Customer Acquisition Cost, or “CAC”, is a critical performance metric for B2B marketers. The costs of acquiring a new customer can vary, but according to KeyBanc, the CAC ratio for new customers in the software industry was 1.6 in 2020. Since every marketing team has a limited budget, it is critical to deploy resources strategically that prioritize high quality prospects and minimize your CAC.
The key to lowering your Customer Acquisition Cost is to selectively target the prospects that have the highest conversion and win rates. Doing so will ensure that budget is not being wasted marketing to prospects who are unlikely to actually buy.
While lead scoring is an important tool for demand generation, there are a few reasons why most lead scoring frameworks are not sufficient enough to prioritize segmented brand awareness and demand generation campaigns:
- Lead scoring efforts often only examine engagement activities prior to a lead entering the sales funnel, rather than behavior throughout the deal lifecycle. This can waste investments in efforts designed to nurture and move prospects along in the sales funnel.
- Lead scoring sometimes does not take into account win rates, meaning that they do not objectively identify the leads that are most likely to close. Rather, they only look at the impact of demographic and behavioral factors on opportunity creation. This means that lead scoring could ultimately fuel pipeline with junk, lower win rates, and increase CAC!
- Lead scoring sometimes focuses only on the account attributes which miss the attributes and nuances of the people the company is trying to target.
- Many marketers forget to optimize their lead scoring model for continued accuracy over time. Those who rely on a “set it and forget it” lead scoring model will not recognize the impacts of market trends and changes on win rates, weakening the reliability of model over time.
To unlock a truly objective lead score, companies need to regularly run sophisticated analytics that optimize lead scoring accuracy over time. However, this can be a difficult and time-consuming effort without the right resources.
That’s why Discern.io simplifies objective lead scoring through the power of machine learning. By helping customers understand the unique account and contact attributes that have the greatest impact on win rates, Discern.io can accurately score leads. Furthermore, by using logistic regression at regular cadences, Discern.io seamlessly updates lead scoring formulas to reflect the impact of market changes and ensure the most accurate methodology is always being used.
Once you have an objective lead score, you can then differentiate marketing spend to focus on the high quality prospects that are most likely to purchase your solution. Doing so will not only reduce CAC, but will also increase the win rates for marketing-originated business!
