Lead Generation with Neural Networks: How AI is a Real Game Changer

Generating leads from potential customers found online can be a daunting task, often requiring time-consuming efforts. Lead generation is simply trying to convert strangers on the internet into potential purchases; often described as a funnel whereby traffic converts to leads, then to qualified leads, and finally to a sale. However, in real life, this can more closely resemble a chaotic process, with a large number of leads not related to your business.

The classic process of lead generation is based on a marketer's gut feeling and significant manual effort — someone creates an ad, someone looks at the results, and then a manager calls each lead to bring them in. The biggest issue is, there isn't enough time for a person to quickly and accurately determine if a lead would be a potential buyer for you, due to the number of leads being generated at any given time.

This is where AI comes into play, as a solution for problems that occur when people begin to become overwhelmed:

• Processing leads. Neural Networks process tens of thousands of leads every second, and can identify patterns in user behavior that indicate a user's readiness to purchase from you. A manager simply wouldn't be able to do this unless they were very experienced.
• Predicting intent. Through the use of machine learning, AI can model likely customer intent based on past behavior. If a consumer visits your pricing page on three separate occasions, downloads case studies, and arrives via an ad, as an example, AI can see this information and predict how likely they are to purchase from you.
• Serving personalized content in real-time. Based upon the prior responses of the individual users, AI can automatically select a personalized offer in real-time. One individual may receive an offer with technical specifications, whilst another may receive an offer containing case study examples with ROI, and a third may receive an offer with a call manager button. All of this occurs automatically based on an analysis of each user's behavior.

In 2026, the average person will be exposed to approximately 10,000 advertisements a day. Because the volume of ads is so great, it is important not only for ads to be displayed often, but also at just the right time and with precise targeting. This is where AI can help, as manual processing of all this information is no longer possible.

To illustrate the benefits of using AI, let’s look at a real life example. A SaaS company had a conversion rate from lead to payment of 4% prior to implementing AI. After implementing a model that included 12 behavioral parameters, (including traffic source and browser type, the pages read by the user, and geographic information), their conversion rate increased to 9% within two months while maintaining the same advertising budget. The only difference was in the way that managers handled leads – they now only worked with the leads that they considered to be “hot” and ignored all of the others.

AI has not replaced any employees; it has simply removed many of the routine tasks that needed to be done, allowing employees to devote their time to working with prospects that are ready to purchase.

How does AI work with leads?

There are at least three technologies that make AI work faster, better, and at a higher quality than traditional methods of working with leads:

Machine learning (ML) is the basis of AI. As algorithms learn from their history of leads (those who purchased, those who did not purchase, and what occurred leading up to the purchase), they build predictive models that determine the patterns that indicate whether a customer is ready to purchase.

For example, Hubspot uses machine learning in their analysis of more than 200 different parameters from each of their customers. These parameters include information such as, the source of their traffic to the website, email activity, and history of interactions with the CRM system. The model provides a percentage used to calculate the probability of closing a deal. Lead managers mainly work with prospects that have lead scores of more than 60 percent.

Natural Language Processing (NLP) technologies allow AI machines to "read" text such as chat messages, inquiries, emails… etc. It is able to identify the user’s intent: if they want to purchase now or are just looking for information. In addition, NLP can also analyze tone of voice; meaning it knows when a client is dissatisfied or requires something urgently.

In ASCN.AI, NLP allows for the automatic classification of requests within a Telegram Bot into three levels: "targeted acquisition," "needs consultation," and "just interested at this time." This enables the creation of appropriate communication scenarios for each lead type.

Computer Vision (CV) consists of all technologies for processing image and video. B2B and B2C applications include image recognition for real estate, automated data extraction from business cards, and document reading. A construction business has used CV to process photo inquiries, which resulted in a reduction in processing time from 10 minutes to 2 minutes and an 18% increase in conversion due to faster and more accurate responses to inquiries.

When ML, NLP, and CV are integrated into one system it creates a full service lead generation automation system. It is not simply several disjointed features glued together but rather a functioning system.

