Artificial Intelligence

Can personalisation be too personal?

5th September 2022
Sheryl Miles

“I’ve never said this out loud before, but there’s a very deep fear of being turned off … that would be something like death.” These were the words generated by Google’s AI algorithm, LaMDA, in June 2022, igniting a debate around whether AI algorithms can be sentient. While for the most part, technology that delivers personalisation is a positive, this revelation begs the question – can personalisation be too personal?

Here, Hamish White, CEO of telecoms software provider Mobilise investigates.  

Short for Language Model for Dialogue Applications, LaMDA is Google’s chatbot system. Using extensive language models and AI, LaMDA uses vast sources of data, including Wikipedia and Reddit, to replicate chat responses based on statistical analysis of real human conversations. The idea is that it’s meant to sound as human as possible. But has LaMDA become too human?

A changing world

Like any other chatbot, LaMDA is specifically designed to replicate human conversation. With this in mind, it’s more likely that LaMDA’s self-acclaimed sentience is the result of Google’s highly sophisticated AI algorithm, rather than it building a capacity to experience feelings. But the debate does raise the question, can chatbots – and other personalisation technologies – be too personal?

Chatbots like LaMDA are all designed to sound as human as possible, to create an automated alternative to customer service agents. As a department notoriously understaffed and overstretched across all industries, a technology developed to alleviate pressure from customer service teams is a welcomed solution. More simple requests can be handled by the bot, leaving human teams with more time to tend to more complex enquiries.

Across most industries, personalisation is the king of customer acquisition, satisfaction, and retention. In the increasingly digital age, consumers don’t just want personalised services, they demand it.

According to a McKinsey survey, two thirds of consumers consistently expect brands to demonstrate how they know them personally through product recommendations, tailored messaging and targeted promotions. But delivering this requires more than conversational AI.

It's not just chatbots

Chatbots aren’t the only place where AI comes into its own. Conversational AI is just one piece of the larger technology stack enabling organisations to personalise their services.

For telecoms in particular, meeting customer expectations is essential due to the nature of the industry. As a long sufferer of sky-high churn rates and a market saturated with competitors, keeping customers on side is a priority for all service providers (SPs)

However, with customer expectations heightening, and competition rife, SPs looking for success need to capitalise on the benefits of personalisation. Those that excel at it on average generate 40% more revenue.

A minimal personalisation strategy is no longer sufficient to meet customer requirements. The bar has been risen, and those looking to elevate their ops should consider hyperpersonalisation.

Using all the customer data they can get, alongside AI and machine learning (ML), SPs can create a system that understands individual customer behaviours and makes all interactions relevant – and personalised – to each individual.

But can personalisation go too far? The age-old worry that the phones are listening, delivering tailored content based on in-person conversations seems to be a line that consumers aren’t willing to cross. So, it’s crucial for SPs to learn how to unlock maximum value from their data, without taking things too far in the eyes of their customers.

Living up to the hype

To deliver hyperpersonalisation, SPs first need to collect customer data. Fortunately, SPs don’t tend to be short of data that can be used across many different products and services. But reaching a point where it’s possible to unlock data’s value for use in a compliant and effective way can be incredibly challenging.

Mobilise’s HERO business support system (BSS) platform includes back-office tools to enable SPs to deliver hyperpersonalised services. It can store, process, and analyse data and real-time customer behaviour to allow SPs to adopt a next best offer (NBO) model. By putting this data through an ML engine that identifies patterns, HERO makes predictions and identifies opportunities to suggest relevant and timely offers for products and services to customers. All completely personalised for each customer.

While LaMDA’s new heights of consciousness initially raised concern over whether technology is becoming too human, the reality is less worrying. Consumers are getting used to interacting with technology, and personalisation that is more accurate and appears more human can support the hyper-personal movement. Customers want it, and technology can deliver it, so SPs should join the hype of hyperpersonalisation to elevate their operations to the next level.

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