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The Problem

Speed to lead is the time taken for a company to respond to a lead via a phone call or email.

Any marketer and salesperson will tell you that making sure you’re getting somebody up and on the phone and into a conversation quickly increases your conversion by a huge amount.

And for Christie Horsman, CMO at Thinkific, the whole process looked like a black hole in terms of which leads coming from Marketing were being prioritized by the Sales team and in what time frame.

The Sales team had a huge variation of speed to lead, with SLA times way below the industry average. There was no clear structure of when the MQLs that were coming over from Marketing were being picked up, and the team was arbitrarily cherry-picking who to contact first because they didn’t trust the quality of the leads coming over.

Likewise, Marketing really felt like they were sending leads over into a bit of a black hole and that feedback loops were really broken with Sales.

Not knowing which leads the Sales team needed to pick up faster was causing inefficiency and missed revenue opportunities. It was also causing a lack of trust between marketing and sales because the leads that were being worked by the team were qualitatively different.

Thinkific had a problem: The sales team was highly inefficient. Leadership was going over to the sales team and really couldn’t understand how leads were getting picked up and in what time frame. The whole process was a black hole: how the sales team was picking up leads, which leads needed to be contacted first.

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The Hypothesis

Christie knew they had to get to leads faster; she knew they needed to want to get into the sales conversations faster, but that they couldn’t just say, “Hey, I need you to pick up the phone and talk to this person within five minutes” when the reps have a queue full of unprioritized leads and didn’t trust the quality of most of the leads. 

Christie knew she needed to help sales prioritize the leads to talk to them as quickly as possible. Her hypothesis was that implementing lead scoring would give the Thinkific sales team a more efficient and organized way of operating when they were going after the leads in their queue.

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The Solution

1. Align marketing and sales

Right from the get-off, as she started implementing Thinkific’s first scoring model, Christie got her marketing and sales team together to align on two specific things:

a. Find out who your Ideal Customer Profile is.

An Ideal Customer Profile (ICP) is a detailed description of the type of company or individual that is best suited for your product or service. It outlines specific characteristics, such as demographics and firmographics, which help identify the most valuable prospects for your business, the ones you want to speak to first.

Christie and her team used Reveal to answer questions about who their ICP was both in terms of Fit (demographic and firmographic data) and Activity (engagement and intent.) By running a complete Reveal analysis of the contacts, they were able to identify who their best contacts were and what they did on their online properties.

Once Sales and Marketing were aligned on the definition of the ideal customer, they knew who to target and who to speak to first.

b. Agree on what metrics you want to move together.

Once Marketing and Sales were aligned on Thinkific ICP, the next step was to decide which metrics they wanted to focus on and improve together. Christie knew that it was crucial to set the same foundation of who their ICP was and how they wanted to drive them into sales.

The metrics they decided to focus on were lead quality, MQL to OPP conversion rate, and speed to lead.

Before setting up this shared value system, the typical process would involve Marketing driving MQLs into the top of the funnel, and Sales flipping half of those MQLs to unqualified, saying they didn’t look great as they didn’t trust the lead quality,

After implementation, Marketing is still driving MQLs into the top of the funnel but focusing on how to drive lead quality as well. Both teams are now also thinking about improving the MQL to OPP conversion ratio and the overall lead quality.

2. Create a lead scoring model

By using Breadcrumbs’ automated scoring system, Thinkific was able to prioritize leads that were ready to buy and surface them to the Sales team. 

Breadcrumbs lead scoring uses machine learning and data analysis to surface a contact’s fit (demographics and firmographics) and activity (intent and engagement level). Each contact is then scored according to a co-dynamic system which gives a letter (A-B-C-D) to fit and a number (1-2-3-4) to activity where A1s are the leads with the highest fit and engagement, and D4 are the leads with the lowest fit and engagement.

Breadcrumbs’ co-dynamic scoring system allowed Thinkific to not only rocket A1s to the Sales team directly but also segment the different buckets into ad hoc campaigns in order to move potential customers that were still not ready to buy through the funnel with fewer touches from the Sales team. 

With this system, the Sales team was able to focus on getting on the phone with the most promising leads and still move the leads that were not ready to buy through the funnel.

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The Impact

Lead scoring proved to not only increase lead quality and MQL to OPP conversion rate but also gain back trust between Thinkific’s Marketing and Sales teams.

Using a lead scoring system able to surface leads that were ready to buy in real-time, Christie and her team were able to set that priority into what leads to target and contact first. That agreement got sales and marketing on the same page and reduced Thinkific’s speed to lead dramatically, which led to more closed deals and increased revenue.

After aligning Marketing and Sales on a shared lead scoring system and metrics, the Thinkific speed to lead dropped from 2-5 days to the 10-minute mark, with a total increase in SLA times of 99%.

This allowed Thinkific’s sales team to close deals faster and gave the marketing team the opportunity to reinvest those resources into keeping their pipeline full. With the help of lead scoring and better communication between teams, it was clear that Thinkific had achieved a much faster MQL-to-OPP conversion rate and increased its bottom line.

The Sales team’s efficiency increased dramatically. 

In Christie’s words,

“The Sales team can now sit there and know the SLAs associated with their queues, and they can prioritize their day. It’s a much easier job as a sales rep as well. When you walk in, you know exactly what you’re looking at; you know exactly how your day is going to be laid out.”