Professor$Leigh$Sparks,$ Ins9tute$for$Retail$Studies,$ …quarter.cs.stir.ac.uk/courses/ITNPBD4/lectures2015/13... · 2015-11-09 · Morrisons in 20, coming in second and third respectively

www.stirlingretail.com

Big Data and Retail

Professor Leigh Sparks, Ins9tute for Retail Studies,

University of S9rling


Structure

•  What do retailers do? •  How is this changing? •  “Big Data” as panacea •  Data •  What are the retailer problems?

•  Big data/retailer fit and issues

•  Beyond retail problems


What do retailers do?

•  Sell stuff (oAen single item) to the final consumer

•  Mainly through the noDon of the shop

•  The shop is not a staDc concept •  Retailers are consumer not

producDon 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)



•  Retail is omni-‐channel business –  Amazon –  Asos – now global brand –  Tesco, £5bn e-‐commerce business

–  Retail sales now 12% online, predicDons are possibly 20% by 2020



•  Consumers in control – MulD-‐channel, mulD-‐access

–  Always on and social media

– More volaDle and less loyal

–  Discerning and quesDoning

–  Paderns of behaviour have changed

The experiment consisted of 10 UK retailers, including Tesco, Sainsbury’s, ASDA, M&S, John Lewis, Co-op, Argos, B&Q and Homebase, and was carried out by four Veeqo team members. The Veeqo team asked three questions to each retailer in separate tweets. The following questions were posed: “Do you do free delivery?”, “Do you stock X product in X store?” and “What time are you open till today?” Based on the average response time, the best customer service was attributed to B&Q, who responded on average in 9 minutes, whilst Sainsbury’s responded in 14 and Morrisons in 20, coming in second and third respectively.

Over 1 million people view tweets about customer service every week. 63 per cent of brands have multiple accounts, whilst only 2 per cent do not have a Twitter account at all. Customers’ expectations of brands show that 53 per cent expect a brand to respond to a question they ask via Twitter within an hour. This goes up to 72 per cent if it is a negative remark (for example a complaint), whilst 89 per cent of customers claim to be more satisfied when they get answers online.


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 –  Paderns –  Tracks –  Views –  InteracDons (P2P) –  CapabiliDes

•  Volume, Velocity and Variety

•  Data “in moDon”/”at rest”


Big Data as Panacea?

•  Retailers always sought informaDon 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

Mr Criado-Perez said that scrapping Safeway's ABC loyalty card scheme would save it £50m this year, money which it would invest in cutting prices.



“Sainsbury, which has tried loyalty cards to adract customers to new stores, yesterday dismissed them as “Electronic Green Shield stamps” that represented poor value for money. It has no plans to introduce them naDonally, it said” (Independent, 11.2.1995, p6)


Tesco Clubcard

•  Introduced in early 1990s

•  Not always got it right from consumer point of view

•  Heart of the business strategy

•  DNA of the consumer – Dunn Humby



•  “The more we collect the more we know”?

•  Big data as fad or fashion?

•  Data will tell us answers to quesDons we did not know we had?

•  Half truths abound


Data

•  Product analysis •  PromoDonal analysis •  Customer analysis

Customer

Transac9on

Data

Tradi9onal Retailing


Buywell

Sauce Customer Base

0

0.5

1

1.5

2

2.5

3

March April May June July August

% C

usto

mer

Bas

eControlIntervention


Data

•  Retailing – Who? – What? – When? – Where? – Why? –  How?

Customer

Transac9on

Data

Modern Retailing

Context:

World;

Behaviours


Data

Data Example

Public Data Government data -‐ transport, energy, health care

Private Data Proprietary – consumer transacDons, RFID tracks, mobile phone use

Data Exhaust Ambient data, passively collected – mobile phones, website searching, purchases, any e-‐interacDon

Community Data Unstructured data (wisdom of crowds) – consumer reviews, ranking, twider feeds (dynamic networks capturing social trends)

Self-‐QualificaDon Data Personally revealed by acDons -‐ Fitbit (stated versus revealed preferences), psychology and behaviour

Source: George et al (2014) Big Data and Management, Academy of Management Journal


Donald Russell

•  Cloud base customer insight system

•  Tailors product and promoDons to customers in real Dme as they are on the web

•  50 segments of customer; preferences, shop type, history, locaDon etc

•  Website movements tracked


What are the Retailers Problems?

•  What –  Prices –  PromoDons –  LocaDons

•  In what –  Context(s) –  Channel(s)

•  Addressed to what segment or target or individual


What Retailers Most Need

•  PredicDve consumpDon •  EffecDveness of promoDons

•  Target pricing precisely •  Understanding the value of the network

•  In store customer acDvity


So Where does Big Data fit in? •  Increased speed and agility

–  Using predicDve analysis –  SupporDng faster decisions –  Real Dme markeDng

•  Projects –  OpDmizing delivery of

messages to shoppers –  Mining for shopper insights –  Demand and assortment

planning

•  PersonalizaDon/more shopper soluDons


Burberry and OMo

Retail Week (2014) Getting inside the consumer’s mind, October.


i-‐Beacons

•  Customers using smartphones in stores

•  So beacons to interact with them via App – may also know where there are in store

•  InformaDon and messaging vs offers and promoDons


Big Data Issues

•  Sources – Social media – Website –  Item level sales – TransacDon data (personalised)

– Mobile devices


Big Data Issues

•  Why? –  Dialogue (or communicaDon)

–  Rapid reacDon launches –  Effect measurement –  Performance – “store”, supply chain, inventory


Big Data Big Issue

•  Privacy •  Acceptability

•  Brand and trust implicaDons and consequences?


Big Problem? Personalisa9on

•  PersonalisaDon is a goal •  But is it acceptable – or more accurately when is it acceptable?

•  When is personalisaDon too personal?

•  The “Uncanny Valley”


EPSRC Neo-‐Demographics Project


EPSRC Neo-‐Demographics Project

•  Aims –  Address systemic failure of UK industry in entering emerging markets •  IdenDfy, 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 informaDon fabric

–  Use covariate and crowd sourced data to test computaDonal behavioural groups


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?


Whose Data is it Anyhow?

•  Big Data Retail •  Engagements •  Loyalty cards •  From Cards to Apps •  Rewards, Nudges, Reinforcement, Peer Groups, Games etc etc


Beyond Retail

Tesco/Diabetes

UK

NHS Diabetes My Way

Tesco Clubcard


Beyond Retail

•  Tesco Employees •  Pre-‐diabetes (so GP records?)

•  Purchase records/loyalty points

•  Tesco e-‐diets system •  NutriDonal 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 (forthcoming)


So…

•  Retailers have data •  Context data becoming available

•  Linking/understanding/acDon

•  Solving retailer problems •  Beyond retailing – retailers part of the soluDon and not the problem


Web: www.sDrlingretail.com Email: [email protected] Telephone: 01786 467384 Twider: sparks_sDrling

Contact Points

Documents

Professor$Leigh$Sparks,$ Ins9tute$for$Retail$Studies,$ …quarter.cs.stir.ac.uk/courses/ITNPBD4/lectures2015/13... · 2015-11-09 · Morrisons in 20, coming in second and third respectively