Young companies tend to possess a strong feel for their customer profile. It’s simply easier to know your target when your sales volume is low and your product caters to a niche (whether due to feature limitations or design choice).
While small organizations may know their ideal prospect, they often make a fatal error when developing a demographic lead score to gate lead delivery to inside sales.
They don’t understand their contact database.
You know in your heart of hearts your perfect target is a network administrator at companies with 1,000 or more employees in the retail sector in North America. This is a very specific profile, which is great!
Let’s assume you selected four fields to use in your lead score calculation:
Let’s also assume you evenly distributed points as follows:
Your maximum point potential is 100 points, right?
Digging into your database we find that Title is always populated because you require it on all your web forms and event scans. Region is identified over 80% of the time because you have IP address enrichment on your web forms and region can be inferred through event locale.
Unfortunately, the enrichment tool you’re relying on for Industry and Company Size is only matching 25% of your leads.
Your maximum score possible is only 50 points for 75% of your database. This is bad news if you set your threshold to 75 points!
By all means, start by analyzing your converted opportunities to develop an ideal profile. But don’t stop there! Pay particular attention to when your data is populated.
All your closed won opportunities contain industry data. This doesn’t help with lead scoring if the information is collected by the sales team immediately before the opportunity is closed. If the field isn’t populated early in the lead lifecycle, it doesn’t make sense to use the field to formulate a score on incoming leads.
If you have the budget, there’s a fix for this. Data enrichment services can supplement your database. Most providers append the data through an integration that’s called during create and update. Don’t forget to ask them to enrich your existing data.
Calculating conversion rates for each possible value and then comparing results to the conversion rate of records without a value (null) is a great way to evaluate whether you should include the data point in your model. If your field is only populated a portion of the time but your conversion rate for populated values versus null is significantly better, continue using the data in your lead scoring.
While it can be tempting to use lead scoring to gate traffic to your inside sales team, I suggest waiting until you are absolutely sure your model is effective. There must be an impressive correlation between conversion rates and your scores. Why?
Once you start gating, what happens to those leads?
They are never prosecuted.
Instead of this salt-the-earth approach, I recommend using lead scoring to prioritize leads for your sales teams. Perhaps the lower scored marketing qualified leads aren’t reviewed as diligently, but it gives your sales team an opportunity to weed through them as they have time. They might find a few nuggets of gold or that your calculations are off.
Do not develop your model in a silo.
The people you pass your leads to have strong opinions. They are already sorting your leads based on their experience. Make sure their voices are heard and include them in your process.
If you don’t communicate who you are targeting and why, you run the risk of lead scores being ignored.
Give the team prosecuting your leads a mechanism to rate the lead score. Not only does this help the sales team feel like part of the solution--you also get real time feedback. Review the data often to determine whether your model needs to be adjusted and tell them their feedback helped.
Finally, if your company’s product feature set changes or you hear sales chatter about a new use case they’ve uncovered, it’s time to reevaluate your model. Quarterly reviews are a standard best practice.
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