How Momentum Solar cut calls-per-appointment by 33% with predictive lead scoring

Since 2009, Momentum Solar has been one of the fastest growing solar installers in the country, helping thousands of homeowners make the transition to solar.

The problem: optimizing the lead scoring process

Momentum Solar relies on their state-of-the-art call center as their main conversion channel, reaching out to leads acquired from aggregators to set up on-site assessments for solar installations.

But Momentum didn't become one of the nation's largest installers by being satisfied with good enough.

A solar installation is a significant investment, and it can be easy to get cold feet. When a sales rep needs to dedicate an afternoon to an on-site visit that can be canceled at any time, the cost to acquire a new customer can get expensive quickly. To help offset those costs, the Momentum team was looking for areas of optimization with the goal of reducing acquisition costs. One area they looked at for improvement was a lift in their lead conversion rate. They believed that better lead prioritization that allowed their call center to focus on the best leads was key to driving this lift.

The obstacle: an incomplete picture of a solar customer

Traditionally, Momentum had leveraged a rules-based approach to lead scoring that focused on a few data points, such as lead source, lead demographics, home ownership status, and location of residence, to determine lead priority. The challenge with a rules-based approach is these data points are often subjectively chosen, and don't account for the full spectrum of both intuitive and unintuitive data points that are key indicators of whether or not a lead will convert.

Additionally, Momentum had a large collection of leads that had not initially converted. A major issue with the rules-based approach to lead scoring is that it's static, even though people aren't. With Momentum's existing approach, a lead's score–and in turn, priority–would never change.

Why Faraday?

Fresh data + models

To optimize their lead conversion, Momentum used the Faraday platform to add a predictive layer to their lead prioritization system.

The value of predictive models is the quantitative, rather than qualitative, approach to lead scoring. Machine learning algorithms run millions of simulations to find patterns in large datasets, meaning they transcend subjective biases.

Instead of scoring a lead based on a few key points, Momentum used the Faraday platform to build a lead scoring model that ranked leads based on their probability of becoming a customer. The model trained on Momentum's existing customer base and the hundreds of first and third-party data points that Faraday associated with Momentum's customers.

Furthermore, the lead scores were updated as new customers' examples were added and lead behavior was observed. This ensured that Momentum's prioritization methodology was never static.

The results

Appointments set 33% faster

To test the efficacy of this new approach to prioritization Momentum split the scored leads into three groups: red, yellow, and green, with red being low probability leads and green being high probability leads. The goal was to see a higher rate of lead-to-scheduled appointments rates for the yellow and green groups (comprising the top four deciles  of leads) and lower conversion in the red group (bottom six deciles).

Each of these groups received the same call cadence and performance was measured against a baseline performance of 180 connected calls per scheduled appointment.

As expected, the green and yellow groups converted at increased rates compared to the baseline vs the red group, which converted at a lower rate. Specifically, the green group saw a +191% lift in conversion rate over baseline, while the yellow group saw a +51% lift in conversion rate over baseline. Equally as important, the red group converted 4% lower than the baseline, further confirming where the call center should spend its time.

Ultimately, the call center needed 33% fewer outbound calls to hit their scheduled appointment goal.