Businesses invest significant resources in their marketing and selling efforts, but there are often a wide range of outcomes. When outcomes differ from what was expected or hoped for, we naturally want to know why. Why did the outcome vary from what was expected?
For example, organizations frequently want to know:
- Why do some marketing campaigns yield stellar results while others fall flat, even when using tactics that are known to produce good results?
- Why do close rates fluctuate even with a seasoned sales team?
- Why do some BDRs outshine their peers, even when using the same sales process and tools?
These questions highlight our ongoing effort to understand and manage inconsistencies within our work.
In the sciences, researchers refer to these inconsistencies as variability, and their mission is to understand as many of the factors that drive this variability as possible.
In this blog, we’ll explore the concept of variability in data analysis and how 6sense Research uses it to understand the complex realities that arise in the B2B market on a daily basis.
Variability Is All Around Us
Variability is a natural occurrence in business and in everyday life.
Consider a customer satisfaction survey.
While every organization would like to have customer satisfaction scores that never vary from the highest score possible, there will always be some customers who are more or less satisfied than the average customer.
Sometimes, the reasons one customer’s satisfaction is different from the average is easy to spot and even anticipate. For example, a customer who didn’t receive proper onboarding may be less likely to give a high satisfaction rating – an explainable cause of lower satisfaction.
Or, an otherwise happy customer might encounter a new bug in your software just before taking the satisfaction survey, which could lead them to give a lower rating than they would have otherwise.
However, other sources of variability will always be outside of an organization’s direct control or at least be very difficult to anticipate and control.
For example, one customer may catch a long line at the coffee shop during their commute to work, which puts them in a bad mood and affects their response to the survey that was in their inbox when they finally arrived at work. Another may find out just before taking the survey that they will not be receiving a hoped-for promotion.
Because there are nearly always many factors that influence the outcomes we are interested in measuring, there is a lot of innate variability in those outcomes. As just illustrated, we can measure and explain some of that variability, but some we cannot easily capture, explain, nor anticipate.
Variability in Human Behavior
Human behavior is notoriously difficult to understand and predict. If you are old enough to read this post, that statement will not surprise you. Even the best statistical models often explain much less than half the variability in any given human behavior.
Last year, 6sense Research surveyed BDRs to learn about their job processes, outcomes, and how supported they feel in their roles. We found that a combination of 7 factors accounted for nearly 10% of the variability in their performance, or, put another way, 10% of the reason why some BDRs meet the average quota attainment and others don’t. Changes in those 7 factors reliably predict changes in BDR quota attainment.
While 10% may seem like a small percentage, it’s not when we put it in context.
A 10% difference in performance could be the difference between a BDR making or missing their quota or making or missing out on their bonus. Likewise, many companies operate with profit margins of less than 10%. The capacity to identify and effectively manage even some of the factors contributing to these narrow margins can have a profound impact on a company’s — or an employee’s — viability.
Variability in B2B Marketing and Selling
In business, understanding the variability in our outcomes is typically easier than understanding human behavior in the social sciences. For example, last fall, 6sense Research studied the purchase processes of over 900 B2B buyers. Despite there being a wide variety of factors that we did not measure, which could be expected to influence the length of a buying cycle, we found that the nine factors in our statistical model accounted for 64% of the reason any given buying cycle differs from the overall average of 11 months.
In statistical parlance, we would say that our model accounts for 64% of the variability in the length of buying cycles (and then do a little happy dance). To understand 64% of what causes a buying cycle to be either longer or shorter than the average of 11 months is truly remarkable.
With multivariate statistics, we can not only understand what influences an outcome, but how much of an influence each factor has. With this, we are able to truly focus on what delivers the biggest impact.
For example, in our research, we found nine factors that influence the length of B2B purchase processes.
If our objective were to manage the duration of the buying cycle, our attention would immediately be directed to just three of those factors, however: the number of interactions buyers have, the cost of the purchase, and how many vendors are being evaluated. These three factors are the most potent influences on the length of the buying cycle.
For example, adding or subtracting just one vendor from a shortlist can extend or reduce the buying cycle by over two months. Likewise, a $100,000 difference in purchase price can add or subtract approximately 15 days from the length of the cycle.
At the other end of the spectrum, the type of solution being purchased, whether the purchase is of a new business capability or renewing an existing one, and the industry of the buyer contributed a mere 1% of the total variability in buying cycle length explained by our model.
In this way, understanding as much of the variability in our outcomes as possible not only allows us to manage these unique relationships but also to predict and navigate the trajectory of our endeavors.
Examining Variability Yields Insights
As we have seen, performance in any area of business or life varies, often for reasons we don’t know or can’t control. However, it’s our responsibility as researchers to illuminate the parts of B2B that we can measure, manage, and anticipate. This is precisely why 6sense Research is committed to going beyond surface-level observations. We do the math to understand how the complex web of factors that influence B2B revenue production impact performance. We hope our research helps you better understand your corner of the B2B world.
A Guide to Statistics for B2B Revenue Teams
6sense Research is proud to introduce our Statistics Glossary. The glossary is intended to help B2B revenue professionals not only read and understand our research, but also to have a better grasp of the kinds of statistics that we often encounter in our professional and personal lives. We include lots of examples for the terms in our glossary, and draw them all from relevant B2B situations. We hope you find it useful.
