Welcome to the Responsiva Data Lists blog

Visit the Responsiva Data Services website

Posts Tagged list broker

List Broker Customer Survey

New customers at Responsiva are invited to register their feedback on our list broker service, around two weeks from receipt of their prospect data list. This survey comprises five key questions and a free-text field for open comments. Approximately 200 customers have taken part.

The first three questions are ranked 1 to 5, with “1” being excellent (or the most positive score) and “5” being poor, or the most negative. For the purpose of the first three questions, Repsonsiva have allocated a percentage score as follows:

Score %
1 100%
2 75%
3 50%
4 25%
5 0%

 

 

 

Q1. Rank Responsiva’s speed of service.

Average score = 1.20. Percentage equivalent = 95%

Responsiva prides itself in offering an incredibly fast service. If we have some counts to run or a b2b data order to deliver then we strive to get the project delivered quickly. Usually (90% of the time) within 30 minutes, and at worst before 9am the following working day.

 

Q2. Rank the price you paid for the data.

Average score = 2.17. Percentage equivalent = 71%.

The price point is always a sensitive area. All companies want to make an honest profit, whilst being reasonable and fair with the price. Had this score been in excess of 75% then Responsiva’s data lists would probably be “too cheap”. And a score below 50% would suggest the price was too high. So we continue to monitor this percentage and strive to maintain it between 60% and 75%.

Some list brokers charge minimum order values of £250, whilst others are as high as £1,000. Responsiva comes in below these figures to ensure that companies looking for a small business list do not pay over the odds for it, priding ourselves as a fair-priced list broker.

 

Q3. Was the data accurately specified.

Average score = 1.48. Percentage equivalent = 88%.

Judging from the occasional comment, customers frequently confuse this question with the quality of data or even their results from a campaign. The question is more akin to the brief, rather than the data quality or campaign results. However, all points are pertinent. Where Responsiva have dropped points tends to be customers who experience unsatisfactory levels of new business appointments from their telemarketing. This is not a fair measurement of the prospect data, as other factors come into the equation. However, on the two occasions in the last three years where customers have expressed dissatisfaction with the data quality (usually a high volume of email bounces in excess of 5%) a refund has been given where appropriate.

 

Q4. Would you buy from Responsiva again?

  • Yes: 83.3%
  • Maybe: 15%
  • No: 1.7%

More than 98% of customers said they would or might buy b2b data from Responsiva again. This is the biggest indicator that our list broker service is fist class, backed up by the fact that the vast majority of orders come from repeat customers.

 

Q5. Would you like Responsiva to introduce a loyalty scheme?

  • Yes: 43%
  • Maybe: 42%
  • No: 15%

The interesting point about a loyalty scheme for business data services is that we have tried this before; in 2011. Customers received points for every prospect data record purchased, redeemable at year end. Only one customer actually went on to redeem those points, despite reminders.

The scheme itself required administration, and for that reason proved more time consuming than it was worth. In general it seems that most customers would simply prefer a great service, accurate data lists and a fair price.

 

Customer Feedback

At the end of the questionnaire, customers are invited to give free text comments about their experience with Responsiva’s data list broking service. The most recent ten comments are listed below, cut word for word;

  1. Thanks very much for all your help.
  2. Thanks!
  3. Great service, called up as we had tried lots of other providers and was helped straight away – really appreciated it. Thanks
  4. Data report not received from telemarketing agency as of todays date so unsure of accuracy of data.
  5. Toby is a great guy to work with. I’ve only worked with Responsiva once, so do not know what can be improved on, as I really enjoyed my experience with him.
  6. Overall the service was good along with good advice
  7. First time I’ve used them, so its too early to say, but overall i have had a good level of service.
  8. Service is attentive and responsive
  9. This was a new venture for me personally, I would get my client to deal direct with Responsiva next time.
  10. We have bought from a few companies and this is the best data we have had so far.

 

 

 

 

No Comments

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.

 

, , , , , , , , , , ,

No Comments