How AI Tools Make Lead Generation Better and Faster

Lead generation is complex. For B2B companies, it’s even more tricky. You spend days finding leads by running marketing campaigns, hosting events, and engaging potential customers across digital channels. Then you research to get more information to qualify suitable targets, build your contact list, and send outreach emails. 

Eventually, you end up with just a fraction of the prospects. And at least half of them wouldn’t be an ideal target audience for your products or services. What if artificial intelligence (AI) could make it all super easy? 

Businesses typically leverage AI-powered tools for lead generation in their sales and marketing operations. Such tools automate mundane tasks such as finding leads, qualifying them, creating customer profiles, personalizing messages, and nurturing the leads. At the same time, the sales and marketing teams can relax and focus on value-add contributions to close deals faster.

In this post, you'll learn about AI tools for lead generation, why it is important, and how your sales and marketing teams can use them.

AI Lead Generation Tools

AI lead generation software helps businesses not only generate and collect leads from various channels but also provide insights from collected data. These insights can be recommendations, predictions, or data-driven decisions that the sales and marketing team can use to convert a prospect into a customer. 

Take a look at these AI-powered lead generation tools available on the market: 

AI-powered lead generation tools

These products often work as standalone tools or alongside your existing CRM software. Besides these tools, there are also many automated machine learning and data science platforms like Matlab, IBM Watson Studio, and H2O.ai that help firms develop their own AI models for lead generation.

Why AI for Lead Generation? 

Digitization has transformed lead generation practices. They’re not limited to traditional ways of hosting events and cold calling anymore. Businesses now use omnichannel outreach via emails, websites, apps, and social media to reach maximum potential customers. 

With so many of these communication channels, sales cycles have become longer and more complex. This has made work highly tedious and time-consuming for both marketing and sales teams. They struggle to generate and keep up with a high volume of quality leads.

Lead generation and research alone consume about 21% of a B2B sales rep’s time and at least half the budget of most marketers. And despite best efforts, nearly 79% of leads never convert. 

No wonder more than 40% of salespeople and 60% of marketers see lead generation as a major pain point. AI can be a perfect support system for sales and marketing to improve the sometimes mind-numbing lead generation and lead nurturing

Here are some reasons why AI can help you generate and nurture better leads:

  • An AI-powered tool can analyze a huge volume of data from multiple sources in real-time “without getting tired.” 
  • It can easily overcome the information overload that humans often face.
  • AI can find patterns in data. Humans may not have such strong observations and benefit from data-driven insights. 
  • Above all, AI can multitask quite efficiently.

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What’s more? It can help generate high-quality leads, guide sales reps on prospects that need attention, and recommend how to reach the right leads with the right message at the right time.

Businesses that implement AI see a tremendous return on investment. Research shows enterprises that used AI-enabled lead-generation tools saw a 15% to 20% increase in sales productivity and 20% in order management throughput.

More than 40% of sales leaders using AI tools have seen major improvements in their lead prioritization, use of their salespersons' time, and understanding of customer needs. Additionally, about 84% of business executives believe AI can give them an edge over their competitors. 

7 Ways to Use AI for Lead Generation

There are several ways AI can boost your lead generation, saving time and effort for the sales and marketing team. Here are a few common applications of AI for lead generation: 

1. Lead qualification 

Businesses collect leads in many ways: social media platforms like LinkedIn, websites, apps, email, live events, online content, referrals, and more. A robust AI system can support this extensive process in several ways. A machine learning software can analyze your customer data, observe patterns, create an ideal customer profile (ICP), and find leads that fit your existing target persona.

AI-driven tools scour the web for people and businesses that match your target audience, creating a pre-qualified prospect list for your sales and marketing to use as a starting point. 

Dell, for example, uses machine learning (ML) algorithms to identify prospects that closely resemble its existing customer base and their purchasing patterns. For instance, if their AI algorithm finds a business that opens a new office more likely to purchase Dell products, it combes the web to find prospects that match similar patterns, boosting the quality of the leads generated. 

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2. Campaign optimization

Besides finding leads, AI software can also precisely locate the right target audiences for B2B marketing campaigns and segment them based on their similarities or differences. 

It can quickly identify campaigns that work for a particular target audience across different channels and automatically allocate resources to those performing well. Moreover, it can optimize the ads and campaigns’ content and call-to-actions (CTAs) in real-time and improve the campaign response rate.   