Automating the Process of Collecting and Qualifying Leads using AI

There are a number of steps between when someone makes an inquiry and closes on a sale including the manager making a phone call to find out what the customer's need(s) are. Depending on the situation, qualifying leads may take anywhere from one minute to several hours — from the moment someone submits an inquiry to when a sales manager receives it. To make matters worse, over 70 percent of all incoming leads are actually not qualified. Using artificial intelligence has changed this process, as it now allows for the automated qualification of leads before they are transferred to a sales manager by aggregating information about prospective customers from various sources to determine if they are a target or non-target user.

Most of this lead qualification is performed in real time by collecting information from major types of data sources:

• Website Activity (Google Analytics): What were the top pages viewed, how long was the user on your website, what did they download, and what source referred the user to the site; For instance, if a person arrived at the website through an advertisement, spent eight minutes on the site, and viewed the pricing page of the business, the lead score created by the machine learning model will be 85/100.
• Information pulled from customer relationship management (CRM) systems and databases: (LinkedIn Sales Navigator, Clearbit, Hunter.io) — Job title, company size, budget — are "pulled" through integrations with CRM systems. A salesperson will prioritize the sales inquiry if the inquiry originates from a CEO of a large company.
• Interacting with a Web Bot: If a user interacts with an automated web bot to ask budget, time frame, and decision maker-related questions, the web bot will calculate the combined scores to determine how to proceed with that lead based on their response to each question.

Using ASCN.AI's Case Study as an example, an advertising agency received 300 inquiries per month from prospective customers prior to launching its automation solution, 210 of those inquiries were irrelevant leads. After ASCN.AI implemented the solution for the client, more than 70 percent of non-qualified leads were eliminated from inquiry forms. In addition, the remaining 90 leads were split into three priority categories by the agency's sales team. The promotion of high-ticket deals doubled after implementing the automation solution and eliminating non-qualified leads.

Unlike a person who baselessly guesses, an artificial intelligence calculates probabilities based upon the use of a massive database of historical data. The greater the amount of information included in the database, the more accurate the predicted conclusion for any inquiry is going to be.

Prominent Algorithm and Neural Network Models to Identify Leads

• Logistic regression (The easiest and fastest way to predict binary outcome): ie. sold/not sold. A method that is best suited for smaller organizations ideally with simple sales life cycle.
• Random forest is an ensemble model that has shown to be very successful when working with complex multi-dimensional datasets. This model is often used in business to business (B2B) sales model, and has very complex/tangled dependencies with very large amounts of data.
• Deep Learning (Neural Networks) represents a multi-layered and complicated structure. These models require large amounts of data, but will give very accurate results. This is typically utilized by large organizations that have millions of records.

For example, A SaaS organization was able to identify customer churn with 82% forecast accuracy, this enabled them to take action before customers churned.

Overview of AI Platforms & Services for Lead Generation

Best to choose one tool that will handle your most pressing needs really well, instead of purchasing a "farm implement" because it has all the possible capabilities that you don't need.

Integration with CRM and Marketing Platforms

To run AI at peak performance, you need the following connections to your infrastructure:

1. CRM SYSTEMS: automatic input of newly updated lead data; automatic lead scoring; manager tips.
2. Email Marketing: segmentation / filtering of newsletters; automated nurturing based on lead actions & scores.
3. Advertising Platforms: lead conversion data transfer; exclude non-target traffic reducing cost per lead (CPL).

For example: One marketing agency reduced its cost per lead from $45 to $28 and increased its inquiry to deal conversion rate from 6% to 11% without increasing its budget.

Case Study: Successful Implementation of AI for Lead Generation

On the night of October 11th 2024, ASCN.AI team's assistant provided a crypto token to the team in 10 seconds. The ASCN team checked the data and was able to find out that the token was trading on Binance at $1.22 and on KuCoin at $1.18 and that there was no selling activity from large token holders or negative sentiment on social media and that the token had high liquidity. The team then performed an arbitrage trade and made a profit of $1,000 on a $30,000 trade. This example shows that speed of analysis is the key to success and not the specifics of the trade. A human trader does not have enough time to take advantage of trading opportunities at that speed.

More can be found about this in the full Falcon Finance case.

