Successful sales and marketing teams who want to handle inbound leads at scale know the importance of lead scoring. Lead scoring—the process of accessing lead quality through a points-based (or other quantitative) system—is a key element to implementing a sales funnel that prioritizes the most qualified leads and keeps your sales reps from wasting time.
However, even with a lead scoring strategy in place, you’re probably still leaving lots of money on the table. Traditional lead scoring models (and funnels) are certainly helpful, but they sacrifice a personalized approach that so many leads need to become a customer.
To keep up with the demands of an increasingly personalized marketplace, sales and marketing teams are turning to conversational marketing tools like Exceed.ai. These ai-powered virtual assistants are not only changing the way leads are scored but using conversational lead nurturing to give leads what they need to mature into a sale.
Let’s take a look at the pain points of traditional lead scoring strategies and how AI solutions can help fix them by not only improving the scores themselves but also supercharging your sales funnel as a whole.
How current lead scoring strategies work—and where they fail
Right now, lead scoring works by tracking demographic and behavioral data on a lead and assigning a point value based on the metrics you’ve decided are most important.
- Demographic data includes information about who the lead is, like their title, the company size, annual revenue, location, etc.
- Behavior data includes the actions your lead actually makes, either online (eg. attended webinar) or in their real life (eg. purchases up to $100 million per annum in robotics components)
It’s important to have both of these data sources. Someone who may be the perfect fit in all of the demographic data points might have online behavior that indicates they aren’t ready to talk to a sales rep (eg. no interaction, unsubscribe from an email list, etc). Conversely, someone with ideal behavior data, (newsletter clicks, whitepaper downloads) may turn out to be a poor demographic fit, like a student or someone too low at the company to make decisions.
These are extremely simplified, clear-cut examples, but when it comes to a proper lead score there may be hundreds of metrics that are worth tracking. The more you track, the more convoluted your lead score calculations become, and the more room for error. Without highly sophisticated (and expensive) data science at play, it’s hard to tell how much different factors should be weighed, especially when shifting behavior goes against things your teams may (incorrectly) assume based on past experience. And even with all that, things are constantly changing—just think of the differences in online interactions since COVID-19. The infinitely complex nature of assessing lead quality is one of the many reasons 79% of leads never convert to sales at most companies, despite the plethora of lead scoring strategies out there.
That’s where AI (Artificial Intelligence) assistants can come in and save the day. These assistants can analyze huge amounts of data (much, much faster than humans can) to come up with a more accurate score, and constantly adapt as you add new blog posts, new webinars, new form fields, and other data points, or as you adjust to new circumstances within your ideal customer base. This deep analytical power is an incredible asset on its own, but the highest value from the virtual assistants comes from their ability to actually engage the leads in real conversations. This means that, among other things, they can ask questions directly to the leads to gain more information and fill in the gaps on what might be missing from an accurate lead score.
To better understand the benefits of ai-powered conversational lead scoring and nurturing, let’s look past the score itself and into what you do with it.
What happens after you assign a lead score?
Yes, the score itself is important, but what’s even more vital is how it translates into action. If you send too many leads to sales, your team will be overworked and swamped with poor leads that don’t translate to contracts. Send too few, and you’re missing out on potential business. Picking leads to send to sales (and how you make the handoff) isn’t the only area for improvement either, since your lead nurturing campaigns are just as important for engaging and re-engaging leads to generate more sales.
Conversational lead nurturing lets you turn your data collection and lead nurturing into a two-way street. It automates communication with leads, letting the AI assistant ask and answer questions, continue threads, direct the lead to the right resources, and even schedule meetings with sales reps. The natural language processing engines these AIs use are 97% accurate in understanding a lead’s responses, and they constantly learn through each interaction.
Giving leads what they need to push them through the funnel
Sales teams know how important it is to listen, but lead nurturing campaigns rarely take the time and energy to truly listen to leads. A barrage of newsletters and webinar invitations can only do so much—but marketers know it’s impossible for a human to personally meaningfully interact with each and every lead.
The same problem pops up when your score tells you to send someone back for further nurturing. With current systems, you’re x5 waiting for the lead to make the move and say “I’m interested again,” instead of diving in and figuring out how you can help them get to a yes.
Through conversational lead nurturing, an AI assistant can step in to do some detective work and figure out exactly what can get that lead ready for sales. They can handle objections like “This is too expensive” or “I’m not ready yet,” which means you can fully understand—not just guess—at exactly what these leads need to become sales-ready. This personalized, one-on-one communication lets you pick up on things you never would’ve caught through cookie-cutter nurturing strategies, without extra work for you.
A perfect handoff
If your AI assistant thinks a human should jump in, it can ping the right person on your team to take over. Plus, it can adapt based on these responses sent by sales and marketing teams as well to know what to do in the future.
More importantly, it can automatically schedule meetings with your sales reps, which means they can spend more time connecting with customers with real potential. Of course, this is also ideal for leads, since the moment they are ready to speak to a real human, they can. Research shows that delaying your response to a hot lead can reduce the odds of qualification by 80%.
Work smarter, not harder by harnessing AI
An AI virtual assistant can ensure that your lead scoring system is as accurate as possible based on the right data, not just measuring what’s convenient or straightforward. It can act autonomously, handling objections, answering inquiries, and asking lead qualification questions on its own, all while updating your CRM with any important data. Even more importantly, an AI assistant can make sure that your leads are getting what they actually need to move through your pipeline. Rather than leaving them to navigate the buying process alone, the assistant can actually guide them through and make the handoff at the right time. All of this means that your team can focus on the right prospects at the right time, without worrying that potential customers are slipping through the cracks.
