Lead Generation using Machine Learning

Lead generation is a marketing term that is used to identify and cultivate potential customers for a business. Whenever a company starts making new customers through various sources, then this process is called lead generation for a particular product or service in a company. Although lead generation can be made through various sources such as Facebook, YouTube, advertisements on TV, etc., nowadays, machine learning is also being used to generate leads for a business.

Besides making intelligent machines or computer software, machine learning is also known to do cool and complex things like targeting the audience automatically for a particular product and service without using many resources. In this topic, "Lead generation using machine learning", we will discuss what exactly lead generation is? And how machine learning is helpful in lead generation. So, let's start with a definition of lead generation.

What is Lead Generation?

Lead generation is defined as the action or process to build a potential customer for a business. It generally consists of the record of the potential customer, such as contact information (email address, phone number & fax) and possibly additional features about the customers like product preferences and demographic data.

Lead generation is a marketing term that requires a significant amount of time, money, and effort to generate potential customers.

Automation using Machine Learning

As machine learning technology can be used to automate the business, which includes automated mail, product recommendation, self-driving cars, chatbots, etc. Similarly, it is also being used to generate leads in businesses, where various ML algorithms are used to run campaigns, suggest products and services, collect information and demographic data of customers automatically.

In simple marketing, we use various sources to approach customers, whereas using machine learning, we should not worry about resources, time, money, and extra efforts.

How do Machine Learning (ML) algorithms help in a Lead generation?

Machine Learning is one of the most popular technologies, which uses various algorithms to solve complex business problems. It is successfully being used in generating leads for businesses. There are a few important steps to successfully generate a lead using ML algorithms as follows:

• Storing new Leads: Machine Learning helps to train machines for storing data in a database through past data. Whenever a new lead appears in the database, it gets automatically stored database on the basis of previous training data and classification metrics.
• Lead analysis: Machine learning algorithms help to determine whether a lead is valuable or not, and based on demographic scores, lead analysis is done by ML algorithms.
• Lead classification: based on demographic score, leads are automatically classified in a system. Whenever the lead score is below the classification score, it gets neglected by the system, and if the lead score is above the classification score, ML algorithms wait for the next possible action of lead.
• Behaviour Analysis: Whenever a lead is successfully classified and takes the next action, machine learning algorithms help to count the sales threshold. And based on this calculation, the system analyzes various details such as lead revert time, link clicks, insights, acquisition, events, web visits, etc.
• Forwarding for the next 'targeted action: Whenever the system qualifies a lead by crossing the benchmark sales threshold, it is forwarded to the next level for further manual/targeted actions such as arranging a call or meeting with leads.
• Enhance calculator function: At this stage, the final output is again utilized in training the sales threshold counting function and demographic counting function. This process ensures the continual refinement of machine learning algorithms.

Methods of Lead generation using Machine Learning

Machine Learning uses various algorithms to analyze the data and generate a new lead in the database. To create that list of lead's data, multiple methods are being used in the industry. After the analysis of a few industrial tools, here are a few ways to provide input to systems that engage or find out the potential leads.

• Contact creation: Whenever a customer visits a website and fills in the required detail for enquiring about a product or service of your business, then it is called lead creation or potential customer. In this entire process, the customer enters a few basic details such as email and phone number. Later, they render the list of most accurate matches from the millions of contracts they have. As the leads are verified, and the contact details are dynamically updated, it works efficiently.
• Automated mail: Machine learning technology is being used for making business automated by various organizations. So, whenever a lead is generated, a system-generated email is a dent to the customer, which is tracked by ML algorithms and tools. Further, based on previous mails status, new mail was triggered. For e.g., the first e-mail was opened by the lead, and a link for service 'ML automation' was clicked. Then the next email will be shared to target that customer by sending a link, which is more likely to nurture the leads.

Here are the following matrices that ML algorithms tracks;

• Link clicks
• Open rate
• Replied

• Chatbots and chat histories

Personal virtual assistance or chatbots are one of the best applications of Machine learning technologies today. ML engineers are continuously focusing on developing advanced chatbots for conversation with the customers in business. ML tools are dedicated to tracking entire chat history based on their geographical location, region, occurrence frequency, text strings, etc. Further, if any customer returns again and again or shows more interest, then machine learning algorithms try to ask for contact detail and save them for you to contact later.

• Competitive technologies stack analysis:

There are millions of websites running over the internet, and many of them may have the same technology which your company is targeting. So, if it is the same, then it can solve many issues in your business-like ranking keywords, most queried keywords, etc. In this analysis, machine learning algorithms and tools are also helpful to find out the competitors/similar websites.

• Website Pixel trackers: Sometimes, a customer visits your website to check home page details but leaves without checking the service details page.

Machine learning allows you to identify such visitors and differentiate them from your target audience as those visitors may come for other reasons instead of buying services.

Advantages of Machine Learning in Lead Generation

Machine Learning has played a vital role in lead generation for products and services in businesses. For a few years, companies used various ways to create potential customers, such as filling a form through email or other sources, but now machine learning has solved the classical approach of generating leads by automation. You don't need to call or meet customers individually to get a question answered; they expect to get all the answers from your website.

There are a few important reasons that make machine learning beneficial for lead generation in your business. These are as follows:

• Remove unwanted form filling: Most of the leads are generated through smartphones, and form is one of the best ways to generate leads through smartphones. Even with the auto-fill feature, no one wants to waste their time accessing a post. Hence, machine learning helps the customers to access blogs without filling multi fields forms, and only they need to browse at their own pace. Sometimes customers are ready to provide their contact detail but don't want to fill out forms; then, in these cases, machine learning algorithms take care of these things automatically.
• Develop a hyper-personalized experience: Machine learning helps to create a truly personalized experience. However, you can create content and target your audience, but without ML, it is impossible to deliver a hyper-personalized experience to customers.
• Allow leads to self-nurture: Machine Learning allows the customer to self-nurture before interrupting the sales and marketing team. It allows the customer to access the content at their own pace and inform them about products and services through personalized content recommendations. However, you can retarget them by social advertisements, but on your site, they can be unrestricted by forms or pushy sales teams.

Conclusion

Machine Learning is one of the most popular technologies that is used in various industries such as marketing, healthcare, finance, banking, infrastructure, digital marketing, SEO, product recommendation, etc. Based on some research, it is found that adding an AI engine with ML in lead generation strategy can deliver 51% more lead conversions instantly. Machine learning is also useful to automate the lead generation process through various tools across your website, such as adaptive content hubs, self-nurturing landing pages, Personalized Exit-Intent Popups, Human Lead Verification, etc. Hence, we can say lead generation is a complex process when you have a large customer base, but machine learning has solved this process by narrowing down your target list and reducing the efforts needed to convert the customer as well as increase the business revenue.