The most common approach for companies who seek a business data list of white collar workers is to review the industry classifications. With around five hundred SIC codes, and two thousand lower level business data descriptions, these will show the vertical market that each company operates within. But does this method truly identify the white collar workers from the blue? Can you be sure that accountants and solicitors are the former, and manufacturers the latter? Imagine two scenarios
Company 1: ABC Solicitors
This firm has ten branches; they are a large firm of solicitors. Nine of their offices are located in major cities. The tenth branch however is the only premise that is located within your desired catchment area, so it is this site that would be picked up by the data. Unlike the other nine sites, this branch is a warehouse premise, where all secure documents are stored. Aside from a general manager, all employees are dedicated to the warehousing and storage functionality of the business. Is this the kind of operation you would want to target for white collar related services?
Company 2: LMN Manufacturing
Similar to our firm of solicitors, this manufacturing company has ten premises and just one of these premises resides within the locality of your target region. The other nine sites are factory premises, manufacturing goods in line with the company’s product range. But the tenth site is the head office premise, where the functions of finance, sales, marketing and account management reside. So in this case we have a white collar operation, despite the overall nature of the company being predominantly blue collar.
These examples are quite extreme, but go to illustrate the point that the industry classification of a company is not necessarily indicative of the functionality of the premise being targeted. And so for this reason, there is a second variable which requires consideration; the business premise code. A warehouse or factory premise is ideal for marketing to for services such as industrial waste disposal, blue collar related training services etc. Whereas these premise types should be excluded if marketing into white collar service companies.
Based on the fact that these examples are quite extreme (the first being more so than the second), as a general rule the industry classification based selection is appropriate where there is no premise type within the data. And there are plenty of office-based companies which are far from ideal anyway; such as taxi companies or couriers. But where the premise type does come into play is as a sense-checking tool. i.e., that the business data specification caters for the removal of prospects which operate from an undesirable premise type.
Another premise type which yields a high volume of anomalies are the companies trading from home. Many services are simply not suited to this premise type. A company may be flagged up as “warehousing services” in the business classification and also be identified as having ten employees. But in reality this could be an individual who previously stated that they have ten employees, where those employees are either contracted or work from a different site. And all the company’s warehousing services are in fact contracted out, with this particular business operating on a commission scheme.
So whether applied as an inclusion or exclusion parameter, the premise type is of particular relevance when considering your marketing data and how best to specify it.