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ESRC Retail Industry Business Engagement Network
Professor Martin Clarke
University of Leeds, UK
• RIBEN is one of 4 ‘Capacity building Clusters’ created, following a national competition, by ESRC to encourage closer dialogue and collaboration between social scientists and UK industry
• Aim: to produce a new generation of university researchers work on industry relevant topics
• Involves 3 leading retail research universities‐Oxford‐Southampton‐Leeds
Management Team
Principle staff:‐ Professor Neil Wrigley – Director‐ Professor Michelle Lowe – Assistant Director‐ Professor Martin Clarke – Associate Director‐ Dr Jonathan Reynolds – Associate DirectorAdvisory Group12 Retail Industry and Government Department members chaired by Kevin Hawkins, ex‐Director of the British Retail Consortium
• RIBEN will receive core support from ESRC over 5 years (2008‐13) valued at 1.45 million sterling
• This supports:‐ 8 Knowledge Transfer Partnerships (KTPs)‐ 15 ESRC CASE doctoral awards‐ 15 vouchers for SMEs‐ 10 ‘business placement’ wards for PG studentsat other British Universities
• However, no FEC support or any admin or travel and subsistence funds – this has been seen as a major constraint on the project
• Collective Research Interests‐ Retail innovation and entrepreneurship‐ Retail competition, regulation, planning and development
‐ Retail marketing and retail information/network planning
‐ Internationalization and retail performance in the global economy
‐ Sustainability‐ CSR and responsible/ethical sourcing‐ E‐commerce and multi‐channel retailing
• Why Retail?‐ big sector of the UK economy, generating 10% of GDP and 11% of employment‐ something the UK is good at – e.g. Tesco’s, Marks and Spencers –benchmarked by glocal best practice‐ Historically, relatively weak links between academia and
practitioner compared with other sectors, such as engineering, medicine, aerospace
‐ Part of this can be explained by different time lines that both parties operate on: retail – weeks/month, academia – years!
‐ Dynamics of the industry changing rapidly – e‐ and t‐ commerce, consumer choices and profiles changing, etc
‐ In the UK over past 3 years many household brands have disappeared – Woolworths, Thresher, Oddbins, Habitat etc. More to follow
Factors Generating Retail Turbulence & Market Opportunity
RETAIL CHANGE
New Delivery Channels & Technologies
- Telephone
- Internet
- WAP/SMS
- Digital TV
New Entrants (start-ups) -Competing Head-to-head with Established Players
Disintermediation -Manufacturers Aiming
to By-pass Intermediaries To
Reduce Costs
Diversification –Players Moving
Into New Sectors
- Supermarkets
- Banks
Globalisation –Traditional
geographical barriers breaking down
Consumer Power –Consumers More
Demanding Than Ever
Deregulation – Markets being Opened Up
Competition – In All Sectors Competition
is Fierce and Increasing
It’s a Battlefield!: Evidence
• Over the past 3years:‐ Growth of deep discount retailers‐ ASDA buy Netto‐ Co‐op buy Somerfield‐ Growth in branded C‐stores (not symbol)‐ Demise of Woolworths, Thresher, Oddbins (next? HMV, All Saints )‐ increased penetration of on‐line
Evidence
• Waitrose: plan to open 300 stand alone c‐stores plus concessions in Boots stores
• Boots health products sold in Waitrose stores• 1250 independent c‐stores closed in 2009 (IGD)
• Sainsbury’s takeaway – Fresh Kitchen• Morrisons acquire 10% stake in US Fresh Direct
Evidence
• Andy Bond – ‘You’re kidding yourself if you think the worst is over and we’ve had a consumer recession – it’s ahead of us’ (6/4/11)
• John Browett (Dixons) ‘ government cuts are having a chilling effect on consumers’
• M&S: clothing and homeware sales fell by 6% in first quarter 2011
• Worse off Wednesday
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Total Sainsburys Total Tesco Morrisons Discounters Asda
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Total Sainsburys Total Tesco Morrisons Discounters Asda
Total square foot in GB, 2002-2010 (Source GMAP Ltd)
Total stores in GB, 2002-2010 (Source GMAP Ltd)
Growth in floorspace provision 2002‐10
Growth in Number of outlets 2002‐10
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Total Sainsburys Total Tesco Morrisons Discounters Asda
Total square foot in GB, 2002-2010 (Source GMAP Ltd)
Total stores in GB, 2002-2010 (Source GMAP Ltd)
A Pincer Movement
BIG 4SUPERMARKETS E‐COMMERCE
TRADITIONAL HIGH STREETRETAILERS
Resilient Retailers?
• Anticipate
• Plan
• Innovate
• Monitor/Measure
• Respond
• Learn/Improve
Examples of RIBEN Projects
• Just to give you a flavour of the kind of things we do …..
Knowledge Transfer Partnerships
SME voucher scheme
School of GeographyFACULTY OF ENVIRONMENT
Estimating tourist demand in retail location models
Student: Andy Newing
• Sainsbury’s location planning team have a major responsibility for generating revenue predictions for existing network and proposed stores.
