By Guest . Posted on October 8, 2024At the core of marketing is the practice of lead management. Identifying potential customers, engaging them, and then guiding them towards conversion. That said, there’s a vast ocean of countless potential prospects with their own needs, wants, and dealbreakers.
As the old saying goes, you can please some of the people some of the time, but not everyone all the time. You won’t get anywhere chasing up every potential lead that comes your way. You need to pick your battles. That’s where optimization via lead scoring AI can help.
What is Lead Scoring?
As the name suggests, lead scoring is about assessing potential leads based on a few things, such as:
- Likelihood of conversion.
- Potential value as a customer.
- Overlap with your target market segments.
Lead scoring is an essential aspect of lead generation, which means making contact with individual prospects and trying to convert them into active customers. This is distinct from demand generation, as a simple demand gen definition is performing activities to build demand in the market as a whole.
Lead scoring aims to quantify the information you have. You’re basically awarding points based on factors correlated with high value or likelihood of conversion. You might use a number rating, letter grades, or any other way you can think of to organize prospective leads into tiers of suitability.
The State of AI
Artificial intelligence algorithms are currently enjoying a lot of time in the spotlight. This is because we can train machine learning tools for all sorts of functions, from reading and translating hand-written documents with optical character recognition to analyzing architectural designs for safety risks.
These days, tech companies are adding AI to just about everything you can imagine. Even so, its most powerful function will always be as an analytical tool. That’s why it plays such an essential role in optimizing lead scoring processes.
The Benefits of AI-driven Lead Scoring
It’s one thing for us to tout machine learning analytics as the vague solution to your lead scoring woes, but quite another to get into specifics. Let’s look at the business benefits of implementing lead scoring AI.
It’s More Efficient Than Manual Lead Scoring
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Since machine learning is still an emerging technology, many businesses still rely on traditional lead scoring to evaluate potential prospects. This is when personnel manually sift through a lead’s data, like brand interactions and demographic info, and assign values based on their framework.
The first problem is that this framework tends to be basic and static, based on pre-established rules rather than current trends. As such, your scoring may not actually capture changes in consumer attitudes.
The second issue is that manual lead scoring is incredibly time-consuming, as personnel have to assess and collate each individual data point.
Machine learning algorithms, however, can comprehensively analyze vast troves of data incredibly quickly. AI is also quite good at identifying patterns and correlations that could otherwise be easily overlooked.
A Data-Driven Approach to Lead Prioritization
With countless people comprising the overall market, it can be difficult to know where best to focus your efforts. Both in terms of return on investment and your long-term brand identity. Do you aim for mass market appeal, or target specific, high-value demographics?
Lead scoring AI’s data handling capabilities remove much of the guesswork. The more information you give it on potential leads, brand interactions and market trends, the more solid its resulting insights will be.
The best lead scoring AI tools include user-friendly data visualization features. This allows human personnel to be the active decision-makers by leveraging AI analysis to provide objectivity.
AI Analysis Offers Actionable Marketing and Sales Insights
Predictive lead scoring algorithms can do much more than prioritize leads. They offer valuable insights to help you engage potential leads more effectively in both marketing and sales processes.
Personalized marketing has become incredibly popular in recent years. Whether it’s targeted ads with interactive content, or bespoke recommendations based on user history. The demographic and behavioral data you collect on leads is fantastic for this sort of thing. Things like:
- Which ads people engage with, and how.
- How people interact with your business (offline/in-person, preferred payment options, etc.)
- Age and gender distribution.
- Geographic distribution.
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You can gain a lot more insight by mixing and matching these criteria. The chocolate cake might be a baking company’s most popular product overall, but analyzing by gender might reveal more women prefer the choc-chip brownies. Similarly, more people might visit the bakeries in person overall, but you might find that people in rural areas tend to order online for convenience.
Machine Learning Analytics are Easily Scalable
Using a lead scoring AI algorithm is far more easily scalable than traditional methods. With manual lead scoring, an expansion would mean needing to recruit more analysts to process all the new data.
That’s because prospecting sales leads with AI optimization is much more efficient. It’s simply more data to feed into the algorithm. Data which, let’s not forget, gets analyzed very quickly. To the point where it hardly matters if you’re giving it 100 leads or 1000.
As you scale up, however, specific kinds of data will become more important, like:
- Geographic data.
- Industry data.
- New comms channels and marketing materials.
However, this is less of a headache than it sounds, as predictive lead scoring allows you to automate a lot of data management. So, at least you don’t have to enter it all manually.
How to use lead scoring AI
Like most AI-powered platforms, predictive lead scoring tools tend to aim for user-friendliness. In theory, this means even someone who isn’t particularly tech-savvy should be able to get to grips with them.
Even so, it can be hard to know where to start when dealing with lead scoring AI for the first time. With that in mind, here’s our basic checklist for getting started.
What you need?
