3 reasons data science fails in sales organizations

Every day of the week, it seems that a new “big data” company comes on the scene. Some of them are good and 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 can serve as a foundation for tremendous efficiencies. When it works, organizations can achieve greater sales and lower their costs. At its worst, when data science is misused, misunderstood or contorted into “management by numbers,” great confusion results and sales leaders’ abilities to impact results are lessened. So many organizations are struggling to define the proper roles that data science and analytics should play in their sales organizations. As such, it’s important to frame the proper roles of data science in our organizations:

Should: Help you make better decisions

Nearly every major decision made throughout a sales organization can be influenced by data science: Which opportunities and leads should get more attention or less attention. Where we should focus our sales, marketing or management resources. Which salespeople need more coaching and training. The variables that created the insight should be clearly understood, so that your colleagues have faith in the decisions you make.

Shouldn’t: Attempt to make those decisions for you

Data should influence decisions, but you were hired to be the final authority on those decisions. No statistical models will ever be driven by perfect data and no model can ever incorporate every conceivable data point. As such, a valuable data model should never produce a perfectly 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.

Should: Help you innovate

A scientific understanding of your team’s sales data will open your eyes to the areas in need of the greatest improvement. When the solutions to these challenges aren’t obvious, data science can empower sales leaders to effectively measure the results of small controlled experiments.

Shouldn’t: Be seen as a replacement for creativity

Data science isn’t a crystal ball that magically shows the future. At its core, it finds historical patterns of data – often very subtle patterns – and compares them to current data to give insights about most likely outcomes. It is not management by numbers. The best sales leaders use data science for the direction that it provides, but constantly look for creative approaches to motivating their sales teams, engaging their prospects, and scaling their processes.

Should: Empower your salespeople

The best salespeople are driven, smart and competitive. They need to win. They are constantly looking for advantages that will help them win the next sale, beat their competition and climb the leader board. Great data science can bring those advantages. It can help focus their limited time on those leads or opportunities that are most likely to win. It can help them remove their own blinders, and give them insights that will position them to play to their strengths and minimize their weaknesses.

Shouldn’t: Replace their humanity

Data science should never be conflated with a loss of humanity. There is no argument that should ever be accepted that would suggest that data science and traits that embody the best humans and the best salespeople – empathy, dignity, perseverance, intelligence, strength, humor – are somehow mutually exclusive. Data science can identify your best prospects. It can identify strengths and weaknesses in your deals. It can find very subtle weaknesses in your sales approach. It can even pinpoint causes for having bad sales data. Data science, however, cannot make an emotional connection to a prospect. It can’t carry a conversation, convey the value of your offering, or address competitive differences. Maybe one day robots will replace all of us, but they aren’t here today.