Improving Sales Conversion Rates at Credit Risk Monitor®
By: Rich Pacheco
Tags: Logistic Regression, Salesfunneloptimization, Credit Risk, Sales Conversion, Sankey Diagram

By Rich Pacheco, William Dunn, Charbel H. Sebaaly, Chuwei Lyu, and Daren Ren

Photo by Micheile Henderson on Unsplash

Background

Our team partnered with CreditRiskMonitor® (CRMZ) to improve their sales funnel conversion rates. CRMZ is a subscription-based risk monitoring tool that predicts the likelihood that a company will go bankrupt within the next 12-months. CRMZ’s customers use the tool to make decisions in extending customer credit, managing supplier relationships, and managing treasury exposures.

Illustration of CRMZ’s sales funnel

In CRMZ’s current sales process, a sales team calls prospects, converts prospects into qualified leads, conducts product demos for qualified leads, and converts these leads into paying subscribers. Sales representatives make an average of 231,600 calls to prospects per year. As indicated to in the chart above, different percentages of customers flow through each subsequent stage. CRMZ is interested in improving their sales funnel conversion rates for each step to drive increased revenue (i.e., increasing conversion rate at each stage by 1% could increase revenue by as much as 41%).

Hypothesis

We hypothesized that four key factors driving customer demand for the product drive sales conversion:

1) Environmental — while macroeconomic events may cause potential customers to become more interested in the risk of bankruptcy of their customers, we did not pursue these factors as our goal is to increase revenue regardless of the economic climate.

2) Marketing Efforts — marketing is likely to drive awareness of their product and CRMZ’s reputation as a market leader, and targeting potential customers with specific data-driven campaigns could move them from awareness to consideration.

3) Types of Customers — certain types of customers have different propensities to use CRMZ, and could be categorized by industry, company size, and job function.

4) Sales Representatives — characteristics and behaviors of the sales representatives, such as tenure and number of calls, also drive sales.

Data Sources

● Customer Relationship Management (CRM) — customer relationship management tool data tracked sales representative activity such as calls and emails to/from prospects and demos conducted.

● Marketing Automation — activities to prospects such as marketing emails sent, email open rate, click-thru rate, and other interesting moments.

● Sales Enablement — activity to prospects related to pitches sent and prospects engagement with the pitches.

● Site Data — activity from paid and trial users interaction such as number of logins, number of public and private companies in their portfolio, and the value of their contract.

● Social Marketing — LinkedIn and Facebook post content and engagement metrics.

Exploratory Analysis

We first analyzed conversion rates based on demographics and found that prospects from the mining, manufacturing industries tended to convert both from prospect to trial user and trail user to paid user faster than other industries.

Sankey diagram of CRMZ’s customer journey

Next, we sought to visualize the customer journeys that prospects typically traversed until signing a contract using a Sankey diagram (see chart on left). For this, we explored the CRM data, which recorded more than one million actions over the past five years for prospects. Given the quantity of data, we simplified the dataset by combining actions and taking only the first occurrence to obtain meaningful insights. Apart from identifying the importance of phone calls in the sales funnel, this analysis also highlighted some important information gaps that the client can use to redesign their data collection process. In particular, we found an unrealistic portion of users only interacted once with the company before signing a contract.

Model

Our main model was a logistic regression with XGBoost, a machine learning algorithm, to predict conversion from leads to demos based on CRM, Site, and Sales Enablement data. Using all the data, features related to sales representative performance dominate prediction (red square below). In addition to sales representative performance, emails and calls are important in predicting conversion to demos.

Feature importance of logistic regression

We next predicted conversion from demos to paid users at four different time points: three months, six months, twelve months, and no month restriction after the first contact. We found that as time increases, PAYCE® score (measuring private company bankruptcy risk), becomes more important, indicating that customers convert sooner when they are interested in information for public companies, aligning with CRMZ’s value proposition.

While our prediction models were strong (AUC consistently > 0.90), we did notice that there were approximately 700 “false positive” customers expected to convert to demos but had not yet converted. Our models also contained a lot of data that would not be readily available for new prospects leading to an issue of using this model in practice.

Recommendations to Client

Based on our analysis and output the client has begun to implement some changes to improve the predictive power of future models or provide additional business value.

First, they are investing in better connections between their CRM data and Facebook/LinkedIn marketing data to understand the customer journeys more comprehensively and get more traceability on pre-funnel activities.

Second, we recommend categorizing calls more granularly as to prospects vs. to leads vs. to trials users to frame the calls according to the stage of conversion. CRMZ had already begun to implement this in their sales process, but this finding reinforces the general trend toward better metadata practices.

Third, as the logistic regression indicated, there was a lag effect for customers interested in private vs. public companies. Understanding a prospect’s desire for public vs. private company information early in the sales process could allow sales representatives to more efficiently allocate resources in approaching these different customers.

Fourth, we recommended conducting Myers Briggs or a similar assessment of their sales reps in order to be able to see if there were consistent traits in high performing sales reps.

Lastly, their sales team should explore the 700 prospects that didn’t convert to paid but were predicted to convert. Our analysis suggests that these false positives are likely to convert and we suggest exploring how these “low hanging fruit” can be efficiently converted or see what qualitative trends the model was unable to pick up.

Conclusion

Overall, we were able to build a predictive model that identifies a prospect’s propensity to convert along various stages of the sales funnel. While the model is not a solution ready for implementation, the process allowed us to provide actionable insights which can increase the business value to CRMZ.