How Conversational Intelligence Is Impacting Sales and Marketing

Conversational intelligence has made its way into the corporate setting and it isn’t likely to go anywhere any time soon (if ever).

There’s a good reason for it, too: AI-powered chatbots, virtual assistants, and voice assistants can simplify so many procedures that it’s hard to ignore them.

Trending conversational tools include Microsoft’s Cortana, Apple’s Siri, Amazon’s Alexa, Samsung’s Bixby (voice assistants), IBM Watson Assistant, Google's Dialogflow, Microsoft Bot Framework, Amazon Lex, LivePerson (chatbots), and Amazon Echo, Google Home, Apple HomePod, and Microsoft Invoke (voice assistants).

The list is expected to expand over time, so keep an eye on developments!

What Is Conversational AI and How Can It Help With Sales?

The term “conversational AI” is explanatory in itself. It implies human-computer interaction through speech or text.

Conversational AI rely on NLP techniques to understand, interpret, and generate human language. NLP allows for understanding the context, sentiment, and user intent.

That’s exactly why it is perfect for enhancing the sales process and improving customer engagement. It’s safe to say that conversational intelligence and sales go hand in hand; in fact, you’ll have a difficult time finding a serious business not relying on this inventive technology.

The most obvious benefit of conversational AI is its 24/7 availability, which makes it perfect for customer support.

The next benefit is engagement with leads, as it can ask questions and score leads based on their responses. This helps CS teams to focus on the leads likely to convert.

Conversational AI can provide detailed information about products or services and answer all relevant questions in this regard. To top it off, it can also guide customers through the order process.

Simply put, conversational AI upholds streamlining at its finest, which directly impacts cart abandonment rates — one of the biggest issues businesses are facing.

AI-Powered App Systematization

Systematization of developing technologies is always challenging, but AI may be an exception.

Namely, a proposed classification (refer to Shai Rozenes for additional info) recognized three levels of AI tools, as follows:

• General AI techniques
• Domain specialization AI techniques
• Application-tailored AI solutions

General AI techniques can be used for various purposes that draw on pattern recognition, data mining, machine learning, deep learning, rule-based reasoning, etc.

Domain specialization AI techniques, as the term portends, focus on a single domain specialization. Typical examples include natural language processing (NLP), speech and tone recognition, face and emotion recognition, gesture recognition, and case-based reasoning (CBR).

Application-tailored AI solutions are deployed for specific user needs.

Domain Specialization AI Techniques

Domain specialization AI techniques are usually the best choice for businesses, as they feature a range of business-specific functionalities, including but not limited to:

• Speech-related cluster (focus on speech analysis and generation)
• Text analysis cluster
• Emotional recognition cluster (focus on recognition of emotions)
• Collaborative cluster (focus on human-computer collaboration)
• Computer vision cluster (focus on analyzing pictures, photos, and videos)
• Awareness cluster (focus on awareness capabilities, “such as self-awareness and context awareness”)
• Building Meaningful Relationships With the Help of Conversational Intelligence

Building meaningful relationships with customers is one of the greatest challenges in the time of infodemic.

Thankfully, conversational AI can tap in, and here is how:

• It fosters personalized interactions by collecting and analyzing customer data
• It is available around the clock
• It ensures consistent (and instant) responses
• It engages with customers via multiple channels (websites, social media, messaging apps, voice assistants…)
• It upholds proactive communication (customizable; typical examples include offering product recommendations, providing updates on orders, and gathering feedback)
• It assists with common support issues
• It can send follow-up messages
• It can identify trends by analyzing customer interactions

In addition, conversational AI adapts over time, as it learns from customer interactions. In other words, it gets better at what it does over time.

Addressing App Fatigue

One of the most curious peculiarities of our time is the so-called app fatigue. It’s the occurrence when users become overwhelmed by or frustrated with numerous apps, which typically leads to disengagement.

The reason why this happens is obvious: there are simply too many apps out there and each of them has a different interface & features and requires different login credentials.

Conversational AI can address app fatigue by consolidating services. Scilicet, conversational AI can function across multiple websites and messaging platforms. In other words, it integrates with various applications, providing a single point of access.

Typically, conversational AI is also more user-friendly, as it features a simplified user interface. Its ability to integrate tasks is, perhaps, its best selling point. Conversational AI can automate reminders, schedule appointments, and send messages — the actions that used to require interaction with multiple apps.

Everything considered, the range of functionalities conversational AI offers can transform businesses and boost their sales and marketing strategies.

Just consider one simple fact: the first chatbot (the MegaHAL chatterbot) surfaced in 1996. It took almost three decades for the technology to reach this level and adoption rate, so who is to say what it will be capable of in a couple of years?

Bottom line, stick to what’s available now and learn on the go!