Steps to predictive sales and marketing success

The art (and science) of selling to businesses has changed significantly; buyers have already researched the market and competitive products before engaging with you and the whole process is now more complex.

Given this new landscape many top modern marketers are looking for more intelligent and data-driven ways of engaging with customers (and potential buyers) and ways to make sense of the huge amounts of data now at their fingertips.

>See also: Predictive analytics: good governance holds key to accurate forecasting

Predictive Sales & Marketing works by taking all of the available data about an organisation that you sell to (at an account-level) and the lead-level information about the people you actually engage with - and use advanced data science and machine learning to advise…

• Who to target?

• What proposition to offer?

• When to target them?

Let’s now consider what steps you need to accomplish to start to benefit from this predictive capability…

Step 1. Mass data collection

True predictive marketing requires ALL (or as much as possible) of the available data about UK (or global) businesses, at both account and contact level. This data needs collecting from hundreds of internal and external sources and indexed on an ongoing basis, before combining for modelling.

>See also: The future of the sales industry

Step 2. Predictive modelling

The next step requires data to be pre-processed, normalised and modelled using a variety of statistical techniques, depending on the outcome being modelled. These models then need to be evaluated and continuously monitored over, typically 100’s of them.

Step 3. Prescriptive / actionable insights

Now thousands of scores and propensities need to be translated into insights that the business can take action upon. This usually involves the calculation of further metrics like customer lifetime value and additional modelling steps to produce actionable recommendations.

Step 4. Access / delivery of actionable insights

All this insight is useless, without a mechanism to deliver to the business and to take action at the relevant points in the customer lifecycle. This usually involves a front end tool for exploring the results as well as integration with other systems like CRM for sales guidance or marketing automation for automated campaign actions.

>See also: How AI is changing enterprise sales

Organisations no longer need to build these solutions in-house or using expensive consultancies. There is a new market of B2B predictive SaaS platform vendors emerging.

It is no surprise that 89% of B2B sales and marketers now have predictive on their roadmap (according to Forester). We are seeing first-hand why Predictive is such a hot topic, with the financial gains being made by those who are leading the pack. Exciting times lie ahead.

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Nick Ismail

Nick Ismail is the editor for Information Age. He has a particular interest in smart technologies, AI and cyber security.