Direct marketing is a science that can be tracked to the penny and continuously improved. Misunderstanding of lists and selects is the industry’s greatest weakness.
1. 99% of Direct Mail is sent Standard Rate (bulk mail) without returns. List sellers rarely receive complaints as mailers rarely know their delivery rate and very few lists even guarantee deliverability rates. Those with a guarantee limit it to the cost of the list so never cover lost/wasted postage and printing costs.
2. 99% of list sources are not aware they sell out-of-date wholesale data. Update dates apply to postal processes, not compiler refreshes. Very few list brokers and others selling lists know much about competing files and are not aware that as “resellers” they never receive refreshes from the compilers’ current-month files. Those that are aware will never tell you they’re selling out-of-date data!
3. 99% of list sources sell what they have. Yearly contracts change regularly where a list source offers data from one compiler one year and a different compiler the next, usually based on price, without regard to quality.
Instead, we’ve been utilizing the same current-month data from the same vendors (those proven to be most accurate) for at least 15-years. We’ll switch sources only when our first-class mail test results prove there’s something better than the current-month Epsilon/Equifax consumer data, and current-month business data our clients require.
4. There is almost no consistency in how a given person selects lists. Some select Zip+4 only, DPV minimums, some use primary and secondary SIC’s, “second class” records, “likely” in addition to those with known data elements, and so on.
This makes it nearly impossible to objectively compare one list to another even if they use the same data-and even the same database. Your success is greatly influenced by their expertise, or lack thereof.
It is imperative to understand the available selects and how they apply to a given file.As an example, the largest compiler offers employees “at site” and employees “total” (for all locations of the entire company). Selecting by employees at site eliminates many of the nation’s largest firms. Selecting employees total eliminates branch locations (some of which are huge and should not be neglected).
Various applications determine when and how to select “location types” (headquarters, single locations, and branches) and whether employees at site or total should be used.
InfoUSA has no employees total select and assigns sales to all locations, even branches that are warehouses that have no sales.
As an example, some Fortune 500’s have a few officers at fancy headquarter offices with thousands of employees elsewhere, yet InfoUSA wouldn’t include them if you selected the nation’s largest firms by number of employees.If you instead selected the largest by sales you will receive their large manufacturing sites (branches), and again not receive the headquarters record.
In fact, the largest compiler once had less than 100 employees at their headquarters yet had 62,500+ employees elsewhere. Their largest offices with thousands of employees were here, in Allentown, PA.
• When selecting the largest by sales you would receive the NY headquarters from them, but not from InfoUSA.
• When selecting employees total you would receive the NY headquarters from them, but not from InfoUSA (again, they have no such select).
• When selecting employees here/at site you would receive the Allentown offices from them and InfoUSA, but neither would provide the headquarters record.
In many cases you will want to exclude branch locations. Or, we often suggest targeting headquarters and single locations of your required size, and branches large enough to have decision-making authority for what you offer.
5. We suggest selecting businesses by number of employees rather than sales volume (at site or total) as employees is almost 70% verified with 30%+ “modeled”. Using sales is far less accurate as it is “known” (verified) on less than 15% of all businesses so is 85+% modeled (based on industry, number of employees, and zip code).
6. Nearly all list selects are largely modeled and only a comparison to other lists and public demographics reveals the accuracy, skews, and coverage of the selects.
Does the count and coverage of a given data element actually cover the population requested? Is it representative? Consumer courthouse data is not gathered in 1,500 of 3,100 counties and homeownership is the most important and accurate basis for all demographics. Here Epsilon has no “home value” coded where other compilers model 100% of their home values.
7. Selects are skewed where some compilers use very liberal models and others are conservative preventing one list from being accurately compared to another. Please see “Compiler Counts Compared” at the bottom of this page for some unbelievable examples of compiler skews.
It is imperative to request “counts” showing the entire population and spread of each data element you are considering.
As an example, if you target households with $50K+-income or business with $1,000,000+ sales, you’ll want to see counts with all income ranges or all sales ranges, including the “not available” bucket. This shows how well the file is coded (how many records have that field populated), the skew (some compilers say many are wealthy and many businesses sell a great deal where others do not), and so on.
8. Nearly all lists (compiled, subscription, and response-email and snail mail) have demographic or firmographic data elements appended. Each compiler utilizes unique ranges (buckets) so you can usually identify the source of the data element.
As an example, an email, response, lifestyle, subscription, or other specialty list has income ranges/buckets that match Epsilon, so you know the models are conservative and reasonably accurate, or they match Experian so you know they are skewed on the high-side and you must accommodate this by requiring a higher income range, and so on.Within business lists you can identify the source by sales and employees ranges (buckets) or by SIC codes. The largest business list compiler and Firmographix SIC’s go to 8-digits where InfoUSA SIC’s are divided into 6-digit subsets.
9. There are only a handful of significant compilers:
Epsilon/Equifax, InfoGroup, Experian, Acxiom, Compass, Firmographix, First American, Knowledgebase and a few others, yet their data and data from their sources is sold and resold under many vanity names, such as SalesGenie, Database101, VisionList, AmericanList, and dozens and dozens of others.
We would never consider lists sold under a vanity name!
10. There are nearly 2,000 businesses listed as “list compilers” and “list managers” in various databases. 99% of these are list resellers working from a spare bedroom buying out-of-date “wholesale” data at very low prices.
Check their references, “whois.com”, Google, Zillow-search the address, and check them with credit reporting agencies before you are fooled by a fancy website.
There’s no need to check the “credit rating” at InfoUSA as it is entirely modeled data based on averages by industry, years in the yellow pages, size, and zip code.
As an example InfoUSA gave The Bethlehem Corporation in Easton, Pennsylvania an “A” credit rating where the largest credit-reporting agency gave them a very, very rare 4 out of 4 (less than 1/100% of all rated companies), meaning they will likely fail shortly with a credit report showing more than 40 suits, liens, and judgments.
99% of these largely 1-person list companies (brokers, managers, and compilers) aren’t large enough to actually compile or manage any data. The largest are multi, if not one hundred-million dollar firms.
In 2010, Experian, one of three dominant consumer credit reporting agencies and the only significant business credit reporting agency beyond the largest ceased selling business mailing lists-they found it too difficult to keep them current (yet they continue to sell business credit reports). They couldn’t resell data from the largest compiler as they are direct competitors in the business credit reporting arena.
We trust Epsilon/Equifax consumer data, but not Equifax business data. We have some trust in some of the largest business data compiler’s data, less in InfoGroup’s business data, or Firmographix, and again, Experian abandoned selling their own business data in 2010 and now resells InfoGroup data.
We have little confidence in InfoGroup (Donnelley), Experian, Knowledgebase, Compass, or Acxiom consumer data and, again,
We would never consider lists sold under a vanity name!