In the days before predictive analytics, the relationship between sales and marketing was tenuous at best. For years, sales blamed marketing for weak or unqualified leads, while marketing blamed sales for not being able to close the leads they did bring to the table. It was a lot of finger pointing without any real proof either way or any clear path to improving the relationship.
Then, with the introduction of big data came a new, easy way for the two teams to collaborate and add value to each other’s work. It started with marketing’s new-found ability to prove the ROI of their efforts and has since evolved into a full-fledged partnership between the two teams. Account-based marketing, for example, relies completely on the two teams to share mutually agreed upon goals and target accounts.
In the last few years, the rise of affordable martech tools for predictive analytics has opened even more doors for marketing to support sales. With better forecasting and improved campaign design intended to yield the maximum number of high-quality leads, predictive analytics can have a huge impact on the sales team’s ability to convert leads. Here are some of the ways marketers are using predictive analytics to support sales today.
#1. Lead Scoring
Lead scoring has been around a lot longer than predictive analytics. Like many marketing strategies of the past, it was more of an art than a science. Today, lead scoring is fully data-driven and determined almost entirely by predictive analytics.
Now, when a prospect converts to a lead through a lead enrichment tool like SmartForms, marketers can use predictive analytics to accurately forecast the potential customer’s likelihood of converting over time based on the previous activity of your already-existing customers. Predictive analytics tools will automatically measure demographic and behavioral data against the dataset of your current clients to figure out:
- Whether or not the lead truly has a need for your product or service. Based on their company size, industry, location, and other demographic information, is it likely that the lead actually has the problems that your company solves?
- A timeline for buyer’s journey. In what stage of the customer journey is the lead currently? What does their timeline-to-buy look like?
- Who the lead is most likely to buy from. Traditional sales organizations are split up into territories, but in the age of big data and predictive analytics, more and more teams are instead passing leads to the salesperson most likely to close the deal.
This more effective method for lead scoring ensures salespeople are only spending their time on the most active, ready-to-buy leads in your pipeline. What is happening with the rest of the leads that are not ready for sales?
Well, predictive analytics helps there, too.
#2. Moving Leads Through the Sales Funnel Faster
The average B2B buyer reviews three to five pieces of content before engaging with a sales representative. Left to their own devices, the process of finding and reviewing that content could take weeks or months — and could end with them finding it from a competitor instead of you.
Predictive analytics does the job of a salesperson long before a salesperson gets involved in the buyer’s journey. Based on a lead’s behavioral and psychological data, predictive analytics can trigger the right content to help answer questions, manage objections, and move the lead through the sales process more efficiently.
Then, when the lead’s activity indicates he or she is ready to make a purchase, predictive analytics can help ensure your salespeople make the right offer, too.
#3. Making the Right Offer at the Right Time
Most companies today with a more advanced marketing strategy do not have a set pricing model, but rather adapt their pricing based on any number of factors, including:
- Company size
- Volume of purchase
- Length of contract
- Predicted Customer Lifetime Value (CLV)
However, figuring out the right offer to present to a potential client is not easy. In addition to setting a price that entices the lead to convert (while still remaining profitable for the vendor), marketers need to figure out the best possible method and time for delivery.
Today, that method of delivery is still largely coming from salespeople (despite some recent research that indicates sales may be going away in the near future). Predictive analytics can help those sales reps know exactly what offer to present, when to present it, and even what to say in their sales script to help increase the likelihood of conversion.
Those sales techniques are not just limited to new customer acquisition; they work just as well with upselling or cross-selling to existing customers.
#4. Upselling and Cross-Selling New Products to Existing Customers
Maximizing your customer’s lifetime value is a huge focus for most businesses today. Marketing and sales teams both understand now more than ever that it is the long-term potential of a client that determines their importance in your book of business, not necessarily the value of the initial sales.
Because of this, marketers are leveraging predictive analytics to truly maximize CLV for existing clients. That means paying attention to trends and patterns indicative of opportunity for salespeople.
If, for example, your predictive analytics tools make note of a series of videos that when watched often lead to a potential cross-sell opportunity, sales reps can be notified to immediately follow-up anytime an existing customer watches that sequence of videos.
The same strategy can prove effective for protecting your existing customers from churn as well.
#5. Retaining Customers
Reducing customer churn is one of the easiest ways to boost company revenue. Unlike converting new customers, preventing churn does not require marketers to “sell” a customer on the value of a product or service because the selling has already been done. Instead, it only requires marketers to ensure the customer continues to receive the value they were initially sold on.
By tracking the activity of customers who have churned, marketers can use predictive analytics to forecast the behavior of potential churn risks in your existing book of clients. Like with upsells, predictive analytics platforms can notify salespeople of triggers that warn of potential churn — like inactivity, substandard performance metrics, or even mentions on social media — so that the salesperson can take immediate action.