Over the past decade, we’re sure that you’ve noticed that your marketing tech stack options have become much more advanced than ever before.
Social media management software doesn’t just let you publish content around the clock; it actually recommends hashtags to add or when to publish.
And website analytics platforms don’t just show you a flat number of site visitors; they show you paths taken through a site, what CTA buttons users have hovered over, and which pages they’ve interacted with for the longest.
We’ve gone beyond basic data analytics, and now different types of marketing and sales technology are using advanced machine learning and high-quality data to deliver better and more actionable insights than ever before.
Lead scoring is another great example. Manually scoring leads is practically migraine-inducing, and traditional lead scoring left much to be desired. Technology today, however, has paved the way for predictive lead-scoring tools, and that’s what we’ll be talking about today.
In this article, we’ll cover what predictive lead scoring is, the difference between manual and predictive lead scoring, and considerations to make before you dive in.
Let’s get started!
What is Traditional Lead Scoring?
Traditional lead scoring—which is also sometimes called “manual lead scoring”—is the process of ranking your leads based on several different factors to determine how likely they are to take a certain action.
In many cases, traditional lead scoring focused almost exclusively on acquiring new customers by converting them from leads that were in the brand’s sales funnel.
Typically, traditional lead scoring relied on giving different demographic attributions and potentially different actions customers could take and giving each one a metric score. You’d add up all of an individual lead’s different attributes for a numeric score ranging from 1-100, and the higher the score was, the more likely the lead was to convert.
Even once lead scoring became more digital and didn’t require someone wracking up the counts themselves, it still relied entirely on team members to determine which qualities or actions deserved what scores. This was a manual, tedious, and prone-to-error process.
This usually involves scouring your database of thousands of contacts for the right combination of attributes that equal the perfect lead–which is by no means an easy feat, and it means that the data wasn’t always particularly accurate.
If you want to try it out, we’ve developed a lead-scoring template that allows you to dip your toe into the world of manual lead scoring.
What is Predictive Lead Scoring?
Predictive lead scoring uses a combination of high-quality and up-to-date data and machine-learning capabilities.
This type of lead scoring will pull in data from multiple sources and then utilize machine-learning algorithms to assess which behaviors and attributions of existing prospects and customers could signal high intent. The systems then create models based on those behaviors and traits so that you can assess new leads against them.
Some tools go beyond initial lead scoring and open the door to what we’ll call “contact scoring.” You are looking for multiple methods of revenue-boosting potential, like upselling or re-engagement.
Traditional Lead Scoring vs. Predictive Lead Scoring
The biggest difference is easy to see: Traditional lead scoring is heavily manual, and it relies on in-depth manual analysis, a lot of strategic thinking, and a bit of luck to really get your lead scoring models right.
Predictive lead scoring will pull data from a single or multiple sources and then do some of the heavy lifting for you. It will determine what your ICP looks like and what traits and actions indicate your best chance of success.
So predictive lead scoring has some strong advantages, but conventionally, it’s also had some serious drawbacks.
Frequently, predictive lead scoring systems have a “one size fits all” solution and models that don’t consider the temporal scoring changes (i.e., changes in business priorities/directions).
Suppose your system has a set model of what it interprets as your ideal customer, and your business changes niches or verticals. In that case, this can misalign your entire scoring system and cause chaos in the sales and marketing department.
In addition, pushing data into a system and hoping for the perfect outcome is fraught with potential issues. Ideally, you should want to prepare your data, the time frame of the data you are using, and ensure it is aligned with your objectives (i.e., more enterprise leads and purchases.)
So What’s the Solution?
You don’t want to be drowning in the time-suck that is traditional lead scoring, especially considering it’s not always incredibly accurate.
And you also don’t want to be stuck with an inflexible predictive lead scoring tool that locks you into models that aren’t adaptable for an always-changing business and market.
The solution is to find an advanced predictive lead-scoring tool that utilizes machine learning to identify high-value traits and actions to help suggest models while allowing you to test and customize those models on an ongoing basis.
That’s where Breadcrumbs comes in, giving you the best of both worlds—the automation and insights of machine learning with the adaptability and control of human oversight.
How Breadcrumbs Works
At Breadcrumbs, we recommend (and offer) a machine learning-assisted system–a blend of the two approaches.
The synchronicity created by your sales and marketing team aligned on your ideal customer attributes, as well as the efficiency of added machine learning behavioral predictions, is one of the tenants of our Revenue Acceleration Manifesto.
This is why we put together five key ingredients and mixed them up with a robust algorithm to cook up the perfect recipe for a perfect lead-scoring model for you and your business.
