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About This Episode
Sometimes trusting your gut isn’t enough.
Sometimes you need cold, hard facts to back up (or disprove) that big move or new process you feel in your gut to pursue.
With so many advances in data technology and solutions, getting down to the science behind what works in sales and sales engagement (and what doesn’t!) is more accessible and reliable than ever.
Our guest today on The Sales Engagement Podcast was Pavel Dmitriev, VP of Data Science at Outreach. Pavel is changing the way sales reps and leaders follow intuition by backing it up with scientific and quantifiable data.
Pavel shared with us a few stories of how his methods and A/B testing experiments have helped his employers’ fast track engagement growth.
Here are the highlights from our interview.
Data Science Is The Key To Improving Engagement
Data science is critical to sales engagement.
Without scientific, quantifiable evidence of effective engagement methods, improvement is limited to guesswork.
In fact, the clearest way to see whether or not these methods are working is through scientific tests and analysis.
Everyone is aware of A/B testing (the process of comparing the effectiveness of two versions of the same email, webpage, phone script, etc.), but it’s often underestimated.
It can actually be used by sales leaders to answer higher level questions, such as the effectiveness of video in emails–it goes beyond click-through rates and email replies.
Many Variables Mean True Scientific Study Is Necessary
Statistical tests, as well as properly configuring and running an A/B test, is not as easy as it sounds–there are too many variables that can cause “noise” in the data.
This noise in the data often comes down to chance.
Say you’re sending an email. There’s a percentage of recipients who are statistically more likely to reply than others, so if the percentage change you are seeing in your A/B test is small, chances are that it’s a noise issue, not a true reflection of one method working over another.
If the percentage shift of effectiveness you’re seeing is small, more data is needed to make sure it’s not noise. A shift of one or two percent, in a small data set, means you need to increase your data set to confirm.
It’s why what Pavel and his team at Outreach are doing is so important. Using scientific methods of study ensure accurate results, which mean sales leaders and reps can make fully informed decisions.
Decisions Made Based On Instinct Should Be Treated As Hypothesis
The results of these tests usually mean a change in process or strategy, maybe even company-wide.
If that change is based on the wrong, or incomplete, data, the decision can have lasting negative consequences for the whole business.
Don’t throw those “gut” or instinctual ideas away, rather treat those ideas as hypothesis instead of absolute truth. Test them out using your A/B process. When you do, you’ll end up learning more and maybe even generating new ideas, which can then be tested.
It’s an evidence-based circle of idea generation that sees your engagement soar.
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