Maya has 5 key uses Banks / FI
Focused on cards & payment
Customer
acquisition
Customer
Engagement
Merchant
onboarding
Under development
Personal Financial
Management
Wealth
Management
Maya has 5 key uses Banks / FI
Focused on cards & payment
Customer
acquisition
Customer
Engagement
Merchant
onboarding
Under development
Personal Financial
Management
Wealth
Management
Maya can be used to drive better customer acquisition
Customer Acquisition
 MAYA generates taste fabric based on enterprise and social data
 Identifies the customers having high affinity towards a promotion/offer for
a campaign
 Generates the interests of these customers
 Enhances acquisition strategies by identifying lookalike customers through
channels like facebook and media delivery platforms (custom audience &
targeting based on interests)
Maya uses the personal taste fabric of existing good customers to
enhance customer profiling of prospects
Behaviour
Profiling
Taste + Behavior
based profile
Enhanced customer profile
given to Bank which in turn
is given to the media
agency/ Facebook for
better targeting
Taste
Profiling
Traverse between "transactions"
from internal data and
"preferences“ from external data
Understand existing
customers as a whole
These enhanced profiles are used to create look-alike segment
acquisition plans on various digital channels
Look alike customer
targeting
Campaign Brief
Identify high affinity
customers for
the campaign
Identify their
interest segments

Facebook Custom
Audience

Facebook Interest
Targeting
Target Segments:

Google Ad words

Fast-food

….

Travelers
Offers/Promotions:

10% off on KFC

15% cashback on
travel bookings
Transactions
+
Taste Graph
Illustration: Facebook campaigns targeting lookalike audiences
Use CRM
contact details
1
2
Combine transaction
data and taste graph to
generate
personalized choices
4a
Create custom
audience on facebook
 Upload contact details of
high affinity customers
(email id / mobile #)
 Select demography (age,
location, sex etc.)
 # customers to target
3
Based on the
promotions for the
campaign, select the
high affinity customers
and identify their
interest segments
Create a campaign
using facebook power
editor
 Select objective
 Select budget
 Select buying type
Likes comedy movies, KFC,
NIKE, Manchester United
4b
Facebook can target
audience based on
interests
 Select demography (age,
location, sex etc.)
 Input interests
Cannot use CRM
contact details
(Ex: fast food, Burger King,
Popular comedy movies,
Football fan pages etc.)
5
Campaign Execution
Based on custom
audience, demography
filter and selected
interests, facebook
identifies lookalike
audiences through its
users’ activity (likes, posts,
shares, pages etc) on its
platform, and targets them
for the selected campaign.
Maya has 5 key uses Banks / FI
Focused on cards & payment
Customer
acquisition
Customer
Engagement
Merchant
onboarding
Under development
Personal Financial
Management
Wealth
Management
3 use cases to drive customer engagement
3
2
1
Customer
activation
Spend increase
When the customer is new to
bank or has stopped using
the card
When the card member
spends only in one category
or when the bank has sparse
data for card member
 Cross-category
 In-category
Drive crossborder spends
When the customer is
travelling to other countries –
where the bank does not
know merchants
How do you activate ID08375 who is new to a Bank?
Internal Data + Maya
Internal Data Only
Time on Books
H
The bank is spamming me with lots of deals on
movies, iphones, restaurants, vacations,..., and
I no longer bother to look at it
Meet ID08375
Personal Data
Age: 26, Male
Resident of Locality A,
Barcelona
Customer since: Oct 2013
Social Data not available
Bank’s Transaction
Data
 4 transactions in
Jan – Pizza
Delivery, Rail ticket
booking, Telephone
bill, electricity bill
 Low monthly spend
– €28
The bank quickly
figured out my
preference for food,
and started sending
me a range of offers
at restaurants that I
truly love"
User Pizza place as
starting point and
map it to external
data taste graph to
know more about
tastes of people
like ID08375
”Wow, they seem
to have known me
better, and they
send me a range of
interesting offers"
Choices for ID08375






