How to Leverage the Infinite Power of AI for B2B Lead Generation
Surely, you must have heard of the ‘dating‘ analogy when it comes to lead generation:
First-time customers compare to your blind dates.
First ‘interactions’ can make or break your dating (think: marketing) game.
Establishing positive word-of-mouth and creating a memorable first impression are key.
In short, like dating, B2B lead generation is a pendulum that can swing both ways. It can either be immensely rewarding or become an equally terrifying experience.
Luckily for us, we’re “Generation AI”– ones who can leverage the multi-dimensional benefits of this ubiquitous, all-powerful technology that is redefining innovation across industries, professional offerings, and personal ambitions.
That said, if you’re someone who is always second-guessing your B2B lead generation tactics and strategies, this blog might just be for you. Today, we are going to look at the five most underrated strategies that can help you embrace and integrate AI into your existing sales framework and roadmap. Are you a game?
Top-5 AI-Driven B2B Lead Generation Hacks Every Sales Marketer Should Know
“Companies using AI for sales were able to increase their leads by more than 50%, reduce call time by 60-70%, and realize cost reductions of 40-60%.” – Harvard Business Review Study
1. Gather Sales Intelligence about Leads & Accounts:
Ask any B2B marketer, and they’ll tell you that accessing and analyzing mountains of customer data is their biggest pet peeve.
Enter Artificial Intelligence. To give some perspective, consider the “cognitive agent” developed by IPsoft. This AI solution can decipher natural language to address customers’ queries and can handle up to 27,000 conversations simultaneously – that too, in multiple languages!
The point we’re getting at is this: AI lead-gen solutions can work with huge customer data sets to extract actionable intelligence and insights. In addition to this, they can:
- Analyze customer profiles, transaction data, and behavioral data from multiple data sources like CRM, marketing automation platforms, and social media.
- Help to arrive at the ideal customer profile by making use of real-time information.
- Analyzes the right technology stack to lure prospects in and recommends the type of communication that is more likely to close deals.
Did You Know?
AI systems are being used to identify customer preferences, interests, passions, key life events, demographics, brand preferences, etc. by analyzing social media photos from platforms such as Twitter, Facebook, and Instagram. Amazingly, it is also being used to identify logos, objects, and demographic interests on the basis of these images.
Key takeaway: AI can help you build the right data sets so that you can draw the most accurate conclusions and act on data-backed information instead of wasting your time and shooting in the dark.
2. Build Smart Lead Lists:
“Around 40% of time spent on sales work activities can be automated by adapting current technologies.” – Harvard Business Review
If you’re wondering about how AI-powered platforms can help you build a rock-solid leads list, here’s how:
- You can find more leads in your Total Available Market (TAM).
- You can extract insights about existing leads and prospective customers.
- You can search and find leads on multiple advanced filter criteria rather than using archaic, traditional prospecting tools.
For instance, you can search for information such as:
“Show me decision-makers from companies that use SAP or Service-Now from the US and are hiring in SAP Implementation.”
“Show me all the decision-makers from retail companies that got funding in the last 6 months.”
That’s not all. AI tools can also read through out-of-office replies to automatically detect new leads in out-of-office replies and add to the lead lists for your convenience:
Moving on, it also helps to know that there are various types of lead lists and methods that you can deploy to expand your lead list, based on your goals and priority, of course. These include:
1. Contact database: This list primarily makes use of lead information such as industries they work in, company names, technologies the potential leads are using, among other things. Once the information is in place, the lead is verified, and real-time updates are made so that you get access to relevant data for maximum efficiency. In fact, AI systems can validate lead contact information and help you find your TAM within seconds.
2. Automated email lists: This type of list makes use of automated emails to understand how often a lead clicks on links, what the mail-open rate is like, and how often do they choose to reply. Depending on how the lead responds, the lead is nurtured further. Here’s an example of how machine learning algorithms work:
Say you’ve sent a lead an email about your company’s progress, and the lead clicks on the link, ‘New Offerings.’ The next email can talk about the new offerings in greater detail to reel the lead in.
3. Chat history: One of the best ways to track potential leads and get their contact details is by analyzing the chat history based on markers such as region, occurrence frequency, text strings, etc.
4. Customer behavior analysis: Say you’ve got a lead who has spent a considerable amount of time surveying your products/services. It goes without saying that this lead can become an ongoing customer. With the help of ML algorithms, you can extract valuable information about the lead in question such as:
- How can you engage with these prospects in an organic way?
- Which channels of marketing would be most appropriate?
- What should the content tone, style, and voice be like in order to speak the prospect’s interests and connect with them?
5. Quality and UX Analysis: In addition to understanding what the lead prefers, you can also understand what challenges they’re facing when interacting with your platform. This kind of feedback becomes invaluable to reiterate your offerings and enhance your UX level further.
Key takeaway: With AI technology and deep learning, you can build lead lists that are dynamic, intuitive, secure, and valuable.
