Experienced sales leaders know how impactful lead prioritization can be on sales efficiency.
It’s the first line of defense in your sales process. Reps can’t focus their time on bad leads.
Chances are, your defense is weak.
For PLG, this problem is tenfold. Manual scoring simply doesn’t work. Learn why here.
AI-based scoring is showcasing much better results for sales teams than human-made lead scoring. It’s time to pass the torch.
Do you have lots of sign-ups and a limited number of sales reps to convert them?
This article will explain:
- How traditional lead scoring models fail PLG sales teams
- How companies like ClickUp and Netlify leverage AI scoring to power their sales motions
Traditional lead scoring fails sales and marketing
Here’s the old (yet very common) way to do lead scoring:
💡 Marketing or growth comes up with a subjective list of criteria for the score, including firmographics, marketing hand-raisers, unrelated customer data and (maybe) product usage data.
➕ Weighted points are assigned to leads based on which criteria hold true.
📤 Qualified leads get passed to sales based on a blindly decided threshold (i.e score of 75+).
Marketers & Growth folks: Read this article to learn why this process makes no sense for PLG.
4 reasons sales hates your lead scoring process
1. Reps don’t trust lead scores
Sales doesn’t define the scores. Marketing (or Growth) does.
Therefore, leads end up representing what marketing thinks are great prospects, not sales.
It’s not that marketers are clueless as to what makes a great prospect. But sales may have firsthand experience or biases towards which types of leads are worth their time.
2. Scores have no context
That’s all you get. Now do a bit of research on the account and try to book a meeting.
Good luck. Here’s an example 👇
Which one of these 2 freemium accounts (below) would you reach out to?
- Are big enough to justify sales engagement
- Have a lead score of 75
- Have 10 users
- Created 5 projects
- Consulted the same marketing materials
Answer: Both. But you reach out with completely different sales narratives.
Sales narrative for account A: They restarted using the product. Ask if their priorities have changed and pitch how your product can help. Perhaps a new decision-maker? Reach out to understand their priorities.
Sales narrative for account B: They’ve hit success right off the bat. Engage highly active users and understand what they’re trying to achieve. Take the learnings and pitch decision-makers with case studies from similar companies.
My point: Reps need context to craft a compelling sales narrative. Lead scores rarely come with context. When they do, it’s rarely actionable and comprehensible.
*Hint: it’s why PLG companies are turning to a Product-Led Sales platform.
3. Scores as CRM attributes don’t work
“We send product data to our CRM system. Our reps have everything they need!”
The worst thing you can do is waste your sales team’s time by having reps look up every account one by one to decide which ones are worth their time.
That’s what happens when lead scores live as CRM fields. SDRs can’t see which accounts are top priority without custom reports, which require work and are always outdated.
4. Scores can’t keep up
Product evolves. Marketing runs a new campaign. Pricing changes.
Sales need leads based on today’s user experience, not last quarter.
Traditional lead scoring can’t identify high-quality leads as more data comes in and take far too long to catch up.
As lead scores get outdated, reps revert to another means of prioritization: their judgment. Leading to human error and wasted sales activity.
How Calixa's AI scoring empowers reps
The challenges described above have for a long time been inevitable. Fortunately, technology has caught up.
Artificial intelligence (AI) can now score leads for your sales teams, and Calixa has the most sophisticated account-scoring model to date. Calixa’s AI-powered account scoring model is used by ClickUp and Netlify to surface & engage leads in pools of millions of users.
TLDR, how it works:
AI lead scoring is a process that uses machine learning algorithms to sort and prioritize leads. Mainly by leveraging machine learning to find trends, correlations, and hidden patterns between your historical data (firmographics, product usage, marketing signals, etc) and sales outcomes (close rate, revenue, churn rate).
It finds which set of actions correlates to revenue potential, and flags accounts that showcase similar behavior. It also does the opposite: flag accounts that show behavior that correlates to low potential for revenue.
Let’s break down 3 ways that AI lead scoring can help your sales team reach goals faster 👇
1. Prioritizing the right leads
Higher win rates
With Calixa's AI-generated lead scoring system, teams focus on hot leads with the best revenue potential. It’s so accurate and unbiased that it is now the main prioritization method for ClickUp’s team.
Reps start their day looking at accounts (or workspaces) with a PQA score of 5. And then only reach out to accounts with lower scores if they have the bandwidth to do so. Same applies if they need more leads within a given target audience.
Stop chasing bad leads
According to Sales Insights Lab, at least half of prospects aren’t a good fit for what you’re selling.
AI can flag leads that aren’t worthwhile for sales, although seem great on the surface.
For example, this account (below) has usage trending up, the company has hundreds of employees, and they’ve even hit paywalls. Ready for sales? Yes! Actually, no!
Here’s why: Most users have registered with personal email addresses, the account has been on the free plan for ages and active users are going down.
Filter lead or account lists by lead score. This way, the most sales-ready leads can be actioned first, increasing closed-won rates and speed through the sales cycle.
In Product-Led Sales (PLS), time-to-lead matters. Set alerts when new leads or accounts have reached a certain lead score (i.e: 4,5), so reps can take action at the perfect time.
2. Personalizing outreach
Crafting your sales narrative
PQL Signals (powered by Calixa's machine learning) help reps understand factors that make up the score so they can craft the perfect outbound message/cadence.
Let’s assume that I’m an SDR at Notion and am alerted of a new qualified account (below).
Looking at PQL signals, it’s clear that:
- Active users are growing
- The account will soon reach the free plan’s limit.
Knowing this, I can engage decision-makers to offer discounted seat pricing on an annual purchase and pitch the benefits of private team spaces (a business plus feature).
Without this context, outreach would be generic and most likely wouldn’t generate a meeting.
Relevant personalization gets more replies
Following up on the Notion example, I can use PQL scoring signals as personalization hooks.
Because scoring signals tell me what users care about:
“John, 52 of your colleagues are using Notion to organize their work. Just this week, 29 lists were created! At this rate, they’ll bust the storage limit pretty quickly. Should we talk about our business plan? Companies like yours have seen tremendous efficiency gains with our private teamspaces.”
Here’s what a rep would do after having identified a qualified account:
- Click on the account. Review product usage history.
- Head down to PQL Signals and loop up why it’s highly scored.
- Craft your message and write down personalization hooks.
FYI: This repeatable workflow is called a Playbook. By using Calixa playbooks, reps know EXACTLY what to do when accounts/leads become qualified.
3. Improving sales efficiency
Automate cadences in high-velocity segments
If your business has a high sign-up volume and low average revenue per user (ARPU), involving sales reps can be unsustainable.
Calixa's account scores can be leveraged to trigger automated workflows.
- Saves reps’ time manually adding users to cadences.
- Focuses efforts on users who show buying interest.
Trust in scoring models removes distractions and eliminates the need for interpretation. It is developed through great feedback loops with reps who use it.
In Calixa, reps can rate each lead to make sure that it stays relevant to their needs.
Finally, AI scores change as new revenue data comes in (as shown below).
This way, sales can focus on what they do best: understand, engage, & help customers.
Ready to add an AI lead scoring model to your sales motion?
You made it!
I hope that I’ve illustrated how powerful AI lead-scoring models can be for your sales pipeline & process.
Setting up Calixa's AI lead scoring model isn’t complicated or expensive.
Here’s what to do: