Regardless of the industry, lead generation is undergoing a massive transformation. The process of attracting and capturing the interest of potential customers is changing. This is primarily because expectations are higher than ever—and new technologies have enabled organizations to meet and exceed those expectations. Enter AI-driven lead generation.
In today’s digital age, potential customers are no longer satisfied with cookie-cutter sales strategies or cold calls and emails. Instead, they expect to be targeted specifically based on their needs, even if they haven’t specified those needs yet. It’s a major reason why, for 61% of marketers, lead generation is the biggest marketing challenge.
Fortunately, a solution is already within sight. As a recent Forbes.com article points out,
The integration of Artificial Intelligence (AI) in lead generation is transforming how businesses identify and engage with potential customers…AI is redefining the landscape of lead generation, offering businesses the tools to find and engage potential customers more efficiently and effectively than ever before.
Over the past few years, AI has made its benefits known to companies and organizations looking to build a more effective lead generation strategy. In a crowded market, AI-driven lead generation has become a vital tool to keep up and get ahead of the competition.
From better market intelligence and more focused segmentation to personalized communication and post-lead follow-ups, these are the seven ways in which AI-driven lead generation will transform your marketing strategy.
1. Deep Audience Insights for Better Segmentation
A core part of modern AI systems is machine learning—analyzing large, sometimes unstructured data sets to gain dynamic insights. For AI-driven lead generation, this process can become a core part of learning insights about your target audience that can be used for better segmentation and targeting.
In most cases, that process begins with feeding current customer information into the system. AI systems like Salesforce’s Einstein take that information, using it to learn which commonalities among current customers can extend to future customers. The resulting data can be used to understand which channels are most effective in lead generation, which segments may be most likely to convert during certain seasons or in certain time intervals, and more.
The result: highly targeted marketing and lead generation strategies that are based on individual and unique customer profiles. Given the rapid rise of account-based marketing, especially in B2B industries, this level of personalization and customization based on complex patterns can be invaluable to building lead generation strategies designed to convert.
2. Scalable Content Analysis to Identify and Solve Pain Points
Beyond analyzing customer data, machine learning mechanisms use the same strategy from a content standpoint. The amount of content uploaded online every minute is staggering and impossible to analyze manually. With AI-based solutions, companies can run analyses on even the highest content volumes, leading to crucial takeaways to inform their marketing strategy.
For example, MarketMuse is an AI-based solution that promises to identify the content that your target audience consumes and engages with. An analysis of that content can provide insights into the types of pain points each user has in different stages of the sales funnel.
These insights, in turn, lead to clear recommendations for lead-generating content specifically designed to engage your audience. Because the analysis is both automated and unique for each organization, it can lead to truly unique recommendations that allow your company to stand out in even the most crowded marketplace and online environment.
3. Predicting Buyer Behaviors to Anticipate Market Needs
The predictive nature of AI and machine learning becomes invaluable to companies looking to optimize their lead generation. Past trends in customer behaviors allow for reliable predictions on how these customers will react in the future, allowing AI-savvy organizations to anticipate market needs in ways that drive their entire strategy.
Combine audience and content insights, and you can build a comprehensive strategy that predicts exactly when your potential customers will take action. That, in turn, creates the opportunity for a more nuanced lead generation strategy that not only offers the right content but presents it to your potential customers at exactly the time when their pain points and needs bubble up.
In Watson, IBM has built one of the most well-known and effective solutions in this area. The tool can predict buying patterns based on historical trends, enabling sales and marketing leaders to anticipate demand and adjust their strategies accordingly. Vast amounts of data that had been impossible to analyze can now turn into structured predictions to improve the lead generation process.
4. Conversational Lead Generation Possibilities
Predictive analytics leads to better outreach, but generative AI also has a massive potential effect on the lead generation process itself. Consider AI-enabled chatbots, which have risen rapidly over the past few years. According to one study, 95% of customer interactions will be at least supported by AI by the end of this year, while another found that 61% of consumers say chatbots sometimes or always influence their buyer decisions.
AI-enabled chatbots are successful in generating leads for two reasons:
- They’re always on, providing a 24/7 opportunity for potential customers to ask questions and receive answers to the questions and pain points that matter most in the buying process.
- They can learn from each conversation and connect to the larger database, allowing the answers to be more relevant to the needs of individual customers.
Through this technology, prospects can get the answers they need immediately—in exchange for only their contact information. But they can also keep the conversation going after converting to leads, creating more in-depth relationships that ultimately make them more likely to become customers. It’s why chatbot solutions like AtlasRTX have made such an impact in lead generation across a variety of industries.
5. Automated, Smart Data Entry for Lead Information
Once leads enter the database, AI systems can also help ensure their information is captured both accurately and comprehensively. In this use case, AI solutions automate the otherwise manual task of data entry, updating databases instantly as new information becomes available.
Crucially, this ability can go beyond simple time savings as well. AI algorithms are now able to identify and extract lead information directly from various sources, eliminating the need to seek it out manually. This data can extend from contact information to online behavior, such as the types of web pages and online properties of your company the lead has recently browsed.
Finally, AI helps after the initial entry as well, cleaning up potentially wrong data through a dynamic proofread and standardization of information. This ability, combined with the potential to comb multiple data sources, frees up sales and marketing teams to focus on more dynamic and strategic outreach based on the information gathered.
6. Dynamic Lead Scoring and Qualification
In addition to data entry, AI also uses information from existing leads to create a better way of qualifying and scoring prospects in your database. Based on the information they provide, AI-enabled solutions are able to leverage machine learning to predict which of your potential customers are most likely to convert.
To accomplish this feat, the solution tends to leverage both data from existing customers that have already converted and actions that leads currently in the funnel are taking. This combination of historical and real-time information is then combined into a dynamic model that assigns scores for your sales team to better focus their conversion efforts.
As is the nature of machine learning algorithms, the lead scoring mechanism within these systems remains dynamic: as new information enters the model, the scoring mechanism adapts. That, in turn, allows it to change quickly as market conditions and customer behaviors change, helping businesses stay competitive even in changing environments.
7. Data Disaggregation for Enhanced and Predictive Takeaways
Finally, and partially connected to many of the above benefits of AI-driven lead generation, machine learning systems can disaggregate any data connected to your leads for more in-depth and predictive takeaways. In addition to analyzing your prospect and customer pool as a whole, that means breaking down data into demographic distinctions designed to inform marketing and sales decisions.
Consider, for example, the consideration of moving into a new market. Through AI systems, you can limit your predictive analysis of market demand to that specific region while also determining whether the pain points of potential customers in that market will be unique. The result can be smarter, more informed decision-making that ultimately increases your ROI.
Unleash the Power of AI-Driven Lead Generation
Make no mistake: AI has transformed the possibilities of lead generation in every industry. With the right approach, organizations can make better decisions, engage more targeted audiences with better content, and focus their conversion efforts more specifically. The result: less wasted spend, better efficiencies across the marketing and sales operation, and ultimately more (and more high-quality) leads that result in more customers.
Getting to that point is not automatic. A comprehensive lead generation strategy that outlines the right tech stack for your environment is still essential. But with the right strategy and tools, every organization in every industry has the potential to unleash the power of AI-driven lead generation.
