Artificial Intelligence (AI) is already beginning to transform several
industries, and it continues to divide opinions on whether it will
transform our lives for good, for bad, or exaggerate existing divides by
benefitting some and neglecting others. One of the areas where AI has
been applied with extremely effective results is in retail, both online
and off, and is a fine example of why AI can be simultaneously good and
evil, highlighting the need for regulation.
Shopping is already an activity that can be easily abused i.e. with
“retail therapy” whereby buyers purchase items as a distraction to boost
their mood in the short term, but due to short-lasting effects, this can
become addictive. Another serious issue associated with shopping is
which can have many different causes. But the majority of us go through
the ritual of buying things we don't need, often in relation to that
hit of serotonin. So how do AI and shopping come together to create a
runaway experience? It has partly to do with why AI has been so
effective in this industry.
The current surge in AI and accompanied hype can be equated largely to
two things; a huge increase in computing power that is required to run
and train machine learning algorithms, and an exponential increase in
rich, real-time data to feed said algorithms. Such data is particularly
rich in the world of retail, where AI systems are able to analyse the
data, recognise patterns and make predictions far more effectively than
a human ever could.
But an additional factor that makes retail such a thriving environment
for AI, is the margin for error. A retail algorithm can be deployed and
tested in action long before it is has been perfected, as an error would
not be detrimental. It can learn on the job, so to speak, recognising
what has worked, and constantly improve itself. Someone not clicking on
an ad is a small lesson, not a costly mistake, whereas other AI systems
may have far more at stake over the outcome of just one decision (take
self-driving cars as an example), making progress in other domains seem
Several sections of retail lend themselves to AI, from handling customer
interactions and brand reputation to creating an engaging user
experience, perfecting marketing, and more.
Retail companies have been able to jump on general AI trends like
chatbots to improve the customer experience and build trust by offering
24-hour support in addition to cutting costs, as well as using social
listening and social media scraping to monitor how customers are feeling
and resolve any issues that they are expressing to protect their brands
reputation. In fact, most companies can make use of these
tools to strengthen
their brand and protect its reputation, but the retails industry has a
few more tricks up its sleeve that we’ll look into now.
Keeping your users happy has always been key to improving that customer
lifetime value, and e-commerce brands are approaching this by creating a
more engaging experience in a number of ways. AI can be applied to
create more intuitive search functions that bring up more relevant
results through the application of natural language processing, or even
employing visual search, such as Pinterest’s Lens
or ASOS’s style
that can recognise items in photos and suggest similar products.
Being able to search and actually find what you want is great, but
accurate recommendation systems could be even better. You don’t even
have to search to be shown things that you ultimately want. Many types
of companies use recommendation systems, from retailers through to
media. Machine learning algorithms feed off rich data that reveal
patterns such as what actions a person is likely to take based on the
actions that other users with similar data took. As the algorithm
collects more data and adjusts, accuracy of personalisation, and hence
The user experience can be optimised in other ways, too. For example,
online clothes brand
responded to their customer data and after noticing that most of their
users shopped via mobile, specifically using an iPhone, they decided to
improve their mobile app and added unique and relevant features like
Apple Pay as a function. Artificial Intelligence can play an important
role in optimising the user experience assisting UX designers and
Users feel closer to a brand when optimisation and personalisation are
so fine-tuned, as if the brand really knows and understands them. This
brings us on to our next topic.
Marketers are well aware of how personalisation increases engagement,
with 72% of consumers now only
responding to personalised adverts. So just as with optimised user
interfaces, marketing thrives on plentiful data to create
hyper-personalised deals. Everywhere we shop, whether its online fashion
websites or grocery stores with loyalty cards, or through location
tracking on our phones, our behaviours are logged and turned into data
that can be used to make powerful predictions and personalise marketing
For example, your grocery loyalty card can pick up patterns in your
buying behaviour, like how often you stock up on your cupboard staples,
and present you with personalised offers, perhaps when it calculates you
are due to run out, and personalised rewards that keep you buying with
the same company. It might also combine similar customer’s data and
suggest new products that other customers go for when purchasing some of
your regular items. Store data can also be used at scale to streamline
the inventory processes by picking up subtle locational patterns or up
and coming trends and stocking for them.
This seems pretty useful when it comes to groceries, but these methods
are reflected throughout the rest of the retail world, where purchases
are generally far less vital. We’ve all conduct a Google search to find
that suddenly all our online adverts are trying to sell us that specific
thing. Since many websites and apps now share data, your online
activities can be traced and manifest themselves in highly targeted
offers that are difficult to resist.
There is some
about whether or not Facebook listens to your conversations, applying
Natural Language Processing (NLP) to process the topics you discussed
and generate explicitly targeted adverts. It can seem questionable that
something so specific could come up by chance, and many experiments have
been done to support this theory, however Facebook has repeatedly denied
this accusation. They claim that their predictive models are just highly
accurate, which is also plausible.
Another influential factor in someone’s buying decision is the price.
Predictive pricing can help marketers push the right promotions to the
right people at the right time in order to yield the biggest benefits.
Studies show that more half of mass promotions do not break even.
Predictive pricing ends that, ensuring that offers are temping enough to
certain people, without giving away more than necessary.
Analysts must take into account multiple factors at once to get pricing
dynamics right, which is why a good machine learning algorithm can do it
so much better than a human. An algorithm’s ability to analyse multiple
factors at once, such as seasonal trends, competitor’s prices, and
individual buying habits of a customer, means it consistently gets it
right, or at learns and improves from the cases where it got it wrong.
We’ve seen the many different ways that retailers are capitalising on
artificial intelligence and the amazing ways that it can boost their
revenue, but what about how it affects the population of consumers? Data
has become a powerful weapon, collected from the masses and now used
to influence their own decisions.
With the abundance of data now available around our shopping habits and
preferences, marketing campaigns become painfully relevant and hard to
resist, resulting in huge wish lists, or in some cases an accumulation
of repeat buying “offences”. Consumers get tricked into buying more
things that they can’t afford and don’t really need, getting carried
away and not realising the overall impact when spending is split across
multiple smaller purchases.
In China, shopping addiction is already a
and Gartner predicts it to become so much of an issue in western
civilisation that retailers will soon be forced to take more
responsibility around these exploitative practices, just as casinos and
gambling sites must issue warnings around playing responsibly.
What is your take on this? Is AI in retail working wonders in connecting
brands to their target customers and helping consumers easily find the
things that they want, or is it adding to the problems of society by
serving up easy fixes to unsuspecting consumers? What kind of
regulations do you think should evolve as AI and retail integrate to
create such a personalised and addictive experience?
Don’t forget that Nightingale HQ can
help you find data and AI consultants to work with to help improve your
business, guide you through processes and help you comply with the laws
that are slowly adapting around data.