Analysing Banking Data to Provide Relevant Offers to Customers

© Strands Inc. 2014

ANALYSING BANKING DATA TO PROVIDE RELEVANT OFFERS TO CUSTOMERS

Jim Shur, Chief Architect

Ivan Tarradellas, Product Manager Marc Torrens, CIO

Enric Plaza, Research Professor


• Strands, what do we do?

• Banking Industry: Innovation or Death

• Card-Linked Offers

• Financial Data Analysis

© Strands Inc. 2014 3

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


“Banks are losing their monopoly on banking.”– Francisco González, BBVA

THREADS IN BANKING

© Strands Inc. 2014 5© Strands Inc. 2014

“We are on the leading edge of a technology revolution in financial services.”– Anthony Jenkins, Barclays CEO

LEADER’S VIEWS


“They all want to eat our lunch.”– Jamie Dimon, JP Morgan Chase CEO

LEADER’S VIEWS


“(…) 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

THE OPPORTUNITY


Banks are faced with a great opportunity to move from being a mere provider of financial services to become a provider of solutions.

A NEW ROLE FOR BANKS


TECHNOLOGY VISION

Transforming Data into Knowledge to produce

Actionable Insights for innovative Financial Software

Transactional Data

BIG DATA MACHINE LEARNING USER EXPERIENCE

Knowledge Actionable Insights

> >


HELP!

CARD-LINKED OFFERS


HELP!

CLO

How do I find the

most relevant

offers?

How can I attract

new customers?

IsHow well do I know

my customers?

CARD-LINKED OFFERS


Strands CLO is a marketing tool that enables retailers to target highly relevant

deals to card-holders through digital banking channels

UNLEASH THE VALUE OF CUSTOMER DATA


CARD-HOLDER’S PERSPECTIVE

Accept Offer in Digital Banking

Buy at Merchant Pay with Bank Card Get Cash Back in Your Account

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

HOW IT WORKS


Hi, my name is Mario and I like wearing trendy clothes

Hi, my name is Sarah and have a shop selling trendy shoes

VIDEO


THE PROBLEM

Maximise the overall Performance for all offers

I want RELEVANT offers I have MARKETING strategies


THE PROBLEM

Maximise

I want I have MARKETING strategies


RETAILER VIEW


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)


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:

Win New Customers

Reward Loyal Customers

Increase Frequency

Increase Loyalty

Increase Spending

…

SMART MARKETING STRATEGIES

MERCHANTS

SELECT STRATEGY

Increase Loyalty

Win New Customers

REFINE (OPTIONAL) TARGETED AUDIENCE


THE PROBLEM

Maximise

I want RELEVANT offers I have


CARD-HOLDER VIEW


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)


MONTH TO PREDICT

LAST YEAR

2 YEARS OF DATA

Jan Feb Mar Apr May Jun Aug Sep Oct Nov DecJul



h✓(x)

Training Set

Hypothesis

Supervised Learning

Seasonality (12 Months) Customers (Millions) Categories (Hundreds)

L IKELIHOOD OF BUYING IN A CATEGORY


…tree1 tree2 treen

k1 k2 kn

voting

k

12 Months Month Transactions Month Amount

LIKELIHOOD OF BUYING IN A CATEGORY


THE PROBLEM

Maximise the overall Performance for all offers

I want RELEVANT offers I have MARKETING strategies


AR = campaign accomplishment ratio

USER-CENTRED CAMPAIGN SALIENCE indicates the relevance of that campaign for the customer.

combination of behavioural features +

demographics

TR = time ratio gone for a campaign

%

CAMPAIGN SALIENCE indicates the priority of a campaign for the system.

OVERALL SALIENCE


Comprehensive and interconnected set of solutions to leverage the value of customer data.

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THANK YOU!

Analysing Banking Data to Provide Relevant Offers to Customers

Engineering

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