The Workplace Dimension
Accurate market intelligence is a key driver of business success, contributing big-time to NPD and the roll out of new concepts. Not surprisingly then, lots of retailers (and those distributing via retail channels) look at the kinds of people living around each store. Yet, for many outlets, the daytime population is quite different from – and often much larger than – the population of local residents. This is because so many people work outside the areas in which they live – often in city centres.
SPA's Workforce Analysis
But for its age, the Census would be a good source of daytime population figures – combining data about local residents not in work with data on local employment. And all the information comes from one source too. Sadly, however, the Census is too old. And it would be difficult to combine new employment figures from a business database, where employment is split by business type and size, with elderly Census data broken down by age, sex and social class.
SPA therefore treats the local workforce as a second population. We have assembled detailed data on the workforce so we can analyse the workforce in each area in just the same way that we analyse the resident population there. As you’ll see, we can even map it. We can’t break down the workforce by age, sex and social class, but we do have very useful splits by industry (SIC) and business size. There is a small extra advantage in that some businesses can sell not only to local workers, but also to local businesses. This makes workforce data a good indicator of the overall potential offered by both local employers and their employees.
Data Sources
SPA’s Workforce Data comes from the leading business data specialist, Market Location Ltd. The ML’s 2 million strong business database gives business name, address, activity, number employed (by site) and premise type – all kept up-to-date by an in-house research team. And because the database is by site (and not by Company), it reflects where people actually work. To quantify the workforce in each locality accurately, SPA has estimated the employment total of each business where this is unknown. ML data does not fully cover every last home office and one-man-band, but many of these are people working from home so their characteristics are strongly correlated with those of the resident population of standard geodemographics.
Workforce Insights: Some Examples
Concrete examples show where workforce analysis may lead you.
Chameleon Bar? Are the kinds of people who work locally so different from local residents that you need to consider a totally different offering for lunchtime and early evening?
Catering for the Lunchtime Rush. The size of the lunchtime potential can influence how much effort a supermarket puts into sandwiches & snacks – and any matching changes to layout. Where workplace potential is large, pubs and restaurants may adjust pricing, menus etc. so they get their fair share of customers and can serve them quickly.
Targeted Lunchtime Promotions. Not all branches have enough potential – or potential which is “right” – for targeted lunchtime promotions to be worthwhile. Workforce data can tell you how big the potential is, and how up-scale (or otherwise!) local workers actually are.
Hit List of Local Businesses. Outlets serving mainly local residents & workers may be able to tap extra potential from local businesses. Off Licenses, pubs and restaurants do this at Christmas. Stationers, sandwich shops, garages, and tyre & exhaust centres might use a similar approach.
Technical Applications
How exactly is workforce data used? What types of analysis might point in the directions just listed?
Beware of Dogs. In parts of retail, some workplace environments are bad news. Workforce analysis of potential new sites can help avoid such “dogs” – branches that never trade well. Analysis may even suggest new offerings that might be more profitable in such sites.
Profiling Residents and the Workforce. The aim might be to summarise the social mix of the resident and workforce populations, and to understand the implications of this for the kinds of services and products likely to be required. SPA has done several projects where the social class mix of the workforce was the key. And it’s still very much a hot topic!
Outlet Segmentation. Classifying outlets into types using just workforce data can help decide where to roll out the quick lunch menu or where to target that new promotion. But using standard Geodems too can be useful for spotting chameleons and other odd-balls.
Turnover Prediction. Workforce data adds a new dimension to outlet turnover models, and can help improve the accuracy of predictions considerably – especially for pubs, restaurants, dry cleaners, sandwich shops and convenience stores. Model estimates of this kind can be invaluable inputs to site selection and branch performance reviews.
Lunchtime Outlet Performance. Workforce data is invaluable for predicting trade at lunchtime and early evening. And comparing actual with predicted sales can pinpoint outlets which should be doing far better – and hence where efforts should be concentrated.
B2B Profiling. Before buying lists of local businesses, it’s worth analysing existing business customers. Looking at the industry sector and size may raise questions about the kinds of businesses currently being served, the kinds that offer the best potential, and what needs to be done to attract them. B2B Marketing must not be allowed to go off at half cock!
Data for YOUR GIS?
Workforce, Business and Employment Data can be supplied at most geographical levels for use in Geographical Information Systems and Prospect Management software. See Databases for more.
Interested?
To discuss how Workforce Analysis can help you get more out of your business, Contact us, email us at info@spamarketing.co.uk or call SPA on 01926 334978.