This document discusses how Strands, a provider of personalization and recommendation solutions, works with financial institutions to analyze banking data and provide relevant offers to customers. Strands uses machine learning on vast transactional and customer data to understand customers' purchasing behaviors and interests in order to target them with personalized, salient offers from merchants. This helps banks strengthen customer relationships and merchants increase sales through more effective marketing strategies and campaigns.

1. ANALYSING BANKING DATA TO PROVIDE
RELEVANT OFFERS TO CUSTOMERS
Jim Shur, Chief Architect
Ivan Tarradellas, Product Manager
Marc Torrens, CIO
© Strands Inc. 2014
Enric Plaza, Research Professor

2. WHAT DO WE DO?
Strands is a global provider of personalisation and recommendation solutions to innovate in two
sectors: financial institutions and online retailers.
Strands Finance Strands Retail
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3. THREADS IN BANKING
“Banks are losing their monopoly on banking.”
– Francisco González, BBVA
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4. LEADER’S VIEWS
“They all want to eat our lunch.”
– Jamie Dimon, JP Morgan Chase CEO
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5. THE OPPORTUNITY
“(…) the good news is that we still have one significant advantage, which is
the vast array of financial and non-financial data that we accumulate.”
Francisco González, BBVA CEO
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6. A NEW ROLE FOR BANKS
Banks are faced with a great opportunity to move from being a mere
provider of financial services to become a provider of solutions.
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7. TECHNOLOGY VISION
BIG DATA MACHINE LEARNING USER EXPERIENCE
Transactional Data
> >
Knowledge Actionable Insights
Transforming Data into Knowledge to produce
Actionable Insights for innovative Financial Software
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8. TURNING DATA INTO BUSINESS
CUSTOMERS AND
MERCHANTS
VAST HISTORICAL
DATA AVAILABLE
NEW APPLICATIONS
IN CONSUMERS’
DIGITAL LIFE
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9. How do I find the
most relevant
offers?
HELP!
CARD-LINKED OFFERS
CLO
How can I attract
new customers?
How well do I know
Is
my customers?
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10. HOW IT WORKS
CARD-HOLDER’S PERSPECTIVE
Accept Offer in
Digital Banking
Buy at Merchant Pay with Bank Card Get Cash Back in
Your Account
RETAILER’S PERSPECTIVE
Get Charged from
Bank
Upload Offer in
Digital Banking
Monitor Offer
Campaign
Sell
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11. Hi, my name is Mario
and I like wearing
trendy clothes
Hi, my name is Sarah
and have a shop
selling trendy shoes
VIDEO
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12. THE PROBLEM
I want RELEVANT offers I have MARKETING strategies
Maximise the overall Performance for all offers
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13. THE PROBLEM
I want I have MARKETING strategies
Maximise
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14. RETAILER VIEW
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15. CAMPAIGN AUDIENCE FILTERS
• A CAMPAIGN is defined by an OFFER and an AUDIENCE
• An AUDIENCE defines a group of consumers by a set of filters:
• Demographic filters (e.g. 20-30 years, married, in Barcelona)
• Behavioural filters
• Behavioural filters are based on Commercial Interests
• Loyalty: how loyal is the customer to the merchant (loyal, shared, competitor)
• Frequency: how frequent is the customer buying to the merchant (low, med, high)
• Purchase Segmentation: the buying segment of the customer (low, med, high)
• Location: where is the customer buying (in, out)
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16. MARKETING STRATEGIES
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17. MARKETING STRATEGIES
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18. MARKETING STRATEGIES
SMART MARKETING STRATEGIES are an easy way to build audiences for different marketing
goals, so merchants do not need to be expert marketeers and use complex filtering:
SELECT STRATEGY
Increase Loyalty
Increase Spending
Increase Frequency
Win New Customers
Reward Loyal Customers
…
SMART
MARKETING
STRATEGIES
MERCHANTS
REFINE (OPTIONAL) TARGETED AUDIENCE
Increase Loyalty
Win New Customers
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19. THE PROBLEM
I want RELEVANT offers I have
Maximise
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20. CARD-HOLDER VIEW
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21. USER CENTERED CAMPAIGN SALIENCE
• The degree of interest of a campaign to a specific user:
• Likelihood of buying in the category of the campaign
• Proximity of the user to the merchant of the campaign
• Activity of the user with the merchant that is making the campaign
• Loyalty of the user to the merchant of the campaign
• Merchant Fitness of the user considering the median of the merchant’s selling prices
Likelihood (LK)
Proximity (PX)
Activity (ACT)
Loyalty (LY)
Merchant Fitness (MF)
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22. LIKELIHOOD OF BUYING IN A CATEGORY
Supervised Learning
Seasonality (12 Months) Customers (Millions) Categories (Hundreds)
2 YEARS OF DATA
LAST YEAR
MONTH TO PREDICT
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Training Set
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
h✓(x)
Hypothesis
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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23. LIKELIHOOD OF BUYING IN A CATEGORY
12 Months Month Transactions Month Amount
tree1 tree2 … treen
k1 k2 kn
voting
k
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24. THE PROBLEM
I want I have
Maximise the overall Performance for all offers
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25. OVERALL SALIENCE
CAMPAIGN SALIENCE indicates the priority of a campaign for the system.
AR = campaign
accomplishment ratio
TR = time ratio gone
for a campaign
USER-CENTRED CAMPAIGN SALIENCE indicates the relevance of that campaign for the customer.
combination of
behavioural features +
demographics
%
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26. © Strands Inc. 2014
Comprehensive and interconnected set of solutions
to leverage the value of customer data.
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THANK YOU!