Updated February, 2022.
It used to be that when a lead comes in, your business development representative (BDR) would manually gather all the relevant information at your disposal, ask a few questions, and make the call on how close that lead is to an ideal customer.
Over time, the lead qualification process has become much more sophisticated. It’s now a widespread practice to rely on customer relationship management (CRM) tools for lead scoring, or assigning a numerical value to leads by screening on-page data.
These predictive scores are typically based on rules derived from a small set of data and subjective opinions of what constitutes a good lead. Even so, traditional lead qualification helps businesses prioritize their sales efforts based on expected customer value, as opposed to pursuing everyone on the first-come, first-served basis.
What Are Predictive Lead Scoring Criteria and Attributes?
Predictive lead scoring uses software algorithms to automatically process data about your leads — a process which used to be done manually and take a lot of time.
Essentially, a predictive analytics algorithm can look at everything you already know about a web visitor from an existing database (e.g. your CRM tool), determine which information is the most important, then complement that knowledge with other data sources, including on-page behavior (e.g. “Clicked on Pricing Page”).
The algorithm can therefore accurately predict which of your known customers are more likely to purchase by assigning scores based on the information about them in the database plus their on-site actions.
Predictive lead scoring can become even stronger by learning what all converted customers have in common (as well as what unconverted customers have in common).
Marketing automation solutions that include predictive lead scoring are already available to any business today. Let’s sift through the pros and cons of using the predictive model approach.
- Saves time by processing data automatically through an algorithm
- Boosts the quality and accuracy of scores with rules and weights
- Improves based on common and uncommon traits in hot leads
- Relies heavily on an existing database to work with (e.g. a CRM)
- Remains subjective due to using rules and weights initially designed by humans
- Doesn’t account for any web visitor that isn’t already known
Is artificial intelligence the future of predictive lead scoring?
Although predictive lead scoring is a fairly new and exciting leap in scoring technology, it has already been superseded by a new ground-breaking approach that Lift AI calls “buyer intent scoring.”
Imagine being able to score leads not only based on existing CRM data but also by taking into consideration your visitors’ pre-action behavior — looking at the data you have and augmenting it with external data from vast arrays of third-party sources as well as machine-learning models that can determine which attributes actually inform buying behavior. That’s buyer intent scoring.
The benefits of buyer intent scoring over predictive lead scoring include:
- Utilizing much more data and real-time input analyzed through machine-learning tools that eliminate guesswork, improve accuracy, and automate an otherwise impossibly complex process
- Instilling objectivity in decision-making, freeing you from human errors
- Achieving greater marketing insights that illuminate previously unseen patterns
- Evaluating leads who were completely unknown to you previously
- Enabling real-time actions on high-scoring leads
- Freeing up and then focusing your BDRs to work on high-potential leads instead of time-consuming, manual lead-scoring tools
In addition, buyer intent scoring is highly cost-effective. Just take your BDR payroll and multiply it by the percentage of unqualified leads you’re getting today to calculate how much money is going to waste. Every call pursuing a bad lead is an opportunity cost for not getting the lead you want.
Since generating leads in today’s competitive online market is hard enough, making the most out of your existing lead flow is critical. Thus the benefits of testing new lead-scoring methodologies significantly outweigh the risks.
Does intent scoring require emails and contacts from potential customers and users?
There’s really no reason to avoid using machine-learning tools like buyer intent scoring by Lift AI — it works with any company size, revenue level, and data you have in your CRM. It even works on visitors who are not in your contact list and have no email associated with them.
Some may fear that machine-learning models could assign inaccurate scores to leads depending on the quality and accuracy of input data. That said, algorithms, unlike humans, are not prejudiced and are able to continuously get better in their judgement. If the machine-learning model is based on millions, if not billions of data points, then you can rest assured that it’s more accurate than any human calculations — even when supported by basic algorithms like predictive lead scoring.
If you’re not sure, you can always check — go over the scores manually and compare them to what you’d have assigned as a best guess, given the contact information you have, at least for the first few days. It’s not a perfect verification method, but it can’t hurt to compare.
Additionally, algorithms lack charisma, in the sense that buyer intent scoring is as objective at evaluating potential prospects as possible.
For example, your highly skilled BDR might occasionally upsell a low-scoring lead and turn them into a profitable client that a buyer intent scoring model might have determined is not worth pursuing. But this argument doesn’t hold water if you estimate how many ideal customers the same BDR would be able to acquire instantly from high-scoring leads provided by machine scoring. The volume is simply incomparable.
Get your company started with buyer intent scoring services today
Lift AI is an evolved form of predictive lead scoring powered by 15 years of machine-learning data. Right out-of-the-box, Lift AI uses more than one billion profiled website visitors, 14 million sales engagements, and real-time behavioral data to score every single visitor on your website, even if they’re completely anonymous (which 98% of them are). That’s why buyer intent scoring is so valuable.
Here’s how Lift AI works. After a high-buyer-intent visitor is identified (about 9% of the overall traffic), Lift AI connects them directly to your BDRs through online chat, thus significantly increasing the rate of conversions. Medium- and low-scoring visitors get assigned to a nurturing bot or a self-help guide instead.
As a result, anywhere from 2 to 10 times the increase in chat conversions within the first 90 days can be expected. Drift, for example, has converted 9 times more conversations with Lift AI. Formstack grew its chat pipeline by 88%, whereas PointClickCare saw a 4 times increase.
Stop relying on outdated forms as your main source of conversions and try the Lift AI way for free for 30 days. Signing up now will also get you a complimentary Revenue Opportunity Assessment for your website. No credit card is required, so why not try?