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How is AI used in Lead Generation
AI Lead Generation Tools
Why AI for Lead Generation?
7 ways to use AI for lead generation
i. Lead Qualification
ii. Campaign Optimization
iii. Predictive Lead Scoring
iv. AI powered lead engagement
v. Chatbots
vi. Personalization
vii. Predictive Recommendations
Conclusion

By: Radhika Kanswal

AUG 08, 2023

Artificial Intelligence, Lead Generation

Web development industry has already fully adopted Artificial intelligence (AI) to increasingly performing more complex tasks. If you are planning to have your online presence, then you should be familiar with how AI is changing the game.
The objective of this blog is to explore AI tools for lead generation, their importance, and their uses for sales and marketing teams.

How is AI used in Lead Generation

Lead generation is a complex process for both B2C and B2B companies, and it is even more challenging for the latter. It requires a significant investment in running marketing campaigns, hosting events, and engaging potential customers across digital channels. It also involves extensive research to identify qualified targets, build contact lists, and send outreach emails. Despite these efforts, the outcome often yields a small number of prospects, with a good proportion of them not being an ideal target audience for the company’s products or services.

The use of artificial intelligence (AI) can simplify this process significantly. Sales and Marketing operations for lead generation can be simplified using AI-powered tools. These tools automate mundane tasks such as finding leads, qualifying them, creating customer profiles, personalizing messages, and nurturing the leads. This allows the sales and marketing teams to focus on value-add contributions to close deals faster.

AI Lead Generation Tools

AI lead generation software not only helps businesses to use various channels for leads generation and collection but also collects data and provides insights. These insights can be in the form of recommendations, predictions, or decisions based on data which sales and marketing teams can use to convert a prospect into a customer.

There are several AI-powered lead generation tools available on the market, which can work as standalone tools or alongside existing CRM software. Additionally, there are 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

The digitization of lead generation practices has transformed the way businesses reach potential customers. With the use of omnichannel outreach via emails, websites, apps, and social media, sales cycles have become longer and more complex. Plethora of Sales and marketing channels has made the work of these departments highly tedious and time-consuming for both marketing and sales teams. The leads are however not of that much good quality as is expected and data generated is honduras. Lead generation and research alone consume about 21% of a B2B sales rep’s time and at least half the budget of most marketers. Despite best efforts, nearly 79% of leads never convert.

AI can act as a perfect support system for sales and marketing to improve lead generation and lead nurturing operations.

AI-powered tools can analyze a huge volume of data from multiple sources in real-time, easily overcome the information overload that humans often face,
AI can find patterns in data, and multitask quite efficiently.
AI can help generate high-quality leads, guide sales reps on prospects that need attention,
AI can recommend how to reach the right leads with the right message at the right time.

AI has improved return on investment for businesses tremendously. Research shows that 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 affirmatively said that using AI tools have improved their lead prioritization, salespersons’ productivity, and their understanding of customer needs. Additionally, about 84% of business executives strongly believe that implementation of AI in various Sales and marketing processes can give them an edge over their competitors.

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7 Ways to Use AI for Lead Generation

There are several ways AI can boost lead generation, saving time and effort for the sales and marketing team.

Lead Qualification

Businesses can gather leads in a variety of ways, including through websites, applications, email, live events, online content, referrals, and social media sites like LinkedIn. This complex procedure can be supported in a number of ways by a strong AI system. An ideal customer profile (ICP) can be created by using machine learning technologies, which can also find leads that meet your current target persona.

AI-driven tools search the web for individuals and companies that fit your target audience, producing a pre-qualified prospect list that can be used as a starting point for sales and marketing.

For instance, Dell utilizes machine learning (ML) algorithms to find prospects with purchasing patterns that are similar to those of its current clientele. As an illustration, if their AI program discovers a company opening a new office

Campaign Optimization

The digitization of lead generation practices has transformed the way businesses

In addition to locating leads, AI software can accurately pinpoint the proper target markets for B2B marketing campaigns and divide them into groups based on their similarities or differences.
It can swiftly discover successful campaigns for a specific targeted population across many channels and automatically direct resources to those initiatives thus improving the probability of higher conversions.
Additionally, it can boost the campaign response rate and real-time adjust the content and call-to-actions (CTAs) of advertising and campaigns. For instance, if the AI technology discovers that advertisements with “call now” as the CTA perform better among prospects than those with “book a call,” it will immediately alter the advertising to include the better-performing CTA. Using an AI-powered tool by Adgorithms, a Harley Davidson dealership in New York was able to optimize its lead generation campaign and saw tremendous increase in sales leads.

