Salespeople rely on their intuition . . . but does that mean they should always follow their gut?
How turning to basic statistics and embracing data analysis can make you a better seller.
A tried and true recipe for a stressful situation
Take 100 clients sprinkled across the globe, add one startup committed to at least 30% year-over-year growth, an end of the year trade show accounting for approximately 20% of the book of business, and no starting point. Mix it all together and you’ll be ready to unravel in no time.
If you’re one of the 14 million people in the US that call sales their occupation, the situation above probably resonates. The typical next step is to comb through the book of business lead by lead, targeting “big-spend clients” in search of the mega-sale that will break quota. I started my career in sales, and these types of scenarios are exactly where I found myself — relying on my gut and scouring my book in search of the needle in the haystack. I’ve always lived by the motto that nothing beats hard work and so work hard was what I did, burning the midnight oil until I had exceeded my quota. The stress, balancing act, and sheer lack of sleep had me thinking that there must be a better way.
Fast forward five years and into data & analytics consulting, I often reflect on my selling experience through this new lens (and I’m usually left with a healthy case of “hindsight is 20/20”). I still use my gut to guide my decision-making, but I will always turn to data to point me in the right direction. For example, nowadays instead of scanning a sales report for which clients generate the largest sales, I use basic statistics to build a story around purchasing habits and drive forward from there.
The classic dilemma
Here’s an illustrative example: “Don’t worry about the numbers so much. It’s all about the relationships. You’re young. Focus on building your relationships and the numbers will come!” That’s the response I received when I asked my manager to see a sales report leading up to our biggest trade show at the end of the year — the one that represented approximately 20% of my book.
“Focus on building your relationships” is sound advice that every salesperson needs to hear, but how do you qualify this? Where do you start? With a territory that spans hundreds (for some, thousands) of clients, how do you make the best use of time to build the most valuable relationships? This is where we can turn to data.
If we peel back the onion on the “relationship building” strategy through data, what do you think we will find? Perhaps you can enhance your relationships (and your profit) in one fell swoop. To demonstrate, I’ll use a sample sales data set from Kaggle for my analysis. (Note that data manipulation was performed on this file, so outputs may differ.)
Let’s start by looking at data on a book’s overall performance. By calculating basic descriptive statistics such as the mean, median, and standard deviation, and so on, we can start to build a story around the data.
Let your gut drive, but use data to keep it in check
It’s time for some basic statistics. Consider this set of numbers: {2, 4, 6, 8, 10}. We see an evenly distributed set where the mean (average) is 6 and the median is 6. What does this tell us? It tells us we have a symmetric or a “normal” distribution. In other words, we have just as many numbers above and below the middle number (6) and our average is smack dab in the middle of all our numbers as well. This is what we’re looking for as our benchmark.
Now look at Figure 1. below. The sales mean is significantly greater than its median. What’s going on? For starters, we know that we aren’t dealing with an evenly distributed set like we were in the example above. We also know that because the average is [total sales dollars] / [number of sales]. For the mean to be larger than the middle number, there must be some large sales pulling the average above the median. What could be causing this? This is where data starts to drive intuition. Perhaps this book had a few atypically high value sales that created a skewed right distribution and increased the average sale toward the upper end of the distribution. Data visualization confirms this — check out the skewed right histogram in Figure 2. . . .Bingo!
All those large sales toward the right end of the histogram are pulling the mean above the median. Look at the right end of the histogram: There are plenty of sales >$3,200. The larger the sale amount beyond the median, the more the average is pulled to the right.
Why is this important? Salespeople typically rely on their gut to make critical decisions in a finite amount of time. Decisions like figuring out where to allocate their time in the lead up to a rapidly approaching trade show. By interpreting data using basic statistics, you can enhance your understanding of your book. And the more you back our intuition with data, the less you have to wonder if you’re making the correct decision.
Want to chase that big deal? Think twice.
