Updated Jan 11, 2024: We’ve added a new section on challenges in predictive sales, practical tips, and FAQs.
There are two fundamental ways for any sales team to sell more.
You can either hire more BDRs to achieve your goals or you can make sure that each of your sales reps is more productive by improving the quality of what they spend their time on.
Without question, investing in the latter tends to have a much greater ROI.
However, to increase BDR or SDR efficiency, you need to have a clear picture of all your website visitors, known or anonymous, understand their conversion potential, and know how to get more of them.
How and where do you get this data?
Most sales teams still rely on Excel spreadsheets to track their progress and a CRM for conversion insights. The problem is that this type of analysis happens post factum, such as when a deal falls through or after you’ve spent days pursuing a prospect who wasn’t a good fit for your solution in the first place.
To really get ahead, you need to be able to see into the future, which means using predictive sales analytics.
In this article, we’ll cover the basics of sales forecasting through predictive sales and showcase the possibilities of AI when applied to leading predictive sales models.
Tip: The best way to measure predictive sales is to accurately identify buyer intent. Get buyer intent readings in real time with Lift AI.
Understanding Predictive Sales
Definition and Significance
Predictive sales are about evaluating data that you have today to make decisions that will help you sell more tomorrow. By using predictive sales, your team can identify the optimal path of engaging any lead that’s coming in.
The reason predictive sales have become essential in the past few years is that traditional sales outreach channels like cold-calling and emailing have decreased in efficiency and require significantly more effort for the same outcome.
Using predictive sales is a way to bring efficiency back into your sales process.
How Predictive Sales Works
The way predictive sales analytics works is by leveraging vast amounts of data you already have on your website, in your CRM, and in other sales and marketing tools. Predictive analytics then processes all that data with predictive algorithms and mathematical models. Some predictive sales analytics even let you set your own goals and rules, and then process your existing data accordingly.
So what are the benefits of predictive sales forecasts?
Benefits of Predictive Sales
Accurate Sales Forecasts
Without the power of predictive sales analytics, it’s hard to say whether your sales quota is too low or unrealistically high. As a result, you can’t optimally allocate sales resources and make consistent progress.
For example, you can use predictive sales analytics to identify cross-selling and upselling opportunities. Since organizations rarely sell a single product or service, it’s much easier to retain and upsell customers than acquire new ones. Selling someone a compatible product or a higher-tiered plan is a good way to meet your sales targets.
Leverage analytics to scan all your SKUs, matching them to customer buying patterns, and get suggested products that you can sell to certain customer profiles.
Enhanced Customer Relationship Management
If your product or service is based on recurring revenue (e.g. subscription software), maximizing your customer lifetime value is paramount. The most effective way to do this is to ensure that your customers are happy and have no reason to leave — in other words, decrease your churn rate.
Predictive sales AI can help you single out customers who have a low probability of making another purchase. You can then come up with a plan to improve customer retention and get the right customers to stay (e.g. a loyalty plan or special offers).
Optimized Marketing Campaigns
For many industries, marketing is a guessing game with unpredictable results. While half of marketing campaigns might be working, no one knows which half for sure.
With sales forecasting based on historical data, companies can improve their lead generation and drive conversion growth by focusing on the areas that have performed well in the past.
Using a data driven approach to marketing can get your team more sales for the same budget, improve brand perception, and reach the right leads at the right time.
Risk Reduction
Pricing is a true business art and one of the largest risks that every company has to deal with. There are many ways to go about pricing. You can charge everything upfront or agree on installments, add training or installation costs, charge per team member or per user, or simply create a few tiered subscription plans.
How do you know when you’ve reached the perfect pricing equilibrium? In the end, your best guess is as good as anyone’s. That’s where predictive sales analytics can help visualize different pricing strategies, compare their implications side by side, and test your assumptions. As a result, you can achieve a simpler pricing model with better outcomes and lower maintenance costs.
Challenges and Solutions in Predictive Sales
Common Challenges
It’s good to keep in mind that predictive sales is not a panacea for improving business operations.
You might face challenges with data collection, sales data accuracy, finding high-quality employees that are skilled in machine learning and AI, etc.
Depending on the scale of your operations and the amount of historical data available, the costs associated with predictive sales might be hard to swallow.
Overcoming Challenges
The best way to effectively solve challenges associated with introducing predictive sales analytics is to automate, train, and outsource.
Relying on automation tools you can save on costs by decreasing the amount of work that has to be done by your team. For example, having a lead-scoring tool can instantly cut down on time that your salespeople spend prioritizing leads.
Investing in extra training could help decrease the need for extra specialists. For example, if your account executives know how to create predictive sales dashboards, you might not need to hire an AI engineer.
Finding existing tools and solutions, such as Lift AI, instead of building your own is another way to get started quicker and cut on future costs.
Predictive Sales in Action: Lift AI’s Approach
How Lift AI Enhances Predictive Sales
While working on upselling, churn, and pricing will, without a doubt, increase your sales, the best thing you can do to unleash the true power of your BDRs is to make sure they only chat with leads that are worth pursuing.
After all, spending days talking to irrelevant prospects has considerable opportunity cost.
Traditional predictive sales analytics uses your CRM data along with software algorithms to calculate the score that tells you how likely any given leads is to buy.
