From its humble beginnings, predictive analytics has burgeoned into a crowded, bustling, noisy marketplace. There are some great platforms out there which could revolutionize the way you do business — but it can be hard to discern which they are amid all the noise.
Demand for predictive intelligence solutions is rapidly rising too. 65% of B2B businesses who aren’t already using predictive analytics said they were considering adopting it in 2016, according to a survey earlier this year.
So how can you know which solution is right for you? Here are a few things you should be looking out for when selecting a predictive solution.
1. Integrating predictive into your existing marketing stack
One of the main advantages of predictive analytics is that it brings together huge amounts of disparate data to provide a model for success.
So the ability to integrate predictive into your existing marketing technologies — and particularly your data management platforms — is crucial. Predictive models are meant to provide marketers (and sales) with the intelligence to act on your data, so the closer together you can use them, closer you are to a “single source of truth.”
It’s also a good way of separating the men from the boys: if a predictive platform can’t be integrated into your Salesforce or Marketo, it’s probably not worth its salt.
Does the predictive model have a proven track record?
These days, it seems that every big data company does “predictive analytics”, and new “predictive” vendors are continually popping up all over the place. In many cases, however, what they’re offering is at best a rudimentary predictive platform, and at worst something more basic dressed up as one.
Vendors of this kind contribute towards a muddled, sometimes even skeptical impression of the industry as a whole. But predictive analytics should actually be relatively straightforward.
It’s important to drill down beyond the generic, jargon-filled claims and big promises to identify which predictive models actually work. More specifically, you need to know if what they have to offer suits your business needs.
The only way to safely determine this — as with any other product or service — is by checking out their track records. Who do they already work with, and what solutions have they provided them with?
The best way to find this out is by looking at case studies. Again, ignore the jargon and pinpoint what precise needs they filled in each case. In particular, you should be looking out for customers with similar needs and objectives as your own company.
Look for patterns as well; for example, if they’re doing the same thing for every customer, it’s safe to assume their platform is fairly wet behind the ears. On the other hand, if they’re offering a variety of services or products, even their more basic services are likely to be more sophisticated.
Which brings us to our next point…
They do other things, too
Once you’ve ruled out the sub-par options, there are a number of quality predictive analytics solutions out there.
One major differentiator at this point is how they handle your data.
Black box solutions will take your data, run it through an algorithm and provide you with a predictive model. They’ll rank your contacts and leads according to how likely they are to buy, based on past behavior.
It’s a “black box”, because you won’t actually know how they came to those conclusions. That might not matter initially if the predictive model is at least reasonably accurate, but you could see gradually deteriorating ROI as the different variables within your data begin to change. For example: leads move jobs, get fired, retire, get promoted, etc. Simultaneously, certain factors or traits which initially appeared significant could turn out to be less influential over time than first thought.
A black box model won’t allow you to see these things, and could therefore preclude injecting valuable — even crucial — human input into the mix.
What’s more, most predictive vendors simply apply predictive to your existing first party data. The problem with this is that AI/machine learning solutions like predictive will only perform as well as the data allows them to — and odds are your existing databases are at least partially inaccurate or out of date, and missing crucial insights. To really get the most out of predictive, that intelligence needs to also come with plenty of third-party data as well.
It might sound cliche, but ultimately it’s the people behind the computers who make a predictive analytics solution truly great.
This is true professionally as much as technically. For example, if your predictive model is being built by a team with firsthand experience in the field of marketing automation, that will help inform the solution they provide.
Have you using or seriously considering predictive analytics for your business? Do you agree with our suggestions, or have any of your own? Let us know in the comments below, or tweet us @Leadspace.
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