Wouldn’t it be nice to know in advance which B2B marketing campaigns are going to work, before you spend time and resources on them? Wouldn’t it be nice to identify new revenue opportunities before they actually appear? And wouldn’t it be nice to know what the customer is actually thinking instead of just guessing?
Well, that’s why “predictive analytics” is already becoming such a huge buzzword – it promises to answer all three of those questions. Given enough data, the thinking goes, you can build powerful enough algorithms and computing systems that don’t just tell you what’s happening now with your marketing campaigns – they give you important insights about what’s going to happen in the future.
Here are just three ways that B2B enterprises can put predictive analytics to work:
Find new revenue opportunities – This could be huge. The idea is that you combine all of your CRM data on a customer, combine that with all known behaviors and preferences within the marketplace, and you will be able to tease out new revenue opportunities via predictive intelligence. The real power of this is being able to convert those behaviors and preferences – very subjective factors, to say the least – into data that a computer algorithm can recognize.
Improve the ROI of existing marketing spend – The old adage is that marketing and advertising is an 80/20 game. 80% of your marketing and advertising doesn’t work, but the 20% that does is golden. New predictive analytics packages claim to solve that problem, by telling you in advance what’s going to work, and what’s not going to work. This also could be huge, even if it’s not as sexy as finding new revenue opportunities. Maybe now advertising is not an 80/20 game, it’s a 70/30 or 60/40 game.
Create new models for lead generation – The first two key selling points of predictive analytics assume that what you’re doing now is basically good, you’re just looking for a few incremental tweaks here and there to squeeze all the value out of your marketing dollars. But what if the whole system is somehow flawed? That means you might need an entirely new model for lead generation. It could be that your industry or sector is in transition, or in the process of being disrupted, and you need an entirely new approach to marketing and sales.
The big caveat in all this, however, is that if your data is of poor quality, the predictive insights are also going to be of dubious value. “Garbage in, garbage out,” as they say in the computing world. There are a lot of qualitative factors that go into any customer model, and the key is being able to code these in a way that turns them into quantitative factors. It’s one thing to record the average sales price of a particular product, it’s quite another to record something subjective, like “desire or willingness to upgrade existing systems.”
Yet, many of the most forward-looking companies don’t seem to view that as a problem. By some estimates, more than one-third (36%) of high-growth companies will invest in predictive analytics packages over the next 12 months. The chance to drive revenue and profitability, it appears, just can’t be passed up.
Dane
Dane Cobain is a published author, freelance writer, book blogger, poet and (occasional) musician from High Wycombe, UK. Before going freelance, he worked as a social media marker, now specialising in writing about publishing, technology and marketing.