There are two ways for any sales team to sell more. You can either hire more SDRs 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. Needless to say, investing in the latter tends to have a much greater ROI.
To increase efficacy, you need to have a clear picture of all of your site visitors, known or anonymous, understand their conversion potential, and how to get more of them. How do you evaluate all of this data?
Most sales teams worldwide still rely on spreadsheets in Excel to track their progress, and their CRM to provide valuable insights. The problem is that this type of analysis happens post-factum, such as when a deal falls through or when you 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 you can do with predictive sales analytics.
In this article, we’ll cover the basic benefits of predictive sales analytics to what’s now possible through the use of artificial intelligence.
Recap: What is Predictive Sales Analytics?
Predictive sales analytics tools evaluate the data at its disposal before the action happens, not after. As a result, it’s able to predict your lead’s behavior and identify an optimal path to engaging them.
How does predictive sales analytics do it? By leveraging vast amounts of data you already have in your website, CRM, and other marketing automation tools, then processing that data through the application of predictive algorithms and mathematical models. Various predictive sales analytics tools let you set your own goals and rules, and then try to process your existing data accordingly.
So what are some examples of areas where predictive sales analytics could be helpful in improving your team’s performance today?
Identifying cross-selling opportunities
Rarely do organizations sell just one product or service. Since it’s much easier to retain a customer than to acquire a new one, selling someone a compatible product or high-tier plan is a good way to meet your sales targets.
For a long time, SDRs had to manually detect cross-selling opportunities for each customer. Predictive sales analytics tools are able to analyze all your SKUs and customer buying patterns to suggest products that fit certain customer profiles best.
Decreasing customer churn
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 analytics can help you single out customers who have a low probability of making another purchase. You can then come up with and use tailored loyalty plans to retain them.
Tweaking your pricing strategy
Pricing is a true business art. There are so many ways to go about it. You can charge everything upfront or agree on instalments, you can add training or installation costs, you can charge per team member or per user, or you can 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 you visualize different pricing strategies, compare their implications side by side, and test your assumptions. As a result, you could achieve a simpler pricing model with better outcomes and lower maintenance costs.
Assigning more accurate lead scores
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 SDRs 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.
Predictive sales analytics uses your CRM data along with advanced behavioral algorithms to calculate the score that tells you how likely any given lead is to buy. However, this approach relies heavily on a hypothesis of what behaviors you think represent a high potential visitor. These algorithmic rules only represent a small percentage of the many pathways that high potential visitors take through a site. Consequently, most high potential visitors are still missed, typically more than half. That’s half of the full potential of your website!
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.
However, when it comes to optimizing your lead scoring methods, predictive sales analytics can only work with the data you already have in your CRM (relying on its accuracy) and specific actions (e.g. web visitors clicking on certain pages). 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 utilize Machine Scoring.
From predictive analytics to machine-scoring
Most companies still use web forms and BDRs to qualify leads on the site. It’s expensive, frustrating to both the visitor and the BDR and typically engages less than 30% of the high potential visitors. Adding identity intelligence from your CRM or third party vendors provides more context to any chat, but only if the visitor can be identified and most cannot.
Figure 1 shows the evolution of lead scoring from simple qualification to state-of-the-art machine-scoring.
Lift AI is the only machine-scoring model available today that’s able to accurately predict 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 anywhere from 40 to 90% of your web traffic who are 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.
Unlike predictive sales analytics that guesses what path might be taken by a high potential visitor, machine-scoring actually measures each and every path to determine each and every high potential visitor with 90%+ 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 SDRs time will be well spent and the visitor will be helped through their buying journey.
Instead of deploying chat hoping the right visitors will click-to-chat, you can now proactively present chat to the all the right visitors.
Use Lift AI with any live chat for sales tools 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 and either connect them directly to your SDR via live chat for sales or direct them to your chatbot based on their unique score.
As a result, from this moment, your SDRs will only spend their time working with leads that have the highest conversion probability, and you’ll watch your sales grow in no time.