•Their in‐house spatial interaction model is generally successful, but consistently under‐predicts of revenue in certain areas with a high proportion of non‐residential demand.
• It is difficult to accurately forecast the impact of tourism on store sales and revenue as visitor expenditure is driven by a combination of local and broader factors with a variable spatial and temporal pattern.
•This RIBEN CASE award PhD asks: “Is it possible to identify and account for the factors that influence visitor demand within SI models?”
• The PhD aims to :Develop a SI model and spatial decision support system that can be used by grocery retailers to incorporate visitor demand.
Estimating tourist demand in retail location models
Why are retailers interested in visitor demand?
•Visitor demand is spatially clustered around resorts, destinations and attractions and often driven by the supply of accommodation.
•Grocery stores are an important part of the services required by visitors –particularly those using self catering accommodation.
•Demand also originates from second homes, people hosting friends and relatives, operators of small accommodation establishments and others catering for visitors.
•Stores in some resorts demonstrate a clear seasonal pattern to their overall trading figures and out‐of‐catchment trade.
Visitor grocery expenditure
“in many holiday locations, during the summer season tourists will complement revenue derived from residents for a range of retailers such as supermarkets, chemists,
newsagents, pubs and cafes”
(Dudding and Ryan, 2000, p302)
School of GeographyFACULTY OF ENVIRONMENT
•
School of GeographyFACULTY OF ENVIRONMENT
Newquay Store
Trans. from
within the store trade area
External trade
originating outside the store trade
areaProportion of total Nectar transactions
66.2% 33.8%
Proportion of total Nectar spend
64.1% 35.9%
Spatial pattern of Nectar transactions by week
Trade composition by OAC Supergroup
Expenditure by OAC Supergroup
Subsequent research
Visitor demand layer
Accommodation supply by type,
location and occupancy
Spending by businesses, and by
those hosting friends/relatives
etc.Day trip visitors
Seasonal passing trade
Demand disaggregated Supply disaggregated
Consumer insight from loyalty card data
SI Model
Ability and willingness of visitor type to travel
to stores
Attractiveness of retailer by type to
visitors
SDSS
Food miles & carbon footprint
• Road transport up 30% since 1980: average distance travelled up 60%: 30bn km per year, 10m tonnes of CO2
• UK consumers use the car more (up 60%): 1995 750 miles 2005 – 900 miles
• Regional distribution depots• Agricultural specialization• More processed foods (i.e ready meals)• Economies of scale in manuf.• That said: nb. life‐cycle assessment
Food Miles
Food transportation
factory shopfield homefactory shopfield home
Internationalisation Centralisation
“An everyday meal is now a minor piece of socialhistory. Each ingredient will have travelled somedistance, probably be based on a plant which has beengrown thousands of miles from its biological origins,and the foods will have been trucked long distances.”
(Lang and Heasman, 2004:237)
High quality, low cost food
Greenhouse gas emissions
soupLeeds, UK
olive oilItaly
2,171 km leeksCambridge, UK
233 km
potatoesPerthshire, UK
441 km
onionsSpalding, UK
185 km
stockSwitzerland1,375 km
saltFrance
1,158 km
pepperBrazil
8,901 km
Localsupplier
Localsupplier
Localsupplier
Localhub
Asdastore
Asdastore
Asdastore
Transportation of local foods
3. Asda2. Research aimMap showing the location of Asda’s local hubs and stores
e.g. Jeff the Chef’s Chicken Parmo
e.g. DyffrynTywi Ice Cream
e.g. Lake District Cheese
e.g. Elveden Estate Chutney
e.g. Malcolm Allen Sausages and Pies
e.g. GrainstoreLocal Ale
Retail Industry Business Engagement Network (RIBEN)
• Encourage collaboration between academia and industry professionals• Provide researchers with first-hand experience of working on issues of relevance to the retail sector
• Transform data collected (such as questionnaires, official registries) into actionable information which helps clients understand customer preferences, improve customer acquisition and retention, predict consumer behaviour.
• Chief Executive John Mayer - “ The biggest company you have never heard of”
Research
Retail Spending and Store Location during a Recession: An Analysis of Changing Consumer Behaviour and Interaction Patterns
Aims
• Aim•The broad aim of this research project is to quantify the impact of the recession on the grocery market from a consumer (demand side) and a retailer (supply side) perspective in Great Britain.
• Objectives – Slightly changed since last time• Review and gain a comprehensive understanding of the extensive literature on the recession, household expenditure and store location techniques.• Validate Acxiom data against existing data sources on grocery expenditure.• Identify supply-side changes in the food retail industry over a period of recession.• Analyse the changes in food expenditure during the recession by different household types and geographic areas.• Analyse the changes in customer patronage for British retailers by household type and geographic areas during the recession• Construct a disaggregated spatial interaction model to evaluate future opportunities in the grocery market.
