The impact of Artificial Intelligence on retail.
Research(R) predicts that artificial intelligence (AI) in retail will grow at a
CAGR of 34.4 percent, reaching $19.9 billion by 2027. For many small to medium
retailers AI can be regarded as unobtainable technology, only accessible to the big
end of town. Many believe it will cost squillions of dollars, require teams –
preferably ex Googlers or Amazonians – and can take years to build.
However, what we have
seen over the past year is definitely an acceleration of SaaS-type platforms that provide
plug-and-play capabilities without the significant investment. Giving small to
medium retailers access to Amazon-like technology will, over time, level the
Put simply, AI may be the
ability for machines to consider huge amounts of data, analyse it, organise it and
then make decisions or solve problems similarly to the human brain.
The opportunity for retailers is to use Artificial Intelligence to improve systems and procedures, reduce costs and increase revenue. Ultimately the biggest return will be where AI can be applied to improve the customer experience.
Here are the areas Artificial Intelligence is impacting, plus some practical use cases.
AI continues to be applied across multiple areas in the payments ecosystem, from fraud to payment processing. Within an environment where COVID-19 has forced us to rethink the implications of queueing and provide customers with a greater degree of speed and convenience, contactless payments will see huge growth. One of the most exciting case studies is Amazon Use the US. Amazon Go is a cashier-less, cashless store. Shoppers sign up to the mobile app, enter the store and once they have chosen their product/s they just leave the store. The payment is then automatically debited from their account upon exit. Known as “just walk out tech”, the technology uses a complex mixture of algorithms. Essentially it detects activity like a person picking up a product from a shelf, it distinguishes between the various products, and then uses image recognition to recognize an individual.
“One of the biggest opportunities for AI in retail is in personalisation and guided selling.”
Some may reason that
this could result in job losses, however there is an opportunity to use this
technology to release instore staff. This would allow them to provide guidance
and support to customers instead of being stuck behind a till. Amazon Go now
offers the ability to leverage this technology in their own stores. It will not
be well before there are a growing number of competitors within this space.
Guided selling and personalisation
One of the biggest
opportunities for AI in retail is within personalisation and guided selling. In
today's environment retailers need to have a deeper understanding of their
people to develop a relationship, increase loyalty and drive repeat
purchases. Machine learning not just has the ability to understand customers'
intent, sentiment and buy behaviour, but it is also able to predict when
and what they will buy. It's no secret that up to 30 per cent of Amazon sales
are generated from personalised recommendations.
Dynamic Yield is
another illustration of mass personalisation and its opportunities. Recently
acquired by McDonald's, it presents your menu based on previous purchase
behaviour, time, weather and local preferences.
Dynamic Yield also
powers the e-commerce solution of Lamoda (Russia's leading online fashion
retailer), which reported a $15 million uplift in gross profit within the first
year of implementation as well as an 8 per cent increase in revenue per session. To
achieve this, the company created over 160 unique visitor segments and
automatically targeted each with personalised offers and messages according to
purchase behaviour and product preferences.
For smaller retailers
there are numerous plug-and-play options that allow you to embark on the
personalisation journey. Shopify has over 150 apps that offer the ability to
give personalised recommendations and upsell or cross-sell. Klaviyo, an
AI-powered email platform, provides the ability to predict purchases, future
spending, customer lifetime value and probability of churn. Flag Nor Fail (an
athleisure wear brand in america) reported a 270 per cent increase in purchases
after implementing Klaviyo.
One of the most
frustrating experiences for customers is the search process. Search for “yoga
pants” on a single of the biggest retailer sites in Australia and you are presented
with jeans. This can lead to a subpar user experience and boosts the chance of
customers leaving your site. Over time our searches have become more specific
and, as technologies improve, we are shifting from one-word searches to typing
the way you think (natural language). For instance, “red party dress” would be
“a red party dress for a 40th birthday party”.
This is where
artificial intelligence makes sense. With potentially thousands of
components of retailers' catalogues, or even 100, it's impossible to manually tag
best of luck with the right information. Manual tagging needs a huge effort
and is extremely inefficient. With visual recognition and tools such as Okkular
AI, which is able to recognise style, patterns, shapes, lengths and apply
automated tags to products, retailers can return better results and
Fulfilment and logistics
COVID-19 has also
driven an enormous shift in consumer behaviour. Based on Australia Post, 5.2
million Australians shopped online in April – a rise of 31 per cent
when compared with 2021. This resulted in a huge Rise in packages being delivered.
to realise that logistics is now a fundamental part of any e-commerce business.
Prior to COVID-19 it was seen as an add-on, but now it is core to the customer
companies using AI for optimal routing will drive costs down and allow
retailers to offer more competitive shipping rates. Shipping cost is the
number one reason for cart abandonment – actually, Dynamic Yield estimates that
$18 billion in revenue is lost per year as a result of customers abandoning
advertising continues to be driven by creative rather than being data driven. When
customers receive personalised ads, product revenue grows by 38 per cent.
complex in Shepherd's Bush, London, has recognised the value of personalised
ads using AI facial recognition. Cameras can define a shopper by age, sex and
even their mood, and then display digital advertisement boards according to them.
When chatbots first
launched they were the most frustrating experience. Asking a few questions
would normally result in a “computer says no” response. The first chatbots
were not artificially intelligent; instead these were a set of rules based on
user input. When the user input was not the same as the preprogrammed text, the
question or enquiry was rejected.
Chatbots have evolved
given that they first came to market around Ten years ago, and with the market set to
grow 30 percent by 2025 it is a clear chance of retailers to connect and
build relationships customers.
AI chatbots learn and
understand things like variations of the same question, context, and emotion.
The strongest opportunity for chatbots is to know when you should hand over to a
real human – to understand when a customer is getting frustrated and/or is
going to drop out of the sales funnel, making a seamless transition to a real
AI should not be seen
as technology that will allow the big end of town to consider over the world
and leave us all jobless. It should be viewed as an enabler. Investment and
implementation should be committed to only if there is a clear additional benefit
towards the customer experience, which in turn saves costs or increases revenue.
This story first appeared in issue 31 of the Inside Small Business