Tim Hortons
Carbone
Mondrian
Daikaya
La Barbecue
Nico Osteria
Bank offer
External
offer
Only info
Enhancing the internal data with external, tastes and affinities
data ; helps solve the cold start problem
Less Transaction data available; hence mass mailers
are sent to the customer
L
Data
Differentiate bank’s cards proposition in London by delivering
personalized choices to their customers.
David Miller
Age: 34, Male
Residence: London
Customer since: May 2014
Social Data not available
Bank’s Transaction Data
 7 of 10 recent transactions were dining
 Restaurants visited : Bistro Union, Grain store, Jar
Kitchen, Honey & Co in London
 Average spend on dining: USD 21
 Total spend in Jan: USD 300
 Internal Data on David’s
spend on dining
 Existing bank segmentation
does not deliver
personalized choices to
customer
Taste Graph & Choice Engine
Exciting Dining offers for You!
Bank
Dear David,
Wishing you a Merry Christmas & a Happy New Year!
Festive season is around and its time to take your loved
ones for a family dinner
Click the below link to know about exciting deals and
options on dining
Click Here
Taste Graph
Taste graph uses transaction( & social
data, if available) to identify most
relevant cross category choices
Offer Cross-border choices for bank’s customer travelling abroad
Scenario: David goes to Singapore
Bank’s Transaction Data
Internal Data Only
Mr. David Miller
Age: 34, Male
Residence: London
Customer since: May 2014
Social Data not available
What are some good
restaurants around
my hotel?
Where should I go for
shopping?



