How is AI-Driven Deal Engagement Scores Helping Sales Teams Achieve KPIs?
Sales managers and their sellers know that winnable deals fall through the cracks without any actionable insight or an effective way to prioritize certain prospect accounts. At the same time, some are swept away by competition.
How would this scenario change if they have an unbiased and data-driven perspective by combining qualitative feedback from sellers with quantitative data?
Deal Engagement Scores, an AI-driven metric, offers a powerful perspective on the health of prospects. Sales teams could close most deals by pairing this score with feedback from their representatives and their intuition. They can decide which deals will most likely convert and which ones need some more attention.
Measuring engagement scores is the best way to:
- Identify the most engaged trial accounts that you could convert into paying customers, including when they are ready to open discussion with you and convert.
- Which trial or freemium users may not be ready to engage further to see the product’s value?
- Which premium user is ready to pay for a higher package and receive greater value?
Why Should Businesses Care and Who Should Care for the Engagement Scores?
Businesses must care for the deal/customer engagement score because they are successful only if they have people use the product and engage with it.
- Disengaged trial users never buy the premium version, and businesses spend money on supporting trial users seeing nothing in return.
- Disengaged users churn, causing a loss of revenue.
- Customers who do not find value after turning premium customers may not upsell. They may instead churn by the next subscription due date.
As the usage levels grow, customers may move up to a higher package, increasing your revenue without you having to win more accounts proactively. Everyone must care about these scores.
The scores reveal how much the product meets the customer’s expectations or demand for the executive team. The product team could use it to determine whether customers engage with updates or additions to the product. Sales and marketing teams could use the scores to prioritize accounts to process and reduce the time wasted on washout deals.
Deal Engagement Score’s Role in Maximizing Productivity
Organizations must adopt a customer-centric approach to deliver on revenue targets and consistently win. Besides, businesses must simultaneously plan for the long-term and tactically execute today, putting pressure on revenue teams to collaborate, share best practices at scale, stay on the messaging, and double down on successful strategies.
As sales teams grow, it becomes difficult to pivot and control messaging with prospects. The right technology must empower sales teams to build predictable revenue models. With significant insights, sales teams can create standardized processes for teams to follow.
In-platform analytics track activity and give sales leaders real-time data on how their sellers execute that model. Additionally, sales leaders can evaluate closed opportunities and revenue with sales data to understand if and where the process is driving success.
Along with the numbers, sales managers must make sense of their team’s pipeline. The scores also provide insights into why a score is high or low and how sellers could raise it. This level of transparency is critical to an AI-based platform.
Sellers and their managers can check the algorithm’s key factors against their intuition and awareness of the deal in play. They can make informed decisions about what actions will improve outcomes.
Draup’s sales intelligence platform allows you to micro-target using its dual taxonomy-driven technique giving hyper-contextualized insights. With deal engagement scores available at parallel, sales teams can drive the message and aid in the faster closing of quality deals.