However, this approach relies heavily on a hypothesis of what behaviors you think represent high-potential visitors. For example, you might decide that a visitor downloading an ebook is worth 20 points, or going to the pricing page is worth 5. These “rules” are then given to the software, which follows those rules exactly to tally up scores for those visitors.
Since algorithmic rules represent only a small percentage of the many pathways that high-potential visitors take through your website, most of them still get missed — typically more than half. And that’s half of the full potential of your website!
Not to mention, this approach only works with website visitors that have been successfully ID revealed, which is typically 30% of traffic on average.
So, existing tools miss at least 70% of website visitors and provide an overly simplified view of their intent to convert based on human bias and basic rulesets.
As you can see, predictive sales analytics is essential for any sales team today that wants to eliminate the guesswork out of their business development.
While better than nothing, these techniques don’t work on anonymous leads and thus already lag behind the industry front-runners like Lift AI that use machine scoring.
AI and SDRs: A Powerful Combination
Most companies still use web forms and BDRs to manually qualify leads on their website. Alternatively they’ll use a conversational marketing platform to engage visitors through chat.
However, without knowing exactly which visitors are likely to convert and which aren’t, the BDRs end up targeting chat too broadly across every page (which isn’t possible to service with humans, and leaving it to chatbots won’t be able to successfully sell). Or, they target too narrowly, just putting chat on pages they think are likely to have buyers such as the pricing page, however studies show that 88% of buyers don’t even visit the pricing page.
This exercise is not only expensive but also frustrating for both the visitor and the BDR, typically engaging a small fraction of high-potential visitors or giving them the wrong experience.
Adding identity intelligence from your CRM or a third-party vendor can provide more context to any chat, but only if the visitor can be identified — most remain anonymous.
Figure 1 shows the evolution of lead scoring from simple qualification to state-of-the-art machine-scoring.
Lift AI is the only machine- learning scoring model available today that’s able to accurately predict the conversion probability for anyone who visits your website in real time.
Even the most advanced predictive sales analytics tools can’t assign correct scores on approximately 70% of your web traffic, which remains anonymous.
Leveraging more than 15 years of global sales data, 14 million customer interactions, and one billion customer profiles, Lift AI gives you actionable insights right out of the box with no additional training required for your website.
Unlike predictive sales analytics that guess what path might be taken by a high-potential visitor, machine scoring actually measures each path to determine every high-potential visitor with 85% accuracy.
When your machine-scoring predictions are this accurate, not only can you engage more high-potential visitors who you would otherwise have missed, but you can proactively engage those visitors knowing that your sales team’s time will be well spent and the visitor will be helped throughout their buying journey.
Practical Tips for Implementing Predictive Sales
Instead of deploying chat hoping the right visitors will engage with your team, you can now proactively present chat options just to those right visitors.
How do you integrate predictive sales analytics and chat? Use Lift AI. It works with any live chat platform you already have, be it Drift, Intercom, Salesforce, etc. When someone visits your website (whether for the first time or not), Lift AI will automatically assign them a score that’s updated in real-time as they navigate, and either connect them directly to your BDRs via live chat for conversion or direct them to a chatbot for nurturing, qualification, or escalation based on their unique score.
As a result, your BDRs will only spend their time working with leads that have the highest conversion probability, and you’ll watch your sales grow in no time.
Conclusion: The Future of Sales with Predictive Analytics
The results of predictive sales analytics speak for themselves. Lift AI, when integrated with your marketing stack, tends to increase chat conversions anywhere from two to 10 times within the first 90 days.
Chronos, one of Lift AI’s customers, got 85% more leads from its pipeline than before. The company also converted those visitors 8.5 times more effectively.
Another company, Formstack, used Lift AI with Drift (a chat platform) to get 420% more monthly recurring revenue, increasing its ROI by 18 times.
Finally, Truckstop used Lift AI to create sales team buy in and success as they were spending their time on traffic that is converting 3x higher than general traffic (9.72% vs. 3.28%)
All this and more is possible due to Lift AI’s ability to determine the buyer intent of every single website visitor.
Best of all, Lift AI offers a free proof of concept for companies with over 20,000 monthly visitors. Just reach out to the Lift AI team to see how the impact it can have on your sales.
FAQs
What is predictive sales?
Predictive sales means leveraging historical data to forecast future sales performance and adjust different variables for the most optimal results.
What does predictive AI mean?
Predictive AI incorporates not only your first-party historical sales data but also a powerful machine-learning model trained on billions of data points and customer interactions.
How can AI help sales?
One of the most effective ways of using AI in sales is by letting it accurately score leads (using Lift AI) based on their behavior, freeing up your sales team’s time and increasing conversions.
What is the difference between predictive AI and generative AI?
While predictive AI is an analytics-first approach that gives you useful information, generative AI produces new information on its own (e.g. an intelligent chatbot).
How does predictive sales differ from traditional sales forecasting?
The key difference between predictive and traditional forecasting is the much larger amount of data and nuance that can be evaluated automatically by predictive sales tools.
How can I implement predictive sales in my sales process?
The easiest way to implement predictive sales powered by AI is to get started with Lift AI’s buyer-intent system for anonymous website visitors.
