- Performed a series of data analyses that provided important business insights
- Proved the phone numbers being flagged as spam didn’t cause underperforming cold calls
- Revealed other factors affecting the cold call performance, such as lead demographics, etc.
- Suggested building an AI system to detect the low cold calling success rate
- Built the MVP for the suggested AI solution and passed it to the customer to test in the fields
About the Project
A midsize insurance business specializing in home and car insurance contacted Intelliarts regarding their problem of a low cold calling success rate. This was the company that we collaborated with for a long time. We also knew our partner for its commitment to improving operational efficiency and cutting business expenses. To achieve this, the customer was used to monitoring new ways for operational optimization, especially with the help of innovative technologies.
Challenges & Project Goals:
Cold calling is one of the key strategies for how the company attracts new clients. However, phone numbers used by the company’s insurance agents are periodically flagged as spam, and the insurer suspected this could affect the effectiveness of their cold calling sales channel.
The project’s goals were to test this hypothesis while also helping the insurer solve the problem of underperforming cold calls.
We performed a series of data research analyses that proved the underperformance of customer cold calls didn’t correlate with their phone numbers being marked as spam. We then investigated other factors affecting the cold call performance, such as the agent who was calling, the timing of the original and follow-up calls, etc.
In the end, our data science team suggested creating a machine learning system that could detect the underperformance of phone numbers, track the factors causing the low success rates of cold calls, and avoid such patterns in the future. After building the MVP for this custom solution for insurance cold calling, we passed it to the customer to test it in the fields.
Business Value Delivered:
- The comprehensive data analysis that Intelliarts conducted helped us form a set of recommendations for the customer with further action needed to solve their business challenge of low cold calling performance.
- We built the MVP for the AI-powered solution for phone management, which, in the long run, can help the customer detect which factors specifically cause the low performance of cold calls and avoid such patterns.
The most impressive thing about Intelliarts was their willingness to learn about our business so that they could build the most effective models.
VP of Product Management
We completed a series of data analyses to summarize the main characteristics of the phone call data, discover patterns, and test assumptions. Our main conclusion was that the underperformance of the customer’s cold calls didn’t correlate with their phone numbers being marked as spam. Specifically:
- This was confirmed by the general phone call analysis, which helped us check how the amount of successful cold calls grew over time and whether there was any correlation between effective cold calls and all calls generally at the moment when the numbers were flagged as spam.
- We reached the same conclusion after performing the number migration analysis. At this stage, we monitored the effectiveness of cold calling over time and expected the performance to drop after the number was marked as spam. This did happen, but occasionally the performance grew up again after a few more calls were made from the same number, proving no strong correlation between the two events.
- We also contrasted the low performance of a particular number to spam detection during the underperformance vs. spam analysis. We discovered that the low success rate of a specific phone number didn’t correlate with the same phone number being marked as spam later. As a result, we ensured that, while marking phone numbers as spam did provide extra management efforts related to phone number management, it didn’t have any direct influence on the overall effectiveness of cold calls.
- Despite having no evidence suggesting that the underperformance of cold calls was caused by the company’s phone numbers being marked as spam, we discovered other factors that mattered. Among them, we singled out the demographics of the lead, the agent who was calling, the timing of the original and follow-up calls, and the strategy of how the cold calls were managed.
- At the end of the project, we provided the company with a range of business insights concerning the underperformance of their cold calls and the underlying factors affecting their cold calling strategy.
- Our main conclusion was the absent correlation between the low cold calling success rate and the customer’s phone numbers being flagged as spam.
- Our team of data scientists suggested building an AI-powered cold calling solution for the insurance agency that could detect the underperformance of phone numbers, track the factors causing underperforming cold calls, and avoid such patterns in the future.
- We built the MVP for this solution and passed it to the customer to test in the fields to collect feedback from the insurance agents.