Commentary

What Predictive Analytics Means For Marketers

The explosion of big data has been both a blessing and a curse for modern B2B marketers. A blessing because, in one way or another, every time a prospect interacts with your brand — through your site, ads, emails, etc. — it’s now tracked. Because prospects interact with so many touch points along the path to purchase, even if an interaction doesn’t translate directly to a conversion, every touch is producing valuable data indicative of prospect intent. The curse portion of the equation comes most often from the mouths of marketing leaders as they struggle to make sense of this avalanche of data.

Most marketers know that big data does contain valuable information that, when used properly, can transform their marketing. The problem is that these large data sets are often unstructured and “noisy,” and manually mining data to uncover patterns is often time-consuming, inaccurate, and can feel like looking for a needle in a haystack.

The outlook is not all bleak for marketers, however, and many are turning to predictive technologies to help reduce the noise and derive actionable insights from their data.

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Predictive analytics automates the processes that were once carried out manually by marketers and their teams through in-house algorithms and a barrage of spreadsheets and pivot tables. Using a combination of native data and public, external sources, predictive analytics can remove the speculation from marketing. Consider some of the ways that predictive analytics is already benefiting B2B marketers:

Sales Alignment: The sales department can work more efficiently by focusing its time on the leads and opportunities most likely to close, and marketing and sales can be closer aligned on lead volume, quality, and follow-up expectations.

Campaign Optimization: By automatically identifying and analyzing patterns in historical campaign performance, predictive analytics helps marketers understand ROI in real time, rather than waiting until the end of a sales cycle to evaluate campaign performance. Using these insights, marketing can see where to increase investment and where to cut, in order to ensure that they’re tracking toward organizational growth and revenue goals.

Demand Generation: With a better understanding of the ideal customer profile, and the channels most effective in reaching them, the marketing department can produce highly targeted campaigns reaching the right prospect, at the right time, with the right message — and doing so in a cost-effective manner.

Forecasting: Unlike sales,  primarily concerned with meeting near-term (monthly and quarterly) quotas, marketing needs to plan multiple quarters and even years out.  With predictive analytics, marketers can get a data-backed reality check on pipeline health and apply conversion probabilities to more accurately predict revenue.

While predictive analytics for B2B may be in its infancy, if you’re still on the fence, you needn’t look any further than the consumer-facing applications that you use on a daily basis. Amazon, Google, Netflix, and even Facebook have all been leveraging predictive algorithms for years — to better serve users, track engagement, and forecast how changes will impact usage, loyalty, and company revenue goals.

B2B is not as dissimilar as you may think and, while prospect engagement may not be confined to a single website, technologies have emerged to help better understand how B2B prospects interact with a brand along the path to purchase — wherever they’re touching your brand.

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