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Predictive analytics in the B2B world: Why is it so popular and what is it good for?

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26th Oct 2015
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Predictive analytics is a type of analytics that is used to make predictions about future events.

Predictions are made from applying a variety of techniques (such as artificial intelligence, machine learning, statistics and modelling) to current and known data. Through looking at the patterns found in this data, predictive analytics identifies risks and opportunities in the enterprise.

Predictive analytics is so hot right now

Although predictive analytics has been around for several decades - pioneered in risk management, healthcare and financial services industries - it’s only now that B2B organisations are getting serious about implementing the technology for sales success.

One reason for this is that the amount and types of data that are available to be analysed have grown exponentially. A decade ago, predictive analytics was limited to the information in your CRM database. This would typically be a contact name, company title, product holding information and job role. Whilst these were (and continue to be) very useful data points to work with, they hardly encapsulate the dynamic signals and complexity of a customer relationship where prospects are continually evolving in their interests, needs and motives.

Now, there has been an explosion in unstructured data; buried away across emails, reports, spreadsheets, contracts, advertisements, marketing materials, PDFs, call centre transcripts, ordering information, surveys, social media feeds, and blog posts is a whole raft of customer insight. These examples of unstructured data hold vital information about the customer journey which until recently have had to remain outside of the traditional CRM environment - and therefore outside of the purview of predictive analytics for sales.

The result is a Big Data boom which has been a boon to predictive analytics in the sales environment. Suddenly there is more data than ever to analyse and the outputs of predictive analysis can be neatly delivered through predictive CRM.

Problems predictive analytics solve in the B2B environment

I’m a big believer that we shouldn’t celebrate or obsess over shiny new technologies for the sake of it. Technology needs to solve an actual business problem - that goes for predictive analytics, too.

So, what are some of the problems predictive analytics can solve in B2B environments?

Reduces unused content

One of the great scandals of B2B marketing is just how much content is wasted. SiriusDecisions believe that 60-70% of content produced by B2B companies goes unused, whilst Corporate Visions suggest it is even higher (90%). In all cases, it presents a pressing issue: whichever figure you believe, at least 60% of budget is being wasted on content that is created and then never used or distributed.

Predictive analytics will identify the pieces of content that will be most engaging to your prospects and customers, and make recommendations as to which pieces you should send to them. This saves salesperson the time of having to wade through a central database and having to learn about all the content that is available at their disposal.

Preventing lead churn

Any demand generation exec knows that it’s one thing to create a lead. It takes a whole ‘nother set of strategies to keep that lead engaged until they are ready to engage with a sales person. Marketing automation tools help marketers scale their marketing campaigns but they do not have predictive capabilities. The result is that each time you launch a new campaign or comms strategy there is usually attrition from your subscriber database.

Predictive analytics can identify leads that are likely to churn and take action using your content. One example of this is personalization: rather than uniformly showing prospects in a segment the same content, predictive analytics will examine each person’s content consumption history and display the most engaging content to retain their interest.

Improve lead handover

The marketing-to-sales lead handover is a crucial point in lead management. It’s also the site where most revenue is lost – for a variety of reasons.

Predictive analytics solves a variety of issues during the lead handover point. For a start it enables salespeople to prioritise leads. Predictive analytics also enables marketers to pass on a large amount of lead intelligence (such as their emerging needs and interests) to salespeople for a more informed sales discussion.

Better lead scoring and prioritisation

Lead scoring refers to cumulative score typically captured and generated by a marketing automation tool for how engaged a prospect is with your marketing. As a marker of prospect interest, it’s great, however it doesn’t tell your sales rep what to say to them or give them any context beyond ‘this person is very active’.

Predict analytics builds on the foundation of basic lead scoring to look at the trends of other leads with similar scores. Now leads can be ordered and ranked in order of their likelihood to convert. Furthermore, when using employed through Content Intelligence technology, predictive analytics can examine the content consumption patterns of other active prospects and tell salespeople which topics of interest to talk to their lead about.

Conclusion

The coming-of-age of predictive analytics in the sales environment makes for an exciting time.

For B2B companies that are using content to nurture prospects over complex and prolonged sales cycles the benefits of moving from a reactive to a proactive company with greater lead intelligence, less lead churn and more efficient use of content should become increasingly apparent.

Andrew Davies is CMO and cofounder of idio.

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