Here’s How Generative AI Is Transforming Retail Personalization
The adoption of generative AI in the retail industry will empower retailers to enhance user experiences, streamline operations and ultimately drive business growth. As generative AI continues to evolve, retailers must embrace the latest AI technology in order to stay competitive in the rapidly changing market landscape.
We’ve all seen futuristic movies where a truly personal shopping experience is delivered with the power of artificial intelligence.
Perhaps, like us, your first thought was the iconic scene from the movie Minority Report. "Hello, Mr. Yakamoto. Welcome back to the Gap! How did those assorted tank tops work out for you?"
With Generative AI technology, even in its early stages, this vision is closer to reality than ever before. The adoption of AI in the retail industry will empower retailers to enhance user experiences, streamline operations and ultimately drive business growth.
As generative AI continues to evolve, retailers must embrace the latest AI technology in order to stay competitive in the rapidly changing market landscape. Here are the areas within retail where we foresee that personalized AI will have the highest impact.
1. Personalized product descriptions
In retail, product descriptions play a crucial role in selling products. It's an area customers actually take the time to read in order to understand more about their potential purchase. In a recent study conducted by Convercart, it was found that 87% of customers feel product content is the most important factor when deciding to purchase an item online as they cannot physically see or feel the item.
However, writing product descriptions can be time-consuming and therefore expensive. This is especially true for large companies with a vast amount of inventory. Using AI, organizations can effectively streamline the process by:
Creating targeted descriptions: AI can tailor product descriptions at scale to various different personas, emphasizing features that are likely to be most relevant to them. With segmented audiences in place, organizations can tailor their descriptions to be more personal according to various factors like age, geographical location, browsing history and returning or new customers.
2. Personalized customer support
The use of chatbots in customer support is nothing new — however, they have always been rather impersonal and tend to stick to a very specific flow. Now, with LLMs, organizations can drastically improve and personalize the way they connect to their customers.
Here are a few examples of how AI can help customer support teams handle complaints or queries — while also communicating to customers that they care about their needs:
Offer 24/7 customer support: AI systems can answer customer queries at any time of the day, providing a consistent level of support without the constraints of human working hours.
Support instant responses: Use AI to reduce wait times and improve customer satisfaction with instant responses to customer questions.
Understand context and provide answers: Advanced AI algorithms can understand the context behind customer queries, enabling more relevant and personalized responses. AI can also be prompted to answer customer questions from a given database of text.
Allow for scalability: AI can handle an unlimited number of queries simultaneously, offering a scalable solution to customer support.
3. Personal shopping assistant
There's nothing worse than feeling like someone is clocking your every move as you browse through a store. However, there are those times where you need help locating a specific size or color of an item. AI personal shopping assistants can seamlessly guide customers along the buying journey in the following ways:
Provide personalized recommendations: AI personal shopping assistants can analyze customer behavior, preferences and past purchases to make personalized product recommendations — providing a unique shopping experience for customers that encourages more sales.
Use conversational language: AI assistants can understand and engage in human-like conversations with customers using Natural Language Processing (NLP). This allows for a more humanized experience, enabling retail companies to build on high quality customer engagement and perception.
The future of retail is here with personalized AI
Recent advancements of LLMs and Generative AI have given retailers a major leap forward in creating content. This has significantly reduced the cost of content, giving retailers the power to easily create personalized content. In order to tackle the significant challenges that the retail industry faces, retailers need to leverage the power of personalized retail with AI. Your customers and your bottom line will thank you.
Ready to transform your online retail store? Contact us to learn how you can personalize your retail experience with Generative AI today!
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In August 2021 we released Jurassic-1, a 178B-parameter autoregressive language model. We’re thankful for the reception it got – over 10,000 developers signed up, and hundreds of commercial applications are in various stages of development. Mega models such as Jurassic-1, GPT-3 and others are indeed amazing, and open up exciting opportunities. But these models are also inherently limited. They can’t access your company database, don’t have access to current information (for example, latest COVID numbers or dollar-euro exchange rate), can’t reason (for example, their arithmetic capabilities don’t come close to that of an HP calculator from the 1970s), and are prohibitively expensive to update. A MRKL system such as Jurassic-X enjoys all the advantages of mega language models, with none of these disadvantages. Here’s how it works.
Compositive multi-expert problem: the list of “Green energy companies” is routed to Wiki API, “last month” dates are extracted from the calendar and “share prices” from the database. The “largest increase“ is computed by the calculator and finally, the answer is formatted by the language model.
