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Taxi Business Data Lists

Taxi companies will appear on most business data lists, yet they are probably the most unwanted of all industry classifications.

So why are they such an issue, and what can be done about it? There are four key reasons that taxi companies become an issue when specifying business data for marketing;

 

1. Vertical Market Selection: Transport Sector

Taxi companies sit within the Transport Sector. And although it would be normal to consider this sector to predominantly contain couriers, shipping and road haulage companies, taxis also reside in this sector. If considered carefully, to what other possible sector could they belong?

A similar example would be to select the “Education Sector”. Within this sector you would expect schools, colleges, universities, training companies and could even understand nurseries. But the industry classification which is perhaps the least expected is driving schools. There are many thousands of them, and they are not the kind of environment where you may wish to market books, pencils and other educational equipment.

 

2. Company Size Selection

Another typical business data brief would be to select all large companies (say, 50+ employees) within a given catchment area. And here is where the unwanted taxi firms pop up yet again. There may be just two or three people actually employed within the company, with each driver being self-employed or contracted. Either way., for the purpose of reflecting a company’s size, taxi firms will generally tot up the total number of people working for the company; and that includes all the drivers on the books. So it becomes commonplace that taxi operations are classified as having more than 50 employees from the perspective of the business data selection.

 

3. Office Premises

In marketing to office premises, businesses will usually have an office related product or service. Such as office cleaning, office equipment, stationery or even IT services. Each one of these services will usually target businesses by a minimum employee size (e.g. 10+) to ensure a reasonable sized sales opportunity. Cleaning an office with 10 staff will possibly mean 2 – 4 hours worth of cleaning per week. But a taxi firm is typically one person behind a desk, operating a phone and single p.c.. So the IT company would not be best chuffed with this opportunity either; 10+ employees in an office will typically mean there is a server linking all the p.c.s, where as a taxi operation is unlikely to have this. But how else could a taxi company be classified? They are not factories, medical centres or even retail outlets in the regular sense.

 

It becomes easy to appreciate why business data selections should apply a “special case scenario” to taxi firms; although some marketing campaigns would welcome them, in many cases they are a blatant and unwanted anomaly. And this is made worse by …

 

4. Population Count

There are some 17,000 taxi companies within the UK. Taking one postcode area as an example (Birmingham), a general business list selection from this region would include more than 300 taxi firms. That is a lot of unwanted data if these are not your target market. By contract, there are only circa 60 taxidermist companies UK-wide, so if these were unwanted (from a pool of over 3million records) then they would hardly dilute the power of your marketing initiative by including them. And that’s the point; there are so many taxi firms that their undesired inclusion would weaken any marketing database.

 

So What Can Be Done?

At Responsiva, one of the first questions you are asked is what product or service you supply. And the very reason for this question is so that consideration may be given to potentially undesirable industry classifications and other pitfalls. It is commonplace that a company would tell Responsiva that they have historically purchased a business list which includes them, ridiculing the b2b data supplier for not understanding their target market. It is true that business data suppliers sell on volume, so by including undesirable taxi firms within the marketing list usually means their sale value is higher. But it would be wrong to suggest any malpractice that they have attempted to pepper the file with useless prospects simply to up the sale value. It is far more probable that the client was serviced by an inexperienced account manager who simply didn’t understand the business universe well enough to know that taxi firms are one of the main anomalies when it comes to selecting business data.

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White Collar Business Data List

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.

 

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Business Data Samples

There are three reasons to get business data samples when sourcing a prospect list;

(1) Data Qualification

(2) Quality Testing

(3) Review the b2b data Fields

 

DATA QUALIFICIATION

The first, Data Qualification, is your opportunity to manually check each sample record to ensure it meets with your business data brief. Some examples include; if you need every record to contain a director-level contact name then check the job title of each contact name contained within the samples. If there are any absent or managerial contact names then the samples have not married up to the brief. Check the geography of each record (town, county & postcodes) to make certain they are all within your desired catchment area. Check the employee size and/or turnover information to be sure every line marries up to the target market company size. And perhaps the most important data field to review is the industry classification (sometimes referred to as SIC code). Quite often our customers say that “any business” is an appropriate target, but in reality they did not consider that some business types are undesirable; government, schools, churches, care homes etc. So by reviewing the industry classification column you should be looking for any b2b data which (upon manually qualifying the samples) aren’t desirable after all.

So what the samples enable you to do is sense-check the business data you would be buying. And it is imperative that you do check each record.

