Predictive Sales Analytics: Using AI to Drive Sales

Predictive sales analysis refers to the AI software and processes that businesses use to make accurate predictions about the future by analyzing data corresponding to various factors, including historical sales data, economic conditions, customer trends, and more.

These AI-based predictions help sales leaders and managers make accurate sales forecasts they can trust, which enables sales teams to effectively plan, budget, and anticipate pipeline risks and opportunities.

Predictive sales analytics projects are delicate and multi-step, so it’s common for businesses to use sales analytics software to streamline the process. These AI-powered platforms typically integrate with your CRM and other data sources and analyze that data according to the insights you want to generate. Some CRMs also come with predictive analytics features.

Let’s look at the many facets of predictive AI sales analysis, including how it works, benefits, challenges, key tools, and the future of this emerging use of AI in sales.


6 Benefits of AI-Based Predictive Sales Analysis

Predictive sales analysis using artificial intelligence has many benefits for all types of businesses, from increasing the accuracy of your sales forecasts to helping you spot and plan for potential risks like cash flow slumps or economic downturns.

Below are five of its biggest benefits:

Increase the Precision of Your Sales Forecast

Predictive sales analytics tools improve the accuracy of your sales forecasts in various ways:

  • Automatically gather relevant past and current sales, marketing, and financial data from your data sources and AI CRM.
  • Clean the data, enrich datasets, remove redundancies, and fill in missing fields.
  • Apply machine learning and statistical techniques to effectively turn the data into insights and sales predictions.
  • Predict industry and competitor trends that will help you make better forecasts.

Using predictive analysis software also ensures that your predictions aren’t corrupted by human error.

And when you’re confident in your forecasts, you’ll be more likely to use them to inform your business and sales plans. Doubt is the enemy of effective execution.

Understand Your Customers on a Deeper Level

By analyzing customer data such as past purchases, survey feedback, and social media activity, AI-based sales analytics helps you form a clearer picture of your customer segments and how they’ll change going forward.

With a better idea of their future needs and behavior, you can build sales processes and offers that resonate with those buyers.

For example, you might predict that in 2025 your target customers will be seeking out more self-service options for exploring your solutions.

In that case, you could integrate a better AI chatbot into your website that can automatically send them content and recorded demos related to their interests. This helps you win customers from competitors who aren’t taking this into account.

Predictive Analytics

This predictive sales analytics tool by Clari summarizes topics and offers additional guidance for sales reps. Source: Clari. 

Enhance Your Marketing Campaigns

Insights into the future desires and situations of your audience will help you create data-driven marketing campaigns that reach and engage your buyers.

By reviewing past campaigns and other data, predictive analysis can help you find the marketing channel with the best ROI, identify the attributes of your highest-quality leads, and write copy that moves buyers to take action.

To learn about AI for customer relationship management, read our guide: Top 8 AI CRM Software 

Effectively Allocate Your Assets

AI-based analysis can help you predict future events like slumps in sales numbers in a certain territory, which will enable you to improve inventory management and staff assignment.

With predictions like that at your disposal, you’ll also be able to put your effort and money into high ROI-activities and market segments while avoiding the ones that look unpromising.

Improve Sales Performance

Predictive analytics tools can tie into your CRM and the other platforms your sales teams use.

This enables you to track key sales metrics and figure out which processes and tactics will work in the future and which ones need adjustment.

These tools can also assist in predicting shifts in the marketplace, which will affect which sales skills you focus on developing.

If, for example, it looks like your leads are going to increasingly come from social media, it’s important to train your sales team in social selling.

More Proactive Risk Management

Imagine a meteor was heading for Earth.

With predictive analytics, the scientists could identify the timeframe and likelihood the meteor strikes, but that’s not all — they’d also be able to examine the effectiveness of the various meteor-prevention strategies.

For instance, the military’s option might be to “blow the darn thing up.” Using predictive analysis, the scientists could then assess the effectiveness by predicting the weather patterns at that time, where the pieces would fall, and how likely their missiles were to hit the target.

This is the power of predictive analytics for businesses.

Sales operations leaders can spot potential causes of future drops in sales and make predictions about how well each avoidance strategy would work. This gives them the greatest chance of eliminating or at least mitigating the risk to the pipeline.

To gain a deeper understanding of today’s AI software for sales, read our guide to AI Sales Tools and Software

How does Predictive Sales Analytics Work?

Predictive sales analytics uses AI, machine learning, and statistical models to find patterns in data and forecast future sales trends.

The data it analyzes depends on your forecasting goals but often includes things like:

  • Customer information
  • Market trends
  • Economic conditions
  • Past sales performance
  • Marketing data
  • Financial numbers

Usually, businesses use a predictive analytics software, or sales forecasting tool, to automatically gather and update relevant data, clean it, analyze it, and auto-generate reports based on questions they need answered.

Some businesses may still use spreadsheets. However, this method is comparatively inefficient and prone to human error compared with using an AI-based analytics approach.

Example of a Predictive Sales Analysis Project

Imagine you run a retail chain. In order to forecast demand for seasonal products, you use predictive sales analytics to analyze historical sales data, customer behavior, and external factors that affect foot traffic, like weather patterns.

The results empower you to optimize inventory levels, plan promotions, and intelligently allocate resources, resulting in increased sales and fewer inventory shortages. The value here is leaving behind the classic retail “gut instinct” approach and focus on a strictly data driven approach, bolstered by AI.

