Last month, I discussed intent data, one of a trio of datasets that assist with lead scoring.  This month I’m touching upon Fit data and next month I’ll be discussing Opportunity data.

Fitness data consists of firmographics, technographics, and verticalized datasets that help define whether a company is a good prospect.  Biographic values such as Job Function, Level, Skills, and Responsibilities should also be employed when evaluating contacts or leads.

Firmographics are the basic variables that have long been used to define a good prospect.  Firmographics include location, size (e.g. revenue, employees, assets, PE/VC funding, and market cap), industry, and year founded.  Other commonly used dimensions include Ownership Flags (Minority Owned, Woman Owned, Veterans Owned, SOHO, Franchise), Ownership Type (Public, Private, Nonprofit, Government), and Parent/Sub/Branch.

Ownership flags are used for both inclusion and exclusion with SOHO and Franchise flags generally used to exclude small businesses and those with limited purchasing authority.  Subsidiaries and Branches are often excluded as they also have more limited purchasing authority, but are included when looking for locations to sell into after an MSA is signed or when evaluating entry into overseas markets.  In these cases, knowing all of the locations of current accounts and top prospects is quite valuable.  Likewise, logistics companies look for companies with many locations.

Several vendors support radius searching around a ZIP code.  This select is valuable for both event planning (e.g. 50 miles from a tradeshow) or for sales reps when traveling and looking to include additional accounts and prospects on a trip.

A recent study by Dun & Bradstreet found that three of the top five dimensions used when targeting B2B accounts are firmographic (Location, Industry, and Company Size).

Furthermore, Account specific lists for ABM generally employ firmographic criteria when building or extending ABM lists.  (Online activity is an intent variable which was discussed in my last What Is.)

Technographics are an example of a verticalized dataset.  Generally they consist of vendors, products, and product categories.  Originally, such data was only available from technology sales intelligence vendors such as DiscoverOrg and HHMI (now Aberdeen Services), but HG Data built and licensed a technographics dataset which is now widely available in data marketplaces, predictive analytics, and sales intelligence platforms.  Aberdeen followed suite in licensing their dataset as well.

LinkedIn Sales Navigator offers a set of unique selects for targeting departments, department headcount growth, and employment growth.  Unfortunately, this data is not downloadable or available for lead scoring.

Biographic variables are also important when determining fit.  Job function and level help determine whether a lead is likely to be a decision maker, influencer, or noise.  Most vendors map job titles to taxonomies of between 8 and 60 job functions and 4 to 8 levels.  Other biographic variables include education, years at company, former companies, and interests.

Data availability and currency may also play into Fit both directly and indirectly.  If a select is weakly populated (e.g. Education, Skills), then many potential targets will be omitted from lists or given low scores.  In some cases, lowering the lead score due to a missing field makes sense.  Lead scores should incorporate the availability of emails, direct dials, and LinkedIn handles because this information increases the likelihood of successfully communicating with a prospect.

TIP: When evaluating vendors, ask about the fill rates on key fields you anticipate using in your lead scoring or prospecting.

In a similar vein, last update dates should also be used as a filter.  Data from SHRM indicates a 2016 average contact decay rate of 27% when accounting for job departures, lateral moves, and title changes.  And this is only at the contact level.  The rate is even higher when including company name changes, relocations, and bankruptcies / facility closures.  Thus, the last update field is a relevant fitness variable for prospecting but not inbound lead scoring.

In short, lead fitness can be defined by a broad set of who, what, and where variables related to companies and contacts.