We all know how difficult and challenging Marketing Intelligence is. Fortunately, advances in database technology makes it much easier to pull together vast amounts of data and use machine learning to analyse it and understand what is relevant and what influences customer behaviour. This is known as Predictive Modelling. 

In a B2B environment, people don’t buy things they do not need. There is less emotional or impulse spending, making it much more predictable. In reality, the combination of need, events and actions drive a purchasing decision. Good data is available for all of these factors, which when used intelligently can not only improve our sales but also satisfy customer needs.

You may consider hiring a data scientist and working with your IT team to build a marketing intelligence platform internally. But for most companies this will not be cost effective and high risk due to the difficulty of recruiting data scientists and the expense of implementing advanced, new big data, IT solutions. To make sense of all this fragmented data you need machine learning on a fast, big data platform that can intelligently combine and analyse all the available data and present back the insight in a way that both marketers and their systems can use intelligently – this is Predictive Marketing Intelligence.

Predictive marketing works by taking all the data available including account level (business information) and lead level information about the people we actually sell to and applies modern data science to answer questions like:

  • Who will my next customer be and what will they buy?
  • How can I find more customers like my best ones?
  • What do I need to do to convert them?
  • Which customers are likely to leave, what should we do to keep them and how much should we spend on them?

Predictive Analytics and Big Data have emerged as potentially the most important technology, to deliver a significant competitive advantage, for B2B marketers to adopt.

Using Your Data More Effectively

We now have lots more information available to us at the account level, yet most marketers still focus their activities at the contact level. By considering more account level information marketers are able to significantly improve effectiveness of customer retention, growth and targeted customer acquisition.

Account level buying signals are often the earliest signals presented publically, sometimes preceding contact level buying signals by weeks and months. Such signals could be company growth trends, technology usage, hiring patterns, funding, patents filed, technology usage, news reports, social media activity and many more.

Online ad spending can be personalised not only to the person accessing the site, but also to the account by using inbound IP address identification. This now makes it possible to personalise the content to specific accounts, making it more relevant to provide a much better customer experience.

Mobile Device & App Data

Increasingly we are seeing more mobile device data from apps, including additional signals such as: device, location, interests, social media information, usage stats. As B2B companies find new services to offer their customers using mobile apps, this new data will become increasingly important to help understand customer’s needs.

Complexity In Matching Internal To External Data

Complexity in matching internal data to external data means many marketers simply ignore the rich insights available when the two are combined. The forward thinking marketers are beginning to embrace new software and data analysis techniques to enable more dynamic, relevant and usable customer segmentation, using all the data available. This can be used at the heart of customer acquisition (lead generation), retention and growth campaigns (upsell/ X-sell) to improve marketing performance. Dynamic customer profiling is something all marketers should get excited about.