Hacking Content Marketing With Predictive Analytics [Slideshare]

It’s time for content marketers to change their thinking. We all have heard the cliche “quality over quantity” message, but we’re always left wondering how exactly to execute on such a nebulous strategy. 

Last month, we co-hosted a Meetup with DNN to talk with Bay Area marketers about how predictive analytics can better inform their content marketing strategy. We had a great time sharing simple tactics and strategies for how they can use predictive insights to gain instant feedback on their content marketing programs, and gather insights like:

  • Am I reaching the right audience (i.e., those that are most likely to buy your product)?
  • What are my top-performing content pieces?
  • What are my best content distribution channels?

To learn more about how predictive analytics and content marketing go hand-in-hand, you can view the deck from the event below. Additionally, we sat down with Sean Zinsmeister, Infer’s Senior Director of Product Marketing, to find out how his marketing team uses predictive insights in their own content marketing strategy, his predictions for the modern marketing organization, and more.

Check out the presentation below, or view on Slideshare.

Q&A with Sean Zinsmeister

What is predictive analytics?

I’ll riff off of a definition I really like from Predictive Analytics Today:

“Predictive Analytics is the brand of advanced analytics used to make predictions about unknown future events. Predictive Analytics uses many techniques from data mining, statistics, predictive modeling, machine learning, and artificial intelligence to analyze current and 3rd party data sets to determine a likely outcome.”

Predictive analytics are able to find patterns in historical, transactional, and behavioral data that identifies opportunities and risks before they ever occur. Predictive models analyze relationships across many data points to assign a score, or weightage. Businesses that successfully apply predictive analytics are able to easily and effectively interpret big data, and reap massive benefits beyond just sales & marketing.

How is Infer different from other players in this space?

With over 150 customers, Infer has the largest customer base in the category and is one of the few vendors born-from-predictive. While other vendors have pivoted into the predictive space from areas like social media and demand generation, Infer’s foundation was built upon helping companies leverage data science to model the untapped data sitting inside and outside the enterprise. Finding a mature vendor is an important consideration for businesses who are exploring getting started with predictive analytics, because it’s vital to work with someone that has deep expertise in building and deploying models into all types of environments and workflows.

Our product portfolio also sets Infer apart from the pack.

  • Instead of multiple modules, our unified predictive sales and marketing platform offers profiling & segmentation, predictive scoring, and account-based marketing all in one place.
  • Infer offers both fit and behavior models, which gives companies a more complete way to assess leads. While fit models help to identify prospects that are a good fit to buy your product, behavioral models determine which are in-market and ready to buy that product. We have experienced the most success deploying behavior models, which have spiked in demand this year. From a sales perspective, reps need more insight into when they should target different types of leads, while marketing needs more information about how well their ABM programs are performing. Because they capture how engaged a prospect is with your company, behavioral models can determine how likely a prospect is to buy within a certain time frame (i.e. 3 weeks), which helps alert sales to the best timing for their outreach. For marketers, they help to measure engagement so teams can assess if their outbound campaigns are successfully reaching their target audience.
  • We believe in empowering sales with more transparency and control, which is why we put sales at the center of the universe with our sales and account-based intelligence products. We strongly believe that in order to drive adoption, trust, and confidence with sales reps, they need transparency into why a lead is scored the way it is. To do this, we use machine learning to summarize the predictions and top signals behind each score — such as account intelligence about a prospect’s business model, what technologies they use, their website engagement and other details — in one convenient place. This detailed, at-a-glance information about an account, lead, or opportunity enables reps to personalize outreach and better pre-plan conversations.

Do you use predictive analytics in your product marketing role?

Absolutely. One way my team uses predictive analytics is to drive more value from our content.  For example, say we have a great customer playbook that we want to leverage in a campaign. We will leverage signals from our predictive model to hyper-segment our audience so we can target the people who would be most interested in this piece of content. Predictive analytics allows us to rely less on educated guesswork and more on data so that we make sure the right content gets to the right buyer, and increases the likelihood they will convert to customers.

The other primary use case is channel feedback. When we launch new products, we use predictive analytics to find out in real-time how successful our launch was — gone are the days of waiting for downstream metrics in order to calculate the success of launch programs! We can tell right away if we attracted the best types of prospects, and fine-tune as needed for the next time around.

How will product marketing be different five years from now?

As the modern marketing organization evolves, product marketing will become the core of the function and will give rise to a new best-of-breed marketer. As the “message owner,” PMM can be a helpful bridge to other go-to market teams to ensure content and campaigns are unified in their messaging.

With PMM as a strategic centerpoint to ensure message-to-content alignment, the standard chain of command will look like this:

Product > PMM > Corporate Marketing

Product defines what the product is, its functionality, the raw value it brings to the customer, and the market problem it solves.

PMM defines how to tell the world about the product, the go-to-market strategy, pricing, packaging, channel planning, and more..

Corporate Marketing, which includes functions like Content Marketing and Marcomms, manages outgoing messages and tailors them to external audiences, then amplifies them through various channels.

We’re going to see this chain continue to evolve as more and more startups make their first marketing hire a product marketer. The demand for good product marketers is high and steadily rising, which will create a driving force for more entry-level marketers to begin their careers as a PMM.

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