By Frans Van Hulle
Note: This is the fifth article in a series on technical needs for lead generation.
Part 1: The Importance of Technology and Automation in Lead Gen
Part 2: Tech Requirements for Lead Gen
Part 3: Designing a Successful Lead Gen Form
Part 4: How to Automate Lead Verification
The lead generation industry has a complex web of interconnections, as well as a rather obscure distribution model. This has attracted a lot of fraudsters looking to make a quick buck.
As a result, the lead gen industry has moved away from a model that puts emphasis on only quantity, and shifted to a system where quality matters. This is why lead verification and scoring have gained immense importance over the last few years.
An effective lead scoring model has a couple of key advantages:
Greater Sales Efficiency
With the help of lead scoring, sales can be focused on leads that are estimated to be most valuable and likely to convert. Salesmen can simply focus on leads with the highest scores and filter out bad data.
Greater Marketing Efficiency
By determining which channels or strategies produce the most leads with favorable characteristics. This can help marketers to target leads more appropriately.
Alignment between Marketing and Sales
By scoring leads, both sales and marketing departments can establish what characterizes the value of a lead.
Lead scoring and verification go hand-in-hand. While lead verification validates lead data and eliminates users who are not contactable, lead scoring takes it a step further and rates the quality of leads based prospect identity, interest and intent.
When looking at the prospect’s identity, individual lead data is evaluated based on “who they are,” and if they are a good fit based on location, age, gender, position, industry, etc. But, it is also vital to take into account implicit data relating to user behavior on the lead gen form that can give insights into prospect interest and intent. Implicit factors that give insights into how sales-ready the lead is include how long did the user spend on the form; how many clicks were made; whether or not the lead is a returning visitor, etc.
After defining the explicit and implicit factors that determine the quality of a lead, it is important to assign categories and criteria in order to assign values to parameters, especially how they relate to each other. For example, what if a lead is located in the right state and has the right age, but only spent a few seconds on your form. Or, how are you going to rate leads that visited your website three times yesterday vs. leads that visited your website three times last week. It is important to have a clear system in place to weigh factors and categories in order to establish how data points relate to each other. Depending on how you weigh parameters, your lead scoring can be significantly fine-tuned.
After calculating quality scores based on characteristics and engagement, you can put a system in place that established how to follow-up on leads with different scores, i.e. they need urgent, priority follow-up or should they go into long-term lead nurturing programs.
Developing lead scoring is a sophisticated undertaking that requires a lot of data analysis. Since there are many criteria that have to be taken into account, weighed and calculated, it can easily become an intricate, complex and time-consuming task. Furthermore, lead scoring is something that only companies who have already collected a lot of data can do. You’ll only be able to correctly identify correlations and parameter weighing if you have access to a large amount of data. If you don’t have the skills or technical resources, it may be advisable to outsource lead scoring services to external companies that offer lead scoring services.
Frans Van Hulle is CEO/co-founder of ReviMedia.
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