Whether you do your shopping online or in store, your retail experience is the newest battleground for the synthetic intelligence (AI) and machine learning revolution.

Major Australian retailers have begun to understand that they’ve rather a lot to achieve from getting their AI strategy right, with one currently recruiting for a Head of AI and Machine Learning supported by a team of knowledge scientists.

The newly developed Woolworths division WooliesX goals to bring together a various group of teams, including technology, customer digital experience, e-commerce, financial services and digital customer experience.

All about crunching the information

To understand the opportunities and threats for all major retailers, it’s useful to know why artificial intelligence is back on the agenda. Two crucial things have modified for the reason that initial forays into AI a long time ago: data and computing power.

Computing power is straightforward to see. The smartphone in your hand has tens of millions of times more computational power than the bulky computers of a long time ago. Companies have access to almost unlimited computing power with which to coach their AI algorithms.

The other critical ingredient is the size and richness of knowledge available, especially in retail.

Artificial intelligence systems – especially learning techniques reminiscent of machine learning – thrive on large, wealthy data sets. When fed appropriately with this data, these systems discover trends, patterns, and correlations that no human analyst could ever hope to find manually.

These machine learning approaches automate data evaluation, enabling users to create a model that may then make useful predictions about other similar data.

Why retail is suited to AI

The rapidity of AI deployment in several fields is determined by a couple of critical aspects: retail is especially suitable for a couple of reasons.

The first is the flexibility to check and measure. With appropriate safeguards, retail giants can deploy AI and test and measure consumer response. They may also directly measure the effect on their bottom line fairly quickly.

The second is the relatively small consequences of a mistake. An AI agent landing a passenger aircraft cannot afford to make a mistake because it would kill people. An AI agent deployed in retail that makes tens of millions of selections on daily basis can afford to make mistakes, so long as the general effect is positive.

Some smart robot technology is already happening in retail with Nuro.AI partnering with grocery behemoth Kroger to deliver groceries to customers’ doorsteps within the United States.

But lots of essentially the most significant changes will come from deployment of AI slightly than physical robots or autonomous vehicles. Let’s undergo a couple of AI-based scenarios that may transform your retail experience.

Your shopping habits

AI can detect underlying patterns in your shopping behaviour from the products you purchase and the way in which during which you purchase them.

This may very well be your regular purchases of rice from the supermarket, sporadic purchases of wine from the liquor store, and Friday night binges on ice cream on the local convenience store.

Whereas inventory and sales database systems simply track purchases of individual products, with sufficient data, machine learning systems can predict your regular habits. It knows you want cooking risotto every Monday night, but in addition your more complex behaviour just like the occasional ice cream binge.

At a bigger scale, evaluation of the behaviour of tens of millions of consumers would enable supermarkets to predict what number of Australian families cook risotto every week. This would inform inventory management systems, routinely optimising stocks of Arborio rice, for instance, for stores with a lot of risotto consumers.

This information would then be shared with friendly suppliers, enabling more efficient inventory management and lean logistics.

Efficient marketing

Traditional loyalty scheme databases like FlyBuys enabled supermarkets to discover your frequency of purchase of a selected product – reminiscent of you purchasing Arborio rice once every week – after which send a proposal to a gaggle of consumers who were identified as “about to purchase Arborio rice”.

New marketing techniques will move beyond promoting sales to customers who’re already prone to buy that product anyway. Instead, machine learning recommenders will promote garlic bread, tiramisu or other personalised product recommendations that data from hundreds of other consumers has suggested often go together.

Efficient marketing means less discounting, and more profit.

Pricing dynamics

The pricing challenge for supermarkets involves applying the precise price and the precise promotion to the precise product.

Retail pricing optimisation is a posh undertaking, requiring data evaluation at a granular level for every customer, product and transaction.

To be effective, infinite aspects have to be examined, like how sales are impacted by changing price points over time, seasonality, weather and competitors’ promotions.

A well-crafted machine learning program can consider all of those variations, combining them with additional details reminiscent of purchase histories, product preferences and more to develop deep insights and pricing tailored to maximise revenue and profit.

Customer feedback

Historically, customer feedback was attained via feedback cards, filled out and placed in a suggestions box. This feedback needed to be read and acted upon.

As social media increased, it became a platform to precise feedback publicly. Accordingly, retailers turned to social media scraping software so as to respond, resolve and interact customers in conversation.

Moving forward, machine learning will play a job on this context. Machine learning and AI systems will enable for the primary time bulk evaluation of multiple sources of messy, unstructured data, reminiscent of customer recorded verbal comments or video data.

Reduction in theft

Australian retailers lose an estimated A$4.5 billion annually in stock losses. The growth in self-service registers is contributing to those losses.

Machine learning systems have the flexibility to effortlessly scan tens of millions of images, enabling smart, camera-equipped point of sale (POS) systems to detect the various varieties of fruit and veggies shoppers place on register scales.

Over time, systems may also recover at detecting all of the products sold at a store, including a task called fine-grained classification, enabling it to inform the difference between a Valencia and Navel orange. Hence there can be no more “mistakes” in entering potatoes when you’re actually buying peaches.

In the long run, POS systems may disappear completely, as within the case of the Amazon Go store.

Computers that order for you

Machine learning systems are rapidly improving at translating your natural voice into grocery lists.

Digital assistants reminiscent of Google Duplex may soon create shopping lists and place orders for you, with French retailer Carrefour and US giant Walmart already partnering with Google.

An evolving AI retail experience

As you progress through life stages you become older, occasionally get unwell, it’s possible you’ll get married, perhaps have kids, or change careers. As life circumstances and spending habits of a customer change, models will routinely adjust, as they already do in areas like fraud detection.

The current system involves waiting for a customer to begin buying nappies, for instance, to then discover that customer as having just began a family, before following up with appropriate product recommendations.

Instead, machine learning algorithms may model behaviour, reminiscent of the purchases of folate vitamins and bio oils, then when offers ought to be sent.

This shift from reactive to predictive marketing could change the way in which you shop, bringing you suggestions you perhaps never even considered, all possible due to AI-related opportunities for each retailers and their customers.

This article was originally published at theconversation.com