6 Effective Ways To Use Predictive Sales Analytics For Sales Forecasting

There are many ways predictive analytics and AI sales tools can help you create more accurate sales forecasts in less time:

  • Create Ongoing Forecast Refinement: Predictive analytics tools can tap into real-time data and adjust your forecasts accordingly.
  • Leverage AI Chatbots: Use AI chatbots to gather more customer data and feedback that will fuel your predictive analyses and sales forecasts.
  • Uncover Relevant Economic Trends: Track economic indicators to predict certain fluctuations in market demand, and use this to inform your sales forecasts.
  • Use Detailed Deal Tracking: Sales analytics platforms can estimate the likelihood that a deal in your pipeline will close and bring these estimates into the sales forecast.
  • Collect More Customer Data: Set up your predictive analytics tool to auto-collect customer purchase and behavioral data so you have more data for your forecasts.
  • Create Better Lead Scoring: Improve your lead scoring model with AI-based lead generation tools that measure a wide range of variables; this will help you more accurately predict which leads will turn into sales, once again improving your sales forecasting.

Whether it’s forecasting next year’s customer behavior trends or estimating the number of sales in a given future quarter, predictive analytics software can dramatically improve your process by making it easier to collect and make sense of data from various disparate sources.

3 Predictive Analytics Tools For Your Team

Below are three powerful predictive analytics tools that will help your sales team make accurate sales forecasts in an efficient manner.

Alteryx icon.


Alteryx is an AI platform for enterprise analytics. Its automated data preparation, AI-powered predictive analytics, and easy-to-use interface allows analysts to create predictive models and transform data into sales insights.

If you’re a big organization looking to create a more data-driven culture that uses AI-based predictive analytics across departments, this is a great option for you.

Visit Alteryx

Clari icon.


Clari Forecast is a predictive revenue and sales forecasting software that uses AI-based sales analytics and deal inspection to make accurate sales forecasts.

It takes into account rep inputs, sales performance across segments, deal context, and other predictions into one powerful forecasting model. The tool will also recommend tactics for hitting your revenue targets.

Visit Clari

Zendesk icon.

Zendesk Sell

Zendesk Sell is an affordable sales CRM with various sales forecasting features, including win probability forecasting, sales forecasting, monthly forecast analytics, and pre-built analytics dashboards.

It’s designed to give salespeople their sales performance via accurate data-driven insights and predictions based on various CRM metrics, like salesperson performance and pipeline value.

Visit Zendesk Sell

3 Challenges of Predictive Analytics In Sales Forecasting

The major challenges of predictive analytics in sales forecasting include poor data quality, complexity of the projects, and the unpredictability of significant market-altering events (also known as “black swans”).

Lacking Accurate or Sufficient Data

Poor data quality and insufficient data are common hurdles for businesses attempting to do predictive analytics. After all, these AI tools require sound inputs to make their predictions.

If you haven’t been tracking relevant metrics, say the open rate of past email campaigns, then the analysis won’t be able to use that data in its predictions.

To compensate for a lack of in-house data, sometimes companies use data scraping tools, which allow them to extract data like product prices or lead data from public sources.

They may also use tools like B2B database platforms like ZoomInfo to gain more information about their customers or to enrich their current database with more information.

Also important: remember to make sure that your predictive analytics tool integrates with your various data sources. If not, your project will quickly run into limitations.

Predictive Analytics Projects Are Delicate, Multi-Step Processes

Due to the complexity of predictive analytics, expertise and care are required to properly integrate it into your sales forecasting methodology.

Sales teams will likely need training and support to get to a skill level where they feel comfortable using predictive analytics and the required tools.

Of course, there are user-friendly predictive analytics platforms and CRM features that make it easier for non-techies to conduct predictive sales forecasting.

Global Events, Economic Conditions Are Highly Unpredictable

Regardless of the soundness of your methodology and the quality of your data, the sales forecasts you make with predictive analytics are still not guarantees; they’re estimates.

Many unforeseen factors, from political shifts to technological advancements, can pop up and render your forecast inaccurate.

That said, quality data is becoming increasingly available, and so are the AI tools that allow you to use it to understand the future.

Therefore, you may be able to integrate economic and other types of data into your sales forecasts to make them extremely robust.

Future Trends of Predictive Sales Analytics

Predictive sales analytics will continue to become more available and advanced as AI companies continue to develop and offer more robust predictive capabilities.

Further, in the next few years, expect to see more CRMs coming out with AI-based sales forecasting to meet the rising demand among sales professionals and executives for artificial intelligence.

Also, large language models (LLMs) and AI chatbots like Chat-GPT will make it easier for users of all technical skill sets to make sales predictions.

Assuming the generative AI tool is trained on company data, a VP of Sales can simply ask the generative AI certain questions, such as, “what will be our sales in Chicago in May 2026?”

Overall, predictive sales analytics will become more democratized as software companies aim to make their platforms easier for professionals to use.

Bottom Line: AI Drives Improved Sales Analytics

Predictive sales analytics and AI sales tools are making it easier than ever for businesses to capture data from various sources and make predictions, including sales forecasts, about the future.

This helps businesses prepare for potential risks, budget accordingly, and make sales plans that better fit future market trends and customer expectations.

To see a list of the leading generative AI apps, read our guide: Top 20 Generative AI Tools and Apps 2024

The post Predictive Sales Analytics: Using AI to Drive Sales appeared first on eWEEK.

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