Our client in the Ed-tech sector cold calls prospects who work in administration at schools and large child and daycare facilities.

This works well in their market, but there are two fundamental hurdles. First, prospects are often distracted and busy during the call and ask for a callback. Second, their prospects likely have an existing solution and contract they need to see through before further discussions.

These two factors make correctly logging outbound calls, recording important prospect data points, and understanding overall sentiment crucial for success. Doing this correctly provides ample opportunity to engage and re-nurture leads.

The team uses Gong to record and analyze the call data of their SDRs. Over time, our client noticed that Gong AI couldn’t correctly infer sentiment from the call and that due to the high volumes of calls and the repetitive nature of the task, SDRs sometimes made mistakes when logging call back dates or other customer data points.

While a frustrating inefficiency in their process, their team lacks the time and resources to revisit each call recording and correct any mistakes in Gong.

This is where DataBees comes in.

• • Our team accesses their Gong recordings and transcriptions, reviewing calls according to the client’s SOPs.

• • We checked and corrected any mislabelled calls, ensuring all qualified or disqualified leads were accurately categorized.

• • Missing data points, such as callback dates or prospect capacity, were added to ensure complete and accurate records.

Serving as the final filtration system for their call data ensured that no opportunities were lost due to inaccurate logging. This enabled the client to fully leverage their vast database, significantly improving the efficiency of their outbound efforts.

This ensures:

• • Sales reps’ time is maximized by focusing on accurately labeled opportunities, improving the likelihood of successful follow-ups.

• • Opportunities in the pipeline are properly managed, with no prospects unnecessarily disqualified due to mislabelling.

• • Data quality is optimized, enabling more precise and effective nurturing of leads.

• • A significant reduction in human errors, with a higher degree of confidence in call data accuracy.

This play is particularly effective for:

• • Organizations conducting high volumes of outbound calls and relying on precise follow-up scheduling.

• • Teams struggling with inconsistent call data labeling and management from third-party tools like Gong.

• • Sales and Marketing Operations teams aiming to enhance pipeline accuracy and efficiency through comprehensive data validation.