It’s Not Big Data, It’s The Right Data: How Predictive Intelligence Is Transforming B2B Marketing

Print Friendly, PDF & Email

Amanda_6senseIn this special guest feature, Amanda Kahlow of 6sense outlines how asking the right questions, and exploring different assumptions, can deliver the RIGHT data, versus just a LOT of data. Amanda is CEO of 6sense, and is passionate about bringing the power of predictive marketing and sales intelligence to B2B enterprise and mid-market companies. She makes 6sense customers’ needs a priority, applying her proven aptitude for relationship building and sales. Prior to 6sense, Amanda spent 14 years as the CEO and founder of CI Insights, a big-data services company that used multi-channel analytics to help enterprise companies generate hundreds of millions in net-new business. The ultimate optimist, Amanda is focused on giving back to the community. An advisor to the organization, Girl Rising, Amanda is committed to their mission to educate girls in developing countries as a way to build thriving, prosperous, healthy communities and effect positive social change.

Over 90% of the world’s data has been created in the last year alone. Somewhere in this enormous swell of data are important time-sensitive digital signals that can tell you what your prospective customers want and need and which ones are likely to buy and when.

It’s not just about how much data you can gather. Rather it’s about identifying, aggregating and processing the right blend of data to predict actions, prescribe activities and use the results to vastly improve marketing results over time – from ROI to revenue.

A Blend of Data Versus Just A Lot of Data

Finding the perfect mix of data starts with a basic understanding of data sources and their relative value in making predictions.

Sources include:

  1. Internal data about your existing customers (CRM, marketing automation data and web analytics) to provide a baseline of past buying behaviors on which predictive models about future opportunities can be made;
  2. Firmographic and demographic attributes about target customers (company type, size, industry, purchased technology, job role, job function) to determine whether the target meets your buyer profile;
  3. External data sources where your prospects are searching, researching and comparing products (search, blogs, product review sites, publisher sites etc.) during the buying cycle.

B2B marketers that use only the first and second data sources to match past buyers with prospective customers are now discovering the severe limitations of this approach.

With access to the Internet 24/7 from any device, your buyers are researching solutions, forming opinions and eliminating choices, without any proactive input from you over weeks and months.

That traditional approach to targeting isn’t able to account for time-based activities, multiple contacts with in one organization (who may or may not be known to you) and the dynamic nature of today’s buyer’s journey.

But when all the data sources listed above are aggregated and connected – both at the company and contact level – we not only get the right data at the right time but we can make powerful predictions about which accounts are in market to buy.

Machine Learning: The Power Behind Prediction

Data aggregation and predictive intelligence at the scale needed for today’s enterprises requires use of machine learning for predictive modeling. Machine learning means training a machine to associate known patterns with known outcomes, and then when the machine sees new patterns it can predict new, unknown outcomes.

Machine learning is really about adjusting the knobs of the predictive intelligence engine to get it closer to the right answer. Using machine learning, we’re able to now test whether or not specific actions will take place and to predict a company’s likelihood to buy and offer other data such as time to close or products reviewed. B2B marketing and sales teams can use these predictions to target the right accounts. Machine learning makes it possible to extract meaning from huge and chaotic piles of data. This capability allows us to use the temporal or time-based influences data to identify net-new prospects that are actively exhibiting buying behaviors but are not yet known to you.

The Right Action For The Right Results

All the data in the world won’t help if you don’t actually apply it in your marketing campaigns. With the ability to see your prospects classified by buying stage, you can run data-backed campaigns that will generate much higher conversions. Second, predictive intelligence platforms can ingest all the conversion data from your campaigns into a single platform that continually learns and improves over time so that every dollar you spend is contributing to your company’s predictive models and bringing true integration to your marketing organization.

Some 6sense customers have been able to predict with 95% accuracy when accounts are about to open a sales opportunity. Others customers’ data-driven marketing programs have seen conversion rates that are nine times higher than any campaign they’ve ever run. Those metrics are improving every day.

Predictive intelligence makes a difference in the top and bottom lines by increasing revenues and decreasing inefficiencies by eliminating wasteful marketing to the wrong accounts and contacts. But if you don’t have access to the right data, your ability to see and drive measurable results is greatly diminished. Data-driven programs are a competitive and winning strategy for all companies in all industries that can be measured, modeled and managed.


Sign up for the free insideAI News newsletter.

Speak Your Mind