What if Your Lead Scoring Is Making Things Worse?

December 7, 2015 Alan Reyes

How do you know if your lead scoring is actually helping or hurting your company? This article isn't about any specific leading scoring system. It’s not about tactics. It’s about strategy. How can you sanity check the accuracy of your lead scoring model and make small improvements to improve results?

At NASA they have a saying: ‘garbage in = garbage out.’ Meaning, whatever you feed into your specialized computer program that is supposed to make your life easier, should be something that's healthy for it to ingest. For humans it's kale, for computers it's good, clean data. You can have the best processing in the world, but if you ask it to process garbage, guess what you’ll get.

Space Trash NASA
That right there... that’s garbage that fell from space. Courtesy of NASA.

With any business‐as‐usual operation, it’s easy to let the machines do all the work without sanity‐checking the results carefully. You should never rely on software to be 100% correct. You have to do your diligence to make sure the results you're getting are in fact helpful. So how do you do this with lead scoring?

The Manual Method

I actually always recommend starting out this way. It forces you to dig through your data and get your hands dirty. But you just got the perfect app for doing that. Why keep a dog and bark? There are a couple of reasons why you should get face to face with your own data periodically:

  1. By going through your data, you actually see your pipeline from a macroscopic perspective. You'll personally examine hundreds of leads and you’ll develop a gut feeling about the quality and type of leads you’re picking up. This may make you do a double‐take: your next step might be talking to your marketing department about cleaning up inbound lead quality.
  2. Going through the process once will break the ice of the task. Sometimes, knowing that you should look through your data to make comparisons of scored leads to closed deals will seem like a daunting task. We all have tasks like this ‐ somehow they never make it past #3 on the to‐do list before getting kicked to tomorrow. By getting in there the first time, at least you quickly know how long it takes to do this sort of check up analysis. From being an unknown, it becomes something graspable. Usually, after you do something like this once, and you know it's not a big deal, you won't mind doing it again.

So how do you this?

Step 1: Get A List Of Your Scored Leads

Hopefully, you can export a list of your scored leads to Microsoft Excel or CSV with ease. One thing to keep in mind are your date ranges. You’re going to want to consider your typical sales cycle and download a list of scored leads that range X weeks before the month you’re performing an analysis on (X weeks being your sales cycle).

 

Scored Leads

 

Step 2: Get A List Of Recently Closed Deals

Next up, you’re going to want to get another Excel or CSV export of your recently closed deals or current deal statuses. You should probably always grab data that lines up with the deal statuses that reflect the end of the month – it will keep your analysis simple.

Lead Status

Step 3: Let Excel Sort 'Em Out!

Next, simply paste your data recently closed deals (or current deals) for the month you’re analyzing next to your scored leads. In the example below, the scored leads are on the left and the deal status is on the right.

Put Leads Together

In this example, we’ll use email addresses as the common link between to the two sets of data. The idea is to build Excel formulas to show which deals won and their score. That way you can begin to determine if your lead scoring has any real effect on whether deals close or not.

First, let’s build a formula to match the company from the first set (scored leads) of data to the second set (deal status):

Company Formula

Next, let’s create a formula to match the appropriate role to our second set of data:

Role Formula

Finally, let’s finish off this spreadsheet by building a formula to match the lead score:

Lead Score

If you’re having a hard time finding the formulas, look in the upper portion of each image. You can see the formula created for each starting cell.

Now, other companies may use Account Name as the common link between their sets of scored lead and deal status data. Either way, set up your spreadsheet similar to the one above and simply swap out Email for Account Name.

Step 4: Sanity Check Your Spreadsheet

Sanity check your spreadsheet to make sure it’s performing the matching correctly. You can do this by simply making sure the results (in yellow) are present in the same row in the left data set.

Step 5: Determine If Your Lead Scoring Is Helping or Hurting or Wonky

Finally, look over your deal status and compare them to your lead scores. What percentage of “A” leads resulted in business? What about “B”, “C”, “D” and “F” leads? By knowing the percentage of which grade resulted in real business, you’ll have a good idea whether or not your lead scoring is accurate.

There is a possibility that your lead scoring is giving you the best leads to go after, but for whatever reason your sales team isn’t good at closing high scoring leads. Regardless, the point of this analysis is to know what’s going on so you can investigate further and hopefully improve your sales process.

Now even if your CRM system is set up to tell you this information – you should do this anyway! Don’t rely on software without sanity checking it first! Getting your hands dirty is a must. Find out what’s really going on with your lead scoring and do this analysis every couple of months at the very least.

What About Checking For Something More Important: Customer Retention?

You can also perform a similar analysis every quarter to see if your lead scoring is helping or hurting your customer retention. Sometimes leads that close easily, don’t necessarily mean they’re going to be customers for life. As a matter of fact, it could be the deals that close easily that were misinformed about your service or thought they were getting something else or something extra.

In this case, this could be a serious defect in your sales training and sales process. Obviously, this probably wouldn’t be the fault of your lead scoring system – but it could be a function of how you determine what a qualified lead is. Is this case, doing this analysis may nudge you towards improving your lead qualification process.

For example, a certain budget range might be bringing in a lot of “cheap” customers. By tightening the budget range in your lead qualification parameters, your sales team can focus on customers that can afford to use your product for a longer duration and even have the means to invest in training.

Again this is why this is an important reason to do this analysis monthly. By looking at your sales funnels in conjunction with your lead scoring data, you can make improvements to your overall sales process.

With tools like Kissmetrics, you can import your lead scoring data as a property and track it against customer retention (even closed deals). This can simplify your monthly analysis – but don’t forget to sanity check by hand!

A lead scoring model that finds NEW leads? Learn more here

About the Author

Alan Reyes is a marketing associate for Judicious, Inc.

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