 7 out of 10 transaction on dining
 Restaurants visited : Bistro Union,
Grain store, Jar Kitchen, Honey & Co
in London
Merchants visited: IKEA, Old Navy
Average spend on dining: USD 21
Total spend in Jan: USD 300
Internal + External Data
Dining
Hotels
Entertainment
 Internal Data on David’s
spends in Singapore
does not exist
 Bank does not have a
complete view on all the
merchants in Singapore
 Limited local tie-ups /
offers
Information only, no
redemption
 David’s taste for Food
 Avg. spend
 Restaurant reviews
Serendipitous discovery , using multihop on the graph identify hotel choices
Any entertainment &
recreational parks in and
around city that I should
go to?
Highly personalized choices in
Singapore, for David, based on
taste graph
Maya has 5 key uses Banks / FI
Focused on cards & payment
Customer
acquisition
Customer
Engagement
Merchant
onboarding
Under development
Personal Financial
Management
Wealth
Management
Optimize Merchant Network: Use affinities to enhance merchant
network performance for banks
Identify partnerships which match best
potential guest’s interests…
Identify more opportunities from existing
merchants that match customer tastes best
High
High
Incentivize
Keep
Niche partner
Develop Partnership
Not useful
Critically evaluate
Affinity
Affinity 1
the offer
Kill the offer
Consolidate the
offer
Low
Low
Low
Merchant Value 2
High
Low
1 Affinity
Transactions
– Alignment of the existing merchant with customer’s choice , Merchant Value = ƒx (Spends through Merchant, Customer Response, Commissions,
interchange income, seasonality Index )
2 A qualitative score based on customer segment
& market coverage that the merchant is able to provide
High
Maya has 5 key uses Banks / FI
Focused on cards & payment
Customer
acquisition
Customer
Engagement
Merchant
onboarding
Under development
Personal Financial
Management
Wealth
Management
Crayon’s PFM tool is built on four key principles
1
Ability to set multiple goals (savings targets) with varying priorities. Month end saving is
automatically allocated to all savings goals
2
Automatic categorization of all inflows and outflows in to meaningful income and
expense buckets
3
Month end balance prediction which changes with every in flow and outflow
4
Prescriptive messaging to coach user towards an ideal behavior which keeps him on
track against his goals. Customizable alerts for when a transaction is likely to breach
savings goals
1. Add multiple savings goals with varying priorities
For New User
Money left to play with € 1,100
Get started by setting your first
goal
Money left to play with
You are likely to save € 1,100 this month
How much would you
like to save ?
Saving Goal
My Goals
Set your saving Goals
Add Saving Goal or
track the progress of
existing goals
My Savings
How you are tracking on all your
goals
Trip to Italy
Duration
6 months
High
Your total savings till date is € 35,100
Save
Days Left
125
€1000/50
00
Ford Mustang
€150
0
Cancel
Money left to play with € 1,100
College Fee
Amount
Priority
For Existing User
Days Left
447
€9000/200
00
Rolex Watch
Days Left
18
€380/43
5
€25,435
€10,380
Total Goal Amount
Saved so far
Add
User can add new
goals which redirect
him/her to Add
saving goal screen
2. Automatic Categorization of Expense and Income
Category and
sub-category
classifications
Eg., selected
category
Home>details
Combination of
auto-classified
and user-defined
categories
Money left to play with € 1,100
May 2015
Overview
May 2015
Income
€ 2,800
Expenditure
€ 1,100
May 2015
Home
€ 870
€ 2,800
€ 719.83
Bills
D M Q Y
Likely
Home/rent
€ 300
Home/Utilities
€ 270.56
Electricity
3-4-15,
€ 500
€ 2,000
€ 69.94
05/04/2015
Set Budget
Flexible category
hierarchy to suit
goalsetting
and social
comparisons
Home expense details
Expenditure
€ 6000
Money left
to play
with
Money left to play with € 1,100
Gas/Heating
Rent
Food & drinks
ATM withdrawal
Add
Shopping
Fuel
Policy payments
Set Budget
Details
Screens are illustrative. Reflect type of data used.
€ 35
05/04/2015
Internet
€ 30
05/04/2015
Add
Set Budget
Overview
3. Month-end balance prediction
Balance
prediction on
overall categories
as well as
individual items
Money left to play with € 1,100
Bills & Payments
Money left to play with € 1,100
May 2015
Total Bills
Mapped against
set goals as well
as benchmarking
expenses
May 2015
Overall
€ 2000
€ 719.83
Home
Payments details
Smart mapping
based on
consumption rate
and trends
Set Budget
Electricity bill
€ 123.16
Upcoming
€ 40
Internet bill
Average amount
based on
historical data
Groceries
€ 150
Utilities
€ 200
Home
Improvemen
t
Upcoming
Credit card
Paid payment
€156.67
10-05-2015
Rent
€ 300
Paid
Paid
€ 100
01-05-2015
Add
Lifestyle
€ 1500Food &
€ 100
dinning
Goal Amount
€ 200
Shopping
01-05-2015
Insurance policy fee
€ 200
Bills & Utilities
Mobile
Phone
Add
€ 50
Expense Overview
4. Personalization and timing of interventions
Money left to play with € 1,100
Money left to play with € 1,100
How much would you
like to save ?
Saving Goal
College Fee
May 2015
Adjust your Budget
Additional
savings
required
€ 500
€ 1600
+400
Amount
