You are at the marathon starting line. After months you are mentally and physically prepared. Just as the starting sound blasts, you look down and see….flip-flops? You forgot your running shoes! In these days of automation, Artificial Intelligence (AI) used in sales is the running shoes that support winners. Flip-flops might be fun, but they cannot keep up in today's sales race. 

In this blog, we explore the numerous applications of AI throughout the sales funnel. Automating your sales funnel should fit the particular needs of your product. Wisely applied automation gives you a competitive edge.

Can't wait? See Part 2 and Part 3 here.

Automating the Sales Funnel - Why Should I? Why Shouldn't I?

In the digital age, we buy many products without speaking to a salesperson. The sales funnel has been fully automated for many services in the Direct to Consumer world as well as Business to Business. Google Workspace, Dropbox, and Loom are some prime examples. 

Sales leaders are embracing the latest technologies and designing these self-serve sales processes. Modern B2B sales funnels are largely automated, but AI is now playing a significant part. AI brings personalization and context at scale, which was not possible via standard run-of-the-mill automation. AI is now supporting and working with us as well as for us. For the future of sales, this is vital. 

In this 3 part series, we break down the role of AI in the Top, Middle, and Bottom of the funnel as well as retention and beyond. While not discussed for each use case, it is worth noting that in many of these applications of AI, AI falls well short of human effort, for now. Full funnel automation is not always appropriate for all products and product types. And Humans are needed as architects and safety nets for all uses of AI. 

AI in the Top of the Funnel (TOFU)

During the demand creation and generation stage, the primary objective is attracting potential customers to the brand or showing them that you exist. AI is being used to help in the following ways.

Content Generation

AI-powered tools can help you with essential content and outline. Natural Language Generation (NLG) algorithms generate blog articles, social media posts, and other content types. The goal is to reach and resonate with the Ideal Customer Profile(ICP). It generates quickly and has an endless supply. It can also help adjust your writing 'tone' to best fit your audience. A little human effort can make AI-generated content more engaging and personalized for the top of the funnel. 

NLG can personalize outreach to an individual faster than a human (not always better than a human, just a whole lot faster). While many are shifting to this format, it is worth noting that there are downsides, mainly that AI content can be stilted, inaccurate, and lacks innovative thinking. The best way would be to get the personalized drafts ready by AI at scale and edit them. We have written extensively about the pitfalls of AI-driven content generation here. 

Dynamic Content Generation

This includes various aspects such as dynamic websites, blog display order, keyword-infused landing pages, ad-specific landing pages, and personalized landing pages for Account Based Marketing (ABM). 

AI generates personalized content actively based on demographics and firmographics. Commonly used for highly custom websites and increases the relevancy of the content to the lead. Given the importance of first impressions, this can provide an edge in some scenarios.

Social Media Content

You can make both visual content and written content with AI. There are models built to specialize by the platform to accommodate each platform's inherent differences and usage.

This is most useful in trend adherence, summary reviews, and updates. New and innovative content comes from the wild and creative human mind and less from the hive mind amalgamation that powers AI Content Generation.

Media Sentiment Analysis

Sentiment analysis is used more in D2C, but is still applicable to some B2B. This is when AI algorithms monitor social media platforms, blogs, and online forums to analyze sentiments related to your brand. Marketers and salespeople can tailor their strategies by understanding the ICP's preferences and opinions. It is an easy way to identify potential brand advocates or influencers as well as detractors.

Chatbots

Companies employ AI-powered chatbots and virtual assistants to engage with website visitors, answer their questions, and provide information about their products and services. This interaction is also great for capturing leads and first-party intent data. This improvement in customer experience would be costly to do with human labor. AI chatbots are replacing traditional FAQ pages, although most companies use both. The advantage goes to Chatbots, however, who can respond to the personal request, tone, and keywords the customer uses. 

Personalization and Recommendation Engines

These AI algorithms analyze user data, browsing behavior, and preferences to personalize content. This allows them to recommend relevant products and services. By delivering personalized experiences, brands increase engagement and build strong connections with potential customers. 

Predictive Analytics

AI can analyze large volumes of data to identify patterns and trends. AI enables marketers to make data-driven decisions. Predictive analytics help optimize marketing campaigns, identify the most effective channels, and allocate resources more efficiently.

Targeted Advertising

Everyone wants to optimize ad spend, and AI can help you do it. AI-powered tools can segment audiences based on demographic, behavioral, firmographic, or psychographic data. This allows brands to deliver highly targeted advertisements to specific customer segments, increasing the chances of reaching potential customers.

These are examples of how AI is contributing to brand awareness right now. The future evolves with AI. AI Lead Generation is fed by all these other parts working together to funnel in prospects. By leveraging AI technologies effectively, brands can optimize their marketing efforts, deliver personalized experiences, and increase the impact of their message.

AI in Demand Capture (TOFU)

AI in Forms

Forms are powerful demand-capturing mechanisms. Here are some ways you can use AI to make the most of your web forms. 

Automated Form Completion

AI automates the form completion process by leveraging data from various sources. Auto-population of information from user profiles, publically available data or previous form submissions reduces the time and effort required to fill out forms. This convenience encourages higher completion rates.

Form Analytics

AI can analyze form data to provide insights into user behavior, drop-off points, and conversion rates. You can regularly adjust and optimize your forms by identifying patterns such as bottlenecks and drop-offs. 

Suggested modifications from AI can improve form design, field placement, wording, or length to maximize conversions. Reducing user abandonment is one of the biggest factors in forms. Avoid the rage quit as much as possible.

