Evolution of Web Analytics in the world of AI - Measuring Trust

We all know how difficult it is to stay afloat in today's world with a business that adds value to the customer. The landscape is volatile and technology ever changing! If a business needs to survive, then it must move beyond just its products and services which are actually its most fragile aspect, to something that's robust and immune to change or obsolesce. Businesses need to go beyond, beyond the physical attributes of their products to build a brand that's centred around "Trust".

Customers on the other hand seek more than just a product, they are looking for a solution. Our current market is so saturated with similar offerings that it's more difficult to choose a product than to actually buying one. Customers too are look for something beyond the mere physical attributes of a product, they are looking for a solution that offers them peace of mind and reliability that's once again centred around "Trust". And customers gain Trust in a brand through Consistency, consistency in quality, service, or anything the company delivers to its customer. While measuring Trust is a challenge due to it's inherent abstractness, Consistency thankfully, is easy to measure.

Now we all know that we are on the fringes of another massive technological disruption, AI is transforming everything and altering the way we do business. Businesses which do not recognise its potential and adapt would inevitable be replaced by ones which will have an AI driven culture. AI, apart from all the amazing things it can do is also good at building consumer Trust. It's easier and quicker to build Trust in your Brand through the proper implementation of AI, than without AI.

That said, today it's very easy to implement AI into your existing business website. All you need are a few lines of code and a subscription with one of the AI giants like OpenAI, Krutrim, Meta, etc. But will this do? Will your customers be happy if you have an AI powered chatbot on your website? Will you boost sales just by adding a AI powered product recommendation engine? Will you gain more traffic by deploying AI generated marketing campaigns? Probably not!

One of the biggest risks associated with AI is not in implementing AI itself, but rather in deciding what it represents for your business and "how to measure" it's impact.

Before implementing AI in your business it's very important for the leadership to understand its implications and its impact across the chain of command. I will list a few examples highlighting the frame of mind that needs to incorporated by the leadership before taking a plunge into AI powered solutions:

1. AI powered LLM Chatbots: One of the most popular customer facing use cases for implementing AI today is a chatbot. However it's first important to find out if the presence of a Chat Bot would actually help you build customer trust. If most of your customers who come online are asking for Ticket resolution status or Delivery updates then it shows there is a deeper problem to solve. Your Chatbot might be doing a great job at answering the questions & solving order queries, but is it helping your brand build Trust? NO! Instead of patting yourself on the job for implementing the Chatbot right, do a sentiment analysis to address the underlying motive for customers in asking for resolutions relating to the quality of your service. Ask yourself "Why are there so many Problem Tickets?", "Why are so many of my deliveries not reaching the customers on time?", and solve for these. The best value for an AI powered chatbot on your website / application is when customers use it to research topics related to your services and it responds with right answers and also in turn recommends which products or services they could buy from you.
2. AI powered Voice Support: This is a subset of the above example but is a dedicated solution for problem resolution. So there may be no scope of cross selling or upselling and Trust building would be related to how many tickets your solution can resolve. So the business needs to do a good job of training the AI on identifying the customer sentiment and resolving it in as short a time as possible, and in case the problem is not resolved, how many customers end up in queue to speak to a real live agent. A high problem resolution rate is critical, but even more so for this to be resolved by AI agent and not by a real live agent. Of course you need to fix the root cause causing the customers to call as always.
3. AI powered Marketing: Another popular use case for AI is in GenAI. Building new content for marketing campaigns is all the rage now. While businesses measure the Open Rate, Click through rate etc for the campaigns, when it comes to AI powered campaigns there is more to track. Obviously having an AI solution to generate marketing campaigns will free a lot of band width, so it's important to track what is made of this extra time. How many new campaign ideas were generated? What new markets were tapped? How many new customers were added? In this case you see the measurement KPIs are actually outside the AI tool itself. Having a great AI powered campaign is just half the story, you need to utilise the tools power to target more customers and diverse demographies which otherwise would not have been possible without the use of AI.

As you can see this is an extensive topic and needs a lot of thought. If you are keen to explore AI solutions for your existing business then you can reach out to me either via the comments or via a DM and I can help draft a plan for your business and industry.

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