Perhaps you can better spend your time elsewhere. Notice that our sample standard deviation is significantly large at 1,841.87. A standard deviation tells us how spread out data is from its mean. A larger standard deviation tells us there is greater variance in sales amounts, while a smaller standard deviation tells us that sale amounts are closer to the average sale. Standard deviations are especially helpful in assessing what an anomaly sale looks like. For example, two standard deviations above and below the mean encompass 95% of all our deals (see Figure 3.). In this case, we’ve just learned that it wouldn’t be out of the norm to see orders ranging from the minimum sale of $482 all the way up to $7,237. Sales above (or below) two standard deviations are rare, so you may want to think twice before you scan that sales report looking for the client who generated last year’s highest sale — especially if it was a one-off occurrence.
This is a good start, but let’s keep following our business intuition to gain more useful insights from these metrics. Rather than focus on large deals that bring with them larger variances/standard deviations, or small deals that perhaps do not produce as much revenue, we should direct our focus medium-size deals. In this case, the trade show is in the USA so we could expect that most of the B2B customers in attendance will be USA clientele. Let’s narrow our focus a little more to only USA medium-size deals. Look at the table output in Figure 4.
Notice anything different? The slight increase in both mean and median quantities ordered could indicate that B2B customers are ordering in bite-sized chunks more often than buying in bulk. Therefore, there’s likely to be a steady stream of orders leading up to the trade show. Holding other variables constant, this information tells us that we should keep prices closer to MSRP. Remember that the standard deviation shows how spread out orders are from the average. Because of the increase in mean and decrease in standard deviation, we know that we could expect to see orders ranging from $2,321 to $6,424 (two standard deviations below and above our mean or approximately 95% of all medium-size USA deals) — that’s a big confidence booster compared to our sample!
Now let your gut take over . . . after a sizeable portion of data analysis
Here’s another striking statistic: Medium-size deals are accounting for a greater proportion of all deals and providing more revenue than small and large deals combined (in all territories). With medium-size deals contributing to approximately 50% of total orders, we know that focusing on building relationships with these customers will most likely maximize revenue and profit. This not only allows for larger up-selling opportunities, it prevents customers from falling into habitual small deal purchasing behavior.
Don’t mistake statistical significance for economic significance
After a simple statistical analysis on our sales data, we’re well on our way to maximizing our book’s performance in the lead up to the trade show. But it’s important to note the catch-22: Even the most elegant data & analytics models fall short when they lack business intuition. We can still use our gut to drive our decision-making, but we should always use data to point us in the right direction. If large-size deals take longer to close than medium-size deals, or USA deals close faster than Japanese deals, adding this information to your analysis will help you obtain better results, and make smarter decisions.
Start with a business objective
As you can see, it’s easy to dive down a metaphorical rabbit hole with statistical analysis. Before you do anything be sure to carefully craft a business objective that aligns with any question(s) your team needs to answer. At Slalom, we like to say the first part of any analysis is closing the data tool and starting with a pen and paper. Here’s a brief example:
Objective: We want to focus on the most profitable part of our book leading up to the tradeshow in six months.
Questions: What’s our most profitable segment or deal size? What’s the expected value of sales? How much revenue would we like to generate?
Sales will always be a largely intuition-based business function, but with the power of data, you can give your intuition a helping hand. And in some cases, improve it. How are you using data to drive your book of business? Reach out to me to share your story.
Terms
N = Number of records.
Std. Err. or “Standard Error of the Mean” = The estimate for how far the sample mean is from the true mean (the population mean).
Std. Dev. or “Standard Deviation of the Sample” = The degree to which individual value(s) differ from the sample mean.
Mean LL/UL 95% CI or “Mean Lower Level / Upper Level 95% Confidence Interval = The percentage of confidence for a range of plausible values for the population mean.
LL/UL 97.5% Threshold or “Lower Level / Upper Level 97.5% Standard Deviation Threshold = The range of plausible values 97.5% above or below the threshold value. Ex) UL 97.5% Threshold = 100; 97.5% of values are below 100.
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