My presentation from FARCON http://sched.co/7wPK

2. Listening to the voice of the customer
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1995 1996 1997 1998 1999 2000 2001 2002
Sales Forecast in 1999
Traditional Cameras Digital Cameras
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What Really Happened
Traditional Cameras Digital Cameras

3. The Challenges of Limited Data
Person 1 Person 2
Born 1948 Born 1948
Grew up in England Grew up in England
Married Twice Married Twice
Two Children Two Children
Successful in Business Successful in Business
Wealthy Wealthy
Spend Winter Holidays in the Alps Spend Winter Holidays in the Alps
Likes Dogs Likes Dogs
Like Music Like Music

4. Retail has a multitude of
devices that generate
petabytes of potential
insights
Monitoring and mining
social media data enables
retailers to enhance
customer insights
Open data sources and
internal sources enable
retailers to better
understand customers
Democratization of data

6. Cortana Intelligence
Information
Management
Big Data Stores Machine
Learning &
Analytics
Visualization
Democratization of Tools

8. Furnishings importer delighted customers with
the right offers
Challenge
• Give customers a better
experience and
selection.
• Understand what
customers are looking
for based on online
search.
Strategy
• Combined online and
in-store transactional
and behavioral data
to predict what
products customers
would be most likely
to purchase next.
Results
• Provided more personalized
choices to customers.
• Enabled targeted campaigns.
• Improved inventory management.
“We are continually getting better at identifying what our customer wants, using
Microsoft Azure Machine Learning and resulting data insights.”
— Sharon Leite, EVP Sales and Customer Experience, Pier 1 Imports

9. 10

10. Foodservice retailer built brand loyalty with a
cloud-based mobile app
Challenge
• Improve relationships
with customers.
• Build upon a successful
brand app to reach
more customers.
• Generate upsales and
cross-sales.
Strategy
• Added an analysis and
insights system based
on Microsoft Azure.
• Gathered data on
customers’ behaviors
and preferences
through their app use.
Results
• Expanded app use to millions of
customers in five countries.
• Enabled personal, relevant,
contextual content that adds
value for the users.
• Improved customer loyalty.

13. Random Selection
Offers given out randomly, but you can see each segment prefers a different offer

14. Pre-learned ML Model Activated
ML gives the right segment the right offer to maximise redemptions

15. Impressions
The ML system gave Cheeseburgers to Students and Sundaes to the Employed segments

16. Adaptive MWT
MWT adapts to the new condition it hasn’t seen before. Everyone gets Sundaes, sales are back up!

17. Back to Cloudy - Redemptions Summary
MWT can manage both cloudy and sunny conditions, sales lift is maintained

18. Impressions Summary
ML only knows how to change based on segment, MWT changes with a new variable

33. Big data boosts business for
beverage company
“Over time, this advanced analytics
solution with its statistical approach to
big data will transform the way we take
business decisions through all business
processes”
- Ruben Dario Torres Martinez, Arca Continental, IT Manager
Solution
• Provide executive decision makers
with an understanding of what
drives sales of their beverage
products.
• Understand influencers & answer
“Why did/do we sell?”
• Explain sales variance so that
decision makers know exactly
what caused the variance and
what needs to be optimized
• Analyze SKU profitability and
recommend which SKU’s should
be discontinued
Results
• improved way to measure the
success of marketing efforts.
• can easily view a graph of the
market DNA, which explains how
each different market responds to
each variable.
• revenue growth managers can
now slice and dice the effects of
multiple variables, practically in
real time

34. Key Driver Analysis
• It's important to identify and understand the drivers of key business
outcomes
• Which aspects of your service influence how likely a customer will be
to recommend you to others.
• A key driver analysis investigates the relationships between potential
drivers and customer behavior
• A key driver analysis is often performed using multiple linear
regression to model the primary outcome as a linear combination of
the potential drivers.

35. http://bit.ly/1UoKxGv

36. 37
Sales for Northwest
Territory retailer
Problem: Seattle is
not selling to
forecast
1
2

37. 38
When we drill
down to
Seattle, we can
see a problem
in soft drinks
Click and see
further details of
Seattle sales
1
2

38. 39
Sales driver analysis
– build a model that explains what
drives sales
Sales delta analysis
– use the model to see problems
3. How can we fix sales?
– apply the model to fix the problems
21 3

39. 40
25.6% variations
explained
Internal transaction
and marketing data
include variables as:
- Stock Up
- Price Elasticity
- Radio Advertising
- TV Advertising
- SKU presence
Transaction dataset
in AML experiment
1
2
3

40. 41
Variations explained
improves to near
50%
External weather,
demographic, and
competitor data
include variables as:
- Temperature
- Precipitation
- Household size
- Annual Income
- Competitor Price
Gap
Transaction dataset
in AML experiment
External dataset
enters the model
in AML experiment
2
1
3
4

41. 42
IoT dataset enters
the model in AML
experiment
Variations explained
improves to 89%
New IoT, research and
online activity data
include variables as:
- Survey research
- Web traffic
- Social media
traffic
- Mobile traffic
- Store traffic
- Shelf traffic
Transaction dataset
in AML experiment
External dataset
enters the model
in AML experiment
2
4
1
3
5

42. 43
Monthly ∆ by sales
driver
Let’s first zero in on
the sales impact of
price gaps, as they
are the biggest
problem
Competitor price
gap caused 7,598
less units sold than
previous month
Click one of the
controllable variables
to see what would
happen if we take
some actions
21
3
4

43. 44
See the impact on
physical sales if
we reduce the
price gap by
different levels
See the impact on
profit if we reduce
the price gap by
different levels.
When it is
reduced by 15%,
we would be able
to achieve 4.5K
incremental profit.
Select
competitor
price gap as it is
a controllable
variable
It would be
recommended to
decrease the
competitor price
gap
2
1
3
4

44. 45
See the impact on
physical sales if we
increase social
media engagement
by different levels
See the impact on
profit if we increase
social media
engagement by
different levels.
When it is
increased by 20%,
we would be able
to achieve 12.6K
incremental profit.
Select Social Media
Engagement as it is a
controllable variable
It would be
recommended to
increase social
media engagement
2
1
3
4

45. 46
See the impact on
physical sales if we
increase
advertising by
different levels
See the impact on
profit if we increase
advertising by
different levels.
When it is
increased by 20%,
we would be able
to achieve 7.1K
incremental profit.
Select Own Brand
Advertising as it is a
controllable variable
It would be
recommended to
increase our own
brand advertising
2
1
3
4

46. 47
See the overall impact
on physical sales if we
take the recommended
actions
The sales will
continue sliding
down if no actions
are taken
1
3
Within the budget
constraints, select the
recommended
actions
2