Balto Ideal Customer Profile

Background

For this project, I was asked to help identify our company’s ideal customer profile. Our company, Balto, sold an AI tool for contact centers that gave agents real-time suggestions of what to say on every call.

There were three specific areas that I was asked to focus on:

• Industry- We had customers across a variety of industries, and our executives wanted to know if we should target or avoid any of those industries in particular moving forward.
• Tier- Our Revenue Operations Manager had developed a tentative tier system based on what he thought would be the traits of a good customer. There were 3 tiers, and my task was to validate whether these tiers did indeed line up with positive results.
• Integration type- Our product had to integrate with the customer’s softphone system. It was easier for us to integrate with some systems than with others, so we had developed a softphone scoring system. I was asked to verify that this scoring system was correlated with positive outcomes.

Data Collection

I collected data from a variety of sources. The main one was Salesforce, but a lot of data came from Google Sheets, Pendo, or even individual contracts.

I compiled data on all of our current as well as churned customers.

Some of the most important data points to consider were the following:

• ARR (Annual Recurring Revenue)
• Churn rate- What percent of customers within a given industry stopped using our product
• Sales cycle length- The number of days between when we first made contact with a potential customer and when we signed a deal

Analysis

I performed all the analysis in Google Sheets, with different sheets for the industry, tier, and integration analysis for both current and churned customers, as well as some sheets for direct comparison between the two.

Here is a link to the dataset, as well as all the analyses. I have restricted the access to invitation-only at Balto's request to protect proprietary interests. Additionally, all columns that show customer counts or raw ARR figures have been hidden- all customer and ARR statistics use percentages. All company names and softphone brands have been changed, and many other aspects of the dataset have been changed as well.

Conclusions

Here is a slide deck with a summary of key takeaways, which I presented to executive stakeholders upon finishing the analysis. Its access is also restricted.

A more in-depth look at results by industry can be found within this sheet of the analysis.