You’ve probably been hearing the phrases “predictive marketing” and “predictive analytics” being used more and more. What the heck is predictive marketing, really?

We’re going to break predictive marketing down in plain language, right now!

What is predictive marketing?

Predictive marketing simply means using data science to make smarter marketing decisions by predicting which marketing actions are more likely to succeed, and which are more likely to fail. This deceptively simple statement has profound implications throughout the marketing process

predictive marketingTo understand how predictive marketing fits into the marketing technology landscape, it helps to look at it as part of a continuum of data and analytics capabilities of varying sophistication.

Forrester, in their report on emerging data technologies, relates predictive marketing data services in general, dividing data providers into three categories:

  • Data aggregators – collect and append general business data – contact info, firmographics etc.
  • Data enrichers – collect and enrich marketing and sales activity data with insights relevant to the marketing process
  • Predictive modelers – apply mathematical algorithms to the data to match patterns to best-fit criteria

In Forrester’s framework, these stages of sophistication in data use correspond to the development of the marketing organization, so that as your marketing becomes more sophisticated, you will naturally progress from data aggregation through data enrichment to real predictive modeling.

Gartner uses a different framework – they place predictive on a continuum of analytics capabilities:

  • Descriptive – tells you what happened
  • Diagnostic – tells you why it happened
  • Predictive – tells you what will happen
  • Prescriptive – tells you what to do next
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Source: Gartner Report September 2015, Extend Your Portfolio of Analytics Capabilities https://www.gartner.com/doc/3054119/predictive-analytics-transforming-bb-selling

Predictive Analytics Are Transforming B2B Selling

Interestingly, predictive is not the most sophisticated method conceived in this framework – it would be even more advanced to take the technology one step further into actual automated decision-making. It is along this frontier that the true potential of predictive marketing lies. If marketers can make better, more scientifically accurate decisions about what to do next, they can produce better results.

How does Predictive Marketing work — in simple language?

Predictive marketing vendors collect data from a growing list of sources, consolidate it, combine it with your marketing and customer data, and use that data to build a predictive model that is customized to your business. This model is used to predict the success of your marketing efforts. The technology behind the curtain is complicated, but you don’t need to understand it in order to use it – basically, you give the vendor access to your systems, and the vendor gives you back scores and insights. The difficult part is figuring out what to do about that insight and information.

How is it different from predictive analytics, predictive lead scoring, and good old fashioned data science?

Predictive marketing is the application of predictive technology to the entire marketing process, across the entire buyer’s journey, and across every channel of communication. It means not only having predictive insight into the future through predictive analytics, but also using that insight to make better decisions about who and how to engage, and then build better content, campaigns and programs. Predictive lead scoring, which ranks leads in order of probability to close, is only one aspect of predictive marketing – other applications include predictive segmentation, predictive lead generation, predictive forecasting, predictive messaging – basically applying predictive technology to every part of the marketing process.

As for old fashioned data science, the key difference is that predictive marketing systems are being built to scale as SaaS systems. Gone are the days when only huge companies with teams of expensive data scientists could take advantage of this technology. The advent of scalable, cloud-based predictive platforms puts similar capabilities into the hands of business users.

How does it relate to and integrate with Marketing Automation, Marketing Cloud and CRM platforms?

Ultimately, we believe that predictive technology will be woven deeply into Marketing Automation and CRM systems, so that many of the features of these systems become intrinsically predictive. However, at the moment, predictive platforms function as a separate technology that integrates with existing MA and CRM systems. The integration is simple in concept – the predictive model extracts the data it needs to make its predictions from MA and CRM systems. It returns data in the form of a lead score for each contact, along with varying degrees of information about how the score was calculated (this varies a lot from one vendor to the next). Some predictive platforms also provide a view into the underlying attributes or traits that lie underneath the score. And some predictive platforms take that even a step further and provide you the data itself via contact enrichment services (for your existing leads) or predictive list buying (for net new contacts and accounts).

Who are the leading vendors in the predictive space?

(in alphabetical order)

6sense – A very new player in the market (founded in 2013), 6sense entered strongly with claims of exclusive access to intent data and the ability to predict the actual moment prospects reach their decision point. They focus specifically on intent scoring, in which they claim special expertise and access. Clients include Dell and Lenovo.

