To be successful in sales, you need to have a thorough understanding of what you’re selling, to whom, and why what you sell matters to them. The more precise your understanding is about the pain points you’re specifically solving, and whom you’re helping, the more targeted your lead generation efforts will be.

But, it takes a lot of time and effort. Identifying the right prospects, engaging with them and building trust, understanding the right pitch that’ll resonate with them, following up, and more.

It’s logical but not easy. And then you need to do this at scale—to hit your revenue numbers, which means, you need to consistently be talking to high intent prospects. And that’s where AI really helps.

In this blog post, we discuss how you can automate high intent lead research at scale using AI. We will see how automating lead research not only streamlines your lead generation and prospecting process but also makes it more precise and effective.

What is lead research?

Lead research is the foundation upon which you build your marketing and sales strategies. It’s the systematic process of identifying potential customers who might be interested in your product or service. Essentially, you're trying to find people or organizations that have the potential to become your next big customers. This involves gathering data, analyzing it, and then using it to inform your marketing and sales efforts.

Traditional lead research involves a lot of manual work—scouring social media, checking out competitors, compiling lists from various databases, and so forth.

With AI, however, businesses can leverage technology to enhance their lead research processes significantly. First and foremost, AI saves time. Instead of spending countless hours manually compiling and analyzing data, you can leverage AI to do the heavy lifting for you. This frees you up to focus on more strategic tasks, like crafting compelling marketing messages or closing deals.

Secondly, AI improves accuracy. Humans are prone to errors, especially when dealing with large datasets. AI, on the other hand, can analyze data with pinpoint accuracy, ensuring that your lead lists are as accurate as possible. This means you’re less likely to waste time pursuing leads that aren’t a good fit.

Let’s break down the lead research into two key components:

• Identifying Ideal Customer Profile (ICP)
• Ensure Product Market Fit (PMF)

Identifying Your ICP

The first step in effective lead research is to identify your ideal customer profile (ICP). An ICP is a detailed description of the type of customer who would benefit most from your product or service. It includes demographic information, behavioral traits, and even the specific problems your product solves for them.

With AI, identifying your ICP becomes much more streamlined. For example, here’s how Plena adds to your ICP filtering on LinkedIn 👇

Plena completely reads through every profile shortlisted (based on the search filters you defined) and extracts values that will be beneficial for strategically targeting when you run campaigns. 

Plena’s AI captures over two dozen data points like industry,  systems used, social media activity, headcount of company, open jobs  etc. from different sites to determine the intent. 

For more on how it specifically helps you make the most of LinkedIn, you might want to dive into this one:

The result? You get a highly accurate, data-driven ICP that you can use to focus your lead generation efforts.

Ensuring Product-Market Fit

Another critical aspect of lead research is ensuring product-market fit. This means that your product or service effectively meets the needs of a specific market segment. Without product-market fit, your lead generation efforts will likely fall flat, no matter how targeted they are.

With AI you can find and validate product-market fit by analyzing market trends, customer feedback, and competitive landscapes. For instance, with a tool like Plena, you can scour social media, review sites, and more to gauge the general sentiment toward your product. Machine learning models can also predict how changes in the market might affect your product’s reception.

Leveraging Intent Data in AI-Powered Lead Research

Data, specifically Intent Data, is the lifeblood of AI-powered lead research. However, it’s not just about quantity; quality matters too. Not all intent data are the same. 

This includes data on your current customers, data on your competitors, and even data on broader market trends. The goal is to collect as much relevant data as possible, so your AI algorithms have a robust dataset to work with.

Poor-quality data can lead to inaccurate understanding and research, and subsequently result in building suboptimal lead lists. 

Here’s what a customer of Plena said after switching to Plena from their previous intent data providers:

We used to build a list using Zoominfo and LinkedIn Sales Navigator and then manually shortlist it further for intent based on manual research. It was tough until we signed up for Plena. With 67+ intent filters in Plena, we went from 10 new high-intent leads/ week/rep to 60 without any hassle.

How Plena’s AI-Powered Lead Research works

So, how exactly do you implement AI in your lead research process? It's not as intimidating as it might seem. Plena works exactly like how a human SDR would. 

With Plena, here’s how you build a high intent lead list:

You just need to assign the Plena bot a starting point—some high-level filters, and topics for intent signals. Our users most commonly pick from one of these sources as a starting point:

• LinkedIn
• Sales Navigator
• Google Maps

However, you can ask the AI to read any website.  For example you might want to target people who left reviews on G2 OR attendees of a specific trade show OR specific job posting. You can even upload a file as a starting point for the bot’s research. 

First the bot filters the contacts and companies based on the high level filters. Then it crawls the internet to scrape all useful publicly available information. The AI reads every available detail such as:

• Profile summaries 
• Job descriptions
• Contact data,
• Interests
• Social-media activity, likes and dislikes, 
• Current company’s stats and descriptions
• Recent PR releases
• Revenue
• Headcount
• Job postings, and much more. 

Once it gathers all the information, it processes all that information to ascertain if there is any intent based on the topics or keywords you provided. If the answer is yes—a lead will be added to the list. 

The bot builds the list on demand and can take several hours to come back with a result. You’ll have a list of active leads with 50+ customized data points that are thoroughly researched and scored for intent. 

So if you act upon the list fast, your response rates will be astronomical!

Once you have the list and data points, the tool also helps you personalize your email copy and reach out across multiple channels such as email, voicemails, SMS, LinkedIn, and more.

Monitoring and adjusting your AI lead research

Implementing AI in your lead research process is not a one-and-done effort. To get the most out of your AI tools, you need to continuously monitor and adjust your strategy. This involves regularly reviewing the performance of your AI tools and making adjustments as needed.
As AI technology continues to evolve, we can expect to see even more innovative applications in lead research. For example, advancements in natural language processing (NLP) could allow AI to analyze and understand unstructured data with greater accuracy. This could provide even deeper insights into customer behavior and preferences.

Until then, we highly recommend:

Don’t buy lists! They’re mostly crap. Build your own lists manually, and once you understand your ICP, use AI to scale list building. But never buy lists.

Summing up…

In conclusion, AI offers a wealth of opportunities for scaling lead research. The key to successful lead research is to continuously monitor and adjust your strategy, ensuring that you are always getting the best possible results from your AI tools. By doing so, you can optimize your lead generation efforts, drive growth, and stay ahead of the competition. So, don't be afraid to embrace AI and see how it can transform your lead research process.