Hey Siri–what’s the deal with AI and why is it taking over so many aspects of our lives? 

Siri? Bueller? 

AI is the latest buzzword on everyone’s tongue in tech. Replacing support teams with AI-powered robots saves companies some major money–right? Siri doesn’t need health insurance, and Alexa will never call out last minute with the flu. 

But what about the conversations that get lost in translation? We’ve all been there–these AI-based natural language processing systems are amazing, but they can only go so far. Are your buyers really interested in clicking through seas of predetermined multiple-choice conversations with a robot? How many give up after one too many close but not quite interpretations? 

That’s why we’ve taken a slightly different approach to the chatbot. There’s a lot that AI can do, but there’s so much nuance when it comes to selling, and we don’t like leaving that up to chance. AI-based natural language processing bots certainly have their place in tech, but the limitations could be setting your team back and creating more friction for your buyers than you realize. 

Siriously? The history of NLP

Natural Language Processing (NLP) is a technology that can “listen” to you speak and interpret your words to search a database for an answer to your question. It’s a truly incredible advancement when used in the right way. The Siris and Alexas of the world might feel like a brand new invention, but we’ve been trying to develop machine translators since the 17th century. 

It took a few hundred years, but in 1978, Arpa’s Network Speech Compression transmitted the very first spoken sentence over the Internet, or, at least what was kind of the Internet back then. That’s a whole other blog post.

Since the late 70s, the Internet has taken on many forms, and speech transmission grew at a similar pace. The very algorithms and systems today’s NLP AI are based on were developed in the 80s and 90s. 

In 2001, Yoshua Bengio and his team proposed the first neural language model, using an artificial neural network that doesn’t use loops or cycles to form connections, but instead moves data in one direction–resulting in a feed-forward neural network that helped the next evolution of NLP begin. 

In 2011, Siri was born–one of the first successful NLP assistants to land in the hands of the mass market. Siri, like its other NLP peers, relies on its Automated Speech Recognition module to translate human speech into digital concepts. It doesn’t have to be exact to trigger the right response, because machine learning helps bridge the gap between human speech patterns and the appropriate reaction. 

All of this comes together in the current state of AI-powered NLP tech in the B2B sphere: chatbot marketing. These systems are powerful enough to engage in a back-and-forth conversation, and almost feel human, but aren’t quite capable of the nuanced thought patterns and emotions that humans are. At least, not yet. 

1600s Descartes proposes a universal language, leading to the theory that machine learning is possible.

1940s After WW2, academics reprioritize this machine learning effort in hopes to be able to automatically translate from one language to another. 

1970s NLP research embraces new logic-based paradigms, opening up the field to new possibilities and new contributions to other technologies.

1990s Building on the earlier NLP models and the wider availability of information thanks to the internet, NLP becomes more focused on information extraction and generation.

2011 Siri becomes the first mass-marketed NLP assistant, opening NLP AI up to the public.

2022 The Chatbots have taken over.

Where bots break down

When our founders, Kraig Swensrud and Sean Whiteley, decided to build their next SaaS company, they knew what problem they wanted to solve.

They set out to completely transform how companies generate pipeline. Companies allocate a huge amount of time and money to getting people to their website, the next step simply can’t be a form that prospects fill out and hope to hear from someone in a few days? Weeks? Never?

Qualified was created to allow marketers and sales teams to seamlessly transfer the baton between landing a visitor on your website and getting them in front of a sales rep, all while keeping visitors engaged and serving them meaningful, personalized experiences. And one of the ways the Pipeline Cloud does this is by leveraging custom chatbots to engage users at critical points in the buying journey–with the goal of converting these prospects into real-time conversations with powerful tools your team can use to create memorable, stand-out experiences. 

NLP-based chatbots are programmed to recognize relevant topics that your website visitors might be interested in, taking them through a tree of different Q&A interactions that can serve them helpful content. We use automated bots on our website to make sure our visitors get what they need, when they need it, knowing that 80% of buyers prefer to research on their own before they talk to our team. 

Custom chatbots are standard across most modern B2B companies’ corporate websites–which is why we love them when scaling up your operation. They’re helpful in these early research phases, or after-hours to help an interested buyer book a meeting or help qualify unknown site visitors.  

But nuance is impossible to achieve with AI-powered bots. Interact with any of the AI-powered chatbots you can find across the internet for more than a simple conversation, and you’ll start to see where it falls apart. 

The best of both worlds

Qualified Conversations allows for teams to scale with this AI bot tech, without a robot taking over and getting in the way of more complex human conversations. Our philosophy is that humans sell, and bots help scale. With Qualified Conversations, you can leave the nuanced sales conversations with high-intent buyers to your sales teams, and use custom chatbots to interact with unknown visitors.

With the Pipeline Cloud, AI is additive, not a replacement, and we like to use the power of AI in other parts of the buyer journey. 

We start before your buyer even engages with your website. With Qualified Signals Research Intent Data, our AI model aggregates data points from the research your buyers are doing to predict when they’re getting ready to seriously start talking to your team. Your marketing and sales teams can see what other competitors and topics your buyers are researching so you can be over-prepared when they come to your door with relevant information. 

When buyers do start engaging with the content of your website, Qualified Signals tracks this as Engagement Data, alerting your teams when a buyer is heating up and likely ready to start talking. 

Qualified Conversations gives your sales teams incredible visibility into everything your buyer has touched, integrating data from your Salesforce instance and pairing it with insights  Qualified Signals has learned about your buyer. Not only that, but your teams have live views into what your buyers are interacting with on your site, what else they’ve already seen whether it’s an ad they’ve clicked or a chatbot interaction, and any engagement they’ve had with other team members. 

All of this intelligence comes together to create a pivotal moment in the buyer journey–a live chat, with a human who is more than equipped with everything they need to provide a hyper-personalized experience. 

AI powers the Pipeline Cloud, but your people power the sale. 

The bottom line here? Qualified Customers see an average of 4224% ROI in the 12 months after implementation. The leading AI-based chatbot service, Drift, averages 670%. Want to do the math yourself? We’ve created a handy ROI calculator to help you see what the Pipeline Cloud could do for your team. 

Want to learn more about keeping the power with your people on your sales team? Read our book, Bots Scale, Humans Sell.