Three Problems with Lead Scoring

Sales leaders don’t always recognize problems with lead scoring

The typical North American sports team has a CRM with hundreds of thousands – or more – contact records. Teams look to target their “best” prospects by leveraging various lead scoring tools and methodologies. Major cash investments into these systems, in theory, should provide these sales teams with some real efficiencies. However, most teams realize that many of these tools aren’t delivering the efficiencies or impacts promised with their investments. Many teams are looking for new tools. Others bring their lead scoring efforts entirely in-house. This post explores three problems with lead scoring that lead to disappointing results. Sales leaders and B.I. leaders who understand why these approaches fail to drive expected results will source more reliable tools with confidence in the output.

#1 Pre-defined categories

A typical sports team’s CRM has dozens of data points on each prospect relating to:

Demographic AttributesBeing something…rich, poor, old, young, nearby, far away.
Behavioral Attributes: Doing something…buying tickets, opening emails, visiting the website, submitting a form or survey.
Account Attributes: B2B vs. B2C, Business size, Business location

However, most lead scoring methodologies arbitrarily pre-select a small handful of factors and then jam all contacts through the same machinery. This approach spits out a singular score, which is supposed to help sales leaders decide which contacts are good leads and which contacts are not.

If this sounds like a reasonable approach, ask yourself the following questions:

Does your team track the exact same data as all other teams?

Does your prospect universe identically mirror the prospect universe of any other team?

Do you have the same sales resources and capabilities that any other team has?

Of course not! There may be high-level similarities. However, an approach that pre-selects certain data fields to score prospects – without regard to specific team data – it ignores the nuances of your team, your market, and the purchase patterns of your prospects.

#2 Everything Changes….Except Lead Scores

People change. Situations change. Everything in life changes. Except, it seems, many teams’ lead scores.

Most teams update their lead scores very infrequently. Once a year or less, in many cases.

To illustrate the opportunity cost of static lead scores, consider the following example:

John Smith, a fictional sales prospect for the fictional Isotopes baseball club, has the following contact attributes:

  • Previous Purchase History: Has never purchased from the team
  • Email: Has never opened an email from the team
  • Website: Has never visited the team’s website
  • Previous Sales Activity: Told a sales rep “no” last year
  • Lives 75 miles away

Now let’s say that the team ran its annual lead scoring exercise and some combination of these factors resulted in a low lead score for John Smith. However, the day after the scores were processed, John bought four tickets for the next home game. The day after the game, John opened an email from the team and clicked on links that took him to a page on your website that discusses partial season ticket packages and their prices.

Its probably a safe bet that John is a better prospect on Day 3 than he was on the day that the lead scores were assigned. However, a static lead score won’t reflect John’s improved buying profile. An organization that puts significant value in these lead scores may overlook a sales prospect who is doing everything short of standing outside the stadium with a bullhorn and yelling “CALL ME!!” Further, John’s interests may be diverted by the time the scores are recalculated next year.

By ignoring the fan who was in the right place at the right time, the team likely missed a great first sales opportunity and any potential of developing a lifelong patron.

#3 One Size Fits All

Broke college students tend not to purchase courtside season ticket packages. Multibillion-dollar corporations tend not to buy packages in the nosebleed sections. However, most lead scoring models jam every contact into a single lead scoring model that boils every prospect down to a single number.

See a problem here?

Failing to consider meaningful differences in the buying populations of different products results in less reliable lead scores. This is a costly mistake. Instead of achieving sales targets with smaller prospect pools, team have to expand their prospect pools. The only way to effectively communicate with a larger-than-necessary prospect pool is with a larger-than-necessary sales and marketing team.

The value of understanding problems with lead scoring methodologies

There is a tremendous upside to understanding problems with lead scoring methodology. As understanding increases, teams gain deeper insights into their own data. They also realize cost savings and efficiencies that drive more revenue. Perhaps the greatest advantage, however, is knowing how to choose a lead scoring vendor with scrutiny.