3. Leverage Lead Recommendation:
“Around 71% of B2B marketers are interested in using AI for personalization.” – A Survey by EverString and Heinz Marketing
Did you know that Tradera, a subsidiary of PayPal, boosted its gross revenue by a staggering 125% on its website? How did it manage this incredible achievement? We have two words for you: predictive recommendations.
AI systems can help you create a wealth of personalized data. Plus, they help you to go through large customer data sets so that you can learn about the ideal customer profile and gather valuable insights. As you can imagine, this helps B2B marketers to recommend accounts and leads in their TAM that are more likely to close and turn into loyal customers.
Handy tip: If you’re an absolute beginner into the AI world, we recommend you read the book, “The Sales Funnel Book: How To Multiply Your Business With Marketing Automation,” to gain meaningful insights into marketing automation.
Key takeaway: A robust blend of intelligent automation and predictive analysis can create healthy leads and generate repeat business.
4. Engage in Lead Scoring & Qualification:
You could have the most exhaustive lead list in the world, but if you don’t know which leads to prioritize, all that valuable information becomes pointless. This is where a smart, AI-enabled CRM assistant comes into play. It allows you to boost conversion rates by prioritizing the leads and opportunities that are most likely to convert.
This is why you need to create an accurate lead scoring system.
Put simply, lead scoring is the process of ranking prospects against a scale to understand the perceived value of each lead. Note that most lead scoring systems use 100 as their tipping point.
You can achieve all this and more with the help of Machine Learning technology. Here’s how the 5-step process works:
Step 1: Lead Analysis
AI analyzes a new lead’s past behavior, previous training data, and specific identity-related attributes to prioritize and score the lead.
Step 2: Lead Qualification
If the lead’s score falls below the qualification range, it is no longer pursued. In cases where the lead does qualify, the next course of action is determined. All in all, AI can qualify leads so that the sales personnel can execute their job more efficiently.
Step 3: Behavioral Analysis
In this step, the sales threshold is calculated as soon as the lead takes some action, such as link clicks, downloads, website visits, etc.
Step 4: Taking Manual Action
Here the sales personnel engages in ‘targeted’ actions such as meeting the lead or calling them to drive the engagement further.
Step 5: Capturing Data to Enhance Calculation Functions
Critical data that is achieved throughout the process is used to improve and continually refine the algorithm’s output.
Key takeaway: AI can help boost the lead-to-opportunity conversion rate by taking the guesswork out of the lead gen process and targeting the right customer at the right time.
5. Convert Leads to Deals in a Jiffy
“Top-performing companies are more than twice as likely as their peers to be using AI for marketing (28% vs. 12%).” – Adobe Report
All said and done, generating leads is one thing, but converting them is a whole different ball game. This is where AI-powered solutions can come in handy and ensure that you gain access to quality leads by providing an ‘inside-out‘ understanding of your existing as well as potential customers. In addition to this, it helps to automatically contact, engage, nurture, qualify, and follow up with leads through a natural, two-way email conversation until the lead converts into an opportunity.
In sum, here are some all-important questions AI can answer for your business and tackle for your Sales team:
A. What does an ‘ideal lead’ in your database look like?
B. What does an ‘ideal lead’ outside your database look like?
C. What are the best practices and steps to take in order to convert a lead?
D. What kind of content and imagery will appeal the most to your leads?
E. Which leads are worth pursuing and will take the leap of faith with your brand?
Key takeaway: AI can bridge the gap between potential leads to long-term deals and embed true value across your business requirements – only if you have the right set of tools, tactics, and techniques in place.
Closing Thoughts: Embracing the Era of AI for Lead Generation
“AI can create $1.4 trillion to $2.6 trillion of value in marketing and sales across the world’s businesses.” – McKinsey Report
In a series of firsts, AI was deployed into the healthcare domain – and with success! The acclaimed cancer hospital, Apollo Proton Cancer Centre, adopted an AI-powered marketing suite, “ADHOM,” to run campaigns that focused on creating brand affinity and boost the number of leads. This AI software made use of big data analytics and ML techniques to deliver highly personalized communication across multiple devices and channels.
John Chandy, the chief operating officer of Apollo Proton Cancer Centre in Chennai sums up the results of this effort as follows
“From day one, the hospital had a 100% increase in their lead generation without increasing a single dime in the ad budget.”
Throwing light into the true sentiments of using AI among marketers, Gartner’s recent report summarizes:
“AI’s capacity to transform marketing is obscured by a fog of hype, but the breakthroughs are real.”
To that end, if there are three things you can take away from this blog, let it be these:
As an intuitive tech tool, AI lead generation solutions can skyrocket your lead generation efforts, organically.
As a sales and marketing mindset, AI lead generation processes can personalize and improve your behavior as well as perception towards your leads.
As a digital strategy, AI lead generation software solutions can lock in on leads who are engaged, interested, and hooked to your brand, now more than ever.
Finally, remember that AI lies at the intersection of your lead-gen goals and your lead. Need we convince more?