As a B2B marketing professional, we know that you're worried about not getting enough leads. That's every growth team’s worry because leads are the lifeblood of your business - the healthier the flow of leads the faster your business will grow.
Although you should focus on getting more leads, at the same time you need to be looking at identifying your best leads. To do this, you must figure out who is interested and able to buy your product and pick them out from the noise of all the tire kickers who are not a fit and will never buy what you're selling. Once you know which leads are the best, you can target your growth efforts at the lead sources with the highest ROI.
Of course, filtering your best leads from the noise is easier said than done. Marketers create a scoring strategy and develop ranking systems to accomplish this task. Leads that are more likely to buy are “scored up.” When they clear a certain threshold they are “qualified” and passed to the sales department, which engages the prospect and works to close a deal.
The more accurately you are able to score your leads the better your business will perform. Today, AI is the most accurate way to score leads. Cutting-edge digital marketing teams use machine learning to build predictive models that stack rank every lead they capture. But what is AI lead scoring, and how does it work? Let's take a look.
If you are not already using AI to score your leads, you are almost certainly doing some kind of traditional lead scoring. Before jumping into AI modeling let's take a few steps back and look at the basics.
Lead scoring is where you use values or points to score the sales leads that you generate for your business. Every company with a sales team has some mechanism for scoring leads and prioritizing outreach to the best leads in their CRM system of choice (Salesforce, Hubspot, etc). Traditional scoring systems range from basic first-in first-contacted methods to more sophisticated points-based systems.
You can use attributes like a lead’s personal information, customer data, social media data (LinkedIn, etc), and behavioral data around how they've responded to your marketing efforts and engaged with your website and brand (email opens, etc). The lead scoring process helps you prioritize leads that are sales-ready, increasing your conversion rate.
Let's look at a basic example of a traditional lead scoring strategy - your sales data indicates that your most successful customer profile is between the ages of 40 and 50 and that before purchasing they attended a webinar, read a case study, and downloaded a whitepaper.
Leads matching these characteristics will have higher close rates, so you set up some basic behavioral and demographic scoring criteria, and then you create a threshold for “marketing qualified” leads (or MQL’s). Leads that are over the MQL threshold are the best leads so you pass the leads with higher scores to your sales team to focus on them, improving the efficiency of revenue capture.
Now, basic lead scoring already sounds like it improves your conversion rate, right? And it does. The problem is that it has a few weaknesses, so let’s spend some time looking at lead scoring pitfalls.
Lead scoring systems have a habit of aging and becoming obsolete: traditional systems are often developed once and then calcify into an organization's operations. Point values are often assigned by executive mandate (best guess) under a time crunch. Sales and Marketing teams are afraid to change the process in case conversions fall and they miss their numbers. These scores are rarely revisited.
Incentive systems can cause teams to optimize for incorrect behavior. One example of this is when a customer requesting a "quote" scores up instantly (that must mean they are ready to buy right?). The Marketing team is compensated on the number of qualified leads - so they hide the product's price and add a call to action “request a quote.” Now anyone who wants to understand the cost becomes “qualified” even though they may not be a good fit. Conversion percentages drop over time, but on paper, it looks like the Marketing team is succeeding.
So, yes, lead scoring can improve your conversion rate, but a smarter system can avoid these common pitfalls and supercharge your sales funnel. And this is where predictive lead scoring using artificial intelligence shines.
Predictive lead scoring uses your historical conversion data combined with machine learning to surface your best-fit customers. Let's face it; no sales rep wants to lose time on a potential lead that won't convert. By using predictive lead scoring, sales reps will have more accurately qualified leads, allowing them to crush their revenue goals.
Unlike traditional lead scoring, predictive lead scoring algorithms use every available piece of data to identify the patterns that make leads likely to end up closed-won or closed-lost. By recognizing the leads that are most similar to your existing closed-won business, machine learning-driven lead scoring helps eliminate the guesswork in scoring leads.
Leveraging AI means you have more accurate and reliable scores for your sales team to action. It will also pick up on hard to identify data points that you may have missed. And because predictive lead scoring works easily with big data, the longer you’re in business, the more data you generate, the larger your company size, the more accurate your lead-score predictions will become.
You probably have some idea of what AI lead scoring could mean for your business. In simple terms, it:
The biggest benefit of all is that you’ll make more sales. When you can instantly re-target your marketing efforts to focus on generating high-quality leads, close rates will go up, your sales team will exceed their numbers, and your business will grow faster than ever before.
You can look at it like this, you can have the best salesperson in the world, but if a customer doesn't want to buy your product, it won't be of much use. So, wouldn’t you rather point them towards leads that you know will probably convert?