Converting Words into Data

by Oct 22, 2019Productivity, Traq, AI

Today, nearly every aspect of business has become data-driven.

Data is everywhere, especially in the digital world. Our clicks on websites, the data we provide to retailers or businesses, the applications we interact with all leave behind data points that companies use to better understand the needs and wants of their customers and target them through marketing.

But while organizations are spending ample resources on tying this abundance of digital data to their marketing effort and media strategies, they’re not applying nearly the same resources toward leveraging that data in their sales organization.

To date, many sales organizations have come to rely on fractured and questionable data to power the most important revenue function in the company.

We know about the behaviors of our sales team: the software allows us to track how many calls they make, or how many meetings they set with clients. And we have a fair amount of structured data around the sales engine like how long it takes for deals to close or the average dollar amount for each sale. But the most valuable information that surrounds every sale isn’t easily captured in the numbers and spreadsheets.

As the sales process unfolds, reams of incredibly valuable information get captured in casual and conversational moments on phone calls or in meetings. Most salespeople jot down notes, create documents, or send emails to capture that information, and a lot of those elements live only in their own files or workstreams and not in the central source of intelligence on the account.

When it comes time to forecast revenue or predict the likelihood of deals closing, today’s salespeople are asked to take those conversations, analyze them, and somehow convert this unstructured, language-based data into something as rigorous and structured as a sales report. Does the conversation they had on the phone last week mean that deal is still being qualified, or is it further down the pipeline? Should they interpret that most recent email to mean a deal is 50% or 75% likely to close?

The core challenge is that the most valuable intelligence in the sales process is conveyed through language, not numbers.

In almost every case today, we’re depending on the very subjective analytical skills of what can be an overly optimistic salesperson to interpret the language to provide pipeline guidance, then management treats that pipeline forecast as if it’s founded rigorous data.  But it’s not…yet.

At Traq, we’ve unleashed artificial intelligence on this problem, and are dedicated to converting words and conversations in sales to data that can be analyzed, measured, and used to better shape sales strategies and revenue forecasts.

For the first time, it will be possible to convert the sales narrative – all of the conversations, movements, and actions that help move a deal along – into objective data for the purpose of sales pipeline analysis. AI and machine learning will analyze the dialogues and language across notes, emails, documents, and conversations and determine objectively where the opportunity belongs in the pipeline, how likely the business is to close, and help uncover patterns, commonalities, and signals that can help sales professionals more accurately and proactively manage toward closed-won deals.

Until now, sales planning and management have been all about gut, intuition, and artistry.  We believe it’s time to quit the sole dependency on these subjective approaches to data analytics and more structure, rigor, and insight through artificial intelligence.  The Art of the Sale may be timeless, but the digital era demands that we bring the Science of the Sale to the table, too.

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