MoneyMaster
€ 1500
Duration
Priority
6 months
Overall
Budget
-400
Cash
ATM
withdrawal
€ 700
Saved € 300
Home
High
Groceries
€ 150
Utilities
€ 200
Savings Required
€ 250/monthly
€ 62.5/weekly
The intelligence behind
managing user’s finance
and suggesting next
course of actions to
achieve the target
€ 8.5/daily
Current monthly saving rate is lower
than required
Home
Improvement
€ 100
Saved € 100
Lifestyle
Food &
Drinks
Adjust your Budget
Add
Shopping
€ 100
Expense Overview
Ex-User cut down the
expenses from ATM
withdrawal and Home
improvement to
accommodate the
additional savings
required to achieve the
new goal i.e. College fees
Maya has 5 key uses Banks / FI
Focused on cards & payment
Customer
acquisition
Customer
Engagement
Merchant
onboarding
Under development
Personal Financial
Management
Wealth
Management
Summary
1
The current traditional wealth model is broken resulting in up to 80% wastage across the
industry.
2
This can be fixed only when advisors throw away their current toolset and truly solve the
problem of choice for their clients.
3
At Crayon, our solutions are based on deep & massive algorithmic analysis of observed
behavior - NOT stated preferences of clients.
4
By combining our algorithmic insights with banks’ internal data, Crayon transforms how
advisors discover their clients and deliver choice.
5
Helping deliver superior and sustained outcomes for the client, advisor and the firm
Case Profile : A Client Advisor
 Works at a leading wealth
firm
 Has a book of 250 clients
assigned to him
Meet John
 Has been given a roster of over
100 products to select from to
construct his clients’ portfolios
 A mix of low-risk and high-risk
products
 A subset of 20 high-risk products carry the
highest margins.
 His clients are all busy people,
traveling all the time.
 Finding them, let alone getting
quality time with them is a real
challenge.
 John gets only a few minutes with his clients for
pitching in new investment idea
It is a tough life as a client advisor
and often stressful.
Fact: Advisors waste 80% of opportunities to engage client assets
Focus on 20% of clients and
20% of products
Advisors focus on “top 20%” clients who buy investments
Skew towards higher-margin products, 20% of available range
Constant churn- 40% clients likely to move in next 6 months
Advisor doesn’t deliver
actionable choices to client
Investment research high level, user-unfriendly, often post-facto
Choices presented are product-centric, not client-centric
Poor efficacy of last gen predictive analytics based on sparse data
Last century toolset used
for client discovery
Goal identification & Risk profiling tools are of limited value
Stated preferences often do not reflect actual behavior
Discovery process is too mechanistic
Case Profile : Mary, A Wealth Client
Meet Mary
 Young successful entrepreneur with a busy travel
schedule
 Maintains a wealth management account with a
leading firm for over a year.
 An amount of US$500k from the sale of her
previous digital venture remains in low-yielding
deposits
 Soon after the account was
opened, her client advisor John
called to briefly talk about her
goals and preferences
 However, subsequent choices of structured
products and dual currency investment presented
by John did not interest her.
 She has not received any calls in
recent months from John.
 She receives standard researchtype emails from the firm however
finds them mostly irrelevant and
often post-facto.
Mary sees many investment choices
in the market however
has no time to research them
Fact: Many investors like Mary face the unhappy problem of choice
Risk tolerance, control
orientation (Do-it-for-me,
Do-it-with-me, Do-it-myself),
composure, values and
beliefs
Advice from Client
Advisor, Friends, TV,
Internet
CONTEXT
Schedule, Buying
mode, life events,
news
PREFERENCE
WEALTH CLIENT
ADVICE
BEHAVIOUR
Previous investing outcomes,
biases, herd instinct,
rationality, digital native
Financial Choice =  (Preference, Advice, Context, Behaviour)
Maya for Wealth Solves the Problem of Choice
Combine internal client data and external market big data to provide intelligent guided choices to client
advisors and self-directed investors in relation to the growth, protection and preservation of wealth
 For client advisor, guided choice of next action for client management: alert, advise, activate, recommend, sell, ask for
referral, defend
 For self-directed investors, guided choice for next action: buy, add, hold, reduce, sell, leverage, hedge, protect
Apply proprietary algorithms on Google-scale massive external market data and
 Generate affinity-based connections between market movements, marketplace investor actions, and investments (asset classes,
individual securities/commodities/currencies)
 Analyse individual client holdings and generate affinity-based choices of next action for individual securities/ commodities/
currencies held by client
Deliver these to client advisor through their CRM and investor through their app
 Every morning
 As and when significant market events occur
 Based on client preferences