Daily Newsletter

09 April 2024

Daily Newsletter

09 April 2024

Interview: Algolia’s Reshma Iyer on how AI is set to impact retail

Reshma Iyer, head of product marketing at software company Algolia, discusses the opportunities of AI-powered shopping assistants.

Claire Jenns April 09 2024

AI shopping assistants are rapidly becoming a key tool for online merchandisers such as Amazon to improve the way consumers shop online, making their buying journey tailored and personalised through support in product selection.

Consumer expectations regarding personalisation have changed in the decade since 2014. More often shoppers are looking to retailers to provide not only a personalised service but also a tailor-made product to best appeal to them.

This shift in attitude represents an opportunity for retailers to attain valuable consumer data and customer loyalty - and AI technology is at the forefront.

Reshma Iyer, head of product marketing at hosted search engine software provider Algolia, believes that AI can democratise personalisation in retail for consumers of all kinds.

Speaking to Retail Insight Network, she asserts that "AI will continue to improve the retail industry."

The features of an effective AI-powered shopping assistant

According to Iyer, the goal of an AI-powered shopping assistant is to bring the consumer closer to the product.

"Research from Algolia finds that 70% of B2C [business to consumer] retailers consider personalisation to be an integral part of the business over the next year."

AI-powered systems, which are typically aided by machine learning technology, allow retailers to meet this personalisation goal by enhancing recommendations and improving the quality of the shopping experience.

Iyer explains: "Leveraging large amounts of data, like purchase history and demographics, helps to suggest content that resonates with what the consumer wants to see. To be fully effective, the technology used should be able to analyse user data, including the more complex sets, and track any patterns."

The data can then be used to provide insights around how people and products are related, make predictions based on habits and preferences, and expand options for consumers through personalised recommendations.

Crucially, an effective AI model must also remember past interactions. "This will help to drive conversations by suggesting products that align with search history and items that other shoppers have bought along with the item the user is considering," says Iyer.

For retailers, AI technology can also be integrated with inventory ordering and consumption, warehouse capacities and logistics efficiencies.

Iyer points out that in grocery retail, "...we're witnessing innovations like Amazon's 'cashierless stores' and other retailers with AI-powered 'smart carts' that allow consumers to bypass a traditional checkout system."

The challenges behind AI-powered shopping assistants

Iyer notes that alongside the obvious challenges of integrating AI into retail practices such as privacy, retailers will also face backend hurdles.

"For example, understanding where and to what extent an organisation should introduce AI to the consumer is challenging. This means allowing customers to understand when AI is at work."

As AI is rapidly evolving and becoming smarter and faster, Iyer asserts that there's a crucial need to determine how consumers will respond to AI being a large part of their shopping journey.

"This can be addressed by creating roles within a company that strictly focus on AI and consumer behaviour, helping retailers understand how their shoppers feel about AI and where they should and shouldn't implement the technology."

Finally, Iyer believes that the only thing worse than a retailer having no personalisation is having ineffective personalisation, which "is often observed with new visitors on a site or app."

In other words, retailers should invest enough time and finances into getting their personalisation offerings right, rather than rushing the process to no end.

At the heart of an AI shopping assistant's performance lies data.

Iyer confirms that "..as more data becomes available, AI systems learn behaviour, pinpoint patterns and adapt to ever-changing consumer preferences. Continuous learning approaches are key when thinking through how to keep up with consumer preferences and market trends, which are constantly evolving."

The data cannot lie dormant, as ongoing optimisation is a necessity and needs to be applied thoughtfully. There's a need to continually analyse all the new data, identify emerging patterns, and adjust operations when it's rational, says Iyer.

"For example, AI systems will be able to identify spikes in searches, like for eco-friendly products, and acknowledge that there may be a shift in their shoppers' mindset where they're starting to value sustainability more than before."

The relationship between humans and AI is a controversial topic, but Iyer states that "... human effort is needed to ensure the use of highly representative and diverse data, continuous monitoring and as-needed adjustments to ensure the system is acting equitable and fairly."

When this balance is achieved, the information from AI systems can then inform retailers' marketing strategies or inventory management to maximise the potential for success.

The future of AI in retail

Iyer maintains that personalisation is only one of the benefits that AI can bring to retail.

"For example, with marketing retail products to customers, AI can act as an ideation partner. We can lean on AI to help ideate new, creative ways to market trends to the ideal audience."

As for an area that remains untapped, Iyer highlights AI's role in merchandising. "But retailers are catching on quickly, as our report shows that over half (52%) of B2C [business to consumer] retailers plan to implement AI tools to aid their merchandising teams in the next 12 months."

When combined, the use cases for AI-powered shopping assistant technology can bring businesses, retail staff, and consumers into the future awaiting the industry.

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