Predictive Analytics in B2B Sales: Forecasting Revenue with Precision

Imagine a world where you can accurately make estimations about the future — and that’s without asking a fortune teller or magic 8 ball, but rather with the help of cutting-edge artificial intelligence (AI) tools. 

Well, if you operate in B2B sales, this world already exists for you, all thanks to predictive analytics. 

And in the B2B world where data reigns supreme, businesses that harness the power of predictive analytics get an unfair advantage. 

Surprisingly, though, even in 2023, we fail to fully grasp the importance of predictive analytics and are unaware of the untapped value it can bring to the table. 

In this piece, we’re uncovering the benefits tools like predictive analytics offer, especially in today’s fast-paced B2B landscape. 

Think of it as your insider’s guide to making informed decisions, pinpointing high-potential leads, and boosting revenue. Data-driven predictions, not crystal balls and magic.

Ready? Let’s get started.

Role of Predictive Analytics in B2B Sales

For B2B salespeople, predictive analytics offer insights into customer behavior, sales trends, revenue forecasts, lead churn, lead scoring, etc. 

Data about these core contexts will also help you optimize your sales processes by aligning your marketing, segmenting your customers correctly, following up at the right hour, pricing the service at the correct rate, and personalizing customer experiences.

Basically, you can make your pitches more targeted and set better goals and plans of action if you have the insights on hand. 

This strategy turns out to be fruitful regardless of whichever sales method you’re practicing (solution selling, social selling, value-based selling, etc). 

Moreover, predictive analytics aren’t only helpful for sales teams but rather help other teams as well. 

For example, if you find that you’ll see too many sales during a particular month and would require more hands-on-deck, HR and Recruitment teams can begin looking for talent to hire with this advanced notice. 

Or, with the same data on hand, management teams can invest resources in employee training to ensure every pitch looks pitch-perfect. 

Predictive Lead Scoring

Predictive analytics allow you to do dynamic lead scoring. If this sounds like a buzzword culmination, allow us to elaborate further:

Basically, tools like Breadcrumbs use AI and machine learning to identify the ways your lead interacts with your brand — and all of their interactions are based on a point system. 

For example, if they opened an email? That’s five points. Did they sign up for a webinar? That’s ten points. Have they downloaded an e-book? There are five points once again. 

Once you feed data about your ideal customer profiles (ICPs) to this tool and connect it with your customer relationship management (CRM) application, it’ll automatically find the leads most likely to convert based on their engagement level, past data, and behavior patterns. 

This allows sales teams to focus on high-potential leads, thus increasing efficiency and conversion rates. 

Predictive Analytics for Accurate Revenue Forecasting 

Predictive analytics give you insights into customer behavior and future sales trends — it can even help with sales forecasts. Combined, all these factors allow you to understand what your revenue could look like in the future. 

If you don’t have a specific tool allocated for forecasting revenue, you can even use math models for prediction, such as regression analysis, time series analysis, exponential smoothening, etc. 

You can also use predictive analytics for other things such as inventory and resource management, identifying cross-selling and upselling opportunities, setting sales targets, etc. — getting a better handle on all of these factors will eventually help you accurately forecast revenue. 

Predictive Analytics for Inventory and Resource Management

By analyzing past data and identifying current demands based on factors such as seasonality, use behavior, purchasing patterns, etc., predictive analytics can provide you with more information on how your sales are going to look in the future months. 

Based on this information, you can now accurately manage your resources and your inventory. 

For example, if you identify that in a few months, your sales are going to go upward for products A, C, and Z, you can then call for inventory for the following products, allocate your resources (such as labor, machinery, and space) for those products, and call in your other stakeholders to better handle the demand. 

For example, you might need to get in touch with your freight forwarder or your shipping company because you expect additional demand in a month and want them to provide you with a good deal on their charges. 

Predictive Analytics for Cross-selling and Upselling 

Predictive analytics is also an excellent tool for identifying upselling and cross-selling opportunities. Based on intent signals and past purchase patterns, you can push the products your customers need without them even asking for them. 

