Predictive Analytics: Why They Make Your Content More Impactful
July 28, 2016
This byline by Infer’s Sean Zinsmeister was originally published on the Salesforce blog.
Customers today have an insatiable appetite for information, making content a vital part of how sales and marketing teams communicate, educate and influence prospects throughout the buying cycle. But, there are some clear indicators that we need to get smarter about how we view content marketing and its relevancy to our customers and businesses. The stats are sobering: a recent study found that 50% of content gets eight shares or less; another uncovered that 60-70% of B2B content goes unused; and Forrester discovered that 90% of content is ignored by B2B sales teams. It’s clear that there’s misalignment between the content that’s being created and its intended audience.
Predictive Analytics: A Content Marketer’s Secret Weapon
Though the words “data” and “content” are not often thought of as being interrelated, predictive analytics can be a game changer for content marketers. With predictive intelligence, B2B marketers are able to look past vanity metrics — such as number of downloads — to identify top performing content through deeper insights about which pieces attract the highest quality leads, drive larger deals, or accelerate deal velocity. This actionable intelligence enables teams to measure marketing effectiveness in real-time and adjust campaigns to improve content and audience alignment, and ultimately boost marketing ROI.
Predictive models work by analyzing customer data from your internal systems of record — such as MAP and CRM systems — and combine it with thousands of external signals about a prospect to predict whether they are a good fit to buy your product. By leveraging this type of historical data to inform content development, predictive analytics enables you to rely less on educated guesswork and more on data in order to produce content that is relevant and attractive to buyers.
Test and Invest: Using Predictive Insights to Measure Content Effectiveness
Rather than waiting for the full marketing and sales cycle to play out, predictive analytics provides content marketers with a litmus test to help measure the effectiveness of each content program. It lets you look at the quality of your lead sources in real-time as new leads are generated. Now, you can identify which assets deliver the best-fit leads for the business, and direct your marketing spend towards the highest performing content channels. We call this a “test-and-invest” strategy, and it’s something you should repeat over and over again to continuously fine-tune your content marketing efforts.
Let’s walk through this approach with an example from how my company, Infer, uses it to compare several eBook assets and find out which is driving the most high-quality leads.
Step 1: Line up several pieces of content.
For this example, let’s use predictive scoring to measure and compare three different eBooks we are using for demand generation — let’s call them eBook 1, eBook 2 and eBook 3. The content team has put a ton of time and effort into developing these assets to showcase the great use cases and business value of our products. After spending the same amount of money and time to promote each, they assess the download count of each asset.
On the surface, one might assume that eBook #1, which garnered the most downloads of the assets, resonated best with its audience. However, once we apply predictive analytics, a different story unfolds.
Step 2: Score the leads
The marketing team runs each lead through its predictive platform to append a “fit” score to each individual. We use fit-based predictive lead scoring to bucket all of our incoming leads into three groups, with A-Leads being prospects that are the best fit for their product, and C-Leads being those least likely to convert to opportunities. Examining the results, it appears eBook #2 did the best job at delivering inquiries that most resemble our ideal customer — eBook #2 generated 15% A-Leads, while eBooks #1 and #3 generated 2% and 5%, respectively.
While number of downloads can provide a certain level of insight, you can see that it’s often misleading when examined in a vacuum. Predictive analytics fills in the gaps by utilizing the full spectrum of data about a piece of content, giving teams more confidence in their budget planning so they can invest more aggressively in the right content marketing programs.
Step 3: Determine your buyer’s journeys
The key to maximizing this channel is being able to instantly identify high-value prospects so you can ensure they get the proper attention from day one. While A-Leads should get routed to sales reps for immediate follow-up, marketing can take the remaining segments and build out nurture programs around each fit-scoring band in order to educate these prospects about your products and drive engagement. Using the insights generated by a predictive model, your team can develop and deliver impactful content that is relevant to prospects throughout the buying cycle.
There’s a great opportunity for marketers to use predictive intelligence to squeeze more value out of their content marketing strategy. Smart, data-driven content is a great way to generate leads and make a big impact on revenue. And real-time campaign assessment cuts down on sales cycles, helps marketers to make decisions with greater confidence, and lets you invest energy into the programs that are going to drive the most value to the business.
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