Big Data In Industry –

Understanding And Organising

Information Around Your

Customers

Jason Nathan, dunnhumby,

Global Managing Director Capability: Data

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HOW TO CREATE SUSTAINED GROWTH

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The benefits of a global view

500 BILLION RETAIL SPEND

NEARLY 1 BILLION CUSTOMERS GLOBALLY

OVER 2,000 EXPERTS

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$500 million spent investing in innovation

Standard

Analytics

AppStore

Cutting edge research with top universities

Fostering an innovation culture

Share in our innovations

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Mar Apr May Jun Jul Aug

$ 6,000

Mar Apr May Jun Jul Aug

$ 470

$ 6,000 $ 470

Today we know Transaction and Customer Data

Tomorrow we can know all Retail Touch-points

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$

Favourites / Watches / Usuals

Transactional

Social Media

Life Events

Media Consumption

Flyer Distribution

Voucher s/coupons

Email Distribution (and open / click-through)

Social Media (and Click-through)

In Store Location

Retail “Clubs” Data

Participation in Invited Events

In Store Location

Clickstream

(Reviews)

Social Media

Website traffic (identified + unidentified)

In Store Location

Transaction Data

Click and Collect Data

Maps Location

Travel Time to Store

Store Maps Data

Device / App used to access eCommerce Store

Sizes (inc Car Park)

Website Page Load Times

Clickstream

Search Data (outside Retail domain)

Planogram Data

Store Maps Data

Product Attributes

Transactional/Customer

Add to Basket

Favourites / Watches / Usuals

Clickstream

Product Attributes

Price (inc Competitors)

Range Stocked (inc Competitors)

Promos (inc Competitors)

Markdown Data

Scan as You Shop

In Store Location (queue)

Clickstream

Payment Method

eWallet Transations

Banking Data

Subscription Payments Data

Instalments Payment Data

Substitutions

Click and Collect –

location/time/service

Media Streaming Data

Delivery Slots

Social Media

Media Streaming

List builders (Hiku, OutOfMilk)

Quantified Self (Fitbit)

Warranty

Best Before

Social Media

Reviews

Store Feedback Data

Referral Data

Transactions

Social Media

Returns

Customer Complaints

Helpline / Service Desk

A Shopping Trip model is the best way to

understand behaviour and the data generated

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ATTITUDES

• Price perception

• Promotions perception

• Emotional loyalty

• Preferred retailer

CATEGORY ENGAGEMENT

• Spend and visits in each category

• For this year and last year

BRAND ENGAGEMENT

• Loyalty (Shopping Habit)

• Level of involvement in each retailer service (e.g. financial services)

• Staff indicator

PROMOTIONS ENGAGEMENT

• Promotional purchasing segmentation

• Summarised promotion purchasing metrics

• Products / categories only bought on promotion

FORMAT AND CHANNEL

ENGAGEMENT

• Level of involvement in each format (e.g. hypermarket, supermarket, convenience) and channel (e.g. in-store, online)

CAMPAIGN ENGAGEMENT

• Level of redemption of coupons and other offers

• By campaign type

• Propensity to redeem scores

LIFESTYLE & NEEDS

• Price Sensitivity segmentation

• Lifestyle dimension scores (healthy, kids, fresh, big box, small box, etc)

ONLINE BEHAVIOUR

• Dotcom spend and visit segmentation

• Web browsing insight (use of website functionality such as search or favourites

• Breadth and depth of online shop

TRANSACTION BEHAVIOUR

• Spend, visits, basket size

• Spend per visit, items per vist

• This year and last year

TASTES & PREFERENCS

• Gluten Free, allergies, alcohol opt out, etc

• Food Clubs, Wine clubs

• Baby / Kids clubs

• Marketing preferences

DEMOGRAPHICS

• Age

• Income / affluence

• Profession

• Family composition / lifestage

• Neighbourhood segmentation

We can derive a huge amount of insight, scores

and segmentations from a rich, joined data asset

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Clickstream is a particular opportunity today

Site

content

details

Searching

and viewing

details

Customer stored

details and

session

details

Shopping

details

Includes “long

loop” and “short

loop” data too

What did you actually see.