For example, if the algorithm learns ads with "call now" as CTA perform better among prospects than the ones with "book a call," the AI tool automatically changes the ads with better-performing CTA in real-time.   

A Harley Davidson dealership in New York used an AI-powered tool by Adgorithms to optimize its lead generation campaign and saw a whopping 2930% increase in sales leads.

3. Predictive lead scoring 

Time is money, and you don't want to burn dollars or hours chasing the wrong prospects. Once you have a list of prospects, the AI system can use machine learning techniques such as classification, clustering, and regression to effectively qualify and score your leads, saving you time and energy.

AI models study the actual behavioral patterns of previous leads and the company's conversion history to score and rank prospects. It also predicts the time needed to close deals.

With a ranked list, the model recommends qualified leads for the sales team to prioritize to get maximum value. The sales reps can look at the prospect rankings and make data-driven decisions than guesswork and intuition to pursue a lead, boosting their win rate. 

Lead scoring process

With predictive scoring, Razorpay, a payment gateway software, used ML algorithms to prioritize leads and saw phenomenal returns on investment, including a 30-day reduction in lead gestation period and a 50% increase in their monthly gross merchandise value. It also reduced its sales team’s effort by about 70% with the same conversion rate.

4. AI-powered lead engagement

If you don't engage and nurture your leads at the right time, they fall through the cracks. That's bad for business. AI comes to the rescue here too. Using AI technologies such as natural language processing (NLP) and ML, AI tools send automated and personalized emails, messages, and calls to initiate contact with prospects across different channels, ensuring no lead goes cold. 

AI assistants follow your leads promptly and persistently until it gets a positive response. Given how 80% of leads need an average of five follow-ups after initial contact to close a deal, AI can save time and effort for the sales team and eliminate the human error of forgetfulness. It can act as a radar to track lead engagements across channels and provide lead nurturing opportunities. 

Epson America, for instance, employed an AI assistant from Conversica to nurture and qualify leads and achieved a staggering 240% increase in engagement.  

A word of caution here: Sales or marketing automation is not AI. AI tools go beyond automation to analyze data and generate insights while automation just follows a given set of rules.

5. Chatbots

Chatbots are one of the most popular AI lead engagement tools. These intelligent bots use conversational AI to engage customers round the clock across multiple channels. A bot not only converses with visitors who reach out via websites or apps, but it also analyzes real-time interactions, assesses their intent, and qualifies and segregates leads from contacts for the sales team. 

While interacting with a prospect or customer, a bot can seamlessly hand them off to the live sales rep. It helps move a lead forward when it identifies a need for human attention or the customer’s need is too complex for the system to understand. 

Deploying such AI bots greatly improves the customer experience in the age of self-directed buyers. Drift, Landbot, Conversica, and Exceed.ai are some examples of companies providing intelligent chatbot tools.

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6. Personalization  

Another aspect of AI-powered engagement is personalizing content for consumers. Based on past browsing and content consumption habits, AI tools can send hyper-personalized content to leads and online visitors, spiking interest in the business and ultimately driving conversions.

Smarsh, a SaaS company, used AI solutions from Drift and Demandbase to personalize its website content and saw a 13% increase in its conversion rate

7. Predictive recommendations

AI tools can analyze customer sentiments, likes, dislikes, interests, pain points, competitor involvement, and overall prospect engagement during their interactions. They recognize when leads are on track to convert or at risk and offer actionable tips on the best actions to take to accelerate sales. Such predictive recommendations can help the sales team send the right message to the right customer at the right time through the right channel

For example, when an AI tool finds the customer is still learning about the product, it can recommend the sales reps share brochures and demos with the customer. Similarly, if the prospect is almost ready to purchase, it can guide the sales reps to offer discounts to further attract the customer. 

Your sales team can also find opportunities to cross-sell and upsell to existing customers using predictive recommendations. The most common example of predictive recommendation is Amazon’s recommendation algorithm.

Apart from these use cases, AI has multiple applications throughout a sales funnel, from price optimization, and sales analytics to training the sales and marketing teams. 

Build AI-Powered Lead Generation

Firms need to adopt AI to profit and outshine competitors. Smart enterprises are already at the forefront, using the latest AI and data solutions as force multipliers for generating, engaging, and nurturing leads. Others are moving fast not to lose the AI advantage. Businesses are changing. Will you adapt or be left behind?

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