Big Data for Cost-Effective Expansion: What You Need to Know
When it comes to expanding into new locations, the first question is always, where? For the CHRO, the question may actually be who? While the rest of the C-suite may be scoping locations based on customer base or tax regulations, HR will be working to find the best talent in the best location for the best money. To get this done, you will want to consider big data for cost-effective expansion.
Choosing a Location Based on Revenue
The decisions surrounding new locations is never simple. For example, retailers and food franchises not only need to know where potential customers are, but where customers will be over the 10- to 25-year lifetime of the investment in new locations. But for HR, expanding to a new location goes beyond finding where the customers are.
Your organisation may be using big data to identify the main components of existing effective locations and why they succeed, as well as locations most likely to pull in customers. For example, John Crouse, director of Wendy’s real estate services, told Fast Company (1) that the restaurant chain came up with its own “urbanicity scheme,” using “GIS platforms to help break down which blocks in an urban downtown will have high foot traffic and similar factors.” Starbucks, on the other hand, uses data pertaining to “nearby retail clusters, public transportation stops, and neighbourhood demographics” from their “in-house mapping and business intelligence platform” when evaluating new locations in China.
Once your location and potential customers are identified, how do you find the talent to fill positions in your new location? According to LinkedIn (2), you might want to start with the Bureau of Labour Statistics (BLS) to analyse metrics like unemployment rates in the area, to get a better sense of how many people may answer your ad and what kind of compensation they’ll be looking for.
Choosing a Location Based on Talent
If your organisation is choosing a new location based on available talent, you’ll need to decide what elements are the most vital to your organisation’s new office. The Corporate Executive Board (3) (CEB) suggests that CHROs take the same approach to analysing labour markets in new potential locations with fact-based analysis of talent demographics seeking answers to questions like: Which cities have the talent with the right skills? What are the hiring patterns in the cities of interest? What universities are or could be the most logical sources of future talent? How can the firm get the optimal talent at the best price? Where are competitors, partners and suppliers establishing talent skill hubs?
Those determinants need to be expressed in a number value. For instance, instead of stating that an important segment is a “strong labour market,” it should be expressed as the number of universities and colleges within 50 miles of the potential location.
Collect and Analyse the Data
The next step in using big data for cost-effective expansion involves collecting the data for each factor in each location. What are the tax rates in each location? What are average commute times? How many competitors are located near those locations? How many universities within 50 miles? When it comes to compensation and benefits, what is appropriate given local market averages?
It’s at this point that patterns and correlations in the data will become evident, and it can then be compared to benchmarking data from both GIS and human capital management (HCM) systems so that HR teams can analyse data in comparison to industry best practices.
Use Insights to Find New Locations That Match
Considering the large investments required to expand into new locations, organisations should make the most informed decisions possible. By correlating existing data and benchmarking data, CHROs gain insight that informs the best expansion decision possible for their organisations.
Please see the original blog post here: http://www.adp.com/spark/articles/big-data-for-cost-effective-expansion-what-you-need-to-know-9-349
By Bill Cushard