Before you actually do anything, be sure you’ve got everything you’ll need. Here’s a quick rundown of each piece of your infrastructure and its purpose.
Contact center software
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Contact center software forms the foundation of a lot of your existing customer engagement channels. Until recent years, these were commonly referred to as call centers due to largely revolving around phone lines.
However, modern contact centers also handle email, SMS, social media, and live chat functions. Having all of these functions available on a single platform enables more efficient omnichannel marketing.
A lead scoring AI algorithm
Obviously, to do this, you’ll need a predictive lead scoring AI tool like Custom GPT. It’s important to do your due diligence in selecting a tool which is compatible with your existing network.
Your CRM platform
Predictive lead scoring tools integrate with your Customer Relationship Management platform in order to share data. CRMs are systems for cataloging data on customer interactions. This information then provides vital decision-making context for how your lead scoring AI interprets lead data.
Data nodes
If you don’t regularly acquire new behavioral data, your understanding of market trends will stagnate. That’s why you need systems to track customer and lead interactions with your brand, like:
- Marketing stats like CTRs, ad impressions, and social media engagement.
- Customer preferences like purchase methods and product choices.
- Browsing habits, mobile or computer-based, page viewing times, etc.
Contacts
Even the best system in the world is useless without anyone to contact. Your contacts list is every potential lead you can reach out to. As your algorithm assesses different leads for quality, you may end up discarding a lot of contacts without reaching out.
Integration
Before you commit to a given plugin or a platform, you have to be sure it’ll integrate with your existing infrastructure. In large part, that means your contact center suite and your CRM platform. But you also need to check for compatibility with your current data nodes, to ensure the information gets processed.
Then there’s the considerable matter of data storage. For something which doesn’t technically occupy physical space, data can be difficult to store. Machine learning algorithms can process tremendous volumes of data, but you still need somewhere to put it.
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Some organizations use on-site data silos. These offer a great amount of control and customizability. The drawback is that they can be expensive and difficult to maintain or upgrade.
That’s why many businesses instead rely on third-party cloud-based data storage services. This offers flexibility, in that you get a lot of storage without the upkeep responsibilities. The downside is you’re at the mercy of service disruptions. You also have to be sure your provider offers adequate cybersecurity.
That said, some businesses have embraced a third option. hybrid integration platform, which means using a blend of on-site and cloud-based data storage. Provided you can access information seamlessly, it’s a great option for flexibility.
Data Collection and Analysis
Lead scoring AI plays an important role in improving contact center analytics, as well as other areas of lead and customer management. Of course, all of this is impossible without a flow of relevant data.
If you’ve set up the data nodes we talked about earlier, a lot of this will be easy to collect autonomously. Behavioral data like browsing habits and ad engagement, which can be collected behind the scenes through your business’s app or website.
Demographic data can be a little harder to gather. Even geographic info, since VPNs make it easy to seem as though you’re logging on from anywhere. If you can’t gain information passively, your other options include customer surveys and social media engagement.
Automate Contact Center Workflows
The score of any given lead may start to climb as they engage more with your brand. Maybe they started out reacting to a couple of your social media posts, and now they have a user profile on your website to bookmark interesting products.
You can actually automate certain contact center functions (called workflows) to trigger when a lead’s score reaches a given threshold. This can include automated email marketing, timed offers, or even simply sharing relevant content.
Further Analysis
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Once you’ve analyzed and ranked all your leads, you might think you can rest on your laurels, right?
Wrong. After you’ve put insights into practice to convert leads into customers, you need to record what actually happened for further analysis. An ad campaign or promotional event you had confidence in might flop unexpectedly, for example.
Upon reviewing what went wrong, you might realize you need to reassess how to create a lead magnet that catches consumer attention, which leads nicely into our final point.
Review Your Updated Lead Scoring Model
The real benefit of a data-driven approach enhanced by AI is that improvement is iterative. In other words, you’re able to do a little better each time. Think of it like bringing an image into sharper and sharper focus.
As you engage and hopefully convert promising leads, you generate fresh waves of data-driven insights. What works and what doesn’t, how people react to different ad campaigns and sales conversations, and so on.
As lead scoring AI processes these developments, its evaluation criteria shift and adapt. With shifts in consumer behavior, what would previously earn someone a 10 rating might only guarantee an 8 or 9. It’s important to review your algorithm regularly to understand and track these developments over time.
AI is the Last Word in Lead Scoring Efficiency
When it comes to lead scoring AI versus the traditional manual approach, there’s just no comparison. Manual lead scoring is a major drain on work hours which gets worse the more your business expands.
AI lead scoring removes the busywork from processing data. This leaves time for the vital work of deciding how to implement data-driven lead insights. Add to that the fact it significantly reduces rates of error, and the case for lead scoring AI seems pretty ironclad.
If you’re still manually scoring leads, we suggest you evaluate your options. Learn everything you can about the different tools and services, and start using AI to optimize your lead scoring today.
