Originally posted on ITBusiness on February 7, 2014.

The last post I wrote at ITBusiness defined three market segments: predictive analytics, business intelligence, and big data to help frame a baseline of understanding. This blog post I wanted to continue our conversation and focus on why you should care, or with my western heritage, we always used to say, where is the beef?

Why you should care?

“With increased demand for actionable insights from ever-growing volumes of data, broader access to predictive analytics is key,” says Henry Morris, senior vice president for Worldwide Software and Services Research, IDC.

Predictive analytics can be innovatively used in numerous business processes. We will take a quick look at the value of predictive analytics in marketing and sales business processes.

First, there is maturity in the market leveraging predictive analytics in marketing data for numerous approaches such as: optimizing marketing campaigns to analyzing website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer’s predictive score informs actions to be taken with that customer — business intelligence just doesn’t get more actionable than that.

Innovative new market players like Sail Thru or Bulldog Solutions are pioneering in analytic approaches to advance insights using marketing analytics. Of course, market leaders like: Eloqua, Marketo, SAS, Cognos (IBM) are all major players in customer insight intelligence based on marketing actions.

On the sales predictive analytic front, many companies have done predictive lead scoring, such as Marketo, Lattice Engines or Infer Systems, but few have tackled the tougher problem of analyzing all the data sales patterns – everything in sales funnels in all its data formats (CRM data, text in notes, emails, documents, mobile, social media, financial market risk patterns, etc.).

This new era I have named, The Science of Selling™, where advanced math and advanced science, analyzes customer and sales interactions pattern in real-time.

This is the war we are entering and the insights will be informing to advance companies competitive growth edges by 20-40 per cent, from those that simply cannot afford this massive intelligence injection.

In some ways this is an overlay engine to the human race’s interaction patterns, with an incredible sniffer analyzing and predicting our next moves. Companies like Google will increase their acquisitions in predictive intelligence foundations, which also embed advanced search and machine learning methods into sales practices. IP in this area will become invaluable.

Toronto based SalesChoice Inc. is one of the first Canadian companies to tackle The Science of Selling™, with their Sales Predictions Insight Engine™ in leading companies, like Macadamian, and WorldLynx Wireless, already proving out uncanny insights.

Other US venture backed companies include: InferSystems or C9, or Israel’s Sales Predicts are all squaring off to bring advanced predictive analytics into the CRM market segment. Their focus has been primarily in sales predictions on lead scoring vs the tougher problem set that SalesChoice Inc is targeting to solve.

Patent pending SalesChoice may also have a competitive advantage having filed its patent in early 2012; two years ahead of the market momentum build in this space.

What are some of the benefits from predictive analytics?

There are many benefits being projected from predictive analytic solution providers. These are a few, we see in our research, or in our market interactions. If you think of others, we would love to hear about them – talk to me on Twitter – @SalesChoice_Inc.

  • Transform data into predictive insights to guide front-line decisions and interactions.
  • Time savings from being able to focus on the right opportunities that have higher statistical probability outcomes.
  • Predict what customers want and will do next to increase profitability and retention.
  • Maximize the productivity of your people, processes and assets
  • Detect and prevent threats and fraud before they affect your organization.
  • Measure the social media impact of your products, services and marketing campaigns.
  • Perform statistical analysis including regression analysis, cluster analysis and correlation analysis.
  • Help forecast future risks or outcomes (statistical ranges of customer propensity to purchase, or projecting scientifically sales quota attainment), etc.