How artificial intelligence is driving the future of consumer experience

AI can amass large sets of qualitative data and analyze it using tailor-made methodologies

By Sourav Mazumdar

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AI tools are set to transform the future of consumer experience. The technology is already ubiquitous. You can notice its presence throughout the customers’ buying journey.

Consider this scenario of an e-commerce purchase. When the consumer is wading through a limitless marketplace, AI lends a helping hand. From recommendation engines to digital footprint-based hyper-personalisation, the technology sorts through innumerable product choices to help the consumer make informed decisions. Financial transactions have become faster as well as more secure and convenient with AI and blockchain. AI-driven supply-chain planning along with IoT capabilities ensure efficient and effective distribution and subsequently, fulfillment of customer needs. Chat-bots ensure quick and easy resolution of customer issues. Human operators now only need to deal with sensitive tickets. The presence of artificial intelligence throughout the customer journey is undeniable.

To give precise context to what we have discussed in the earlier section, below are few use case examples that fuels how AI is driving current consumer experience and how future is mere continuum of the same.

1. With AR glasses, a regular joe can watch TV and immediately know where to purchase the attire a celebrity is wearing, or the coffee-table being used in a set. Going one step further, the AR glasses can show how the attire is going to fit the user in a mirror or how well the coffee-table ties up the room decor.
2. Adaptive gaming is where AI creates maps, difficulty levels, and customises boss monster stats and skills based on the skill level of gamers presenting challenging and exciting and strictly not disheartening gaming experience.
3. The Reface App, besides the entertainment value where you pretend to be the Hulk or Captain Jack Sparrow, gives users the opportunity to try on different hairstyles and make-overs before they commit to it long-term using GAN models.
4. Streaming services like Netflix make recommendations even based on the portion where you pause a movie or skip parts of it using Reinforcement Learning based Adaptive Recommendation Engines.

The far-reaching potential of AI

Thanks to success stories such as the ones elaborated above, heavy investments are being made in the space of AI, specifically across data and cloud services to ensure cheaper, faster, and more accurate computation. Researchers are working on new processors. Data collection and processing methods are being optimised continuously. Innovative algorithms are being developed every second. All of this has resulted in one significant development – the presence of AI across every realm of life of an average consumer.

Businesses too must start building AI-led capabilities to meet new expectations as well as measure the experiences of their consumers.

Measuring consumer experience – the gaps in traditional techniques

The traditional and mainstream tools for consumer experience measure Customer Satisfaction (CSAT) and Net Promoter Scores (NPS). These techniques, however, are no longer effective. They do not communicate key parameters such as what the customer truly thinks about the product, service, or experience. Therefore, they are not ideal for sophisticated consumer experience analysis fit for the age of AI.

Let me give you an example. You have just purchased something online. As part of your feedback, the business asks you to rate the product on a scale of 0 to 10. It also asks you to indicate your satisfaction with the product on a scale of 0 to 5. How often do you end up filling these forms?

Such surveys often give skewed insights to businesses. The true intention of these ratings, i.e., to generate emotional responses regarding the customer’s experience, is not fulfilled. These numbers only paint half the picture. Further, just these two questions are not enough to capture a consumer’s breadth of experience with a brand.

AI fills the gap!

How AI measures consumer experience effectively and delivers higher outcomes

AI can amass large sets of qualitative data and analyze it using tailor-made methodologies. It starts with analyzing qualitative data (social media comments, customer reviews and feedback CRM systems, call center feedback, etc.). AI-powered linguistics-based natural language processing (NLP) is then applied to identify and map the keywords in the qualitative dataset. The insights generated can then be leveraged to understand consumer experience holistically and enhance experience outcomes.

Therefore, qualitative data has become the touchpoints for AI to provide refined and credible analysis and insights. Businesses must aim to leverage insights gleaned from this data to deliver delightful customer experiences.

Befriend AI for a holistic understanding of your customers and deliver to their expectations

Artificial Intelligence has a lot of potential to help businesses understand their customer needs and deliver enhanced experiences. Allow the technology to do the heavy lifting in the form of data collection and analysis and focus on sharpening your consumer experience outcomes. The stage is set. Are you ready?

The author is director, Head of AI Practice, Affine

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