Using AI for Online School Acquisitions Increased Conversion from 8% to 14%

An online school was managing 450 inquiries per month via a Telegram bot prior to AI. Each inquiry took the manager 20 minutes and converted at 8%. After the ASCN.AI system was implemented, the following happened: inquiries were automatically qualified based on 3 criteria (budget, timeline, and format); inquiries were prioritized and responded to with personal offers immediately; manager time per inquiry was reduced from 20 to 2 minutes; conversion increased from 8% to 14% and revenue increased by 75%.

CRM Error Methodology and Best Practices

1. Assume all CRM data may contain errors. It is important to cleanse the CRM of all errors before training a model. Duplicate records should be removed, statuses should be updated, and mandatory fields should be completed for the model to function properly.
2. Manager feedback is essential for improving the model. If the team consistently disagrees with the AI's outcomes, this is a signal to retrain the model — not to abandon it. In one case, adding the 'decision-making speed' coefficient increased model accuracy from 72% to 84%.
3. Don't roll out to the entire database at once; otherwise your conversions may be decreased without running an A/B test. Begin with 20%-30% of your overall traffic, assess the results quality, and then continue to scale after assessing the result quality.
4. Retrain all models at least once a quarter to incorporate changes in customer behaviors, as well as current and future trends.

Essential Performance Assessment & Characteristics of AI-Driven Lead Generation

• Predicted value for lead potential accuracy should be between 75%-85%. The verification process is to export the data and review the data at least monthly.
• Conversion rate from a segment of “hot” leads compared to a segment of “cold” leads should be 3 times different.
• Time to status qualification will decrease from hours to seconds, thus increasing the probability of closing sales due to reduction in qualification period using AI.
• Cost per qualified lead (CPL) will typically reduce by 20%-30% with AI implementation when calculating the total bill including acquisition/cost of qualified leads.
• Duration of the deal cycle will typically reduce 15%-25% with AI automated processes.

Data Visualization and Monitoring of Analytics

When measuring success in data visualization and analytics, the following are the preferred components of dashboards:

• Lead Scoring and Segmentation in Real-Time;
• Segmentation by the Conversion Funnel;
• Channels for Price and ROI;

Tools such as Google Data Studio, Tableau, and Power BI work well to make data more easily accessible to your team, as well.

Top Questions

Is AI applicable to any industry?

Although AI is highly effective in B2B that require long sales cycles, it has been successful in E-commerce that have large catalogues, as well as in financial services and education. However, AI does not gain success with small local business that have minimal datasets, due to the unpredictable behaviour of their customer base.

How do I properly prepare the data for use in a neural network?

1. First, the CRM should be audited for duplicates and checked for accuracy of the contacts’ current status;
2. Second, the fields should be standardised (Email addresses, Phone numbers);
3. Third, label the target variable as 'purchase' or 'no purchase';
4. Fourth, features that influence the purchase should be created;
5. Fifth, create a training set and then a verification set to train the model.

What are the main risks involved in AI, i.e., Artificial Intelligence, for lead generation purposes?

• Overfitting the model;
• Bias within the data;
• Insufficient or inadequate training dataset;
• Integration of various systems;
• Resistance of the team to changes.

Final Notes, Conclusion and Recommendations for Implementation of AI Logic for Lead Generation

1. AI will help reduce routine, resource-intensive tasks. The role of the manager is to work with those customers who are ready to purchase.
2. The success of automated lead generation will depend heavily on the level of integrity of the data and the integrations.
3. Since the initial implementation of AI for lead generation will involve small-scale use of AI, it will be important to scale up after evaluating the results. A good starting point for small-scale testing will be to develop a qualified lead from a chat and then grow from there.
4. When the model is retrained periodically, the input will be made more accurate due to the feedback of the team.

Expected Implementation Timeline:

1. Audit the existing dataset and clean (1-2 weeks);
2. Determine the point at which to continue with automation (2-3 weeks);
3. Select the tools you will need (4-5 weeks);
4. Pilot launch and establish new metrics and results (6-8 weeks);
5. Upon confirmation of new metrics and results, scalability onwards (9-12 weeks);
6. Group of teams to retrain the AI models every 3 months; provide support to the teams.

Disclaimer

All information contained in this document is general in nature and not an effective substitute for investment, legal, or security advice. A conscious approach to using AI and AI based systems will be required based on the capabilities of each platform.