Source-backed interpretation.
Editorial interpretation is derived from the original DMAasia/WION video description and transcript. Statements by speakers remain attributed to the source; no unverified current-role or performance claim has been added.
The durable question is how AI and automation should improve relevance without losing sight of customer experience.
Source signal, edited only for readability.
Three takeaways worth carrying forward.
- 01
Read AI and automation as the primary operating question in this record.
- 02
Test its connection to customer experience and AI against the speaker’s reasoning in the transcript.
- 03
Keep the 2023 source context visible before carrying the lesson into a current decision.
What the transcript puts on the table.
I don't I think it's OK to fail our customers because the kind of sentiment analysis and we I think went wrong because we're trying to mix too many things.
I'm going to start here with, our ends are using natural language to improve accuracy and relevance of interactions and even sentiment analysis to tailor messaging based on emotions.
Rule number two was resolve is that that was the first time right kind of thing we did.
Original context. Current reading.
Recorded in 2023, this captures the language, platforms and constraints of its time. Read it now by separating those period-specific details from the enduring operating question: how should relevance and AI improve the decision?
Durable tests for the work.
- Start from the customer’s situation, not the channel.
- Use AI to improve a decision or outcome—not to decorate the plan.
What would you keep, change or measure differently if this event intelligence were put to work now?
IP09 · READABLE TRANSCRIPT537 words
We failed miserably. We went back to the drawing board. Rule number two was resolve ASAP upwards of 20 to 25 percent. It can be hyper personalized. I'm going to start here with, our ends are using natural language to improve accuracy and relevance of interactions and even sentiment analysis to tailor messaging based on emotions. Now when you have a product portfolio that wide, how do you take that kind of input or even do you process that kind of input to understand what's the sentiment if I want to loan or what's the sentiment if I want to purchase something? How do you process that? Maybe some insights would help. Yeah, so thanks. So early days, I won't say that as a brand manager, our team has done it all.
And before I start, I must say that when we got off this AI piece and it was a little fancy word in the office with the tech guy saying, hey, listen, let's do some AI related marketing interventions. So it was like the yesteryear days when the last thing in the media plan would be to go a little digital. So that's how we started. And frankly speaking, we said, OK, let's do slap NLP with the call centers and we can try and reduce the resolution time, etc. And I must admit here in front of all fellow marketers. And I don't I think it's OK to fail our customers because the kind of sentiment analysis and we I think went wrong because we're trying to mix too many things. And that was probably something which we could have avoided.
We said, OK, how can it be more outside in? I want to converse my point of view, but the guy who's and I'm specifically talking about service or a customer experience for our existing customers. Understand he's taking out so much of time, energy effort to connect with you. And here you come with your very templated like yesterday as we used to do templated response on the social mediums. But this NLP piece was also very similar to that. And we said in this case, we partnered with with some of the agencies and we found that consumers want to hear or want or would be interested to hear something which they are looking at. So that was the rule number one. Rule number two was resolve is that that was the first time right kind of thing we did.
And these are very basic stuff with whatever loads and loads of data we had. I think the entire the entire transcription was upwards of 1000 hours, which the AI tool really went through, understood what really customers want. And we did apply that and finally we were successful in terms of resolving the first time right. And that in terms of percentage, I would say that there are upwards of 20 to 25 percent pre and post is what we could achieve. And that in terms of the same way. And that in terms of the same way, we could achieve that. So we could achieve that. And we could achieve that. that. And we could do this tool. And