He who would search for pearls must dive below.

– John Dryden

Once upon a time, the VP of Sales concerned himself with his primary responsibility – selling. He crafted the sales strategy, outlined the sales process, coached his sales reps and even took on the mantle of selling on his own. This was all he cared about when it came to his job – nothing more, nothing less.

That VP of Sales is, today, a dinosaur on the verge of total extinction. His focus on sales alone is no longer sufficient to unearth pearls. This old-school VP of Sales is being swallowed up and spit out by a new, ruthless species;

The VP of Sales who is also part data scientist.

There should no longer be a divide separating the ‘cool kids’ running sales and the nerdy geeks analyzing the data. VPs of Sales need to recognize the rare skills that a data scientist brings to the table, along with the critical importance of what they do. The Harvard Business Review even named the data scientist the “sexiest job of the 21st century.” You already know that we think sales analytics is sexy – it’s time for VPs of Sales to fall in line, and add on the role of data scientist to their day-to-day jobs. Here’s why:

1) Data is important…but it’s not just about the data

If it was only about the data, then many organizations could easily find a dedicated analyst or quant to focus solely on breaking down the sales analytics. The problem is that it’s no longer just about the data – there are many disadvantages to hiring a dedicated data scientist, instead of your VP of Sales taking on that mantle on his own:

  • An outside data scientist would not have the familiarity of the business to truly understand what’s going on – for example, not knowing all the different data sources that the business uses

  • They wouldn’t understand the sales applications – applying techniques of regression analysis on sales cycle management without knowing all the nuances of a sales cycle would paint an incomplete picture

  • They might not know the right people to talk to at various tiers of the organization

  • They place too much emphasis on the data – according to Rexer Analytics, 70-80% of a data scientists’ time is spent simply preparing the data, analysis and insight application notwithstanding

The other qualifications of an effective data scientist for a sales organization are too much for an outside, data-focused quant to successfully handle. This is why, according to eBay’s head of EU analytics Davide Cervellin, data analysis is more effective when the Sales VP is also the data scientist:

“It can’t be something you’re detached from; I hate people who are all about numbers only, and so I look for people with strong soft skills – they need to be able to maintain a good level of conversation with executives, telling them what the constraints are, but also helping them to understand what the end product is going to look like before they start working.”

2) Those who fail to learn from history…

Are doomed to repeat it. Or, in an adaptation that might be more resonant with VPs of Sales, are unable to repeat it – it being a predictable, scalable source of revenue. As John Foreman, the author of Data Smart: Using Data Science to Transform Information into Insight, said, “Past customer behavior helps forecast and predict future behavior – historical data can guide your businesses’ actions in the present.” (Click to Tweet!)

The modern VP of Sales can’t just be focused on what’s directly in front of him. He must carefully consider and analyze historical data and performances in order to drive more reasoned business decisions today. For example, how can a VP of Sales set quarterly pipeline goals before first studying his historical pipeline vs. quota to determine his company’s ideal sales-pipeline-to-quota ratio? He can’t, not accurately, at least. Unless you want to repeat the mistakes of your own past – or if you have no interest in identifying successes and repeating them – you have to dive into your historical data.

3) The competitive advantage is shrinking

Simply put, other organizations are beginning to recognize the importance of data and, subsequently, the need for a strong data scientist in-house. Where organizations once stood to gain a serious edge by delving into sales analytics and marketing metrics, doing so today is simply catching up to the status quo and fighting with your competitors on an even playing field.

John Foreman noted that in the nascent days of data analysis in the late 90s and early aughts, basic business intelligence was simply about the capture and reporting of data. Today’s VPs of Sales might balk at having to delve in further. They might be afraid that they don’t have the skills necessary, or they might simply be uninterested in the whole avenue of data analysis all together!

Foreman also noted that, “You don’t need a Ph.D. to do data science. Some techniques are tough, but anyone with the motivation and some spreadsheet skills can learn how to do it.” Well, he’s right! In fact, spreadsheet skills aren’t even necessary anymore – not with great sales analytics software such as InsightSquared, which can collect your data, generate your reports and give you the tools for cutting-edge analysis.

Help me dive below and find pearls!»

 
 

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