Sales Best Practices

The Role of Machine Learning in Sales

Cari Murray's Avatar

Cari Murray

Senior Manager of Content Marketing

Two of our talented data scientists, Yong Liu and Andrew Brooks, recently showcased how Outreach helps sales reps leverage machine learning for their own continuous learning.

At Spark+AI Summit 2020, they talked about how machine learning powers our sales engagement platform, as well as how they solve some of the typical challenges all data scientists face when building enterprise-grade applications.

This work expands on Yong’s session last year about classifying intent with sales emails. Times have changed since then, but one thing remains true: Sales professionals generate a lot of data. Between emails, phone calls, and videoconferences, the average rep juggles dozens of interactions with prospects every day. They're also responsible for tracking various non-selling activities. But with machine learning, automation frees up sales reps to focus on relationship-building techniques that transform the whole customer experience.

When your sales teams have the insights they need to do their jobs effectively and efficiently, they close deals faster. Read on to see some of the ways Outreach helps customers do just that.

Understanding Prospect Sentiment within Email Responses

During their presentation, Yong and Andrew showed how machine learning powers Intent Reporting, which sales managers use to understand the context of their reps' conversations with prospects. This is the first technology of its kind to provide comprehensive visibility and intelligent insights into a prospect's intention.

Intent Reporting automatically classifies email replies into granular reasons for a customer's positive or negative replies. For instance, a positive reply that asks for more information or a negative reply that says they already have a solution gives more information than vanity metrics like open rates. Sales managers now have visibility into recommendations for relevant content, as well as the insights they need to appropriately coach their reps.

Over 1 billion interactions have occurred on our platform. By leveraging natural language processing, machine learning, and AI, Outreach captures the results of these actions, analyzes them, and determines cause and effect to dramatically improve the experience and outcomes for every user. This helps customer-facing sales reps take better action in challenging situations, such as handling objections, while allowing managers to quickly see improvement areas for individual team members.

Our platform also automatically captures key information from customer emails to help reps better engage with their prospects. One example is with out-of-office email replies, which make up about 18 percent of our internal replies. When the platform detects an out-of-office email reply, it automatically pauses the sequences. When the prospect returns to the office, the sequence resumes — making it 46 percent more likely that a prospect will book a meeting with their rep.

Guide Sales Reps to Take the Next Best Action

Yong and Andrew also demonstrated the ways natural language processing improves specific sales engagement scenarios. Guided Engagement is a systematic approach that takes advantage of intent classification by helping sales managers prioritize which plays lead to the most successful outcomes at scale. Based on intent detection and classification of each individual incoming email, our machine learning models guide sales reps toward the best action they can take to overturn recipients’ objections.

After machine learning models identify the intent of an email response, Guided Engagement determines and serves the next best action to the rep. This is particularly useful with newer reps who may not yet have the skill, confidence, and knowledge of available resources to effectively follow up to an objection.

Budget objection is a classic example. Our machine learning models automatically detect the intent of an email that shows the objection is because of budget. Reps can then navigate to the objection handling content collection and find the template that their manager recommended they use in these specific situations. In just a matter of seconds, reps can respond to a tricky objection to budget with a best-in-class, manager-recommended template.

The (Virtual) Road Show Continues

Our data science team will continue to share their best practices in machine learning and natural language processing, starting with the NLP Summit in October 2020: Applied Natural Language Processing.

To learn all about enterprise AI scenarios and digital transformation within sales, make sure to sign up!