Retail analysis and location consultancy

 

Our client was looking to use existing retail data from the Bristol area to create a model and analysis of potential new retail locations. They needed a picking list of the best locations. Other data such as retail potential would be useful.

The challenge

The client needed a formal report with supporting data to indicate a good starting location for furniture retail premises. He suggested a set of possible targets within a study area ranging from the South coast to the M4 and from Wimborne Minster to Royal Tunbridge Wells. Retail figures from existing stores in the Bristol area were provided.

30 minute drive time catchment displaying spend by sector and P2 demographic tree types

The analysis

  • Starting data
    Average weekly retail figures for 5 locations between Weston-super-Mare and Gloucester. Seasonal variation in furniture spend illustrated by providing figures for March to September and October to February.
    Client experience suggested that most customers will travel up to 20 minutes to reach a store. The client also identified the P² People & Places tree type of the source store customers.
    Also provided was a publicly available list of competitor sites.

  • External data sources
    The potential value of the furniture market was based on the data from the Living Costs and Food survey (LCF) indicating weekly spend by P² People & Places tree type per household for furniture and furnishings.

  • Target locations
    The client identified 42 towns within the study area. These towns were geocoded by eye to provide locations in the centre of the settlement.

  • Tools
    We carried out the analysis using Prospex Desktop GIS. Prospex provides the tools to import customer specific data, classify the data using demographics and create catchments based on driven distances or times. Prospex will produce digital maps or export data into spreadsheets such as Excel for further processing.

  • Customer model
    The client supplied data for weekly takings and catchment areas for the stores in the Bristol area. Combining the information with the LCF survey data produced a total possible weekly spend on furniture and furnishings in the catchment area as well as the market share of the individual stores.
    The source catchments were produced using 20 minute drives. Household counts in the catchment for the P² People & Places tree type identified by the customer as their core target were combined with the LCF data to produce a total potential weekly spend.
    We generated a market share percentage by matching the potential spend to the recorded sales figures from the stores.The worst, average and best cases were produced including seasonal variation and annual averages.

  • Retail potential
    A series of catchments was generated using 20 minute off peak driving times from the target location. Using the P² profile and LCF data the total market for each catchment was calculated. The retail potential for the target locations was calculated by applying the market penetration % to the total market

 

The results pack

The client required a formal report that could be used to prepare business plans alongside supporting maps and spreadsheets.

  • Report
    A formal report illustrating the starting scenario, the location of competitors, the methods and data sources used in the analysis. The report included a conclusion paragraph but all of the resulting data was supplied to the client for their own analysis.

  • Images
    Mapping analysis has a visual component where overlaps and gaps in coverage can be seen. To help the client, all the individual maps produced during the analysis were included in the results pack.

  • Spreadsheets
    The data produced from the calculations was also supplied in a series of spreadsheets. The client can see how the analysis was generated and run what if scenarios.

The benefits

The work generated an ordered list of potential sites. The client used the report and supporting documents in the preparation of business plans. The analysis used publicly available datasets alongside customer specific data. The combination of specific and general data produced a report that matched the client’s expectations where he had local knowledge.

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