Uninformed cold calls to personalized warm emails.
Gut feelings to data-driven strategies and results.
The last item on this list is a massive trend across all areas of business. Instead of basing decisions on gut feel alone, smart reps are beginning to rely on data to inform their tactics.
Unfortunately, many teams don’t know how to implement a more data-driven process. But not to worry -- we’re here to help.
Here are five simple steps you can take to start approaching your sales job like a scientist.
1) Develop a hypothesis.
The first step to making your sales team a more data-focused unit is to begin developing hypotheses. In other words, start looking at your traditional tactics through a new lens -- as hypotheses that you can prove or disprove.
For example, a rep might believe that 8:30 a.m is the optimal time to send a warm email to a prospect. But how do you really know? Based on this presumption, this rep now has their hypothesis.
Here are a few sales hypotheses to test:
X time of day is best for sending warm emails
X day of the week is the best time to send a follow up note
X subject line will garner optimum open rates
2) Design your experiment.
Designing your experiment well is critical. As researchers at Yale point out:“It is wise to take time and effort to organize the experiment properly to ensure that the right type of data, and enough of it, is available to answer the questions of interest.”
For example, if a sales rep hypothesizes that 8:30 a.m. is the best time to email a prospect, that doesn’t mean the whole team should start sending all their emails at 8:30 on the dot. This will change your data and findings dramatically. Instead, employ two separate factions of the team to send similar emails at different times.
Also remember to control for variables that you aren’t actively testing. In our example, the main variables are send time, subject line, and body copy. If you can ensure that the subject line and copy are similar across all the emails and the only difference is the time of day the message is sent, you can gather useful data to inform your process going forward.
3) Run the experiment.
This is the fun part: It’s time to start running your experiment. In order to make your effort worthwhile, it’s important you run your experiment for enough time to collect meaningful data. Running an experiment for one to two days will give you data, but not enough to draw a conclusive result.
Ideally, run experiments for 10 to 14 days, and monitor the results throughout. With nearly two weeks of data on open rates, you can clearly see whether or not 8:30 a.m. is the ideal time.
4) Analyze your results.
After you run the experiment, collect the data and visualize it if you can. This makes it easier for people to digest and understand your data. As Haig Kouyoumdjian writesin Psychology Today, several studies have shown that visuals provide a better way to take in information.
Analyzing your results also means drawing conclusions. What can you take away from the data? Look beyond the obvious implications and search for hidden gems. Small details matter, and they can result in developing better, more concise experiments and data collection methods going forward.
5) Develop your strategy.
Once you’ve drawn conclusions, apply them. If you discovered a dramatic difference in email open rates between 8:30 a.m. and the rest of the day, you now know the optimum time to reach your prospects. Skew your sends to 8:30, and watch connect rates rise.
However, it’s important to never stop testing sales strategies. While this approach might work now, the behaviors of the modern buyer are constantly changing. What might be valid data this week could look drastically different next week.
The best sales teams and companies have ditched the traditional sales playbook. They’ve moved towards the new era of science-backed sales. Sales reps who spend time developing a more scientific approach to their jobs are more likely to see a return on that investment … statistically speaking, of course.
Originally published Nov 17, 2015 8:00:00 AM, updated February 01 2017