React to what you buy, then predict what you want to buy. Credit: Shutterstock / nmedia
Whether you shop online or at a store, your retail experience is the main battleground for artificial intelligence (AI) and machine learning revolution.
Major Australian retailers have begun to realize that they have a lot to achieve if they get the right artificial intelligence strategy, with those currently recruiting for the Head of artificial intelligence and Machine Learning supported by a team of data scientists.
The new Woolworths division developed by WooliesX aims to bring together a variety of team groups, including technology, digital experience for customers, e-commerce, financial services, and digital experience for customers.
All about processing data.
To understand the opportunities and threats for all major retailers, it is important to understand why artificial intelligence is back on the agenda. Two important things have changed since the first attack on AI decades ago: the power of data and computing.
The power of computing is easily seen. Smartphones in your hands have millions of times more computational power than many computers a decade ago. Companies have access to almost unlimited computing capacity to train their artificial intelligence algorithms.
Another important ingredient is the scale and wealth of data available, especially in retail.
Artificial intelligence systems, especially learning techniques such as machine learning, develop well in large and rich data sets. When properly fed with this data, the system finds trends, patterns, and correlations that human analysts don't expect manually.
This machine learning approach automates data analysis, allowing users to create models that can then make useful predictions about other similar data.
Why is retail suitable for AI?
The speed of the spread of AI in various fields depends on several important factors: retail is suitable for several reasons.
The first is the ability to test and measure. With appropriate security measures, retail giants can use AI and test and measure consumer response. They can also directly measure the effect on their profits quite quickly.
The second is the consequence of relatively small errors. An AI agent who landed a passenger plane was unable to make a mistake because he could kill people. AI agents deployed in retail that make millions of decisions every day are able to make a few mistakes, as long as the overall effect is positive.
Some smart robot technology has been produced in retail. By partnering with the giant Kroger grocery store to deliver food ingredients to the doors of customers in the United States.
But many of the most significant changes will come from the spread of AI instead of physical robots or autonomous vehicles. Let's look at some scenarios based on artificial intelligence that will change your retail experience.
Your buying habits
AI can detect the patterns underlying your buying behavior from the products you buy and how you buy them.
This could be the purchase of your usual rice at the supermarket, sporadic wine purchases at liquor stores and parties on Friday nights at an ice cream shop at a local convenience store.
While inventory and database sales systems only track individual product purchases, with sufficient data, machine learning systems can predict their habits. He knows that he likes to cook risotto every Monday night, but also his behavior is more complex, like eating ice cream occasionally.
On a larger scale, analyzing the behavior of millions of consumers will allow supermarkets to predict how many Australian families cook risotto every week. This will inform the inventory management system, automatically optimize Arborio rice stock, for example, for shops with many consumer risotto.
This information will be shared with friendly suppliers, enabling more efficient inventory management and efficient logistics.
Efficient marketing
Traditional loyalty plan databases such as FlyBuys allow supermarkets to identify the frequency of purchasing certain products, such as buying Arborio rice once a week, and then sending offers to a group of consumers identified as supermarkets. "Will buy Arborio rice". .
The new marketing technique will go beyond sales promotion for customers who may have already purchased the product. Instead, recommendations for machine learning will promote garlic bread, tiramisu, or other personalized product recommendations suggested by data from thousands of other consumers that often go hand in hand.
Efficient marketing means fewer discounts and more profits.
Price dynamics
The price challenge for supermarkets involves applying the right price and the right promotion for the right product.
Optimizing retail prices is a complex task, which requires analysis of granular level data for each customer, product, and transaction.
To be effective, endless factors must be examined, such as how sales are affected by price changes over time, season, weather and competitor promotions.
A well-designed machine learning program can take into account all of these variations, combining them with additional details such as purchase history, product preferences, and others to develop customized perspectives and prices to maximize revenue and profit.
Comments from clients
Historically, customer comments were obtained via comment cards, completed and placed in the suggestion box. This feedback must be read and practiced.
When social networks increase, it becomes a platform to express comments publicly. As a result, retailers are turning to social networking tracking software to respond, resolve, and engage with customers.
In the future, machine learning will play a role in this context. Machine learning and the AI system will allow for the first time a massive analysis of various irregular and unstructured data sources, such as verbal comments or video data recorded by clients.
Reduction in robbery
Australian retailers lose around A $ 4.5 billion every year in stock losses. Growth in self-service records contributes to this loss.
The machine learning system has the ability to scan millions of images easily, enabling a smart point-of-sale (POS) system equipped with cameras to detect the various types of fruits and vegetables that buyers place on a registration scale.
Over time, the system will also improve in detecting all products sold in the store, including tasks called fine grain classifications, which allow you to distinguish the difference between Valencia and Navel Orange. Therefore, there will be no more "mistakes" when entering potatoes when you actually buy peaches.
In the long run, POS systems may disappear completely, as in the case of the Amazon Go store.
The computer that you ordered yourself
The machine learning system quickly increases the conversion of your natural sound into a shopping list.
Digital assistants like Google Duplex will soon be able to create a shopping list and place an order for you, with French retailer Carrefour and US giant Walmart that has been linked to Google.
A growing retail experience.
When you go through life, you grow old, sometimes you get bad, you can get married, maybe you have children or change careers. When the circumstances of life and habits of client consumption change, the model will automatically adjust, as they have done in fields such as fraud detection.
Current reactive systems involve waiting for clients to start buying diapers, for example, to then identify the customer as someone who has just started with the family, before continuing with the appropriate product recommendations.
Instead, machine learning algorithms can model behavior, such as purchasing folate and biological oils, and then estimating when bids must be sent.
These changes from reactive to predictive marketing can change the way you shop, give you advice that you might not even consider, all this is possible because of the opportunities for artificial intelligence for their retailers and customers.
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