The concept of quality vs. quantity continues to be an evolving concern for many advertisers who spend media dollars with the hopes of generating leads—and when I say leads, I mean the crème de la crème of leads. Their desire for valuable leads over volume of leads is understandable. After all, what is the value of a lead that doesn’t develop into something more? Analyzing lead quality and characterizing what makes a lead “Good” or “Bad” is an essential step in making campaigns more efficient, especially when cost per lead and lead volume goals are aiming to be met.
At HYFN, we work with a considerable number of advertisers that look to us to drive quality leads. Specifically within the Education and Finance verticals, clients seem to have their own internal methods for classifying leads, and HYFN’s role is to make data-informed decisions to determine when and where to allocate spend. Within Education for example, our campaigns may be algorithmically optimizing towards leads. However, what we really want is a contact; what we really, really want is an application; and ultimately what we really, really are working towards is a student that completes the first class, and then signs up for a second class, to then one day return for grad school.
By classifying and tracking lead quality across one of our brands over a twelve month period, we were able to group leads into one of three categories: “Good”, “Bad” or “Un-Contacted”. These were derived from a sublist of categories that were provided by the brand at the audience level. For example, “One Class Passed” would be characterized as a “Good” lead. “Incorrect Contact Info” would be a “Bad” lead, etc.
Armed with this information, we were able to calculate the percentage of “Good” leads that make up the targeted audience. As a result, we saw a direct relationship between audiences that were producing a higher percentage of “Good” leads with a lower cost per application.
Interestingly, we have repeatedly seen an inverse relationship between cost per application and cost per lead with one particular advertiser, suggesting that the more leads we drive, the more expensive our cost per application could become. On average, high volume of leads tend to results in a higher CPA only when the lead rate is greater than the application rate, though if the leads are of a sufficiently-high level of quality, application rate will be greater than the lead rate causing CPAs to decrease when CPLs decrease. Based on this analysis, we have begun implementing this method of lead quality classification to support our decisions in budget allocations based on cost per applications, ensuring that we are putting spend where we are seeing success outside of low cost per leads or a high volume of leads.
In addition to using lead quality classification to support spend allocations, we can leverage learnings to determine whether broad audiences need to be narrowed to reduce the number of poorer quality leads.
Below are ways HYFN has been able to implement this in order to improve lead quality:
1. Develop a way to classify leads
- As mentioned above, assigning leads to a tier aids in analyzing how effective campaign targeting is, and can be key in understanding composition of audiences to better allocate budget and ensure the best results are being generated. Continue to gather this data and find trends in correlating metric performance to better understand how to optimize campaigns.
2. Narrow it down & put your money where it matters
- Disperse your budget to what you know performs well with regards to generating good leads with a low CPA.
- This may be a no brainer, but setup campaigns to make certain budget isn't being spent on already generated leads and exclude bad leads from a CRM list of bad emails (for example).
4. Be more specific
- Tailor your lead forms to weed out bad leads. The fewer questions the better, and make sure these are quality questions that are important to generate submissions. Make fields required and think about asking for personal information in a more trustworthy manner to avoid false or fictitious information.
- Test different ways to generate a lead. Maybe that is using the lead generation objective vs. a conversion campaign optimization towards leads. Another test may be different landing pages. The key here is to continue testing what works and what doesn't in order to always be improving campaign performance.
Working with clients to achieve better quality leads allows us to see correlations outside of just a cost per lead. This is very important since we wouldn’t have assumed an inverse relationship between CPA and CPL, as the relationship is usually positively correlated. This also extends to the inverse relationship between audiences and their proven lead quality, and proves that running these analyses are paramount in determining the true relationship between steps in the conversion path instead of relying on commonly-held assumptions. Continuing this conversation and working with clients to understand and analyze their data is essential in making any campaign the most efficient it can be.
Want to learn more about our solutions? Get in touch with the team.
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