4Com offers leading business phone products, mostly in the United Kingdom. The company’s core product HiHi2 is an innovative desk phone with a tablet and business phone system. They approached us with the task to validate whether AI can improve the overall sales rate by identifying high quality leads more likely to convert during the sales process. They wanted to find out if the available data can be analyzed deeper to draw conclusions and used more efficiently to help telemarketers close more deals.
The client presumed AI could help create a more predictable and scalable business model and wanted to prove this before going all in with a company-wide adoption.
The client presumed there are dependencies between specific leads and their conversion rate. Validating this hypothesis would later help telemarketers focus only on the leads that have high chances of closing.
During the AI Sprint, our data scientists build a set of models that either proves that a business case can be solved with AI or that it’s not possible with the available data.
The client did not have a lead scoring system helping identify the leads with high chances to convert. Thus, telemarketers had to talk to leads that were unlikely to become clients.
There was no single data funnel in place and leads had to be processed from three different funnels thus making it difficult to track relations during the process.
Telemarketers kept a lot of data about their leads in their databases. This could be used more efficiently during the sales process and help increase lead conversion rate. There was also a lot of useless information that had to be filtered.
The leads were managed manually, which created a dependency on one key person in charge of this process. With manual work dominating the process of managing leads and data collection, the client faced increasing costs of keeping the system in place.
During the duration of the AI Sprint, slightly more than 3 weeks, the client wanted to test out if the team of our developers can work efficiently with their in-house team remotely before engaging in a long-term cooperation.
The client provided us with ready to test data about call history. After analyzing the records, our data scientists decided to drop certain rows as their quality was low. This process helped our team train the model in a more efficient way and, as a result, create an efficient and robust AI model.
During the AI Sprint, our data scientists created the first model with the data the client supplied us with. The model would help validate the hypothesis that AI can be used to overcome the client’s business challenge.
We met the client’s team for a 2-day workshop to set the goals and the hypotheses for further validation. We discussed the available data and set up success criteria for the project. During the next 3 consecutive weeks, we built the first model that validated the hypothesis and confirmed over 91% probability of success for AI adoption.
During the AI Sprint, we have validated the hypothesis saying that telemarketers’ efficiency can be increased by assigning the right leads and leftover data in a more efficient way.
The certainty for success of AI adoption was rated at over 91% and the client decided to proceed with further model optimization and AI adoption.
After the AI Sprint and presentation of our findings, the client decided to continue working with our data science team on the company-wide adoption of AI. Apart from continuing work in the AI area, we also started a web development project.