Announcing New Propensity Model & Mobile Recommendation for All: Prioritize the Next Best Tasks First

Structuring sales activities with cadence is powerful, but it’s quickly becoming table stakes. Your basic cadence tools can’t answer one of the most important sales questions: what’s the next best thing for you to work on?

Reps only close deals when they make contact with the right people at the right times. When they don’t, they fill their pipeline with garbage and spend too much time working on deals that will never close.

Connecting to more members of the buying committee, earlier on in the buying process,  is a huge factor in accelerating your impact on revenue.

As a leader, you already know you can’t afford to leave the decision on how to spend time entirely up to reps. It’s not their fault. The human brain just isn’t equipped to do the analysis required to align all our efforts to the limitless nuances in our customers’ behaviors. And yet we expect them to do this in real-time? Fuhgettaboutit!

What’s New: Propensity to Engage Model & Verified Mobile Recommendation

We’re excited to continue bringing the full arsenal of our Buyer Intelligence to all customers. Our new Propensity to Engage Model and Verified Mobile Recommendation feature amplify the power of collective data and AI for customers as soon as they go live, without any coding or custom modeling.

If you’re not familiar with Buyer Intelligence, here’s a quick summary: The Playbooks platform actively and securely captures all interactions and outcomes with buyers in real-time. That’s Collective Data. The engine powering Playbooks then builds buyer and company profiles from the data, and scores leads, contacts, and accounts based on their likelihood to engage and buy. It also identifies behavioral insights in the data about how to engage and when to engage. That’s Buyer Intelligence. (For more detail on Buyer Intelligence click here)

Propensity to Engage Model

Our new baseline Propensity to Engage Model is the first-of-its-kind and scores a prospect’s likelihood to engage with reps out-of-the-box. 

So, if basic CRM fields are populated and reps actually follow the model’s recommendations—assuming they’ve got more work to do than they have time to do it in—this model will prioritize activities with leads and contacts that have the highest chance of success.

Think of it: no more wrestling with what to work on next.

Let me break it down further, first with a graph of what’s going on behind the scenes:

It basically says:

• Every CRM record has a propensity to engage score between 0-100
• We break those records up into 10 equal-sized buckets 
• The graph reads from right to left, your best records in yellow and the ones with low scores on the far left
• That little percentage above each bar is the aggregate % uplift in connections you’d see if you were to work from your best records on down, right to left

And before you ask, the answer is YES! The numbers above the yellow bars are correct! This is pulled from live data.

What does it REALLY mean?

One of our top reps likes to ask customers, “If your reps had 100 things to do in a day but only had time for 50, which 50 would they leave out?” It’s a tough question to answer. We answer it.

Most reps don’t have time to work through all possible tasks and records. So instead of leaving it up to guessing, Playbooks sorts records in order of their propensity to engage score. 

The graph tells us that IF reps follow the top 30% of scores first (the yellow bars), they’ll see an aggregate lift in contact rates of 101% (or double) over their baseline. 

Do the math for your team. What kind of impact on pipeline and revenue would you see if you could prioritize those customers who are twice as likely to have a conversation with your reps?

For most teams, it’s materially huge, and has a compounding effect that trickles down to the bottom line.

The net-net is when reps use scores => more contacts with the right people => more quality conversations => more pipeline => more wins and revenue down funnel.

How easy is it to use? Here’s a screenshot. All you do is sort by CONTACTABILITY and you’re ready to fire. Just one click.

Mobile Recommendation

Now, as if an automated engagement model wasn’t cool enough, we’re also introducing the first-ever Verified Mobile Recommendation feature.

XANT processed over 300M unique phone numbers on nearly 2 billion dials, and mobile phones has emerged as the best contact method. 

Our Mobile Detection feature (which is already in the product) identified millions of verified mobile numbers, giving us the ability to recommend net-new mobile numbers for customer records.

In other words, we’ve already mined the gold. Now, we’re just serving it up to users.

Getting technical on how it works: We procure contact data from reputable data brokers on behalf of customers. Using our Buyer Intelligence, Playbooks matches and verifies data before sharing mobile and direct dial numbers with reps to make sure we provide high-quality recommendations. Users will see the recommendation appear in a “Try Another Number” prompt.

Here’s why it matters: Contact rates for verified mobile numbers are 2-3X higher than baseline contact rates, and people change their mobile numbers far less frequently than landlines. Mobile phone usage in B2B has shot up over 33% in just the last two quarters and yet fewer than 30% of cadences leverage a direct mobile number!

Here’s what it looks like: Just click the number by the mobile phone icon. One-click—easy and automated:

What to do next

Is this all too technical? Maybe, but if you only take away one thing, remember this: 

These new features feed off XANT data to serve one primary purpose: connect your reps to buyers faster.

Do you want your reps guided through their engagement?

Do you want your teams to spend more time on the right things?

Click on the banner below to see a demo:

Talk to one of our consultants today and let’s get you started!