Updated Dec 5, 2023: We’ve added real-world examples, challenges associated with lead qualification, and additional suggestions.
When you launch a business, every visitor to your website is worth their weight in gold. You’re willing to pour as much energy as possible into uncovering their needs and tailoring your solution to their problems — all in an effort to turn them into paying customers.
As your marketing efforts attract more people and your website traffic starts to grow, your new challenge is to figure out which of those hundreds or thousands of leads are more likely to buy and thus require personalized sales treatment. In other words, you need to have a proven lead qualification process in place.
There are lots of different approaches to lead qualification, which continue to evolve based on new technological breakthroughs (e.g. AI) and the needs of sales and marketing teams.
Tip: Scale your lead qualification infinitely by assigning a lead score to every website visitor in real time with Lift AI.
Understanding Lead Qualification
A good lead qualification process is a system of fine-tuned filters that lets you separate leads based on their probability of fitting into your ideal customer profile, or becoming paying customers.
Leads are commonly classified as cold, warm, or hot. Cold leads show little to no interest in your product or service and require significant “warming,” or the building or trust. Warm leads are not yet ready to buy but do show interest and can be converted to customers with the help of a sales rep. Hot leads are ready to buy and are waiting for your sales team to talk to them.
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 lead qualification can vary a lot — from initiating calls and emails to guiding website visitors through tailored funnels based on their likelihood to buy. The quality of the lead scoring system you adopt will dictate the efficacy of each outcome.
Using an effective lead qualification framework will result in more (and better) sales and save your sales reps time and energy due to not pursuing leads that have little to no chance of turning into prospects.
Any goal-oriented company today will benefit from an established lead qualification process. The question then is which one to adopt?
There are a few ways for qualifying leads that have been around for a while, but new lead qualifications frameworks based on the latest technology tend to show even better results.
Old vs. New Methods of Lead Qualification
For decades, lead qualification was a manual sales process. As soon as your sales team got any contact information, they would reach out and ask the sales lead a set of predetermined questions to gauge their level of interest.
A lead qualification checklist like that required your team to identify a lead before they could do anything. This added extra steps and friction to the sales process. Plus, accurate answers from the leads were essential for moving forward.
Lots of lead qualification frameworks have been tested by companies around the world, and a few large corporations even popularized their own. For example, BANT (budget, authority, need, timeline) was originally developed by IBM. Although popular, BANT has 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).
Today, a lot of technological progress has been made when it comes to lead generation and qualification. Old methods of qualification now share the same issues:
- Manual and labor-intensive
- Hard to enact in real time
- Subjective scoring
- Prone to human error
- Arbitrary data as it explicitly relies on the information provided by your target audience or a best guess of their intent
A general solution to these problems is leveraging the power of machine learning and AI, which are able to learn what a sales accepted lead is and assess thousands of website visitors instantly to find the most promising leads.
Key Components of Effective Lead Qualification
Once you look at how to prioritize leads by using frameworks like BANT, ANUM, and FAINT, you’ll notice that they all revolve around similar concepts of authority, need, urgency, and money.
Authority is key for sales and marketing qualified leads because they either need to be authorized to purchase your product or service, or be in direct connection with someone who is. The further away they are removed from a decision maker the less qualified they become.
Need is designed to express alignment between the problems of a potential lead and your proposed solutions. If your product or service doesn’t solve a specific pain point, it would be difficult to persuade someone with that pain point to buy it.
Urgency describes how quickly your high-quality leads are willing to close the deal. The more your leads hesitate and postpone moving along the funnel the lower their lead score should be.
Money might be the most important component used to qualify leads. If your prospect’s company doesn’t have the funds to buy your solution, the deal is not likely to happen.
Regardless of the lead-scoring model you use, making sure these four key components are reflected there will ensure that your sales prospects have the above-average chance of closing.
The Role of Technology in Lead Qualification
As websites have become the go-to tools for sales and marketing, lead qualification has to adapt to the new reality of behavioral data collection. Instead of manually scoring each individual prospect, you can delegate the process to emerging online tools, for example, in the CRM space.
When website visitors volunteer information in forms, surveys, and marketing newsletter signups, it becomes part of your CRM database that contains all the necessary information about every sales prospect. The CRM then uses that data to highlight the most qualified leads for the sales team to follow up on.
