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The Education Industry Guide to Addressing Lead Quality

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First We Need to Track

Before we even start the lead quality conversation, we need to be able to track our leads. We need to know what is happening to our leads from the moment of the click all the way to the student’s first day of school.

Why do we need to track?

Let’s suppose we’re getting a holistic $100 CPL and around 1000 leads per month. From those 1000 leads, 50 enroll as students, which gives us a cost per enrollment of $2000. This is great and all but now we want to see which leads drove those 50 enrollments so we can optimize accordingly.

What we need to track?

We need to be able to see the origin of our leads down to the keyword level if we’re talking search or the creative level if we’re doing something like display or Facebook. Only then can we successfully optimize our lead quality.

How do we track this?

This is usually done by applying a tracking template or a final URL suffix to our campaigns. These add URL parameters to our URLs, which other platforms such as GA and CRMs like Salesforce can pick up and attach data to our leads. We’ve compiled a full guide on how to set up back-end tracking.

From Keyword to Classroom

As we said, we need to know as much as possible about our leads in order to optimize their quality. Generally speaking, we need the following 3 things:

  • Channel (Search, Display, YouTube)
  • Campaign
  • Keyword / Target

Once we have that set up, there are many more data points we can choose to track, and this will also depend on the channel that we’re using. For example we can track things such as match type (search), device, user location, and custom parameters that we invent for specific cases.

The Funnel Steps

Besides setting up granular tracking, we also need to set up a good funnel flow in our CRM so the sales team can manage our leads properly and see where certain leads drop off. It is usually easiest to optimize at the application level since that happens relatively quickly after the lead form is filled out. But as you gather more data you can start optimizing all the way down to the enrollment and school start levels. A hypothetical funnel could have the following steps:

Quality Comes at a Price

High Quality Leads

We all want high quality leads that our sales team can convert with ease. But these leads usually cost an arm and a leg. Competition is as fierce as it gets here.

Example keyword

[apply bachelors law degrees]

What to expect

We can expect to pay up to $200 per lead here. CVR from lead to app can be %5 to %10 or more.

Mid Quality Leads

Mid quality leads are also competitive but because they don’t convert as well their CPCs will be lower.

Example keyword

[law school requirements]

What to expect

We can expect CPLs anywhere between $50 to $150. CVR from lead to app should be around the $5 mark.

Low Quality Leads

Low quality leads can seem quite terrible and could also lead to frustrations in your sales team due to low CVRs. However, their secret weapon is that they can be very, very cheap.

Example keyword

law scholarships for women

What to expect

Expect low CVRs of 2% or lower but the price here can be $30 or lower. If we compare to that to the $200 of the high quality leads, we can see the price can be 6 times cheaper but CVR is less than 5 times lower.

This should be one of your main tools for optimizing your lead quality. Don’t just go after high quality leads. Go after leads with prices that actually make sense. A low-quality lead can be just as beneficial (if not more beneficial) than a high quality one if you’re paying the right price for it. The only restriction to think about is the pressure on your sales team. Low quality leads can put a strain on your sales team if they come in very high volume. But if your sales team is capable of facing this challenge, don’t shy away from lower quality traffic.

When working for large organizations, we sometimes forget some of the basic principles we all apply to our daily economics as individual consumers. For example, you can get the best ice cream in the grocery store, but it will cost $10 or more. You can probably get a less premium brand of ice cream that will taste just as good for half the price. We often see clients forget the reality of their advertising budgets because the money seems entirely fictional or imaginary when viewed exclusively from an Excel sheet. Sometimes, the solution to optimizing your budget is to think about the money as if it is literally your own.

This game is all about smart spending and knowing exactly what you’re paying for!



Now we’re ready to optimize. No matter the quality of the category that we’re going after, it can always be improved. In short, we’re looking to focus on the leads that perform with the highest CVR relative to their price.

High Level Optimizations

When analyzing lead quality data, we should always start with higher level data such as channel or campaign level and work our way down to more granular data points such as device, creative and keyword level. We’ll give quick examples of each to show you what you should be looking for.

Here we can see a very good example of how low-quality leads could be beneficial. We have leads from “Scholarship” campaigns that convert at just a little over 2%. If we compare that to the Medical General category, which converts at close to 8%, it seems that scholarship leads are of very low quality and should therefore be avoided. However, when we factor in the price difference of $35 vs $223 per lead, we can see that the cost per application picture says otherwise. Our cost per application for scholarship leads is $1.5k versus the $2.8k for Medical General. Hence, we should probably invest even more money in the low-quality scholarship leads and look to pay a little less per lead on the Medical General ones. From the table above, we can also see that Competitor leads appear to be extremely efficient up to the application level and we should look to invest more there. Finally, we should remember to think about how our data will play out all the way up to the students starting school, where things might change.

Getting More Granular

After analyzing our channel and campaign data we can go a little more granular to make optimizations based on more granular data points.

We can see that Desktop leads have the highest chance to turn into an application. Hence, we can look to spend more money on Desktop. From the second table we have Phrase match as the best performing match type which is why we should look to allocate more budget towards these keywords.

Other Data Points

There are many other data points we can choose to analyze. The principle always remains the same. We want to invest our money towards the best quality/price ratio leads. Here is another example:

Here, we’ve chosen to analyze domestic leads vs international. Surprisingly, international leads had a very high CVR up until the application level for this client. Later on, however, we found out that these leads end up dropping out a lot more than domestic leads, which made sense. We should always consider the full picture but keep analyzing our data from as many angles as possible.

Most Granular Level

Ultimately, the most valuable data will be the most granular data. We’re talking specific target, creative, keyword level. The one major disadvantage of granular data is low sample size. Having one start from one keyword which generated only one lead doesn’t necessarily mean we should be spending all of our money on that single keyword. We probably just got lucky there. But as we gather more and more data, eventually we’ll start seeing patterns that are extremely useful for elevating our overall lead quality. This will help us spend our money in the right areas. Let’s look at an example data pull of this:

Our top keywords with the most applications are simply scholarships. They have low CVR, but we also know that they’re cheap to get. Something like “medicine schools” was very successful and we should look to push more money there even if the leads are expensive, as its CVR is over 20%. We also have some school names, which are technically competitors. Whatever your data may show, adapt to it and follow the price/quality model for your decision-making process. These steps will lead you to lead quality success!

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