Understand what data science does – and can’t – do
Every day of the week, it seems that a new “big data” company comes on the scene claiming to leverage data science. Some of them provide real benefit. Others prey on the people without a mathematical education and their fears of the unknown. Calling these guys “pond scum” or “snake oil salesmen” seems too cruel to pond scum and purveyors of even the finest snake oil.
At its very best, great data science serves as a foundation for tremendous efficiencies. When properly employed, data science helps organizations achieve greater sales and lower costs. However, when data science is misused or misunderstood, great confusion results. Sales leaders’ abilities to impact results are lessened. Many organizations struggle to define the proper roles that data science and analytics should play in sales. As such, it’s important to frame the proper roles of data science in our sales organizations.
#1 The role of statistical modeling
No model ever incorporates every conceivable data point, and no statistical models are driven by perfect data. As such, a valuable data model never produces perfect binary output telling you to absolutely do something or not. There is no silver bullet. There is no holy grail. An analytics person who tells you that there is only one way to accomplish your end goal is misinformed.
Nearly every major decision made throughout a sales organization is influenced by data science. A carefully crafted data strategy will leverage data science. When leveraged properly, data science will inform decisions. Such decisions include: determining what opportunities and leads should get more or less attention; where to focus sales, marketing or management resources; and which salespeople need more coaching and training.
#2 Pattern detection
Data science isn’t a crystal ball that magically shows the future. At its core, data science detects patterns – often very subtle patterns. As a result, when comparing historical to current data, data science provides insights about most likely outcomes.
A scientific understanding of your team’s sales data will open your eyes to the areas in need of the greatest improvement. When leveraged creatively, data science finds solutions to challenges that aren’t obvious. For example, small controlled experiments used to empower sales leaders can both unearth problems as well as point to potential solutions.
Additionally, effective management is not management by numbers. Data science provides direction. And progressive sales leaders constantly look for creative approaches to motivate their sales teams, engage their prospects, and scale their processes.
#3 The confluence of data science and humanity
The best salespeople are competitive, have drive, and intelligence – and they need to win. They look constantly for advantages that help them win the next sale, beat their competition, and climb the leader board. Great data science brings those advantages – and then some. It helps focus a rep’s limited time on leads or opportunities that are most likely to win. It is powerful enough to remove blinders. And, it provide insights that effectively position reps to play to their strengths and minimize their weaknesses.
Leverage data science to identify your best prospects. It will find strengths and weaknesses in your deals, and identifies very subtle flaws in your sales approach. It even pinpoints the cause of bad sales data.
On the other hand, the use of data science does not indicate a loss of humanity. Among the best traits humans exhibit are: empathy, dignity, perseverance, emotional intelligence, and humor. As such, there is no argument for suggesting that data science can replace these traits. Data science cannot make an emotional connection to a prospect. It can’t carry a conversation, convey the value of your offering, or address competitive differences.
The role of data science in sales success
Making business decisions is a human process. A well-informed business decision combines the best human traits with data science. Therefore, the more colleagues understand your process, the more faith they have in the business decisions you make.
Maybe one day robots will replace all of us. But, until they do, we must balance the best that data science has to offer with our humanity as we work through the process of making business decisions.
