It’s been over a decade since Amazon filed a patent for its anticipatory shipping technology (shipping an item before the customer knows they want it). It may sound dystopian, but the ecommerce giant isn’t relying on a crystal ball or guesswork. Instead, it has looked at historical buying patterns, browsing habits, surveys and demographic data to predict what items will be in demand where. The result? Hard-to-beat prices and best-in-class delivery times.
Since then, AI and predictive analytics have taken centre stage with businesses that increasingly use AI to drive decision-making. The PwC 2022 AI Business Survey reports that 96% of business leaders who responded said that they intend to use AI simulations to improve business performance. The ‘AI leaders’ among them use AI to drive decision-making on technology (74%), operations and maintenance (62%) and customer experience (61%).
This is just the starting point of harnessing the power of AI to drive revenue. A proliferation of generative-AI tools already process customer feedback in real time and adjust their messaging to elicit the best response. As the technology develops, how can businesses make the best use of AI without inadvertently hurting their business or their customers?
Going beyond revenue growth
Sulabh Soral is chief AI officer at Deloitte Consulting and leads the Deloitte AI Institute in the UK. He makes a clear distinction between AI and what most corporates are using today: “The broadest definition of AI is anything that can create rules that mimic things that we would identify as cognitive capabilities. But, if you look strictly at the question of behavioural analytics, a lot of companies still use classical machine learning.”
This may sound like a distinction without a difference, but it’s key in understanding the limitations of the behavioural-analytics models in use today. While deep learning and generative-AI models could generate insights from unstructured data like language and photographs, machine-learning models rely purely on structured data to make predictions. This potentially leaves a treasure trove of data unexplored.
People are happy when they get what they want, but they’re happier when it’s a human that gave them the news
This explains why most businesses today focus on short-term goals like revenue growth and cost reduction. But Soral believes that, as these technologies evolve, business leaders need to move away from this way of thinking and focus on long-term, customer-centric objectives instead.
“Most behavioural analytics today is based on how a response should be elicited and not whether the customer requires the product. But how would you train your AI model if your goal were customer happiness or the amount of savings they could make in a decade?”
Generating new revenue isn’t the only way in which behavioural analytics could contribute to business growth. Ellen Loeshelle, director of product management at Qualtrics, an experience management platform, highlights that behavioural analysis can be just as valuable to protect existing revenue as it is to generate new revenue.
“[Our product] takes behavioural data from the past and projects it onto things in the future. If you can tell us that this cohort of customers filled out a survey beforehand and then they quit, we can then use that as training data to project for future surveys that come in.”
These insights can then help businesses take preventative measures and ensure customer concerns are addressed in time.
Zooming in on consumer welfare
A new tool seems to emerge every day with the enticing promise that it can help you work better, faster and smarter. But Stefano Puntoni, a professor of marketing at The Wharton School at University of Pennsylvania, warns that automation isn’t always the right answer to drive business performance: “It’s important to understand the role that technology plays in people’s lives to make predictions about how they’re going to react to it.”
For example, when the items we buy are closely linked to who we are as a person, automation can backfire. Think of a cooking aficionado who loves spending time in the kitchen. If you suddenly automate part of the cooking process, you’ve then created a problem, not a solution.
Another scenario where automation is less effective than human input, Puntoni says, is in interactions where customers are “assessed” by your business (for example, loan or credit card applications). Here, companies usually worry about how automation will handle negative outcomes for the customer, but Puntoni emphasises that it’s the positive outcomes which require human interaction.
“People are happy when they get what they want, but they’re happier when it’s a human that gave them the news and not an algorithm,” he says. “When it comes to rejections, however, we don’t observe that being rejected by an algorithm or a human makes a difference.”
AI governance
As AI takes over more of our business interactions and decision-making, it’s inevitable to think about the ethics around its use. In the PwC survey, 98% of respondents said they plan to make their AI responsible but fewer than half have planned to take specific actions.
David Wright is a partner in Deloitte’s intelligent automation team and works with clients across the private sector. He believes that the rule of governance will become key as more businesses rely on data to anticipate consumer behaviour. He points out that the “rule of governance is incredibly important, particularly if you start to automate off the back of AI predictions or recommendations”.
While internal watchdogs are already present in highly regulated industries like banking, insurance and healthcare, they’re less so in hospitality and retail. Loeshelle, however, thinks this is about to change.
“We’re seeing it right now primarily in the States but it’s starting to bubble up in the UK – [companies] insist on putting our technology through model risk management reviews,” she explains. “This is an internal AI governance board that is responsible for evaluating the integrity and the purpose of each AI-related tech that the company is purchasing or building.”
There’s no doubt that AI will play a key role in how businesses and consumers interact in the future. But if companies are attempting to predict consumer behaviour with AI, it’s important to understand the implications for consumer experience and the safeguards that must be in place from the outset.
It’s been over a decade since Amazon filed a patent for its anticipatory shipping technology (shipping an item before the customer knows they want it). It may sound dystopian, but the ecommerce giant isn’t relying on a crystal ball or guesswork. Instead, it has looked at historical buying patterns, browsing habits, surveys and demographic data to predict what items will be in demand where. The result? Hard-to-beat prices and best-in-class delivery times.
Since then, AI and predictive analytics have taken centre stage with businesses that increasingly use AI to drive decision-making. The PwC 2022 AI Business Survey reports that 96% of business leaders who responded said that they intend to use AI simulations to improve business performance. The ‘AI leaders’ among them use AI to drive decision-making on technology (74%), operations and maintenance (62%) and customer experience (61%).
This is just the starting point of harnessing the power of AI to drive revenue. A proliferation of generative-AI tools already process customer feedback in real time and adjust their messaging to elicit the best response. As the technology develops, how can businesses make the best use of AI without inadvertently hurting their business or their customers?
