Machine learning and the future of sales: how will AI impact your career?

When sales and science work together, amazing things happen. Long-held myths and traditional practices are exposed through the lens of data, making way for discovery and innovation to improve business outcomes and drive a culture of excellence.

Nowhere is this more evident than in the impact of Artificial Intelligence (AI) on the sales process. More than just automating workflows, AI and its subfields, such as Machine Learning, are helping sales teams gain visibility and control over their pipeline, productivity, and performance.

While AI in general still elicits fears of job loss and dehumanization - not to mention a robot apocalypse - some of its aspects actually move reality towards the opposite direction. In sales for example, machine learning complements the human touch, enabling sales professionals to scale their outreach without diluting personalization when and where it matters.

In the webinar Amplify the Art of Sales with the Science of Machine Learning, Glassdoor’s Global Head of Sales Development, Kamal Suffoletta, and Outreach's CEO and Founder, Manny Medina, discuss how Machine Learning is flipping the world of sales and spawning a new generation of sellers with superpowers.

• Part I of the webinar explores the state of Machine Learning in sales, how it impacts each step of the sales cycle, and how sales careers are evolving differently with this new technology.
• Part II covers the types of insight Machine Learning can generate for sales leadership and how this information will influence business decisions.
• Part III is a Q&A with Kamal Suffoletta and Manny Medina.

Common sense vs. real-world data: Who wins?

The on-demand webinar explores a broad range of topics and is filled with the presenters' personal experiences and that of their sales teams.

A case in point: email outreach commonly generates three possible reactions from your prospects: positive, negative, or no response. Now which reaction would you advise your sales reps to prioritize in order to drive the highest number of meetings?

With a playbook based on intuition and “common sense,” most sales teams follow up on the positive responses first because doing so should generate the highest number of qualified leads, right? Why would anyone prioritize negative responses, anyway, when doing so takes precious time and effort away from engagements with people who are ready to talk to you?

Well, surprise surprise. It turns out that (in this case at least) “common sense” translates to “common error.” Using a sophisticated Machine Learning solution, the sales team at Glassdoor discovered that treating negatives as positives when it comes to response times delivers the best business outcomes. So instead of ignoring negative emails, Glassdoor’s top-performing sales development reps used customer objections as conversation starters.

The results are conclusive. Addressing both positive and negative reactions immediately and without distinction results in a higher number of qualified opportunities, easily 4x to 6x times more than when only positives are prioritized.

Discovering this counterintuitive - but needle-moving - insight requires a scientific perspective. Specifically, measuring data and validating hypotheses through the experimental process of A/B testing.

What about next steps? Where should sales organizations start and which actions should they take to improve results? How can you leverage AI and Machine Learning to elevate performance?

A sales team without AI is an entity without a future.

Artificial Intelligence already performs much of the heavy lifting for many successful sales teams today. A sales organization that chooses to engage prospects and customers without the pinpoint-precise insights of Machine Learning operates with a major disadvantage, and misses many opportunities to achieve optimal business results.

On the other hand, teams who choose to embrace Machine Learning dramatically push the limits of their potential. This webinar will help you learn —

1. How Machine Learning is driving the next generation of practical insights which lead to innovation and revenue efficiency
2. Why Machine Learning is not meant to take over human sales jobs, but rather aims to empower salespeople
3. How Machine Learning can spur dramatic improvements in your key metrics
4. How even small and simple Machine Learning-driven changes translate to significant uplifts in performance