The average Salesforce instance has thousands of prospects, leads, and customers. Here’s the problem. Each of those contacts has a uniquely different propensity to buy.
As a salesperson, every single day you have to find the proper balance between selling activity and non-selling activity. For example, you have to balance making calls and meeting customers with prioritizing your leads and opportunities.
Did you know that the average salesperson spends 145 hours every year manually prioritizing leads?
Now you may be thinking, “Yeah, but that’s only a couple hours per week, I can manage.”
Maybe, but we’re not just focusing on the time-suck that is manually prioritizing your prospects, leads, and customers. We’re also focusing on the fact that 66% of the time, the average salesperson uses “gut-instinct”, geography, the alphabet, and company size to prioritize leads.
Now you’re thinking, “Well, how else should I be prioritizing leads and opps?”
I’m glad you asked. In this article, we’re going to dive deep into this topic, especially the automated data analysis of propensity to buy that is forever changing the lead prioritization game for salespeople in 2022.
What is Lead Prioritization?
Lead prioritization is the process by which you prioritize your outreach to leads based on their likelihood to engage and buy at that moment.
As noted earlier, traditional salespeople have always used the same archaic battle-tested practices to prioritize outreach. Sorting spreadsheet columns in alphabetical order or company size. A salesperson’s intuition. The list is long and definitely not best practice for 2022.
Today, we’re focusing on SMART lead prioritization.
By SMART, I’m referring to the use of automated analysis of sales data to rank leads based on their real-time likelihood of engaging and/or buying.
This may seem too good to be true. You may be thinking, “Is lead prioritization the new crystal ball of sales?”
We wouldn’t go that far, however, we would say that this sales technology most definitely improves your chances of engagement and shortening the sales cycle. Before we get into the techy knitty-gritty, let’s first clarify the difference between prioritization and qualification.
What is the Difference between Lead Prioritization and Lead Qualification?
Whereas lead prioritization is focused on ranking leads immediately prior to outreach, lead qualification is focused on ranking leads from the very beginning.
Lead qualification is the initial process of determining how well a prospect fits your ICP, or ideal customer profile.
Both of these sales concepts have to do with ranking leads to improve sales efficiency. Lead qualification removes low quality prospects that are not a fit and less likely to engage, and lead prioritization concentrates on the highest quality leads that are a perfect fit and very likely to buy.
In short, you can’t have one without the other.
What is Sales AI and How is it Used in Lead Prioritization?
Artificial Intelligence. While it may bring to mind Arnold Schwarzenegger and the Terminator movie series, it should also bring to mind improved sales productivity and efficiency.
Sales artificial intelligence is the use of a unique set of algorithms, analytical tools, and machine learning to automate non-revenue-generating activity.
With Sales AI, salespeople are able to spend more time on the activities that produce revenue, such as prospecting and meeting customers in person, rather than the activities that cost time and resources.
These non-selling activities can include data entry, admin tasks, and most importantly—lead prioritization.
AI for Sales is constantly analyzing and tracking the sales data for every single prospect, lead, and contact for their propensity to engage and buy.
While each artificial intelligence sales platform is unique, they’re set up with a smart lead scoring system based on best practices of specific sales roles, industries, or another determining factor, thereby prioritizing leads and executing other functions that will make a sales person more efficient and productive.
Let’s now focus on the details of this data analysis and lead scoring.
What is a Propensity to Buy Model?
The propensity to buy model of Sales AI helps you understand which leads are more likely to engage or buy, when and WHY. It constantly tracks your engagement with leads, your leads’ sales cycle times, their inbox activity, and other ICP factors to learn and improve the predictability of their buying behavior.
Salesforce has been around for over 20 years, and salespeople are just now waking up to the fact that the data set they have is enormous and impossible to manually sort through. For many, the data just sits there unused and uncorrelated.
Data-driven insights are now the best way to prioritize leads, but in order to automate and deliver these insights in real-time, a salesperson must use Sales AI with a propensity model.
Only then can they learn to predictably close more deals and meet the rising standards of their customers.
What is Predictive Lead Scoring?
Predictive lead scoring is the use of algorithms to constantly process and analyze sales data. During analysis, values are assigned and adjusted in real-time so as to improve the predictability of buyer behavior.
While there is marketing and sales software that allows you to manually set the scores of certain buyer behaviors, predictive lead scoring focuses on the data that contributes to shortening the sales cycle and influencing purchase behavior.
As data progressively signals to the model its correlation with buying, so will the scoring increase in contrast to other data, thereby prioritizing leads with high scores for a salesperson’s outreach.
Examples of Sales Data used in Lead Prioritization
When you look at a lead or contact record in Salesforce, you can only imagine how many more data records are processed by propensity models and lead scoring.
To highlight some examples, here are a few of the popular data records that are scored and used in prioritizing leads for Veloxy’s Sales AI.
Number and Recency of Opens and Clicks
Our software addresses each user’s sales activity uniquely. With that being said, inbox activity has been one of the most popular factors for prioritizing outreach. This isn’t a mere open and click. Algorithms will pick up on account-wide email and email-attachment activity within a small window of time, increasing the likelihood that you are a talking point and the decision making process is in its critical stages.
Average Sales Cycle Time / Previous Purchases
When you sell to repeat customers, knowing their past engagement and buying behaviors is very useful for prioritizing outreach. As you continue to engage with the customer, our models will compare and contrast their behavior with that of those same behaviors in the past. While humans and organizations may change, our models have discovered there are some fine data sets that typically do not.
Days and Times of Prior Engagements
You may have very tight relationships with a handful of customers. You likely know their birthday and anniversary, and you likely also know their daily or weekly habits. But for the thousands of other prospects, leads, and customers, you could benefit from knowing their phone and inbox behaviors. When using Veloxy, and a series of leads are queued up in your quick dialer, rest assured it’s partly due to that day and time being their preferred engagement window.
Start Using Lead Prioritization to Predictably Close More Deals!
Now that you’ve bought in to the concept of automatic lead prioritization, it’s time to start selling smarter and faster!
If you’re a field sales rep, start using our mobile lead prioritization app and its 35+ other selling features (free for 15 days).
If you’re an inside sales rep, you can begin prioritizing leads by using our inbox extension app and its 20+ other selling features (free for 15 days).
However, if you prefer to see a live demo of this sales feature in action, you can request a demo today!