Just think about the “Recommended for You” emails you get from your beloved brands. 

David Janovic, Founder of RJ Living, stresses the importance of predictive analysis for upselling and cross-selling by saying, “In eCommerce, we want to cross-sell and up-sell in a way that makes the most sense for our clients.

We’re essentially connecting them to more of the products they want, and we can do that by analyzing collective and individual data and matching customers to the right products at the right time. So we’re simultaneously building real value for the customer and our brand’s customer lifetime value.”

Predictive Analytics for Setting Sales Targets

Another thing predictive analytics can help with? Setting the right sales targets. Pushing too high or too low on your sales targets can lead to easy burnout of your sales teams or a loss in profits. 

Knowing what your sales will look like in the future month (based on past data and current market conditions) will allow you to set better sales targets that allow you to optimize for profits without burning out your sales teams.

Predictive Analytics for Customer Behavior

With predictive analytics, you can also get highlights on customer behavior (such as their purchase patterns, lifetime value, churn predictions, content preferences, etc.). 

Moreover, you can even go one step further to segment your audiences, provide them with personalized experiences, share product recommendations, and even dynamically price your product (e.g., many software companies consider price parity based on the location of their users). 

Some businesses also use the data they get on customer behavior to create retention strategies. For example, some companies employ strategies like account-based marketing (ABM) to keep customers engaged, improve their experiences, and increase their lifetime and present value. 

Best Practises for Implementing Predictive Analytics

Some of the best practices for implementing predictive analytics in your sales teams would be to:

• Define clear objectives: This is the first step for implementing predictive analytics. Find out your goal. Create a strategy for the steps you’ll undertake to achieve this goal and write down the results you expect to find. 

• Get quality data: Your output and results will only be as good as the quality of data you feed it. Give your software access to historical data to see past trends and connect them with CRM systems so they can analyze real-time trends and customer behaviors. Make sure the data you feed is free of any biases towards any trends. 

• Work with the right people and tools: Take your time to research the right predictive analytics tools and find domain experts who can help you undertake the tasks of researching your market — they’ll be able to take raw insights and turn them into actionable plans. 

• Train your model continuously: The models and software you work with are guided by the data, ICPs, and other information you feed it. Sometimes, you might need to work with different mathematical models and algorithms to ensure the insights you get are fairly accurate. 

• Align your internal teams: Your sales and market research teams must align to ensure that both departments have the right information, have understood it correctly, and can implement it without any noticeable issues. 

• Keep an eye out for competitors: Finally, to ensure you’re doing predictive analytics right, you also need to keep an eye out on what your competitors are doing (this is because you need to analyze the use cases they’re using predictive analytics for). 

For example, if you’re in the healthcare space, a B2B competitor that could serve as inspiration is Henry Meds. Why? They use predictive analytics to find potential B2B partners, predict market trends, and tailor their offerings to specific needs — aka, they go beyond the box to use predictive analytics to its best potential.

The result? They’ve landed seed funding from Heron Rock Fund and a 4.5-star rating on Trustpilot. 

Future Trends to Consider 

As data and technology advance around us, there’s no doubt that in the coming years, multiple sales teams and small businesses are going to harness the power of predictive insights for better decision-making. 

Perhaps the only thing that’ll stop anyone from opting in is the belief that the software required to do predictive analytics might eclipse their budget.

But we have exciting news… 

Leverage Predictive Analytics to Improve Your B2B Sales

It’s time to bid budget concerns goodbye, as solutions like Breadcrumbs that offer predictive lead scoring also have free tools. Yep, you read that right — Breadcrumbs’ Copilot tool is now available for free, so you can find your most valuable leads in no time. 

Can’t believe the offer you’re seeing? Head on to our Copilot page to see what it can offer your business!

Your top line will thank you. Now, sit back and watch the sales come rolling in.