Improving Lead Quality In And Out Of The Platform

Sometimes the paid media world can feel like it’s split into two camps. You either work in DTC/ecommerce, or you work in lead gen. This is for people in the lead gen camp.

When working as an agency or freelancer, it can be difficult enough as it is to generate leads at an efficient rate for clients, but the more important part is driving qualified leads that ultimately bring in revenue.

I’ve seen the tone of multiple client meetings do a quick 180 because our agency team was excited about all the new leads we were bringing in, but the client team (disappointedly) revealed that the majority of these leads were low quality.

As a result, I now spend a lot of time analyzing what factors drive quality leads, so I’m sharing my processes behind my analysis and the subsequent optimizations I make.

Starting With An Analysis Of CRM Data

Similar to the beginning of many of my customer behavior analyses, I start by looking at data within my clients’ CRMs. I’ve found it helpful to pull a report that contains:

• Contact information (name, email, company, etc…)

• Deal stage

• Deal won/lost

• Won reason/lost reason

• Initial message/request (if possible)

This report helps me to identify which specific contacts and deals I should be focusing on in my analysis. I like to bucket all of these entities into two groups; non-SQLs and SQLs. When I categorize the CRM data in this way, I can quickly see which common themes the SQLs share, and which themes are common among the poor quality leads.

While pulling this report and categorizing leads is fairly quick and simple, I also like to take the time to manually comb through messages and correspondence between leads and sales reps.

No, this isn’t very efficient, and it doesn’t scale well. However, I’ve found that understanding the specifics and nuances behind why leads went one way or another brings an entirely different context to this analysis. Granted, if there’s an overwhelming number of leads to sort through, I won’t get this granular for all of them.

Improving Lead Quality With In-Platform Adjustments

With the insights captured from the above CRM analysis, I can now start making adjustments within ad platforms to improve lead quality. There are two fairly simple actions that I like to take here:

1. Importing lead lifecycle data into the ad platform for optimization. This is now easier than ever through direct CRM integrations, or a simple Zapier connection. By optimizing toward an SQL, or even closed deal, it’s possible to improve the quality of leads that a campaign generates.

   1. A quick caveat is that this will be dependent upon the length of a sales cycle and data volume. If a brand has an average sales length of 90 days, and is closing five deals a month, I wouldn’t recommend only optimizing toward closed deals. This won’t provide any ad platform with enough data to make quality bidding decisions.

2. Update audience targeting, specifically focusing on exclusions. While it can be intuitive to set up the audience you want to actively target, I’ve seen that adding in audience exclusions can be an equally impactful optimizations to improving campaign performance. I lean on the data from my CRM analysis to guide what characteristics or demographics I should be excluding.

The combination of these two simple changes can help to refine a campaign’s targeting to better align with business outcomes.

Improving Lead Quality Outside Of Ad Platforms

I’m sure there are many ways to do this, but one of the easiest and most impactful tactics that I’ve turned to in the past is to make landing page updates.

Circling back to the CRM analysis from the first section of this post, there are often specific qualifying criteria that leads need to meet for a business to consider them an SQL or high quality lead. Outlining these requirements on a landing page helps to disqualify any potential low quality leads.

For example, I often write out statements on a landing page such as:

• We limit our services to companies with over X employees

• Our minimum order size is Y

• Our product can only integrate with platforms A, B, and C

This will eventually train any bidding algorithm to optimize to higher quality leads by default, and the sales teams you're working with will appreciate the time they’re saving by working with fewer low quality leads.

Setting Expectations

Lastly, making adjustments both in, and out, of ad platforms to optimize toward higher quality leads can actually result in a decrease in overall lead volume (at first).

Working in paid media, this can feel uncomfortable. Driving leads everyday provides a much more frequent dopamine hit than those long-term closed deals which can feel few and far between.

I recommend building out regular reports to focus on down-funnel metrics like lead quality, pipeline, and revenue so that the short-term drop in leads is more palatable. You’ll be able to see the changes in SQL rate and pipeline fairly quickly.

Wrapping Up

As with most of my customer behavior optimizations, I start by analyzing CRM data to improve the quality of leads driven from the campaigns I manage.

From there, I usually implement some combination of lifecycle conversion actions, audience exclusions, and landing page updates. These different efforts have helped clients to improve the quality of leads we’re driving for their business.

Have questions, considerations, or critiques? I’d love to hear them! If you’re reading this via email, just hit respond. Otherwise, you can find me on LinkedIn.