Predictive Analytics: Why It’s a ‘Must-Have’ In Your B2B Marketing Stack

How would you describe the relationship between B2B and predictive analytics? If you believe B2B and predictive analytics are born for each other, you’re certainly onto something. For a booming industry that gets its meaning from ceaseless transactions and large customer proportions, analytics are a large piece of the puzzle, and it often completes B2B’s identity. B2B in general generates data at every stage including structured data, which often come with significant opportunities amounting to millions of dollars in some cases. Making predictive analytics a major element within your B2B marketing stack and tying it in with AI and machine-learning can provide many benefits.

How Can Predictive Analytics Help?

The answer lies in the terminology itself. “Predictive” refers to  using machine-learning techniques to anticipate, plan, and make better choices. Whereas, “analytics” refers to the actions and strategies that drive intelligent marketing decisions.

The result is no more guessing games. Then there’s predictive modelling, which can help you reach specific goals by offering a holistic understanding of customer scenarios such as who requires up-selling,  when to transition to next-selling campaigns, and the effectiveness of cross-selling among different people. Any campaign that’s backed by analytics tends to provide the best possible outcomes and ROI.

Why B2B Marketers Need Predictive Analytics

In addition, there are many reasons why B2B marketers should utilize predictive analytics. Gartner shared an interesting prediction that relates to analytics and its future. It was reported that, by 2024, 75% of enterprises will drive a 5X increase in streaming data and analytics infrastructures. When it comes to tech giants like IBM though, they apply AI-fueled analytics solutions like Watson AI and Cognos Analytics to drive forecasts and accurate results. It’s safe to say that predictive analytics work well  for them, and this will likely continue in the years to come. Below are several points that describe why this model is a useful tool for B2B marketers.

  • Drives lead generation and lead scoring: By deploying predictive analytics, marketers can leverage historical data sets and map prospects to create a buyer persona. Due to the use of predictive analytics, marketers can even predict which leads are more likely to convert, and then channel specific marketing strategies accordingly.
  • Enhances price transparency: As a result of data-driven predictive analytics and dynamic deal scoring techniques,marketers can narrow down the time-consuming process of price negotiation.
  • Maximizes customer LTV : Predictive analytics is a surefire way to improve customer lifetime value (LTV). Whereas, machine-learning–based pattern recognition tools can be used to identify cross-sell opportunities and also encourage customers to engage with a brand more often.
  • Aligns resources with right projects: Based on historical and up-to-date data, you can gain insights about which projects are the most profitable and from there, you can allocate resources accordingly.
  • Enables efficient risk management: Accurate and timely responses greatly affect campaign success and ROI.

After learning more about predictive analytics, how likely are you to implement it into your marketing stack?  In the meantime, keep the educational train rolling by learning how to use predictive analytics to boost revenue generation, and keep in mind that focusing on data signals and analytics should ideally be at the heart of your marketing strategy. Get going by first checking out this helpful marketing analytics article!

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