Lead Scoring and Filtering 

AI evaluates form responses and assigns lead scores based on predefined criteria. You can also use Plena to both enrich and filter these leads. Plena verifies work emails, contact details, and matches the list to your ICP. This automated system helps prioritize leads, enabling salespeople to focus on the most promising prospect.

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A/B Testing

AI facilitates A/B testing of forms by automatically creating variations and measuring their performance. By continuously experimenting with different form elements, layouts, or wording, marketers can optimize forms to maximize conversions and improve overall marketing effectiveness. This requires a 'review' component to ensure the variation is still within scope. However, the continual refresh keeps the rage-quit level low.

Dynamic Form Generation

Related to multivariate testing, AI can generate dynamic forms that change with each user. This is done by adapting the fields and content based on user profiles. The personalized approach helps create a more engaging user experience. It improves the conversion rate by increasing perceived relevancy. 

Leveraging AI Lead Generation in marketing with forms streamlines the user experience and provides valuable insight for continuous improvements. What are your form fill rates looking like? How is your form-based Lead Generation game? 

AI in Cold Outreach

Here, we break down the role AI can play in Cold Outreach. 

AI and the Cold Email - How AI Does the Scut Work for You

Personalization

AI analyzes recipient data from public profiles, databases, and social media activities to customized email content. This includes subject lines, greetings, body text, and summaries. Everyone knows customization and personalization are super important. AI goes a level deeper, beyond just customizing demographics and firmographics data. AI curates talking points from their latest activities that help you reach the right person at the right time with the right message. 

Timing Optimization‍

AI looks at recipient behavior as well as historical data to determine the best time to send cold emails. This maximizes open and response rates. At scale, this is usually between 10 am and 11 am in the prospect's respective timezone. But with AI, you can customize sending time for each prospect based on their habits of opening and interacting with emails. 

A/B Testing‍

AI automatically creates variations of cold email templates and tests them to identify the most effective messaging, subject lines, and calls to action. Auto-testing is a wonderful lift to free time preparing and launching slight variations in a campaign. Also, AI provides precise analytics of the results improving design and operational choices by humans. 

Follow-up Automation‍

AI can automate follow-up sequences based on recipient interactions, such as non-response, click-through rates, or interaction with the email content. This is an excellent way to ensure timely, persistent, and contextual outreach. 

AI and the Social Direct Message - How AI is Used in Social Media Outreach

Chatbot Integration‍

AI-powered chatbots engage with prospects through social messaging platforms, providing instant responses to inquiries, offering product information, or directing them to relevant resources. Often they are used for mass initial customized cold outreach. If responded to, the chatbots can continue nurturing or hand off the conversation to humans based on specific keywords. 

LinkedIn Outreach

LinkedIn is the largest professional network and it is imperative that B2B marketers and salespeople leverage it. AI can help you automate some part of LinkedIn engagement such as sending InMails and DMs, linking their activity or endorsing skills. Plena's AI prospector helps you do just that.

Natural Language Processing

‍AI analyzes and understands the intent behind social Direct Messages (DMs), allowing for personalized and contextualized responses. AI is often used to read the tone of the customer and prompt adjustments to the tone of the sales team to match. Tone parity improves outcomes.

Automated Lead Qualification

‍AI evaluates message content and user profiles to determine lead quality, helping prioritize leads for follow-up. These are often linked to CRM systems or able to expand data, such as the Plena bot. Prospecting is one of the chores that AI has shown immense promise in optimizing. Learn more about how you can use AI for prospecting and qualification here. 

Conversion Tracking

‍AI can track conversions' origin. One of these sources is from social DMs. Although this tracker often captures cold emails and cold calls, social tracking across platforms is a newer piece of the puzzle. Conversion tracking allows marketers and sales teams to measure the effectiveness of their outreach efforts. Maximize your strengths and focus improvement efforts on where you are weak. 

AI and the Cold Calling

Call Prioritization

‍AI algorithms prioritize leads based on various predefined criteria. You can use lead scores, demographic information, and behavioral data to ensure sales representatives focus on the most promising prospects. More data points on each lead will improve this process.

Speech Recognition

‍AI-powered speech recognition transcribes and analyzes phone conversations. This is often the fastest way of extracting key insights and identifying actionable points. It is worth mentioning that for many humans, taking notes improves memory, focus, and recall, and the content of the notes is only to spark that recall. At the same time, others desire an accurate, complete record and are distracted by note-taking. 

Call Sentiment Analysis

‍AI can analyze voice tones, pauses, and other vocal cues to determine the sentiment expressed during the call. This helps you understand customer attitudes in order to tailor your responses. Both humans and AI are imperfect at knowing a speaker's or writer's feelings (humans definitely are better at gauging these non-verbal cues). Add in the complexity of understanding why they feel that way, and any accrued data needs to be taken with a block of salt. To understand some of the complexities that can throw analysis off, consider the everyday use of jokes, irony, sarcasm, colloquial negation, and word ambiguity. 

Intelligent Call Routing

‍AI intelligently routes incoming calls to the most appropriate sales representative. This is used in large companies to match callers based on factors like language preference, expertise, or geographic location. Small companies have set up this to switch to any open person to ensure the call is answered.

Call Analytics

‍AI provides analytics on several factors, such as call outcomes, conversion rates, call duration, and other tracked metrics. This level of automated analytics enables businesses to evaluate the success of their cold-calling efforts and make data-driven improvements. 

Continued.... 

This concludes Part 1 of our series Automating the Sales Funnel with AI. In this part, we focused on the TOFU stage to understand how AI can help us maximize demand creation, generation, and capture efforts. 

Stay tuned for Part 2 and Part 3 here, where we will discuss AI's applications in the Middle and Bottom of the funnel.