Predictive Analytics is currently a hot topic in B2B Marketing. But if it is going to live up to its hype, we need to heed the lessons of marketing automation.
Marc Benioff, Salesforce CEO, is quoted as saying “If this is not the next big thing, I don’t know what is”. He’s just launched Einstein, an AI and predictive analytics platform, with much fanfare and rapturous applause at Dreamforce. Since its triumph on the television quiz show Jeopardy!, IBM has also advanced Watson’s capabilities and have announced plans to invest $200 million into its global plans.
It’s the ‘Open Sesame’ to the next big sales deal. We’re no longer looking at data and asking ‘so what’, but ‘what next’?
Sceptics argue that you can never predict human behaviour but predictive tools have been around and have been working well for years, particularly in the B2C space. Now it’s our turn.
The choice of predictive analytics B2B technology at our disposal is huge with 2015 seeing nearly $300m in new start-up investment compared to $375m in prior years combined. More importantly, there is so much more data than ever before. By 2020, the International Data Corporation (IDC) expects the global annual data-creation total to reach 40 zettabytes, which would amount to a 50-fold increase from where things stood at the start of 2010. Staggering.
What this means for B2B marketers is that alongside CRM data, there’s a whole new world of unstructured data hiding in social media, websites, transaction history, blog posts, and satisfaction surveys – all offering digital clues to customer needs and likely intent to buy.
We still await clarity from the DMA and ICO on EU General Data Protection Regulation (EU GDPR). This could have significant implications on how we collect, process and track unstructured data with which analytics tools rely on.
One thing that is certain, is that we must learn from our Marketing Automation experiences over the last 3 or 4 years before jumping in feet first with a global, all-conquering predictive analytics rollout.
Like predictive analytics, Marketing Automation was heralded as the next big thing in B2B marketing, yet Sirius Decisions report that 85% of B2B companies using Marketing Automation believe they are not getting the most out of it, despite significant investment.
That’s a very high percentage but not altogether unsurprising. I’ve worked with organisations who simply expected to pull their automation software out of the box, switch it on and let it work its magic almost overnight.
It’s a path well-trodden, but we know (or we should know) that automation success takes time and requires an ever expanding, well-structured and engaged database all supported by scoring models (that requires continued and sensible re-adjustment) and plenty of content for it to work.
It also doesn’t need to be complicated. Einstein defined genius as “taking the complex and making it simple” – some of the best examples of automation I’ve seen or worked on have been based on simplicity and scaled over time to produce a lead flow that sales are positively purring over. You can see our suggestions for how to run an effective marketing automation programme here.
The same principles need to be applied today. Our automation experiences tell us so. Simply put, B2B companies are more likely to succeed with it if they have invested well in their data and they have already mastered the art of making technology work in their favour.