 

QUALITY TESTING

Business data samples are supplied so that you may test a few records. It doesn’t take long to call through 10 to 20 records and ask one or two quick questions. First and foremost, is the phone active or dead? And when the phone is answered, do they give the company name as listed on the sample database you have been supplied? You could even check that the contact name given within the data list still works for the company and is resident at that premise. Auditing the company premise type is a good sense-check also (although part and parcel of the data qualification process above), but if the contact name supplied works at a different site or building, then the premise type is the first field I would check. Is it the head office or a retail outlet for example?

The quality testing process is there to check the accuracy of the data list; not to audit the data fields or how the file looks in terms of the specification. This step is purely to ensure the business data is current and reflective of what you might expect when ordering a larger file.

 

Review The b2b data Fields

The b2b data fields are effectively the columns within the spread-sheet. Many list brokers charge extra for additional fields. So although they may quote a very cheap price for company name, address & telephone number, the prospect list may be absent of contact names or the other profile variables. Profile variables include the business classifications, staff headcount, premise type etc. So this stage of the checking is really all about making sure that the content of each row is as you would like it. And if any fields are absent then ask; is the extra data available, and does it come at an extra cost?

 

Responsiva’s b2b data Samples

Responsiva supplies data samples with every new-customer quote, for the very purpose of these three processes. We want you to check the data quality, that the data meets the brief and that all the desired fields are within the file. With the exception of email addresses, all available b2b data fields are supplied as standard and at a simple rate per 1,000 records. It may not be the cheapest file around, but you get all the fields without having to pay those niggling costs for all the little extras (like when booking a flight!), which end up making the file more expensive anyway. And we definitely want you to check the quality of the data by making a few calls. A huge amount of money would be wasted on your telemarketers’ time if the quality is not up to scratch.

(the comment that Responsiva only supplies samples to new customers is purely because the repeat customers already know the data quality, fields supplied and qualification of the brief is at a high standard. That said, repeat customers can always request samples too).

 

So the data samples are actually a vital part of ordering business data; they are your opportunity to be satisfied that the prospect list is fit for purpose. If you would like some free business data samples then please contact Responsiva on 0800 118 5000, or send an email to info@responsiva.biz

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Using the business data Premise Codes

The premises type within the business data universe has genuine value to some business types. Most especially those targeting a specific kind of building rather than by industry classification. Three good examples follow;

 

1. Industrial Waste Disposal / Collection

By selecting a prospect list by specific industry classifications will yield anomalies. For example, the manufacturing sector appears at a first glance to be a great sector to market to. However, any marketing list which selects the b2b data by the manufacturing sector will contain an estimated 30% undesirable prospects. Not all manufacturing company premises are factories; many are offices, head offices and sites of administration, marketing & finance. And these could well be located literally hundreds of miles from the manufacturing plant. So by contacting the satellite offices to provide an industrial waste collection & disposal service would quite probably be a wasted marketing piece. The data list should be selected by factory premises; ideally with a minimum employee size to give a fair indication of the volume of industrial waste.

 

2. Fork Lift Trucks & Training

Virtually any business sector can have a storage facility requiring warehousing, though admittedly some are more prone than others. When identifying a business list for marketing, selecting the companies trading from an actual warehouse premises would be much stronger than using the regular SIC coding system. As with the first example, employee size will give an indication of usage (i.e., number of fork lift trucks required for sales or service, or number of trainees). The premise type is vital in so much that a head office based in a commercial tower block will have no fork lift truck related requirement. It could be argues that the head offices may make the decisions, though past experience suggests that they are more likely to allocate a budget for the warehouse manager to make the actual decision on which trucks and related training services are required.

There are two anomalies with warehouse premises however. Many large supermarkets, department stores and other retail premises have a warehouse facility at the rear. But by the very nature of their business they are classified as a retail outlet. And you would not wish to select retail outlets in general when identifying a prospect list for fork lift trucks, or you may scoop up all kinds of dross such as fish ‘n’ chip shops etc. For this reason, many of the warehouses are classified as a different premise type. But also, some businesses classified as a warehouse are chain outlets of a larger home improvements store, or commercial courier company. These can be excluded by the branch count however.

 

3. Office Services

Office services can range from photocopiers, partitioning, stationery and all manner of products which target the office premise. It could be argued that these services are also required in factories, warehouses and other premise types too. So the best example to consider would be p.c. related services (sales, services, networking etc). Specifically, where the employee count should have a high correlation to the number of people actually sat at a desk and using a computer. The office premise is ideal for this, but there are some anomalies. Taxi companies for one; 100 employees could quite literally mean one person sat at the reception desk and 99 drivers out on the road.

 

There are pitfalls in making any business data selection for your marketing. But at Responsiva you have the reassurance that with around 25 years experience these will be pro-actively explored thoroughly before the data is actually ordered. The business lists you order will be fit for purpose, accurate and well-defined.

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