Market share
Changing demand and supply in the Yorkshire and Humber grocery market, 2004‐10 Sources: GMAP (2005, 2007, 2009); Acxiom (2004‐10).
Morrison's
Maintained stable customer base, slight growth in ‘Countryside’ and ‘Typical Traits’ areas.
Primary customer aged 55 -70 years and getting older
Rise in households earning £10-19,000 and over £50,000
ASDA
Marked rise in ‘Blue Collar’ areas and to an extent ‘Multicultural’ since 2007.
Primary customer aged 35 -50 years. Decline in older customers since 2004.
Rise in households earning £10-19,000 and over £50,000
Tesco
Rise in ‘Blue Collar’ areas whilst ‘Prospering Suburbs’ declines
Primary customer was aged -50 years. Falling younger customer since 2004.
Rise in households earning £10-19,000 and over £50,000
Sainsbury’s
Primarily ‘Prospering Suburbs’ areas, rise in ‘Blue Collar’ since 2007
Primary customer was aged 50 -60 years in 2004, this has increased to 60-75 in 2009.
Decline in top earning households and an increase since 2007 in low-income households.
Discounters
Decline in traditional ‘Blue Collar’ households, rise in households from 'Prospering Suburbs’ and ‘Countryside’ areas
Primary customer was aged 35 -50 years in 2004, this has increased to 65-75 in 2009.
Decline in low-income households and an increase in higher earners
Discounters
The percentage change in households using deep discounters as their main supermarket in Yorkshire and Humber, 2007‐09Source: Acxiom ROPs (2007‐2009).
Marks and Spencer
Rise in traditional ‘Prospering Suburbs’ and ‘Typical Traits’ households, decline in 'Blue Collar’ since 2007
Primary customer was aged 50 -60 years in 2004. Since then, there has been an increase in both younger and older consumers.
Increase in top earning households whilst maintaining low-income base as well.
Age and Gender ‐ Ecommerce
46
Online shopping frequency by age and sex , 2011 (Source: Acxiom, 2011)
Internet UsageCode Name 2008 2009 2010A1a Industrial Legacy 15.24 18.40 23.09A2a Struggling Urban Manufacturing 13.26 16.23 20.54A2b Regional Centres 14.88 17.54 21.88A2c Multicultural England 14.40 17.16 21.48A2d M8 Corridor 15.36 18.68 23.88A3a Redeveloping Urban Centres 16.31 19.36 24.04A3b Young Multicultura 17.95 21.01 26.03B1a Rural Extremes 19.26 22.55 27.90B1b Agricultural Fringe 17.26 20.41 25.18B1c Rural Fringe 18.34 21.62 26.55B2a Coastal Resorts 15.63 18.48 22.88B2b Aged Coastal Extremities 16.94 19.97 24.85B2c Aged Coastal Resorts 15.10 18.02 22.27B3a Mixed Urban 16.47 19.68 24.32B3b Typical Towns 17.07 20.09 25.07B4a Isles of Scilly 18.23 29.75 40.42C1a Historic Cities 17.51 20.59 25.31C1b Thriving Outer London 19.76 23.01 28.01C2a The Commuter Belt 20.49 23.77 28.82D1a Multicultural Outer London 16.82 19.83 24.64D2a Central London 20.12 22.79 27.29D2b City of London 25.41 24.31 30.33D3a Afro-Caribbean Ethnic Borough 19.21 22.32 27.76D3b Multicultural Inner London 17.35 20.51 25.93Total Total 17.43 20.67 25.77
Households that purchase goods online “often” by LAD area classification (Sources: Acxiom, 2004 to 2010; Vickers et al., 2003) Households that purchase goods online “often” 2008 to 2010
(Source: Acxiom, 2007 to 2010)
Online Groceries Local Level
Online grocery penetration by LSOA in Leeds (Source: Acxiom 2010)
Internet Usage – Average distance
Miles Offline OnlineMin 0.06 0.04Average 2.54 4.90Median 1.69 2.54Max 38.48 40.84
Miles Offline OnlineMin 0.02 0.10Average 2.12 3.47Median 1.36 2.15Max 22.74 24.57
Miles Offline OnlineMin 0.06 0.07Average 2.44 3.14Median 1.60 2.12Max 30.42 32.14
Grocer online Often Sometimes Never Would Consider TotalUrban > 10k 4.94 11.38 75.73 7.93 100Town and Fringe 7.10 14.79 70.70 7.39 100Village, Hamlet & Isolated Dwellings 7.30 14.43 70.83 7.42 100Total 5.38 12.02 74.76 7.82 100
Households that purchase groceries online by OA classification (Sources: Acxiom, 2004 to 2010; ONS, 2001)
Average distance Asda (Source: Acxiom, 2010; GMAP, 2010)
Average distance Sainsbury’s (Source: Acxiom, 2010; GMAP, 2010)
Average distance Tesco(Source: Acxiom, 2010; GMAP, 2010)
Other Projects
Conclusions
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