How Predictive Analytics Contributes to Pipeline and Revenue Growth

Apurva Pathak | August 4, 2022 | 789 views | Read Time : 05:00 min

Contribute to Pipeline and Revenue Growth
For B2B marketers like you, wouldn’t it be great to know which buyers are ready to purchase and when? It would be, but this doesn’t generally happen. Marketing and sales teams across businesses spend their time trying to find the perfect prospects instead of focusing on selling to the accounts that actually want to buy.

What Goes into Prospecting?

Prospecting is an entire process that is not only time-consuming but might not lead to anything worthwhile. Common steps involved in prospecting include:
  • Finding accounts that fit your target account profile
  • Zeroing in on the point of contact to get in touch with the account
  • Creating messaging to influence prospect’s buying decision
  • Wait for the prospect to answer calls

It is no wonder that prospecting is the most difficult part of sales. According to research by Sales Insights Lab, 50% of the prospects you go after aren’t a good fit for your product. To add to the mix, sorting through incomplete forms, questionnaires, anonymous web visits, and event attendee lists is backbreaking work.

Predictive Analytics: Pipeline Growth and Revenue Covered

ABM relies heavily on high-quality data management and analysis. It is based on quality and, not quantity. It can succeed only if the prospect account’s data like management hierarchy, business practices, pain points, requirements, etc. is interpreted, analyzed and utilized properly. This is where predictive analytics comes in. A predictive analytics model looks at how different parts of an account relate to each other and ranks them. A large amount of data can be successfully interpreted this way. Data mining, statistics, and text analytics uncover different patterns and relationships to give insights into an account’s behavior and outcomes based on data.

A Predictive Analytics Model Boosts Your ABM Strategy

Here is how predictive analytics can boost your ABM strategy:
  • Prioritizing Accounts Based on Rating
Marketing representatives need to approach prospects at the right time to capture expected revenue. Predictive analytics gives real-time data, forecasts into when to approach a prospect to get the conversion. With deep data insights, predictive analytics optimizes ABM and the allocated marketing budget.

  • Personalized Messaging
Personalized messaging is possible only when the data at hand goes beyond account intelligence-based numbers. Predictive analytics goes a step ahead, forecasts buyer behavior and gives marketers a tool to create content that appeals to every individual in the prospect account’s buying group and leads to conversion.

  • Objective Scoring
As accounts near the end of the sales funnel, predictive analytics forecasts the best time for sales overview, so risks like data deletion are bypassed.

Getting Ready to Adopt Predictive Analytics

To adopt predictive analytics in your ABM strategy, you need to follow these steps:
  • Create an ABM-centric organization where content marketers apply ABM
  • Marketing and sales teams need to understand predictive insights and its implementation in ABM
  • Align your marketing and sales teams
  • Decision makers understand predictive insights and how they are used at the grass-root level

Wrapping It Up!

Focus your investments on an intent data set based on predictive analytics and AI learning to make the most of the high-quality data insights without worrying about underlying technologies.

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Lava Performance Marketing

LavaPM’s definition of performance marketing is marketing that delivers measurable business outcomes.

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