The Evolution of Sales Intelligence

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Clik here to view.The picture above features a mainframe computer from the 1960s. Yet despite its sheer size and the two people shown operating it, we can now wear more data processing power on our wrists (hello, Apple Watch) than exists in this photo.

Data and our ability to make sense of it has grown rapidly, particularly in the last few years – into a $122 billion market, to be exact. Yet while marketing has been busy visualizing data and crunching numbers with mere clicks for what feels like ages, sales seems to have been stuck in Excel hell until just recently. (Still there? Do yourself a favor and check out this white paper!)

And now, the next generation of sales intelligence is upon us. We’re about to take a stroll through the evolution of sales intelligence and see that, while each generation has an important place in the sales organization, they have all culminated to the quintessential holy grail of sales intelligence: prescriptive insights.

Descriptive Data

The first step to transforming data into a useful sales tool involved simply making it easy for teams to get answers to everyday questions without having to rely on IT or spend hours crunching numbers in Excel. This type of sales intelligence essentially describes what is happening in your business at a given time in response to a particular query. In other words, think of descriptive intelligence as organized outputs of very specific inputs.

Questions that can be answered by descriptive sales intelligence solutions include:

• What were my total sales last quarter?
• Are my reps performing according to plan?
• How many calls did my reps make yesterday?
• Am I on target to hit my forecast?
• What is the average stage duration for my deals?

As time has gone on, the answers to these queries have grown increasingly granular, and their presentation has become far more visual. Think Tableau or Domo. But while descriptive information is 100% essential for sales teams to have, it is fairly one-dimensional in that it only allows you to look behind you at where you’ve been, but offers no insight as to where you may be going. Enter predictive analytics.

Predictive Analytics

As the name implies, predictive analytics anticipate what will happen in the future. This is made possible by artificial intelligence (AI) as well as an understanding of the events, activities and outcomes of the past. Predictive analytics take many forms, but for the sake of this blog we will take a closer look at three of the most popular.

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Email sentiment: Using natural language processing, predictive sales platforms can detect whether or not an email that you’ve received contains any negative sentiment, signaling that the deal may be in jeopardy. Examples of words or phrases that may trigger negative sentiment alerts include “unhappy,” “need to speak right away,” “disappointed,” etc.

Lead scoring: By isolating the key qualities of your best prospects and customers, as well as identifying common traits shared by high-value businesses, predictive lead scoring assigns a numerical value to each of these signals – industry, title, number of employees, etc. The higher the score, the more likely the lead is to eventually convert.

Forecasting: Forecasting tools estimate the win likelihoods and close dates of your deals based on your previous wins and performance patterns. Using this and other key information, they can then calculate the probability of winning deals to predict expected revenue and help you create more accurate sales forecasts.

Examples of platforms that provide predictive analytics include Oracle Advanced Analytics, Salesforce Einstein and Base’s very own All-in-One Sales Platform. Predictive sales intelligence is extremely valuable, with high-performance sales organizations claiming to be 4x more likely to use predictive analytics than underperformers. However, it’s important to note that these predictions are formulated based on information that is already known, either within the technology itself or within existing business patterns. This means that, while predictive analytics can tell you what might happen, it cannot tell you how to correct course if you are unhappy with your “fortune.”

Prescriptive Insights

Finally, the latest and greatest generation of sales intelligence, prescriptive insights, actually prescribes the exact actions that a sales team can take to achieve a desired outcome. Unlike descriptive data, it can look beyond the situation at hand and analyze potential future scenarios; unlike predictive analytics, it does not have to rely on pre-existing patterns or performance outcomes to make assumptions about the future.

Rather, scientific sales solutions like Base Apollo have the power to dynamically codify and analyze millions of data points at once to isolate the key dimensions impacting your sales performance. These dimensions range from lead source, to rep activity, to stage duration and more. Identifying and isolating these factors gives way to actionable recommendations as to the specific levers that your team can pull to achieve results.

In other words, prescriptive insights don’t tell you what’s happening or what might happen; they tell you why something is happening and how you can increase sales growth. The chart below illustrates the difference between descriptive, predictive and prescriptive sales intelligence:

The Science of Sales

Sales intelligence has come a long way since the days of the 1960s supercomputer. And while descriptive and predictive sales intelligence undoubtedly have their place and purpose within the modern day sales organization, the future lies within prescriptive sales insights. If you’d like to learn more about how to achieve actionable, prescriptive sales intelligence, download this free white paper: Why Your Business Needs the Science of Sales.

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