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Implement Predictive Analytics for Conversion

You know that old chestnut: The future is now. Well, in the business world, machine learning is no longer a future pipe dream. Strides in artificial intelligence have enabled companies to become more competitive than ever in the ways they market to individuals.

Yann Sarfati
Cofounder & CEO

You know that old chestnut: The future is now. Well, in the business world, machine learning is no longer a future pipe dream. Strides in artificial intelligence have enabled companies to become more competitive than ever in the ways they market to individuals. In a recent Infosys study, 98% of respondents who used AI-based systems during their digital transformation could generate more revenue for their companies.

Yet, achieving the goldilocks ratio using predictive analytics to drive conversions remains elusive for many businesses. It’s not always easy to evaluate which tools will deliver on their promise or help companies meet today’s marketplace demands. Read on for more context on predictive analytics and how to implement it in your sales cycle to the best effect.

What is predictive analytics, anyway?

Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. In the context of the sales cycle, predictive analytics can be used to identify trends and patterns in customer behavior, forecast sales, and optimize marketing and sales efforts.

This powerful tool can be used to improve conversion rates by leveraging customer data in order to make informed decisions. By gathering and analyzing information from multiple sources such as web analytics or social media trends, companies can gain insights into customer behavior and preferences which can then be used to craft targeted campaigns tailored to each individual.

Additionally, predictive models can also be used to automate certain processes such as lead scoring or personalized follow-up emails which frees up time for sales reps so they can focus on closing more deals.

Getting predictive analytics right

To ensure that the automation process is running smoothly, businesses should continuously monitor performance KPIs like click-through rates or open rates. Tracking these on a daily, weekly, and, monthly bases will enable you to make any necessary adjustments more easily.

In fact, combining both targeted messaging and automated processes with predictive analytics can lead to improved deal conversion overall. And the more businesses can deliver the personalized experiences that today’s consumers expect, the more likely they are to build trust that least to brand loyalty.

6 Steps to Level Up Your Sales Cycle Using Predictive Analytics

To implement predictive analytics in your sales cycle, follow these steps:

  1. Define your objectives: Clearly define what you hope to achieve with predictive analytics. For example, do you want to improve your sales forecast accuracy, optimize your marketing campaigns, or identify new sales opportunities? Map this all out to decide your plan of attack.
  2. Collect and clean your data: Centralize ****your customer data, like demographic information, purchasing history, and interactions with your sales team. Clean and organize the data to ensure it is accurate and consistent, so you’re not pulling from an unreliable source.
  3. Choose your predictive analytics tool: There are a variety of predictive analytics tools available, ranging from simple spreadsheet functions to complex machine learning software. Choose the tool that best fits your needs and budget.
  4. Build your predictive model: Use your predictive analytics tool to analyze your data and build a predictive model. This may involve selecting the appropriate algorithm, training and testing the model, and fine-tuning the parameters to optimize its performance.
  5. Validate and deploy your model: Once you’ve built your predictive model, you can road-test its performance using a separate set of data. If the model performs well, you can then plan to deploy it in your sales cycle.
  6. Monitor and refine your model: As you begin using your predictive model, monitor its performance and make any necessary adjustments to improve its accuracy over time.

Conclusion

To further improve your conversion rate and reduce costs, predictive analytics remain critical for the success of today’s companies. No matter your current investment level when it comes to AI, its performance continues to spell great value for businesses looking to innovate at speed and scale. For more best practices for using predictive analysis to level up your sales cycle, visit us at Userled.io.