August 9, 2020

How to Gain Cold Call Insights with the Help of AI

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According to The Bridge Group, 65% of B2B organizations see sales productivity as the biggest challenge facing their companies.

One major part of sales productivity that always can improve: cold calls.

Let’s talk about ‘em...on average, 69% of contacted buyers accepted a call from a new salesperson in the last 12 months (data by The Rain Group). 

To put this into numbers, the recommended amount of calls a sales rep should do daily is anywhere from 60 to 100. 20-30 calls might be too little, and over a 100 puts you at risk of stretching your sales force a tad bit too thin. 

An analysis of 100 daily calls made by businesses revealed some of the common pitfalls of cold calling: the majority of these calls were based on minimal to no research and poor qualifying questions, lacked lead definition and understanding, and were poorly controlled or failed to engage the prospect.

Turning call recordings into insights with the help of Artificial Intelligence (AI)

How can you solve burnout from too many calls and low conversion numbers? 

The answer is in three key metrics: Activity, Quality, and Conversation. 

In a nutshell, you need to know how many calls your sales rep makes per day, how many decision-makers were reached, and of those, how many were qualified and moved onto the next step. 

No sales leader realistically has time to effectively track all of these metrics.

Enter: machine learning (ML) and conversation intelligence.  

ML algorithms are redefining the modern landscape of cold calling by first, building a database of transcribed calls for future analysis, and second, transforming those call recordings into invaluable sources of deal intelligence and market insight. 

Conversation Intelligence from a bird’s eye view

Conversation key performance indicators (KPI’s) take on a whole new meaning with the help of AI. 

You can track call length, conversation flow between reps and prospects, and overall tone/sentiment, as well as the average talk time and the client talk time in particular. 

Measuring customer satisfaction and hold time can speed up calls, free up resources, and lead to improved workforce management.

Greater efficiency immediately translates into a lower abandonment rate and higher customer loyalty.

Emotion detection

AI, powered by machine learning and natural language processing (NLP) can even determine customer emotion and tone to bring next level efficiency. 

Words in conversations can be analyzed to identify whether the prospect is happy, angry, or willing to engage. 

You can also assess your sales reps’ performance: usage of certain keywords, for instance, or how many times they name a competitor during the call. 

With this information in mind, you can leverage a better understanding of the strengths and weaknesses of your sales reps.

In today’s landscape, advanced data analytics, powered by ML and AI are key to equipping your sales team with the right training and tools they need to optimize their strategy, leading to a dramatic increase in sales and thus, revenue.

Check out how Pickle is helping sales leaders keep a pulse on their team’s conversations and close the gap between their top performers and newest hires!

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