Don’t let the term “Big Data” scare you off; just because there is a ton of customer data available at your fingertips, does not mean it’s unmanageable. In fact, thanks to predictive analytics, you can take that information and increase overall marketing effectiveness across multiple channels.
So what is big data?
According to IBM, we create 2.5 quintillion bytes of data every day. Did you realize that 90% of worldwide data was created in the last two years alone? This data comes from multiple sources and from every field across the globe.
Big data analytics examines this colossal amount of data to find patterns, relationships and correlations that aid organizations in making better business decisions.
Companies of any size can find an opportunity to analyze big data and use that information to improve their existing processes and fill their B2B sales funnel. But, the big question becomes how can marketing and sales cut through the noise created by so much data to find valuable insights?
A Wall Street Journal study noted that while most companies are deploying these technologies they still don’t know how to derive value from this data. That’s what this 29-minute webinar will show you.
With a predictive analytics model, or as Amanda Kahlow, from 6Sense Insights, refers to it as an Enterprise Sales Predication (ESP) model, marketing departments can show true value to sales based on relative spikes in data points, such as where the buyer is in the buying cycle. Thanks to marketing automation and advanced statistical modeling, this wealth of information can now pinpoint milestone events that present not only a “buy” signal, but also what product that visitor is likely to purchase with an 85% accuracy rate.
The complexity of understanding the B2B sales cycle due to large volumes of numerous digital interactions across multiple channels has grown exponentially. Predictive analytics can tie these different touch points to predict new opportunities. With that information, you can customize appropriate relevant content objects to meet their most immediate needs depending where they are in the sales cycle.
For example, you can set up a visitor early in the buying cycle to receive targeted content for by showing comparison charts versus someone deep in the buying cycle who may respond to a financing offer.
How does the ESP model work?
Predictive analytics aggregates data looking for patterns of activity, and then maps the interaction to create a formula unique to your product or service. Because no two companies are alike and the multiple inbound channels vary, predictive analytics finds the right map to fit your problem and predict sales cycles unique to your company.
Big data can predict sales and revenue forecasts by taking and validating all the incoming possibilities from web and demand-based data to IP sources to pinpoint interest level from a specific company. Unlike a B2C purchase decision, which is based on a one-to-one interaction, a B2B sale may require 100 or more people coming to your business website before you ever receive a purchase order.
This modeling engine analyzes hundreds, even thousands of models, correlating multiple channels of interaction data (web, call, email, CRM, social, search, call center, marketing automation) and storing it in real time. With an overlay “decision engine,” marketing can identify where to route the lead using a rules-based threshold, increasing opportunities and revenue.
While big data may seem daunting, there are steps you can take to navigate this brave new world. Listen to the webinar, Enterprise Sales Predictions: How to Use Big Data to Predict Sales and Forecast Revenue to learn how. Access it now with a FREE trial to the Online Marketing Institute. Get instant access now.