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Retail & Supply Chain

Personalising Shopping Experiences With AI

by Ciarán Daly
Article Image

by Jason Smith & Tjeerd Brenninkmeijer


LONDON - Artificial intelligence (AI) will be used by at least 60% of digital commerce organisations by 2020, according to Gartner. The main benefits of which are a large amount of data available to retailers that can help to influence customer satisfaction and revenues.

To stay competitive, you need to know your customer better than anyone else and most importantly find out what is most valuable to them; data is your avenue into this.

The importance of data for personalisation cannot
be underestimated; it drives deeper shopper engagement and stronger conversion
rates. Moreover, with the cloud and AI, organisations can re-imagine the
customer experience across every touchpoint.

Monolithic commerce platforms' days are numbered.
Businesses need more flexible architectures that can leverage tools and
technologies that truly support their business. A single headless platform that
can extend commerce to any conceivable customer touchpoint makes organisations
future-proof and can integrate with multiple solutions. One such tool is
advanced search algorithms and merchandising tools powered by AI.

AI-powered search and merchandising capabilities
bring additional optimisation to every shopper’s search, browse and landing
page experience from any channel, by learning and adapting to customer
behaviour to deliver a unified, highly relevant and personalised experience. As
a result, you can differentiate your brand with relevant, engaging and customised
experiences across all customer touchpoints. How?

Don’t lose customers at the search bar

Intelligent
search is a vital part of a robust commerce strategy. The majority of search
systems today use algorithms with AI to suggest the best match based only on
text or best-selling product rankings. Whereas, next-generation systems apply cloud
computing, machine learning and AI that not only process data at faster speeds
but garner better results, because they optimise information retrieval and ranking,
and drive more revenue.

This intelligence is made possible by utilising both the merchant’s content on one side and the end user’s query intent on the other to get the most relevant searches. Search powered by advanced AI will take buyer behaviour context into account, e.g. a Nike shoe might be a top seller for elite runners but not casual joggers. Additionally, it will learn from users over time. It will see what searches people make and when they don’t convert, assess if it is because not many searches appear, or if the merchant doesn't tag products in the right way, e.g. shoes versus sneakers.


Related: Introducing AI to a 150-year-old grocery store


Step-up on personalisation

Taking personalisation one step further the latest best-of-breed solutions can also understand individual user preferences to rank products that a customer is more likely to engage with in real-time.

They can even react to specific business objectives, e.g. ensuring low stock items are ranked as highly as bulk items, or promoting white-label products which make a retailer its most substantial profit margins in high performing searches.

This flexibility is also useful for the likes of seasonal marketing campaigns, for example, if it is cold then the filter for colour should be less of a focus than material, as people are more likely to be looking for the warmth of fleece or wool than if a coat is blue.

Targeting recurring visitors with a
different webpage view can also be useful. By adding a rule for a search term
or geographic location you can affect your site’s visitor experience for some
users. This experience might be boosting new products to returning customers
because they value the next release for a brand, or products might only be
suitable for visitors with specific characteristics.

For large retailers, personalisation is also possible across multiple sub-brand sites with the possibility to personalise the experience for each, as well as learn user behaviours across them.


Related: AI affects dynamic retail pricing - here's why


Explore dynamic pricing

Another benefit could be applying AI to pricing structure. For example, a dynamic pricing engine that uses machine learning algorithms could examine customer behaviour, product comparisons and purchase history to offer differentiated or competitive pricing based on an individual's response.

This kind of personalisation can have real benefits to a company's bottom line thanks to the relationship and value building it creates. Gartner predicts that by 2020, smart personalisation engines used to recognise customer intent will enable digital businesses to increase profits by up to 15%.

Experience is everything

The ever-growing mountains of customer data facing businesses make it harder for brands to stay on top of customer preferences and ultimately loyalty. It is, therefore, time to invest in a flexible commerce platform that can integrate with intelligent tools, including scalable, machine learning and AI technology that eliminates manual processes and makes sure customers don’t have lousy experiences.

Are you leaving money at the table because customers are exiting your store or digital channels because you’ve left their experience to chance? It’s time to increase customer loyalty and revenues by offering one-to-one experiences for every customer, at every interaction.


Jason Smith is RVP Sales EMEA & APAC at Elastic Path. Tjeerd Brenninkmeijer is EVP of EMEA at Bloomreach.

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