Insights
September 18, 2020

Lead Qualification Process: Old vs. New Methods

When you’re just starting out, every visitor to your website seems to be worth their weight in gold. You’re willing to pour as much energy as possible into uncovering their needs and, ultimately, convert them into paying customers.

As your marketing efforts attract more people to your website, your new problem becomes figuring out which of those hundreds, thousands, or even hundreds of thousands of leads are more likely to buy and thus require sales treatment. In other words, you need to have a proven lead qualification process in place.

What’s the Best Lead Qualification Process?

In general, a lead qualification process is a system of fine-tuned filters that let you separate your leads based on their probability of becoming ideal customers. Depending on the complexity of your sales process, an appropriate qualification model might be as simple as waiting for a specific action (e.g. filling out a contact form) or as complex as building out a full customer profile that includes various behavioral and demographic criteria. 

The outcomes of your lead qualification can also vary significantly - from calls or writing emails to guiding website visitors through tailored funnels based on their likeliness to buy. The effectiveness of each outcome depends entirely on the quality of the scoring system adopted.

A good qualification model will result in more (and better) sales and save your business development representatives (BDRs) time and energy by not pursuing leads that have little to no chance of turning into prospects.

Any goal-oriented company today would benefit from a lead qualification process. The question then is which one to adopt? Let’s discuss a few qualification methods that have been widely used in sales and how they can be greatly improved by technology used on websites.

Why old lead qualification processes are ineffective

For decades, lead qualification was a manual sales process. As soon as your BDR got any contact information, they would reach out and ask the lead a set of predetermined questions to gauge their level of interest. That process typically required you to have some idea of the identity of a lead to begin with. It added extra steps, friction, and resources to the sales process and relied on accurate answers from the leads to progress forward.

There have been lots of different qualifying frameworks out there over the years and some corporations even popularized their own, such as BANT (budget, authority, need, timeline) originally developed by IBM. Although popular, BANT received a fair amount of criticism for its inflexibility, which led to further development of new methods such as ANUM (authority, need, urgency, money) and FAINT (funds, authority, interest, need, timing).

In today’s world of technological progress, these old qualification methods share exactly the same issues: they are manual and labor-intensive; they’re hard to enact in real-time, their scoring is subjective, prone to human error, and their data is arbitrary as it explicitly relies on the information provided by potential leads or a best guess of their intent. Luckily, we’re able to solve that by leveraging the power of machine-learning. So what do new lead qualification processes look like?

The shift to qualifying leads on websites

As websites became the go-to tools for sales and marketing, lead qualification had to adapt to the new reality of behavioral data collection. Instead of manually scoring each individual prospect, you could simply delegate the process to emerging online tools, namely in the CRM (customer relationship management) space.

Website visitors volunteered information in forms, surveys, and newsletter signups, which in turn informed the CRM database with critical data about each prospect. The CRM would then use that data to highlight the most qualified leads for the sales team to follow up on, but it only worked on known prospects who explicitly gave the website their information and it didn’t cater to the vast majority of visitors who came and went unknown. In the end, such qualification helped distinguish very warm or hot leads, but not much else.

The development of lead qualification soon added the functionality of on-page actions and journey scoring. Marketers could configure the software to assign each website visitor scores based on their actual or inferred company, for landing on specific pages, and for performing certain actions. It was mostly used for nurturing campaigns, but there was some intent to help marketers use tactics like presenting live chat for sales to qualified website visitors on pages they thought were likely to convert. The scoring became more granular but still relied on marketers to set up complex rules and user journeys which could number into the hundreds, while still largely guessing the intent of every website visitor. For most, it was too complex, inaccurate, and cumbersome to maintain.

Lead qualification processes in the age of AI

With the rise of artificial intelligence (AI) and machine-learning, we now have the ability to create a model that could analyze millions of data points instantly, output informed results, and learn from them to improve itself for future use.

Imagine applying the power of AI to lead qualification. There are lots of data points that can be taken into consideration which are hidden from plain sight but readily available to the machines we spend so much time working with. By feeding this data into a machine-learning algorithm, we can quickly process each data point while also taking into consideration past actions, clicks, journeys, interactions, sales data, online behavior and much more — all to inform your lead qualification process and assign reliable scores to visitors as soon as they land on your website.

The best part is that it works on anonymous visitors too, totally bypassing overly complicated and manual on-page methods, yet still augmenting any known customer CRM scores. It’s not just about better-qualified sales leads, but finding new opportunities and creating new sales leads in a volume of visitors that was impossible to qualify using traditional methods.

Machine-learning lead scoring is the true secret of modern qualifying models. Now, instead of trying to pursue every lead with equal effort, you can make decisions based on verified lead scores while compressing the time from when an interested visitor arrives on the site and when they begin to engage the brand.

Want to see a cutting-edge lead qualification process in action? Explore the possibilities of Lift AI, a machine-learning scoring model based on over one billion website visits and 14 million live sales engagements. Request your free 30-day Lift AI trial today to implement a machine-learning lead qualification process, plus an  API that connects those scores in real-time to your live chat for sales tool so you can convert more website visitors when it counts.

More Insights from Lift AI

Ready to turn anonymous traffic into revenue?

Free 30 day trial  •  No credit card required