Speaker: Astrid Wisse

1. How to turn data into
actionable insights?
7 November 2016

2. PwC Advisory
My mission is to transform
data into insights and
insights into actions in
order to solve important
problems
2
• KPN’s D&A team aims to generate
more impact for adequate decision
making within the organization
• KPN D&A analysts have done a good
job but the next step is to show even
more added value towards internal
stakeholders (e.g. Marketing, Sales,
Products)
• Our understanding of KPN’s
requirements is two-fold;
1. Find a partner who can help KPN
D&A moving forward on specific
themes and providing meaningful
insights towards its main internal
stakeholders. The theme to start
with is customer journey.
2. Obtain flexible support (project
wise or other) in areas like specific
deep analytical techniques, project
management, data management,
story telling, change management
and reporting and structurally
boost the skills of KPN’s D&A team
in these areas.
Astrid Wisse
Director Data Analytics
PwC

3. PwC Advisory
Data is everywhere, but….
Doesn't answer my question
What is this telling me?
…making it accessible and
understandable can be challenging.
These graphs are too complex
Mainly looking backwards

4. PwC Advisory
Many companies find it difficult to capitalize on data analytics…
Leading companies in R&C are investing in analytical
capabilities …
… however most of them struggle to capitalize on
these insights
• Trusting less on gut feeling, because of the
complexity of their environment and wrong business
decisions in the past
• Gathering and storing a huge amount of data every
day
• Need for using and combining multiple data sources:
e.g. sales and operational data
• Ambition to build internal expertise and teams for
analytics and BI consulting
• Not knowing where to start. The number and variety
of internal and external data sources is exploding
• Lack of good data management makes it extremely
hard to combine data from different data sources
• BI activities are spread across the organization leading to
many different models
• A large part of the current BI work is backward looking
bringing “nice to know” insights instead of forward
looking bringing actionable insight to anticipate on the
things to come

5. PwC Advisory
…but the opportunities to improve performance by using data analytics
remain significant in almost every part of the value chain
PwC Data
Analytics
Supply chain
• Spend analysis
• Stock optimization
Production
• Demand forecasting
• Overall equipment effectiveness
• Predictive maintenance
Customer
• Basket analysis
• Segmentation
Pricing
• Price promotions optimization
• Price elasticity analysis
Marketing & Sales
• Campaign effectiveness (ROI)
• Channel performance &
optimization
HR
• Workforce planning
• Workforce efficiency
Brand
• Brand loyalty
• Brand cannibalization
Finance / Management reporting
• Revenue forecasting
• Revenue leakage analysis

6. PwC Advisory
Our way of working is an iterative process to learn quickly from insights found
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1. Identify and
diagnose high
priority business
problems
5. Take action4. Learn
from new
insights
found
2. Hypothesis
3. Build & test
with real data
Iterate

7. PwC Advisory
Increase store performance
Case 1
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Step 1
Identify and diagnose high
priority business problems
8
• Issue: Sales behind
budget
• Business question:
How can we increase
store performance?

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Step 1: Analyze store performance to find root causes
Rephrased business question:
How can we increase the basked size for low performing stores?
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Store A Store B Store C Store D Store E Store F
Sales (€) 1,988,878 1,362,104 5,472,055 5,537,453 10,887,708 7,654,320
Sales area
(SqM)
3,150 2,020 3,313 3,451 5,210 3,520
Sales/SqM 631 674 1,652 1,606 2,090 2,175
Traffic/SqM 180 160 190 200 230 210
Conversion (%) 28.1% 28.0% 31.9% 36.2% 34.4% 33.5%
Basket Size (€) 112.50 120.40 167.10 170.45 183,25 185,65

10. PwC Advisory
Step 2
Formulate hypothesis
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• Issue: Sales behind
budget
• Business question:
How can we increase
the basked size for low
performing stores?
• Hypothesis: Low
performing stores are
less successful in cross
selling

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Step 3: Build and test with real data

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Step 4: Learn from new insights found: cross sell potential per store
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28% 23% 10% 8% 1% 0%
Store A Store B Store C Store D Store E Store F
Compared to
best in class

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Step 5: Take action
Store layout Staff utilization Staff selling skills

14. PwC Advisory
Find new locations
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Case 2

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Step 1
Identify and diagnose high
priority business problems
15
• Issue: We need to open new
fitness centers to generate
growth
• Business question: What
are the best locations and
how much additional revenue
can we expect from these
new locations?

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Step 2: Formulate hypothesis
Hypothesis:
There is a correlation between the revenue of a fitness center and the
catchment area of that fitness center (distance < x km)
16
Heerlen
x km

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Step 3: Build and test with real data
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Zwolle
Groningen
Rotterdam
Heerlen
Amsterdam
Eindhoven
Breda
Arnhem
Hengelo
We developed a model to estimate the revenues of current locations using different
catchment areas (in km)
10 20 30
Modelfit
Catchment area (km)
Model fit for different catchment
areas

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Step 4: Learn from new insights found
We developed a model to determine optimal new
fitness center locations
1. Plot existing locations (blue)
2. Plot all zip codes without a fitness center (orange)
3. Select zip codes with a potential revenue > x Euro
(catchment area of 9 km, taking into account other
fitness centers in that area)
4. Iteratively placing a store in the zip code with the
highest potential. Afterwards recalculate the
potential of the remaining zip codes)
Existing Locations All Zipcodes
Potential revenue > xOptimal locations
1 2
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Step 5: Make it actionable
Optimal new location
Location for rent

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How to be successful in data analytics?
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Summary

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How to be successful in data analytics?
1. Start small
2. Involve all expertise needed
3. Define and agree on the business question
4. Work in sprints of 2 or 3 weeks
5. Do not deliver a number of graphs but make it actionable
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22. PwC Advisory
Thank you very much!
© 2016 PwC. All rights reserved. Not for further distribution without the permission of PwC. “PwC” refers to the network of member firms of
PricewaterhouseCoopers International Limited (PwCIL), or, as the context requires, individual member firms of the PwC network. Each
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to clients. PwCIL is not responsible or liable for the acts or omissions of any of its member firms nor can it control the exercise of their
professional judgment or bind them in any way. No member firm is responsible or liable for the acts or omissions of any other member firm
nor can it control the exercise of another member firm’s professional judgment or bind another member firm or PwCIL in any way.
Astrid Wisse
Director | Data Analytics
PwC Netherlands
M: +31 6 20 84 10 29
astrid.wisse@nl.pwc.com