Customer Data & Analytics Blog

Why Marketing Executives Should Care About AI: Part 3

Josh Fayer | 2 minute read

080118_social-mediaOnce you’ve identified specific problems you’d like to fix within your martech stack, you need to look into your data. Insights are powered by data, and data is necessitated by insights. You need to have the right amount of the right kinds of data to truly see the benefits you want out of AI technology.

As goes the analogy, AI without data is a very fancy bread knife without bread—the bread is the base of the problem, and the breadknife is the tool you use to solve the problem of splitting the bread in interesting ways. The biggest challenge here is differentiating a data problem from an algorithm problem; do you not have the technology to produce meaningful insights with the data you already have, or do you lack the data in the first place? Fortunately, even if it’s a little of column A and a little of column B, there’s still a lot you can do to bolster your campaign efforts. Third-party data providers can supply enriched consumer profiles that link together a massive amount of information to the existing records you already have.

Once you’ve evaluated that you have the sufficient data to power your new tech, you’re ready to implement! In recap: you’ve established what your problem is, found technologies that are feature-tailored to fix your problem, ensured you have the right kinds of data to get the most out of the technology—either from first- or third-party data sources—and worked closely with data science team within your organization to smoothly handle the integration with a new service.

Not all machine learning platforms are built identically. If a program is pre-trained off of an existing model, it might take no time at all for the system to integrate with your data. Otherwise, building a model takes time, and isn’t an effortless process. Advancements in AutoML technologies have recently made leaps in progress to allow citizen data scientists to take ownership of their data; but short of fully-automated model construction, your data science teams might need to work closely with the new AI tech as it learns and builds an understanding of your customers and the patterns they exhibit.

Even after your models have been constructed and your new AI tech is producing valuable insights, the work isn’t quite done. The thing that distinguishes intelligent systems from more traditional analysis platforms is their ability to learn and tweak their insights as they gain a better understanding of your customers with time.

What kind of results can you expect from this new technology? And in what ways will it improve your marketing stack overall? Stay tuned for the series conclusion to find out more.

Josh Fayer manages Marketing Communications at Zylotech, where he brings an extensive background in public relations and computer science. Josh Loves music! He sings with an a cappella group, Orange Appeal, (whose recent album Unpeeled he shamelessly plugs at work), and at home he loves to mess around with a variety of instruments. Josh enjoys spending quality time with his adorable dog, and exploring around his new home near the South Shore of Boston.

If you liked this post, check out our other blog post on how to strengthen social media efforts with CDP data.

Topics: Customer Intelligence