The problem with the website-to-CRM process is that it only works on known prospects who explicitly share their information and doesn’t cater to the vast majority of website visitors who come and go unknown. In the end, you can improve lead qualification between warm and hot leads, but not much else.
Another side of web-based lead qualification is the added functionality of on-page actions and journey scoring. Marketers can configure this type of software to assign each website visitor a score based on their known or inferred company, landing on specific pages, and performing certain actions.
Sales and marketing teams use scoring for on-page actions in nurturing campaigns and tactics like giving qualified website visitors an option to live chat on pages where they are likely to convert.
While this type of scoring is more granular, it continues to rely on sales teams to set up complex rules and user journeys, which could number into the hundreds, while largely guessing the purchase intent of every website visitor. As a result, scoring leads like this tends to be complex, inaccurate, and cumbersome to maintain.
Steps to Refine Your Lead Qualification Process With AI
With the rise of artificial intelligence (AI) and machine learning, we now have the ability to create models that could analyze millions of data points instantly, output informed results, and learn from them to improve in the future.
Imagine applying the power of AI to lead qualification. There are tons of valuable data points hidden from plain sight but visible to the machine intelligence we spend a lot of time with on a daily basis.
By feeding data into a machine-learning algorithm, we can quickly process each data point while taking into consideration past actions, interactions, clicks, journeys, sales data, and online behavior — all to inform your lead qualification process and assign reliable scores to visitors as soon as they land on your website.
The best part about machine-learning lead-scoring tools is that they work on anonymous visitors, bypassing overly complicated and manual on-page lead-scoring methods yet augmenting any known customer CRM data. The result is not just better-qualified sales leads but finding new opportunities and sales leads in a volume of visitors that was previously 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 that it takes an interested visitor to arrive on the website and engage with your product or service proposition.
Want to see a cutting-edge lead qualification process in action? Explore the possibilities of a machine-learning model like Lift AI.
Challenges in Lead Qualification and How to Overcome Them
The most pressing problem with the old way of qualifying leads is missing out on all the anonymous website visitors that can make up to 98% of your total traffic. Anonymous visitors are those that aren’t recorded in your CRM and can’t be easily determined by simple lookup tools (e.g. if their IP address is hidden).
Enter Lift AI, a buyer intent solution that can lead-score every single website visitor in real time, thanks to its massive machine-learning model trained on over one billion data points, 14 million sales interactions, and 15 years of data.
It works by analyzing every visitor based on their behavior and updating their buyer intent score in real-time as they navigate through the page or site. With over 85% accuracy, Lift AI is able to find hidden buyers that can be targeted for conversion.
Lift AI can be integrated with your whole marketing stack, from CRMs to chat platforms. In the case of the chat platform integration, Lift AI can automatically connect your sales team with a qualified lead as soon as they’re identified as “high intent”. Unqualified leads can then be delegated to a self-help guide or a chatbot, freeing up your sales resources.
Real-World Examples: Success Stories in Lead Qualification
Companies that use Lift AI have reported improving their chat pipeline by up to 10 times within the first 90 days. This is possible because their sales teams get to proactively interact with every qualified lead without spending time on low-scoring website visitors.
For example, PointClickCare attributed over $1M of extra revenue to its Lift AI integration and increased its chat pipeline by 400%. Similarly, Formstack improved its chat conversions by 420%.
Lift AI is able to integrate with most marketing tools and chat platforms, and installing it is as easy as adding a small JavaScript snippet to your website. Reach out to Lift AI for a detailed demo and watch your conversion rates skyrocket as a result.
FAQ
What is an example of lead qualification?
You do lead qualification when you evaluate a visitor to your website based on their intent to buy your product or service. You can use a particular lead qualification checklist and assign them a low, medium, or high score, for example, based on the company they work at, their behavior, geographical location, and other factors.
What are the stages of lead qualification?
While lead qualification is part of your marketing and sales (or conversion) funnel, it can have a few stages of its own. The most used are cold, warm, and hot categories of leads.
What are lead qualification criteria?
Every company will have its own criteria based on how close a given lead is to its ideal customer profile. These criteria can include demographic, psychographic, and business indicators.
What are the key requirements for a lead to be considered a qualified prospect?
As explained above, when a lead shows appropriate levels of authority, need, urgency, and the ability to pay, it can be classified as a qualified prospect. Marketing and sales might have more specific requirements, depending on the product or service they sell.
