From its humble beginnings, predictive analytics has burgeoned into a crowded, bustling, noisy marketplace. There are some great platforms out there which could totally revolutionize the way you do business – but it can be hard to discern which they are amid all the noise.
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 analytics platform.
Integrated predictive platforms
One of the main attractions of predictive analytics is that it concentrates all your disparate data to provide a model for future success.
So the ability to integrate predictive into your existing data management platforms is important. 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.
That isn’t to say that integration is always 100% critical. For example, even though Leadspace integrates fully into Salesforce and most major marketing automation systems (including the recently-launched Marketo ABM), some of our customers prefer to use our own custom User Interface for a variety of reasons.
Anyway, you won’t find a platform which integrates into every single CRM or marketing automation platform. But if it can integrate into your system, that’s a big plus.
Does the predictive model have a proven track record?
These days, it seems that every big data company does “predictive analytics”, and new “predictive analytics” 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-rich 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 service, and what solutions have they provided them with?
The best way to find this out is by looking at case studies. Again, sift through the jargon and pinpoint what precise needs they filled in each case.
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.
If the predictive analytics vendor has a more diverse range of solutions, they should be offering a more transparent product. For example, Leadspace predictive analytics customers directly benefit from the fact that we also offer real time data enrichment. Our ability to provide readily actionable intelligence means customers have access to all the data we used to build their model – and can adjust things as they see fit.
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.
Predictive analytics is great for Account-Based Marketing (ABM) as well. Find out how it works with our Basics of ABM workbook:
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