This week M&S announced that it will close 30 of its UK ‘department’ stores – this excludes M&S Simply Food. They currently have a portfolio of 30 stores of this format of which 41% are on high streets, 36% in shopping centres, 18% on retail parks and 5% elsewhere. This is not a overly large number of stores when compared to the likes of Next, New Look and Arcadia.
So the big question is which stores are they likely to close? By using LDC’s analytics platform there are a number of ways to look at this and the purpose of this paper is to illustrate how quality data collected in the field by humans can inform the decision. I stress the word inform and not make/automate such a significant process which has wide ranging impact.
There are a number of areas that the LDC data does not take into account which are critical factors. The primary one of these is store profitability which factors in sales (in store or via click and collect), rent and business rates. In addition, lease length and structure is key as it might be cheaper to keep a store open and run it at a loss for a period of time rather than pay a large lease surrender along with dilapidations.
The LDC data provides a comprehensive and current view of every shop in the country wherever they may be and when analysed, aggregated and objectively interpreted can add significant value to a retailer’s strategy. Essentially the LDC data acts as a RADAR, constantly tracking who is opening and closing, what is being reoccupied and what remains empty along with other factors such as demographics, competing locations, footfall and overall health of a location.
I will now illustrate how the LDC data can be used in a number of ways to objectively analyse, using data, the M&S stores of which 30 will close for good and others will move.
1. More than one shop within same town/area
The most obvious view is that shops that are within the same town/area serve the same catchment. Examples of this are where there might be a store in the town centre on a high street, in a shopping centre or in a retail park on the outskirts of the town. There are 35 towns and cities that have more than one M&S store. If you exclude the larger cities then there are 20 towns that have more than one. Milton Keynes is an example of a town that has three stores – see map below.
Towns and cities that have more than one M&S include London (27), Glasgow (6), Edinburgh (4).
3 stores – Bristol , Cardiff, Liverpool, Milton Keynes, Sheffield
2 stores – Aberdeen, Banbury, Birmingham, Bolton, Bournemouth, Brentwood, Brierly Hill, Chichester, Coventry, Durham, Fareham, Leamington Spa, Leeds, Leicester, Manchester, Newcastle upon Tyne, Nottingham, Peterborough, Preston, Richmond upon Thames, Southampton, St Albans, Stockton on Tees, Swansea, Swindon, Tunbridge Wells and Warrington.
2. Small towns with an M&S store
There are a number of small towns (by population) which might not be able to sustain a profitable M&S store based on proximity to nearby centres, competing retailers and of course the massive growth in online purchases.
Some of the towns within this criteria include Barnstaple, Bridlington, Carmarthen, Deal, Ferndown, Keighley, Kidderminster, Matlock, Neath, Sandhurst, Wallasey and Workington.
3. Highest surrounding vacancy rate
The first way is to analyse the surrounding vacancy rate (number of empty shops within a 200m radius) based on the understanding that areas that have large numbers of empty shops are less attractive and will therefore attract less people and subsequently sales.
The image below illustrates what this looks like and identifies those stores which are in high street locations which have above average vacancy rates around them.
Towns that fit this criteria include Ayr, Northwich, Hull, Dundee, Blackburn, Darlington, Bolton, Stoke, Rochdale and Basildon to name but a few.
4. Lowest health index score for the store’s location type (High street – small, medium, large, shopping centre and retail park)
The health index consists of 12 attributes that are considered as key performance indicators for a destination location – these include population, earnings, vacancy rates and how persistent any vacancy might be, competing centres nearby, presence of anchor retailers, food and beverage provision along with cinema presence. For further detail see HERE
Towns in this criteria include Ayr, Barrow in Furness, Blackpool, Bolton, Boston, Dundee, Hereford, Mansfield, Stoke and Sunderland.
The health index is measured against the specific location type a shop is in i.e small high street, medium high street, large high street, shopping centre or retail park.
5. Locations where the health index is weakening
This indicates a potential drop off in attractiveness by tracking the annual change in the health score.
Towns with a weakening health high street index include Dundee, Carlisle, Kings Lynn, Macclesfield, Kettering, Maidstone, Southport, Bedford, Bridlington and Buxton.
6. Locations with the most competition
For the purpose of this exercise I have picked a sample of competitors to M&S (Next, New Look, H&M, Primark and Zara) and picked a radius of 500m. Both the radius and/or drive time along with the competing retailers can easily be changed at the click of a mouse. Analysis as set out above shows that.
Towns that have the most competition include Epsom, Bath, Bolton, Chester, Exeter, Guildford, Maidstone, Nottingham, Swansea and Oxford Street, London. Within this criteria are also a large number of shopping centres.
7. Towns with the least competition
Having little competition might be a bad thing based on shoppers liking to compare and contrast and hence why clustering has always been a retail phenomenon. The map below identifies locations which have one or no competitors based on the competitors selected being Next, New Look, H&M, Primark and Zara.
Towns within this criteria include Birmingham (High Street), Cardiff, Ellesmere Port, Ferndown, Llandudno, Matlock, Sevenoaks, Andover, Bridlington and Deal.
So as you can see there are many ways of looking at a store portfolio and the relevance of each view will vary according to retailer. The overarching aspect in common across all, is the ability to look at temporal data to understand how places are changing which will directly or indirectly impact how a store performs and how that performance changes.
There are other ways of analysing but I will not cover them off here but they include adding a bespoke demographics overlay, adding footfall and conversion rates for the stores and adding spend patterns.
The analysis above covers a vast amount of data that has been distilled through LDC’s online analytics platform but to see how easy it is to do this for yourself then just watch the video of how I did it.