Predictive Lead Scoring

You don’t want to waste time or money pursuing the incorrect opportunities because time is money. Once you have a list of potential customers, the AI system can efficiently qualify and score your leads using machine learning techniques like classification, clustering, and regression, saving you time and effort.

To evaluate and rank prospects, AI models analyze the real behavioral patterns of previous leads and the business’s conversion history. It also forecasts how long it will take to conclude deals. Thanks to AI tools, lead scoring is now more reachable with the convolutional neural networks operating in the background.

The model suggests qualified leads for the sales team to prioritize in a prioritized list in order to maximize value. Sales representatives can review the prospect rankings and can replace guess works with data driven decisions thus improving boost rates.

In order to prioritize leads, Razorpay, a payment gateway software, used predictive scoring and machine learning (ML) algorithms. As a result, they experienced spectacular returns on investment, including a reduction in lead gestation time of 30 days and an increase in monthly gross merchandise value of 50%. With the same conversion rate, it also decreased the effort of its sales force by around 70%.

AI Powered Lead Engagement

Your leads will lapse if you don’t nurture and engage them at the proper moment. That harms business. Once again, AI saves the day. A lead will never go cold thanks to the use of AI technologies like natural language processing (NLP) and machine learning (ML), which automate and personalize emails, texts, and phone calls to get in touch with prospects across several channels.

AI assistants swiftly and continuously pursue your leads until a favorable response is received. Due to the fact that 80% of leads require an average of five follow-ups after the initial contact in order to close a deal, AI can help the sales team save time and effort by preventing human forgetting. It can serve as a radar to monitor lead interactions across channels and find out lead nurturing opportunities.

Chatbots

One of the most often used AI lead engagement tools is the chatbot. These smart bots engage clients 24/7 across many channels by using conversational AI. In addition to communicating with users that contact websites or apps, a bot also examines in-progress interactions, judges their motivations, and qualifies and separates leads from contacts for the sales team.

A bot can seamlessly transition a prospect or customer to the live salesperson while still talking with them. When it recognizes a need for human intervention or that the client’s requirements are too intricate for the system to comprehend, it aids in the progression of a lead.

The customer experience is significantly enhanced by the use of such AI bots in the era of self-directed consumers. Conversica, Drift, Landbot, and Exceed.ai are few of the examples of companies providing intelligent chatbots.

Personalization

The ability to customize material for users is another feature of AI-powered engagement. AI systems may send hyper-personalized material to prospects and web visitors based on historical browsing and content consumption habits, pique interest in the company, and ultimately lead to conversions.

A SaaS firm named Smarsh employed AI products from Drift and Demandbase to tailor the content of its website, and as a result, saw a 13% improvement in conversion rates.

Predictive Recommendation

AI tools can analyze customer emotions, likes, dislikes, concerns, weaknesses, competitor engagement, and overall engagement of potential customers during the process. their interactions. They recognize when leads are on track to convert or at risk and offer actionable advice on the best actions to take to accelerate sales. Such predictive recommendations can help the sales team to send the right message to the right customer at the right time through the right channel.

For example, if an AI tool finds out that a customer is still learning about your product and service , it can suggest a sales representative to share brochures and suggest demos with the customer. Likewise, if the prospect is nearly ready to buy, this AI can guide sales reps to reduce the price to improve the probability of sales closure. Your sales team can also find cross-sell and up-sell opportunities to existing customers using predictive recommendations. The most common example of a predictive recommendation is Amazon’s recommendation algorithm.

Conclusion

Beyond these use cases, AI has many applications throughout the sales funnel, from price optimization and sales analytics to sales and marketing team training.

For businesses to succeed and outperform rivals, AI must be implemented. The cutting-edge AI and data solutions are already being used as force multipliers by smart businesses to generate, engage, and nurture leads. To maintain the AI advantage, some people are moving quickly. Companies are evolving. Will you change, or will you fall behind?

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