big data and retail 2016.ppt - university of stirling - big d… · big data and retail professor...

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www.stirlingretail.com

Big Data and Retail

Professor Leigh Sparks,

Institute for Retail Studies,

University of Stirling


Structure

• What do retailers do?

• How is this changing?

• “Big Data”

• What are the retailer

problems?

• Beyond retail problems


What do retailers do?

• Sell stuff (often single item) to

the final consumer

• Mainly through the notion of the

shop

• The shop is not a static concept

• Retailers are consumer not

production oriented

• How do we get consumers to

keep patronising our business

over other businesses?


How is this Changing?

• Retail is Big Business

– WalMart, $482 bn sales

(2015)

– 7-eleven, 57K stores

worldwide

– Inditex, 6.8K stores in

c90 countries

– Tesco, 3.5K stores in the

UK

– Amazon, $89bn

ecommerce sales (2014)

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• Retail is omni-channel

business

– Amazon

– Asos – now global brand

– Tesco, £5bn e-commerce

business

– Retail sales now 14%

online, predictions are

20% by 2020



• Consumers in control

– Multi-channel, multi-

access

– Always on and social

media

– More volatile and less

loyal

– Discerning and

questioning

– Patterns of behaviour

have changed


Marks and Spencer

• Retail Week Consumer

Experience Conference,

October 2014

– 100m store visits to M&S

per week; 250m website

visits per week

– 52% of women’s clothing

searches done on a

mobile device



• Differences

– Types of data

– Patterns

– Tracks

– Views

– Interactions (P2P)

– Capabilities

• Volume, Velocity and

Variety

• Data “in motion”/”at rest”

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Big Data as Panacea?

• Retailers always sought

information and data

• But not all have

understood why they

need this …

• … or now the range of

data that might be

available or needed

• Data as a cost not an

investment


What are the Retailers Problems?

• What

– Prices

– Promotions

– Locations

• In what

– Context(s)

– Channel(s)

• Addressed to what

segment or target or

individual


What Retailers Most Need

• Predictive consumption

• Effectiveness of

promotions

• Target pricing precisely

• Understanding the

value of the network

• In store customer

activity


So Where does Big Data fit in?

• Increased speed and agility

– Using predictive analysis

– Supporting faster decisions

– Real time marketing

• Projects

– Optimizing delivery of

messages to shoppers

– Mining for shopper insights

– Demand and assortment

planning

• Personalization/more

shopper solutions

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Big Data Issues

• Sources

– Social media

– Website

– Item level sales

– Transaction data

(personalised)

– Mobile devices


Big Data Issues

• Why?

– Dialogue (or

communication)

– Rapid reaction launches

– Effect measurement

– Performance – “store”,

supply chain, inventory


Big Data Big Issue

• Privacy

• Acceptability

• Brand and trust

implications and

consequences?


Big Problem? Personalisation

• Personalisation is a goal

• But is it acceptable – or

more accurately when is

it acceptable?

• When is personalisation

too personal?

• The “Uncanny Valley”

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Are We Upside Down?

• Retailer focus is only one

side of the story

• Consumers have changed

also

• Sugar: we discuss “old style”

remedies alone – info and

tax

• Make consumer lives easier

• How do consumers achieve

goals?www.stirlingretail.com

EPSRC Neo-Demographics Project


EPSRC Neo-Demographics Project

• Aims

– Address systemic failure

of UK industry in

entering emerging

markets

• Identify, acquire and

analyse behavioural data

• Surrogate market

intelligence and novel

data mash-ups

• New business models

• Outputs

– Integrate big data streams

in a privacy preserving

fashion

– Apply novel algorithmic

approaches to behavioural

information fabric

– Use covariate and crowd

sourced data to test

computational behavioural

groups


Whose Data is it Anyhow?

• Big Data Retail

• Engagements

• Loyalty cards

• From Cards to Apps

• Rewards, Nudges,

Reinforcement, Peer

Groups, Games etc etc

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Beyond Retail

• Tesco Employees

• Pre-diabetes (so GP

records?)

• Purchase

records/loyalty points

• Nutritional content

labelling for every

product


A Finnish Example

Source: Saarijarvi et al (2016) Unlocking the transformative potential of customer data in retailing. International Review of Retail, Distribution and Consumer Research


Mi-Connex Perth

• Place Based Solutions

– Gift cards

– Till intercepts

– Independent retailers

• Place Questions

– Who, what, where

– Incentivise e.g. parking

• Retailer Questions

– Who, what, where

– Spend Uplifts


ESRC Consumer Data Research

Centre

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So…

• Retailers have data

• Context data becoming

available

• Linking/understanding/acti

on

• Solving retailer problems

• Beyond retailing – retailers

part of the solution and not

the problem


Web: www.stirlingretail.com

Email: [email protected]

Telephone: 01786 467384

Twitter: sparks_stirling

Contact Points

big data and retail 2016.ppt - university of stirling - big d… · big data and retail professor...

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