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How AI-driven win-loss deal analysis is changing the face of customer interactions

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By Bharat Patidar, COO, Convin

Sales teams win a deal and celebrate their hearts out. When they lose a deal, some drink to cope with their frustration. But is winning and losing the sales deal the last mile?

Win-loss analysis of a deal is the missing piece of the puzzle in maximum sales processes.

Once the deal is closed, the learnings and best practices received from the sales process are buried under the CRM data without any further discussion.

Although managers understand the importance of win-loss analysis, they neglect it due to a lack of data visibility and an inability to process customer data. Surprisingly, atomic changes created by repeatable best practices extracted from win-loss reviews can turn sales and support agents into high-quality scorers. Who, in turn, can transform CX and boost the organization’s CSAT score by 27%.

The next logical question is, where to start? Would it be viable to interview each buyer? The only limitations one can think of are endless time and effort. Then what’s the next possible solution?

Before we talk about the solution, let’s deep dive into understanding what are call centers and managers struggling with due to lack of win-loss analysis.

What are companies missing due to a lack of win-loss reviews?

Our encounter with multiple customers has aided in comprehending the missing pieces due to the dearth of win-loss reviews. Here are a few questions that you can ask yourself:

● How would you create a call script without a structured benchmark?
● Is there a way to discover the call script’s missing and hidden components?
● Is there a logical way to update the call script based on market insights?
● How will you decide the correct call flow for customer conversations at different buying stages?
● Is there a way to extract hidden factors that positively or negatively impact customer conversations?
● How do you decipher which call quality component in the scoring sheet should be allotted more weightage?
● When do you know a competitor/s is replacing you in the market?
● Do you know which of your products or services outperformed or got dismissed by customers?
● Is there a way to deduce which call activity(e.g., handle time, response time, time of the day, etc.) has benefitted the agents most?

The win-loss analysis gap makes the sales and support engagements reactive, resulting in a loss of customers and revenue.

Leaders planning to scale sales teams and generate maximum revenue from the existing reps need to leverage a tool’s win-loss capability, performed automatically. The win-loss review of customer interaction gives a proactive edge to growing companies.

Agents and managers can determine the accurate picture behind why a client chose you, opted for the competition, decided to part ways, or suspended the deal altogether.

And the best way to encourage a proactive way of winning customers is to replicate the best practices that led to securing a previous deal and eliminate actions that sabotage a winning opportunity.

So, let’s talk about the breakthrough win-loss analysis technology transforming agent behavior.

How can growing firms leverage call behavior analysis and assist agents?

AI-backed conversation intelligence solutions can resolve agents’ conversation woos and aid in customer experience management.

The automation engine works on multiple areas of agent-customer discussions where conversation behavior analysis is conducted on Calls, Chats, and Emails to determine repeatable, high-impact customer interaction behavior.

Customer expectations and a successful agent’s behavior can be determined in two ways:

● Categorizing customer conversations under win and loss parameters. Basis previous won and lost data, agents can comprehend what behavior causes positive and negative impacts in a customer conversation. Consequently, agents can replicate the most impactful call parameters in future engagements.

● Access to customer intelligence can empower managers to make better business decisions from rich customer insights. Tagging conversations under different categories, uncovering the words/phrases occurring in those conversations, and tracking those words across previous win/loss deals.

Summing Up

Like AI, customers are constantly evolving. Companies must stay ahead of the evolution cycle by analyzing customer behavior using AI-driven platforms and reviewing winning and losing trends. Here are my two cents: Stop waiting for the right opportunity. Get proactive now!

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