What were the additional

options that you didn’t see

like content, prices, ranges,

sequencing, ordering etc.

Browsing,

searching, dwell

times, sequence,

clicks and mouse-

overs…

Information about a

transaction that we don’t get

in the T-log snapshot e.g.

Add to basket (and remove)

details including offers,

sequence, payment etc.

Beyond what is in the t-log

Time, point of entry, login

details, device ID, IP address,

cookies, captured

shopping list,

customer

preferences etc..

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We find different organisations are at different levels of maturity of their data assets

Ab

ilit

y t

o c

ap

ture

su

peri

or

va

lue

Capability level

LEVEL 1

LEVEL 2

LEVEL 3

LEVEL 4

LEVEL 5

Advanced

Data

Customer,

digital and

competitor

data managed

and used for

specific ends

Joined Data

Data joins

defined (for

customers,

products, etc.)

and data

managed to

allow complex

continual

decision

making and

CRM

Enriched Data

Data joined and

enriched

(segmentations,

scoring)

continuously;

enrichment used

for reporting,

analytics and

CRM activation

Data

Partnerships

Enriched data

used to drive a

self-sustaining

commercial

relationship

ecosystem

(which then

further enrich

the data for

best-in-class

decisions and

customer

outcomes) Basic Data

Sales/PMF

data captured

and used for

reporting

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COMMERCIAL:

What relationships can

create the data asset?

ATTRIBUTE:

What does the data

describe?

(product/customer)

ACCEPTIBILITY:

Is it right to use this data?

AVAILABILITY:

Is the data available? What

potential sources are there?

QUALITY:

How good is the data?

Which sources are best?

TECHNOLOGY:

How would the data be

stored?

GLOBAL:

What is the universality of

the data asset?

VALUE:

Which capability does the

data specifically enable?

Acquisition of data is expensive – we recommend a model for evaluating the investment

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Even where a data asset meets all the other dimensions – it may not be acceptable to use data in that way

Possible

Acceptable

Commoditisation

Legislation

Start Ups

Academia

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Context – 2 of 2

Partnering Harvesting Acquiring

Investing to buy

organisation / capability

Partnering with

organisations to augment

and enrich core assets.

Building Proprietary

Capability to harvest

Our approach reflects the change in landscape and opportunity

Deciding how to acquire data is the key decision facing many organisations today

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Our core assets today, enriched, provide a foundation for what we do

Owned, addressable

and identifiable

Addressable and

Identifiable

1 2 3 4

Customers who you

can have a direct and

full 2 way relationship

with

Customers you can

identify transactions

from, analyse

behaviours of and then

address

Addressable

Customers you can

address but maybe

only at device level?

Identifiable

Customers youcan

identify based on a

token (such as hashed

credit card) and can

analyse behaviours of

Owning the Customer Identifier is the current battleground – are the days of email over?

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FETCH Personal buying app sources lowest-priced garments

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ORIGINAL UNVERPACKT

Packaging-free supermarket opens in Germany

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MARK ONE & FUSEPROJECT

Smart cup automatically detects contents

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WALGREENS Pharmacy chain debuts talking prescription devices

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WALMART Retailer's price comparison service automatically reimburses shoppers

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LA POLAR Retailer lets customers pay with a fingerprint

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FRESHDIRECT Grocery etailer and crowdfunding site collaborate on food contest

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OPTIMEYES Outdoor ads feature facial recognition technology

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HARVARD PILGRIM HEALTH CARE

Insurance policy rewards healthy shopping choices with cash

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SHELFIE App rewards customers for tracking empty shelves

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NEO Smart food jar tracks weight and nutritional value of contents

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Store uses customers' face and hand capillaries to authorize transactions

100% GENUINE IMPORTED FOOD