How to Make Better Decisions with Data

Companies often use A/B testing to optimize their websites, but they rarely use it for anything else. This is a wasted opportunity. It turns out that if you capture enough data, any repeated, measurable activity can be framed as an A/B test. For example, how does breakfast affect your morning work productivity? If you spend a month or two tracking your productivity along with the breakfast meals that you eat, you'll quickly learn if skipping breakfast turns you into a zombie or if having eggs instead of pancakes will help you get a promotion.

Once you internalize the idea that anything you repeat can be an A/B test, you starting seeing optimization opportunities everywhere.

Using Data to Optimize Sales

For example, if you're at a B2B company and have at least a few dozen customers, you can start optimizing your sales process in many directions. A lot of companies just pursue "more" sales, but not all sales are created equal. By collecting data on each customer -- data like the customer's industry or employee count or annual revenue -- you gain the ability to decide what kind of prospects to go after:

Instead of selling blindly, you might discover that your most engaged customers are from a specific industry, or that your quickest sales happen in mid-sized companies that don't yet have a procurement team. These insights give you powerful levers for growing your business, and the icing on the cake is that you can often fill out data retroactively instead of only collecting it for future sales.

Using Data in Other Areas

A few other areas where startups can use data for better decision-making:

Using Data Everywhere

Here's a generalized algorithm for using data to improve decision-making:

  1. Pick an activity you do regularly. The results of the activity should be measurable.
  2. Make a list of attributes that you think might contribute to doing the activity well (or poorly). No attribute is too crazy or too trivial.
  3. Every time you do the activity, write down the value of each attribute along with how things turned out.
  4. After you have a reasonable number of data points, go back and correlate different attributes with outcomes. Some attributes will turn out to predict success, others will predict failure, and the rest won't have any predictive value.
  5. Incorporate what you learned into how you do the activity. Go back to step #1, if desired.

For example:

  1. Activity: selling b2b software.
  2. Details that might contribute to success or failure: experience level of salesperson, how long salesperson has worked for you, prospect's title, prospect's industry, length of initial meeting, meeting venue (e.g. their office vs. your office vs. video call vs. phone call), etc.
  3. ???
  4. Profit!

(In this case, ??? = "Track the details in step #2 for 30 or 50 or 100 sales meetings, then figure out which details lead to more sales.")

Tips

Two things to keep in mind as you optimize your decisions with data:

Homework

If you're doing X repeatedly, whether X is sales or hiring or programming or fundraising, chances are you can be doing it even better. Write down pertinent data and facts each time you do X, then revisit that data once in a while to understand how you can improve your process. Tracking and analyzing data over time will help you do more of what works and less of what doesn't.

If you liked this article, put it to use! Pick one area of your personal or professional life that you want to improve, list out things that might contribute to that area, and start tracking data until you start seeing interesting insights. I think you'll be pleasantly surprised.

Tags: Value of Data

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