How AI Is Transforming Analytics And Improving The Customer Experience
Data is at the core of advanced customer experiences, but many brands struggle to process the data they have meaningfully.
Organizations worldwide are embracing technologies that can revolutionise the customer experience.
Nowhere is this more true than in the Asia Pacific region, which leads EMEA and North America on the adoption of artificial intelligence (AI) and machine learning (ML), according to the “2020 Digital Trends in Asia Pacific” report by Adobe and eConsultancy, which found that China and India are leading the pack.
AI and ML have the irrefutable ability to transform the massive amounts of data being collected from an increasingly connected world into effective insights. Data, according to global consulting firm EY, is a game-changer for creating fast, accessible, and personalised customer experiences and campaigns.
“More and more we’re seeing that the best customer experiences are powered by sophisticated AI and data science models,” said Pramod Sudhindra, partner in advisory services at EY India.
In this exclusive interview, Sudhindra discusses how organisations are harnessing the full potential of AI and predictive analytics to drive customer engagement and conversion. He also shares advice for digital leaders who may be re-evaluating their data strategy.
Thanks for joining us, Pramod. I might start by asking: What do you see as the relationship between AI-powered solutions and the customer experience?
It has become clear that they’re intrinsic to one another. In the past few years, AI has changed the way we create personalised experiences and foster long-term customer relationships. Thanks to AI, marketers have the opportunity to shrink customer segments, identify individual customers, and create a custom offer that services the customer at the right time.
However, the challenge for marketers is understanding how to bridge the gap between the pace at which data is generated and engaging customers in real time.
Then what happens?
Once this is understood, we can start to use AI in deeper, more sophisticated ways—things like demand forecasting, lowering churn rates, and hyper-personalisation models. Many brands don’t yet realise these capabilities are becoming more readily available at a faster pace, certainly more than they were two years ago.
These are exciting areas that have predictive analytics at their core and allow companies to allocate their resources more intelligently and productively.
What are some of the ways you’re seeing it make a difference?
If we take COVID-19 as an example, AI is playing a part in each stage of the global pandemic by processing large amounts of data to find treatments, reduce the spread of the virus, and treat ill patients.
At EY, we’re working with local and state governments in the fight against COVID-19. We’re using AI to more accurately predict the risk of spread so the government can manage the situation in real time.
What else excites you about AI and its ability to enhance customer experiences?
The scale of its potential. Customer experience—that is to say, the ways customers engage with a brand—is the responsibility of more than just the marketing department. AI offers up multiple components and avenues for marketers, CIOs, and data scientists to understand, explore, and work through together to enrich the customer experience.
In an environment where brands are always looking for the best way to optimise the customer journey through design, it is crucial to look at how AI can be orchestrated across customer touch points. The insights from data are more mature and data models far more accurate. This is allowing brands to allocate their resources more intelligently and productively.
Can you give an example of how the team at EY is working with clients to achieve this?
We have found that AI is a common tool used by businesses in the digital TV or over-the-top streaming media space. With high acquisition costs and escalating content costs, operators are looking for a rich content platform that drives user engagement and value with data. By providing richer, data-backed insights into audience preferences and behaviours, AI has provided the opportunity for marketers in this space to cut through the noise of local regional content and create customised offerings for different customer segments through storytelling.
We have been able to apply these learnings to other clients, who are now using large-scale AI and data analytics to predict and understand customer behaviour and create more meaningful engagement along their customer journey.
For example, a client of ours in the quick service restaurant (QSR) segment was seeking a solution to trigger repeat customer orders via their app. The challenge was not to understand the purchasing behaviour of their loyal customers but to identify the trigger for their infrequent, dormant buyers.
We assisted the client by creating a specific micro-targeting model that translated data into more granular insights that informed targeted customer messaging. The model matched the behaviour and needs of customers at the right time and on the right channel.
In situations like these, we are using AI to process data from corporate levels at speed to use it for micro-finance at retail stores. The data is coming from different parts of the business, and the insights are executed at the customer touch point.
What strategic advice would you give to APAC marketers struggling to make sense of their data?
Don’t lose sight of the things that inform your strategy—business objectives that impact the brand and the experience of the product or service. By using technology to trace the end-to-end customer experience, you are able to work with your marketing team to analyse the customer data touch points and create a personalised algorithm that services the needs of your customers.
It’s important to remain agile, open-minded, and understand how marketers, tech, and data scientists each bring a role to the table when working towards the end objective of enhancing customer engagement and experience.
Finally, it is important to break the silos, create a culture of “test and learn,” and find the right people for each role.