Conversational AI: Differentiating Between Relationship & Function

Thought Leadership Piece from our Head of Strategic Marketing, Rob Mead

Rob Mead Headshot

Conversational AI is growing in popularity for customers and businesses, and its capabilities have developed significantly since the technology first emerged.

But as it grows, striking the right balance of its purposes is becoming more and more essential.

So, what’s the difference between chatbots and regular bots? And what makes function-based resolutions and relationship value-add opportunities different?

The difference between “bot” vs “chat”

Chatbots and bots are the product of combining natural language processing (NLP) with traditional software. While both can help customers through typed and spoken interfaces, they aren’t entirely the same.

So, what’s the key distinction between a bot and a chatbot?

The terms themselves give it away. The major difference is that a bot is an automated tool designed to complete a specific task, while a chatbot does the same thing, only with a focus on the conversation.

While bots have mastered automated tasks, chat doesn’t necessarily have a set functional component.

Think of it this way: robots in a factory don’t look like humanoids out of science fiction and instead serve as functional equipment. Meanwhile, setting the world to rights with your friends doesn’t necessarily achieve anything beyond human interaction.

Therefore, the thing that sets apart bots and chatbots is that they have different outcomes. One facilitates relationships, while the other resolves issues with functionality.

Functional Resolutions vs Relationship Value-Add Opportunity

Function-based resolutions are likely to have one single input for a single output. They’re basically a one-in-one-out system.

A relationship value-add opportunity does exactly what it says – it adds value in many ways, with multiple outputs for one single input.

How does this work in practice?

Automation allows companies to create bots capable of answering simple queries and concerns like, “what is my estimated delivery date window?” or “where is my parcel in the delivery journey?”. When a company offers products and services, giving customers an easy option to find out quick information can be essential, and keeps you ahead of the competition that might have slower customer service processes.

Functional resolutions streamline services, give quick and easy access to information, and show that your brand can be trusted.

Meanwhile, relationship value-added opportunities can arise from conversational AI that allows a company to get to know its customers better. Whether this means providing a personal shopper experience or simply using technology capable of personalising customer experiences – customers place value on the relationship a company forges with them.

Building meaningful consumer-company relationships improves loyalty and retention, shows attention to detail, and provides exciting and unique customer experiences.

problem solving

How can both work in a customer mix, and at what stages?

There’s space for function and relationship in the customer mix, but often at different stages.

For example, in fashion retail, an AI program for online shopping that delivers a personal shopper-style experience – wherein the technology learns a person’s style, general fit, and what they want to project through clothing – is worth the machine learning investment because you’re looking to build a relationship.

Using the same example, when a customer wants to know where their order is in the delivery journey, this is a functional post-sale query – of which an automated bot would be better to use.

Although queries can arise at any stage, they’re most important right before a purchase and afterwards. At these stages, customers want a quick, no-fuss response and usually aren’t thinking about the relationship with the company.

Meanwhile, customers might think more about the relationship before they even find your company and during the buying process. As more and more customers switch to buying products and services online, competing for attention will require personalisation.

Understanding where to split investment

Splitting investment requires a deeper understanding of what’s right for your business and customers. After all, functionality and relationship-building tools are needed throughout various stages – hence the need to find the right balance.

Say a fashion retailer is missing an automated bot that can resolve post-sale queries, for example. Customers would then need to find another way to get a response to their questions or concerns. As a result, the customer might feel that the company hasn’t invested enough in this area of the customer journey.

Similarly with a fashion retailer not offering an AI program allowing customers to get a personal shopper-style experience; the customer might feel that the business hasn’t invested enough into creating consumer-business relationships.

Striking the perfect balance between the two will depend on what your customers expect from you, and the type of results you want to see from the investment.

A decent chunk of investment should be poured into the relationship-building side if boosting customer loyalty and retention is the goal. If you know your customers care less about the relationship and more about getting answers, then the investment should flow into automation facilitating functionality.

The future of conversational AI

In my view, companies differentiating between relationship value-add opportunities and functional resolution, while also pouring in appropriate amounts of AI and automation investment, will likely become market leaders soon.

Just look at the current market leaders and how they’re using AI and automation. Most – if not all – will be using the appropriate amount for their customers and are continuing to invest back into the right places. They’re leading the way by embracing AI and machine learning technology.

Article first published in TBTECH

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