Leveraging AI in Retail
?? In this month's edition:
Ever-changing customer dynamics: The evolving landscape of customer behavior in today's fast-paced world
AI empowering retailers: Enhancing retail experience with AI.
Imagine your own personal shopper: Diving into AI objections.
Beyond the Shopping Cart: Unforeseen ways AI is impacting the retail ecosystem
Ever-changing Customer Dynamics
20 years ago, a song typically stayed on the Billboard Top 100 charts for 20 weeks.?Right now, it’s closer to two weeks. Similarly, in retail, the half-life of relevance has never been shorter, and customer dynamics are much more complex.
This pushes companies to accept their customers as ever-changing, complex people, deeply impacted by unpredictable external forces and presses a sense of urgency and efficiency to ensure success, and to stay ahead of the competition.?
These challenges are fortunately met by the rise of Artificial Intelligence, and our ability to address customers’ preferences and needs in complex scenarios.??
AI gives us the ability to address complexity and make it approachable
Never before were we able to measure the real impact of a multitude of variables, including hidden patterns, to understand the behavior of consumers.?
It is not enough however to implement any AI tool to stay ahead, it needs to be able to create value for the retailer and the consumer alike.??
This means improving the customer experience, meeting customers’ needs and expectations, making operations more efficient, and extracting more value from the existing business.
What about the tools before AI?
This is not to say that we hadn’t tools before the rise of AI, companies have been collecting lots of data on their customers and operations to be able to understand them better.
Technology like BI has been used to analyze past data and enable businesses to make better data-driven decisions to improve business processes, customer service, and employee satisfaction.?
We are however talking about a reactive approach and in some cases, basing decisions on traditional personalization and segmentation, which however functional is not the most efficient approach.??
Artificial Intelligence Empowering Retail
What most businesses fail to realize, even the most innovative ones, is the combined benefits of different technologies, like AI and BI.?
While BI is greatly useful in analyzing historical data – using analytical models, AI makes predicting future outcomes a breeze – through the use of human-like problem-solving skills, learning, and judgment.?
Combining the two approaches enables retailers to deliver truly hyper-personalized services to their customers, increasing their brand recognition, customer engagement, and customer satisfaction all in one take.??
Imagine Your Own Personal Shopper
Curiously enough, despite the many arguments against using AI to emulate human interactions, there are already uses that seem to show evidence that contradicts the objections.
It is not difficult to imagine an AI shopping assistant answering questions about products, helping customers with their shopping needs, and making product recommendations whilst enabling product discovery, with a highly personalized focus on the customer.
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A little like having one's own personal shopper at our fingertips. Who wouldn't love that?
This shopping assistant would be trained on historical data and designed to deliver a seamless highly satisfactory shopping experience to the customer, simultaneously generating more data and learning through the interaction.
It would extract even more insights, meaning that the more an AI shopping assistant is used, the better it gets in serving the customers.
This focus on personalization can improve and expand the market to retailers, but also even more so if the entirety of the business is approached through technology. ?
Beyond The Shopping Cart
Besides customer analytics, AI applied to retail can greatly improve logistics, supply chain, internal management, and sales. The opportunity is never-ending.?
With an AI/ML tool retailers can for instance:
Sales & Marketing
Logistics
Real-life use case
For example, 3PM uses AI to protect its clients and their customers from online counterfeiting on marketplace websites. It does so by using machine learning algorithms that can tell fakes from the real thing while becoming more intelligent — and, therefore, effective — in the process.?
In a very different use, Fellow AI uses image recognition to provide real-time inventory management.
One of its robot models, NAVii, is equipped with data capture cameras and can travel up and down building aisles to view what items are present.??
In these examples, the common ground is a combined use of technology and a full integration in the business operation.?
Final Thoughts
The point here is that to extract the most value from AI, retailers and other businesses alike would need to evolve from the typical use case approach, into a more strategic AI approach, combining different tools and harvesting their immense potential.?
The market is evolving at an incomparable pace, and now is the time to choose how to approach it, otherwise the risk of obsolescence is considerably high.
Every step of the retail process can be automated by AI in a way that would increase the accuracy, efficiency, and scaling of operations. From customer acquisition using reliable data to catalogs and inventory management to post-purchase experience – AI can impact retail in a holistic end-to-end manner.?
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