Data Science: The Secret to Driving Sustainable Sales and Growth

Plus ça Change, Plus C’est La Même Chose

Jean-Baptiste Alphonse Karr

In case you don’t speak french (or you forgot almost everything you learned in highschool – looking at you Canadians!). The timeless proverb; “the more things change, the more they stay the same” implies that no matter what turbulent changes we experience, the status quo is reinforced. 

Our first thought jumped to sales operations. Sales ops has come a long way over the last few decades. Before, when many of us thought about sales the first thing that popped into our head was a sleazy, used car salesman, trying to nickel and dime you at every corner. Or the guy in the fedora, going door to door, selling the next best vacuum or tupperware set. Heck, even over the last year we have seen a major shift in sales operations from in-person to almost exclusively meeting our prospects over Zoom. 

Our point is, the sales process has changed a lot over time. And regardless of all the trends and changes in the market, sales operations has always been about selling better, selling faster and selling more. This is a capitalist market-driven economy after all. 

Sales Ops in 2021

It is fair to say that sales has become more difficult over the past few decades. 

Information is more widely available. Competition is stiffer. And for that reason, the modern customer has higher expectations and there must be a sense of trust before a purchase is made. Creating strong honest relationships is crucial to driving sales in the modern day. 

We are also living through the 4th technological revolution. Technology has swept through sales operations and has streamlined many of our day to day processes. Seems like there is a service or a tool for anything you could ever imagine. Then you blink and somehow, somebody has created another tool to make your business even more efficient. 

Despite all the amazing software services and solutions out there, many sales teams are still living in the stone age. Otherwise known as… superstar culture. 

Superstar Culture Sales

Is highly individualistic. It relies on a couple people to carry the team to the sales quota. Energy and resources are poured into the few sales wizards that seem to hold the world on their shoulders. Meanwhile, the rest of the sales team is not being given the support needed to improve their processes.

This culture is bad for two reasons. Firstly, sales superstars are not common. They know their worth, and if they left the company for a better offer, you better pray for another one. 

Secondly, when focus is on the sales wizards, the rest of the sales team is left in the shadows. They are not getting the coaching or mentoring they need to develop into becoming the next up and coming sales wizard. They also know their worth and if work culture is lacking – they will be gone too. And nobody wants a churn and burn culture. 

Data Science Technology Prevents Superstar Culture.

One of the biggest tech changes in recent times (that we would argue is here to stay) is the integration of data science technology into the sales process. This changes decision making entirely. Instead of making decisions based on observation and opinion. We can much more confidently make decisions based on data and numbers. We are not forced to rely on a couple sales superstars to hold things together.

Data science combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. 

By taking a data science approach we are choosing a science-driven culture.

Science-driven culture prioritizes:

1. Technology is used to make our day to day processes more efficient. It is also a crucial tool to collect the necessary data to make informed decisions.
2. Processes are what exactly your team is doing to move opportunities down the sales funnel. By prioritizing processes onus is being put on the functionality of the sales funnel steps rather than the individual. When a problem is identified, we look to how we can fix the process, not the person. 
3. Teamwork is crucial to science driven culture. All processes need to be documented and shared within the organization. When mistakes are made or insights for best practices are discovered they are not to be kept as a secret by the individual. Instead they should be shared to benefit everyone and the organization as a whole. 
4. Skills are acquired when we learn from our mistakes and successes. This is an ongoing process. When we continuously collect data and get feedback we are able to learn and improve in the places we are struggling most. 

Science-driven culture is about continuous feedback, learning and making decisions based on numbers and data. It is about fixing weak processes, not fixing people. 

Why Data Science Works

Data science relies on numbers. Not opinion. Not intuition. But cold, hard, calculable, numbers. 

Metrics must be measurable and objective. If your metrics are subjective, based on observation and personal experience – consider them an opinion. And opinions with all due respect are not important and can’t guarantee results. Without numbers and data your metrics are meaningless and left open to debate. 

Let’s apply the scientific method to the sales funnel using a subjective methodology:

1. Observation: We aren’t hitting our sales quota 🙁
2. Question: Why aren’t we hitting our sales quota?
3. Hypothesis: Hmm, well looks like the sales reps aren’t making enough calls. 
4. Prediction: If the sales reps make more calls we will make more sales. 
5. Tests: *sales reps make more calls*
6. Results: Still not hitting the sales quota.
7. Refine: Well, that didn’t work. Maybe it’s ______. (and repeat).

Using a subjective methodology (aka our intuition), we tend to make an observation, make a hypothesis, and test it.  And rinse and repeat steps 3-7 when your first hypothesis doesn’t turn out to be the problem. Repeating these steps is a massive waste of time and resources. 

However, when we use data science and recognize there is a performance gap somewhere in the funnel. The problem can be easily pinpointed by just looking at the numbers. No need to play the guessing game. From there feedback can be provided and a specific solution can be prescribed that is geared towards that exact problem in the sales funnel. This means a much faster turnaround time when solving issues. Rather than having to repeat steps 3-7 until you are just about ready to give up and try to solve the next problem.

It is beneficial at both an individual and organizational level. From an individualistic standpoint, we can analyze where a performer is successful and where they are struggling. This feedback is personalized and can help the individual to improve where they need it.

On a larger scale, this data can be used by the organization to make a more efficient sales process and provides valuable insights for the next-best action with future customers. 

Got the Data?

Most organizations, once they have reached a certain size, have a formal sales process in place and are collecting data. This ensures that they can continue to scale. A typical sales report breaks down the basic key performance indicators (KPI). Number of calls/emails, conversion rate, opportunities lost, profit margin, customer acquisition costs and so on. Not to say these are bad indicators. They are necessary for every business. The problem is they just don’t tell the whole story. 

KPI’s tend to be high level. They leave out the finer metrics that can give us a more accurate picture of what exactly is going on. It is important to know your industry and know what KPI’s are relevant to the problem you are trying to solve. Furthermore, if you are looking to take a science based approach in sales ops, it is crucial to have somebody who knows how to ask the right questions, can extract the necessary data and come up with meaningful solutions.

Tune in next week,  We’ll be following up with a post on “How to Collect Meaningful Data”