Scoring for Qualified Leads
Without a doubt, lead generation is crucial as the beginning of the sales process. However, generating a lead does not ensure a competed journey through the sales cycle and conversion to a sale. In a B2B sales environment, a marketing team may be focused on overall numbers, generating quantity on the theory that more leads will create more conversions to sales, and thereby more revenue. However, this is not necessarily the case. A prospect must have the intent and ability to purchase.
In B2B environments, collaboration between marketing and sales is important to achieve success. The purchase and sales cycle has become more complex with buyers educating themselves and interacting with sellers late in the process. Many areas of marketing and sales have become more data driven and scientific to improve efficiency and results. As part of that effort, lead scoring is a method to qualify and prioritize prospects, and has major importance in B2B marketing.
For optimal efficiency, marketing and sales need to be aligned and work together. The marketing team launches campaigns, generates leads and demand and nurtures the leads until a prospect is ready for the sales team to present proposals, give a product demonstration, negotiate the contract and close the sale. Timing is crucial. If the sales team comes in late, they may be competing for attention against other more entrenched vendors. If the sales team comes in too early, they may expend valuable resources approaching prospects that are not close to a purchase decision at the expense of focusing on qualified ones who are.
The concept of lead scoring is straightforward – a tracking system to quantify the qualification process. It is based on a model that weights different characteristics, demographics or behaviors, and assigns each prospect a value (often numerical) to indicate whether the prospect is ready to be transferred over to the sales team; requires outreach for additional nurturing; or is unlikely to ever convert and should be abandoned. While simple in concept, with more and more granular and detailed data accessible, lead scoring models can be quite sophisticated relying on analytics and data mining science.
Scoring metrics can be broken down generally into two types: explicit, that is information voluntarily submitted by the prospect or facts obtained by research; or implicit, that is information based upon observed behavior or actions.
Explicit metrics, sometimes referred to as lead fit, generally cover whether a prospect falls within the seller’s target audience, ideal customer or usual buyer. These include demographics or firmographics such as job title, company size, location, revenues; and budget to purchase, authority to purchase, need for the product or service and the time frame for a purchase (which are together often referred to as BANT).
Implicit metrics, based on lead behavior, are utilized to determine the level of engagement, interest, buying stage and timing. Activities that would be scored may include website browsing activity (frequency of visits, pages visited, length of time, downloads of gated content, registrations for a webinar, etc.); email engagement (opens, forwards, replies); and other specific actions or behaviors.
Demographic characteristics and behavioral actions are weighted generally with higher values assigned to those more indicative of a prospect being ready for sales because of intent and ability to buy, such as requesting a pricing sheet or product demonstration. On the other hand, lower values would be assigned to introductory actions indicating initial research, such as visiting blog posts. In addition, negative values may be assigned to certain actions such as an extended period of inactivity or unresponsiveness, or visits to web pages showing job openings.
Evaluation and Iteration
The process of lead scoring is dynamic, not static, as a prospect takes additional steps and moves closer to or further away from a purchase decision. Marketing automation and sales acceleration software tools can track prospects to provide the data, which can then be analyzed with closed-loop reporting and data mining techniques such as logistic regression analysis.
The model and assigned values can be adjusted to reflect the unique mix of characteristics and behaviors most closely associated with a particular company’s business and customers.
As marketing generates leads, lead scoring models help employ data and science for better decisions – identifying where prospects are in the sales and purchase cycle, which need more nurturing and which are ready for marketing to hand off to the sales team.