You’ll notice that the list here considers both fit and activity—many predictive lead-scoring tools only do one or the other. This means that we look at firmographic data like someone’s industry or job title, which can help identify users that match the brand’s ICP, but we also look at the specific actions they’re taking.
Sometimes, you may find a high-intent customer that doesn’t quite meet your standard ICP, but the last thing you’d want to do is neglect them when they’re doing all the right things just because they don’t have a specific company size.
Recency is the first among our lead scoring criteria. This is because it is crucial to know how recently your lead has taken action with your company assets. In this sense, recency allows you to evaluate and prioritize activities that are currently happening instead of wasting time chasing up leads that are 2+ years old.
Because of this, decay is factored into all of our models. Our machine-learning tech will make recommendations, but you can customize the decay’s impact on scores based on your customer journey. It will help your sales team accurately assess leads based on the timing of the actions they’ve taken so they can strike while the iron is hot.
Another important indicator that helps prioritize your leads is frequency. It means you can count how often they raise their hand at what you propose to them. Those who have recent activity with your organization and frequently interact with your company should be the top leads your sales team goes for.
It’s crucial to choose a lead scoring tool that considers both recency and frequency; many don’t, but we do.
3. Online + offline activities
Actions your contacts take on and offline will help to identify what someone is interested in and how they like to consume their information. This is a treasure trove for marketers–both in terms of reaching back to them with the right type of content and what content to create in the first place.
This could include purchases made in-store, pages viewed online, emails opened, demos booked, or tickets filed to customer support.
4. Job title
As a salesperson will tell you, the job title provides great guidance around the level of decision-making and budget authority the person has. Scoring job titles correctly will help your sales team shape that conversation, push the deal forward, and overcome any customer objections with the right person.
5. Company data
A company’s industry, number of employees, and annual revenue are all examples of firmographic data that may help you determine whether or not a lead aligns with your ICP. You don’t necessarily want to try to sell enterprise software to a five-person business, for example.
While these are the standard categories we believe every company should use to score their leads, this is just the tip of the iceberg in terms of what categories you can build your lead scoring system off of.
Going Beyond New Customer Acquisition
Breadcrumbs can help with that. Our contact scoring tool will help you analyze each lead and existing contact for firmographic data (such as Job title and Industry) and engagement data (such as Online and Offline activities) to see which users are prime for a sales opportunity and route back this information to your sales team so they can act fast.
Existing customers who are currently hitting the top limit of their current SaaS plan, for example, are great candidates for a potential upsell. And those who love your copywriting AI service may also benefit from the help of an actual copywriter or a copy strategist— a separate product you offer that they haven’t taken advantage of yet.
You want to maximize revenue anywhere you can, and that means looking at your existing client base, too.
What to Consider When Adding Lead Scoring to Your Tech Stack
By this time, you’re probably wondering how you can pitch yet another marketing tool to your executive team.
When thinking about predictive lead scoring benefits, there are a few considerations as you introduce predictive lead scoring into your lead management process.
- Scale–Think about all the data at your fingertips these days. Data from the CRM, marketing automation platform, chatbots, and usage data–trying to combine all these data sets to form a basis for scoring can be tough. Predictive lead scoring allows you to leverage all these sources (and more!) to make data-driven decisions around what data point(s) are indicative of buying signals without the manual lift. One of the cool things about this exercise is that you can often unearth activities and/or fit attributes on your lead that you didn’t know would significantly impact scoring! All you need is 5-9 data points to help build a good predictive model.
- Change Adoption–Data is literally changing every second within your marketing database. Decisions you made around lead scoring a month ago could potentially be outdated. One of the strengths of predictive lead scoring is that as the data changes, so do the predictions. But those changes need to be informed by the marketer to the AI/machine learning to ensure a sound output to use within your scoring model.
This is the natural evolution of lead scoring, and while AI-based/predictive lead scoring has been around for a while, we’re at the tipping point where you will start to see more companies embrace machine-learning scoring.
Traditional vs. Predictive Lead Scoring: Final Thoughts
There is no one type of data or source of data that is going to solve all your problems. Still, when you combine this data together alongside the objectives of what you are looking to get out of lead scoring, predictive lead scoring can undoubtedly assist you along your demand generation journey.
A contact-scoring software like Breadcrumbs leverages your data to crunch the numbers using machine learning to determine when you need to take action.
We’ll combine data from all of your touchpoints (your CRM, marketing automation tools, and existing product usage tools) to give you a thorough view of what activities are driving behavior—and we’ll let you stay in the driver’s seat, giving you full visibility and control while offering some helpful suggestions where needed.
Ready for the next step? Book your demo so we can talk about how to define your objectives and show you how we can help you develop lead-scoring models to drive real revenue.