Everstring  – A newer player (founded 2012), Everstring claims their late entry into the market gave them the opportunity to build a more robust infrastructure – they say they have built a full DMP sort of “by accident.” – that is, they built a full-featured data management platform as a side-effect of building a robust predictive marketing engine. Clients include Comcast and Zenefits.

Fliptop – Once a stand-alone predictive vendor, Fliptop was just acquired by LinkedIn. See our blog article analyzing this transaction here.

Infer – A strong and well-established player, Infer has powerful data science cred and a focus on strong fit scoring. Their focus has been on building a robust and scalable platform. Founded in 2010, their clients include HubSpot and Tableau.

Lattice Engines  – The established leader in the space, Lattice was founded in 2006 with roots in data science and sales enablement consulting. Lattice currently has the most clients of any predictive marketing vendor, including such names as Citrix, DemandBase and DocuSign.

LeadSpace  – A data provider specializing in social media data, LeadSpace recently added predictive capabilities to their data services and so counts as both a data enricher and a predictive modeler in Forrester’s framework. They focus primarily on bringing in net new leads that are likely to buy. Founded in 2012, their clients include Adobe and Microsoft.

Mintigo – Founded by data scientists from the Israeli intelligence community, Mintigo is based in Israel and was founded in 2009. Mintigo pioneered the concept of predictive marketing beyond just lead scoring, and their proprietary dataset allows them to offer deep insight into the reasoning behind their predictive models. Their clients include Red Hat, Raytheon, and Time Warner Cable.

Radius – Focused on building predictive marketing software “for CMOs,” Radius was founded in 2012. Clients include FiveStars and POSPortal.

SalesPredict  – Founded in 2012, SalesPredict functions primarily as a Salesforce AppExchange app, providing predictive insights including lead scoring and churn analysis. Clients include GE and InsideView.

Well, there you have it! Hopefully this article answered your core questions about predictive marketing and how it fits into the larger picture of modern, integrated marketing, and the ever-shifting MarTech landscape.

This blog article is part of Intelligent Demand’s series on Predictive Marketing. Up next in the series: How would Predictive Marketing drive real revenue results in your company? Where does the ROI come from?

How about you?

Interested in learning more about predictive? ID can provide a detailed assessment of your company’s readiness for success with predictive marketing — and even develop a detailed business case and set of vendor recommendations.

Feel free to reach out if you’re interested in learning more about predictive. ID can provide a detailed assessment of your company’s readiness for success with predictive marketing — and even develop a detailed business case and set of vendor recommendations.

 

Author Eli Snyder

More posts by Eli Snyder

Eli is an old-school geek who fell in love with marketing technology. In addition to writing code and setting up advanced configurations in marketing cloud applications, Eli helps ID clients navigate the ever-shifting martech landscape. At some point, Eli will probably ask you for admin-level access to all of your systems (if he’s working on a project with you, or even if you just meet him randomly at a conference). It’s OK though, he knows better than to touch anything without permission, and with permission, he’ll make them hum like a Tesla roadster.

Join the discussion 3 Comments

  • Tony Yang says:

    Eli – great explanation of predictive marketing! We truly believe that the value of predictive is more than simply attaching a predictive score to a lead and throwing it over the wall to sales.

    I think the next evolution of predictive is in automatically applying/operationalizing the insights derived from predictive to drive marketing decisions and workflows across multiple channels and systems. That’s in part what Gartner is describing as prescriptive analytics. Thanks for partnering with us in educating the market about predictive!

  • Eli Snyder says:

    Hi Tony, thanks for the comment!

    I completely agree about “prescriptive” being the next evolution of predictive, and I’ve always appreciated Mintigo’s vision in this respect. A score by itself doesn’t actually create revenue. You have to make decisions differently as a result of the score for it to have an impact. That means incorporating the score into your decision-making flows, both manual and automated, across your entire marketing and sales process. That’s not easy, and the more the technology can do to make it easier the better.

    Actually, you’re already anticipating our next blog on the subject, which will be about the various use cases for Predictive and how to identify which ones are relevant to your organization. We will definitely talk about emerging and future state use cases as well. Stay tuned!

  • ruth says:

    I totally agree with your points…..Thanks for sharing this write up

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