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Predictive Analytics: There’s Always Room for Growth

By jenny
April 19, 2019
Read Time: 3 minutes

The term “predictive analytics” refers to the use of:

  • Large-scale data
  • Statistical algorithms or models, and/or
  • Advanced machine learning techniques

In order to predict the likelihood of future outcomes based on past results.

In marketing, as well as in business more broadly, we make predictions all the time. Take, for instance, the process of lead scoring. When a prospective lead completes a certain action, like signing up for a newsletter, then we say this lead is more qualified and we prioritize them. When we do this, we’re predicting that they’re more likely to convert down the line. As marketers, we know that predictions like these are enormously helpful in moving customers through a sales funnel.

But we also know that marketing and business decisions in the real world are usually not so simple. In a complex digital ecosystem, it’s possible to track a wide variety of metrics. We can track sign-ups, clicks, scroll depth, etc. However, we can’t unearth all the possible insights from this data without the help of analytics. Using predictive analytics, such as propensity modeling, digital marketers are now able to score leads in a highly detailed and granular way. According to Forrester Research, “Predictive-scoring adds a scientific, mathematical dimension to conventional prioritization methods that rely on experimentation and iteration.”

And predictive analytics can be used in lots of ways beyond just lead scoring. This article from Emerj identifies other areas where predictive analytics are being used to produce valuable insights:

  • Modeling customer behavior
  • Understanding product/market fit
  • Delivering optimized & customized content
  • Driving campaign-level marketing strategies

What sets predictive analytics apart from other methods of forecasting is the use of sophisticated mathematical techniques, like regression analysis, Bayesian analysis, or network analysis. You can combine these techniques with machine learning to discover insights or inefficiencies that would be impossible for a human analyst, or even a team of analysts, to recognize.

As with any sort of analytics, though, the power is in the data. The greater the quantity of high-quality data you collect, the more accurate and actionable your insights will be. When it comes to making smarter, better-informed business decisions, there’s always room for growth.

If you’re looking for cutting-edge ways to leverage your data using predictive analytics, get in touch.

About the Author
Jenny Bristow is the CEO and Co-Founder of Anvil, a digital + analytics agency in St. Louis, Missouri. Anvil focuses on creating data-driven digital strategies that help clients develop scalable, measurable digital marketing programs for clients in the healthcare, education and manufacturing space. Prior to starting Anvil, Jenny launched, grew and sold a digital agency in Seattle, Washington and worked at Amazon.com. She was named one of St. Louis Business Journal’s 30 under 30, won a Stevie Award for Female Entrepreneur of the Year in 2018 and speaks regularly at industry and local events.
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