs) that
would indicate the performance of each strategic option.
• Helpful Tools: Canvas business model, GUT matrix etc.

23. How Does AirBnb Make Money?
• Charge both hosts and guests.
• Guests pay 6-12% of the reservation
subtotal.
• Reservation fee % reduces with price.
• Hosts pay a 3% service fee.

24. Business Challenges – AirBnB
• Strategic Challenge
• How do we improve the rental prospects for our hosts & identify
better rental options for our guests?
• Strategic Options
• How do we leverage the user generated content on our website?
• Is there value in improving the pricing of properties?

25. Firm Belief Map – AirBnB
Consumer
Reactions
Strategic
Options
Market Factors
Key
Performance
Metric

26. Firm Mental Model – AirBnB
# of RentalsReview Sentiment
Price
Property Attributes
Review Rating
Minimum Stay
Property Profit
Gross Margin

27. Data
• What data is available to measure every part of the mental model?
• If exact measurement is not possible, what are the proxies?
• What type of data is it? Structured, unstructured?
• Is the data available for all the customers or only a sample?
• What is the reliability and consistency of this data?
• What are the firm’s values in working with Data?

28. Insights
• Analytics tools help managers understand and generate
insights regarding the behavior of customers and
competitors in their markets.
• The variety of tools include descriptive, predictive, and
prescriptive.

29. Insights (Analytics) - Airbnb

30. Insights (Analytics) - Airbnb
• What predicts if a customer saves a
property to the wish list?
What predicts number of reviews
obtained by a listing?

31. Firm Mental Model – AirBnB
Profit
Per
Property
Price
# of RentalsSecurity Deposit
Minimum Stay
Gross
Margin (%)

32. Insights Gained - AirBnB Properties

33. Transformation
• More than a technology issue, it is a leadership issue that calls for
organizational change.
• Drawing on the tools of design thinking, we can
• identify the necessary organizational changes to implement
data-driven process
• change the organization to make it happen – from building
capacity (e.g., bridging the skill gap) and setting up
appropriate governance to getting stakeholder buy-in
• change values and mindsets around big data and analytics.

34. “In the End it comes down to
What is Implementable”
- Chief Marketing Scientist
Media Industry

35. How to Build the System – The Enablers
• Knowledge
• Horizontal embeddedness
• Vertical consistency
• Merchandising

36. Avoid Perfection Pursuit
“Building a data analytics team
will involve a lot of resources
running into the millions….chances
of CEO approving this without any
proof of success is pretty slim….”
- Business Unit Head
Manufacturing Firm

37. A Systems Approach to Analytics
Beliefs Map
Transformatio
n
Test Updated Beliefs
Data +
Insights

38. 1. Cognitive Inertia
2. Uncertain Outcomes
3. Problem Complexity
Analytics Adoption Obstacles