There are of course many details and challenges in making all this work - training the discrete experts, smoothing the interface between them and the neural network, routing among the different modules, and more. To get a deeper sense for MRKL systems, how they fit in the technology landscape, and some of the technical challenges in implementing them, see our MRKL paper. For a deeper technical look at how to handle one of the implementation challenges, namely avoiding model explosion, see our paper on leveraging frozen mega LMs.
A further look at the advantages of Jurassic-X
Even without diving into technical details, it’s easy to get a sense for the advantages of Jurassic-X. Here are some of the capabilities it offers, and how these can be used for practical applications.
Reading and updating your database in free language
Language models are closed boxes which you can use, but not change. However, in many practical cases you would want to use the power of a language model to analyze information you possess - the supplies in your store, your company’s payroll, the grades in your school and more. Jurassic-X can connect to your databases so that you can ‘talk’ to your data to explore what you need- “Find the cheapest Shampoo that has a rosy smell”, “Which computing stock increased the most in the last week?” and more. Furthermore, our system also enables joining several databases, and has the ability to update your database using free language (see figure below).
Jurassic-X enables you to plug in YOUR company's database (inventories, salary sheets, etc.) and extract information using free language
AI-assisted text generation on current affairs
Language models can generate text, yet can not be used to create text on current affairs, because their vast knowledge (historic dates, world leaders and more) represents the world as it was when they were trained. This is clearly (and somewhat embarrassingly) demonstrated when three of the world’s leading language models (including our own Jurassic-1) still claim Donald Trump is the US president more than a year after Joe Biden was sworn into office. Jurassic-X solves this problem by simply plugging into resources such as Wikidata, providing it with continuous access to up-to-date knowledge. This opens up a new avenue for AI-assisted text generation on current affairs.
Who is the president of the United States?
Joe Biden is the 46th and current president
Jurassic-X can assist in text generation on up-to-date events by combining a powerful language model with access to Wikidata
Performing math operations
A 6 year old child learns math from rules, not only by memorizing examples. In contrast, language models are designed to learn from examples, and consequently are able to solve very basic math like 1-, 2-, and possibly 3- digit addition, but struggle with anything more complex. With increased training time, better data and larger models, the performance will improve, but will not reach the robustness of an HP calculator from the 1970s. Jurassic-X takes a different approach and calls upon a calculator whenever a math problem is identified by the router. The problem can be phrased in natural language and is converted by the language model to the format required by the calculator (numbers and math operations). The computation is performed and the answer is converted back into free language. Importantly (see example below) the process is made transparent to the user by revealing the computation performed, thus increasing the trust in the system. In contrast, language models provide answers which might seem reasonable, but are wrong, making them impractical to use.
The company had 655400 shares which they divided equally among 94 employees. How many did each employee get?
Each employee got 7000 stocks
(No answer provided)
6972.3 X= 655400/94
Jurassic-X can answer non-trivial math operations which are phrased in natural language, made possible by the combination of a language model and a calculator
Solving simple questions might require multiple steps, for example - “Do more people live in Tel Aviv or in Berlin?” requires answering: i. What is the population of Tel-Aviv? ii. What is the population of Berlin? iii. Which is larger? This is a highly non-trivial process for a language model, and language models fail to answer this question (see example). Moreover, the user can’t know the process leading to the answers, hence is unable to trust them. Jurassic-X can decompose such problems into the basic questions, route each to the relevant expert, and put together an answer in free language. Importantly, Jurassic-X not only provides the correct answer but also displays the steps taken to reach it, increasing the trust in the system.
Do more people live in Tel Aviv or in Berlin?
There are more people living in Tel Aviv than in Berlin.
Berlin and Tel Aviv are roughly the same size
(First hit is a comparison between Tel Aviv and Berlin)
More people live in Berlin than in Tel-Aviv
[‘Return population of Tel Aviv’; Return population of Berlin’; Return which is bigger between #1 and #2’] Step 1: Population of Tel Aviv. Result - 451523. Step 1: Population of Berlin. Result - 3664088. Step 3: Which is bigger, #1 or #2. Result - Berlin.
Jurassic-X breaks down compositional questions, answers the basic sub-questions, and puts together the answer. Importantly, this process is transparent to the user greatly increasing the trust in the system
Dynamic information (like weather and currency exchange rates)
Certain types of information change continuously - weather, currency exchange rates, share values and more. Such information will never be captured by language models, yet can easily be handled by Jurassic-X by integrating it with a reliable source of information. We performed a proof-of-concept on two such features - weather and currency exchange rates, and the design enables quick integration with more sources to solve your use-case. Weather - a loosely phrased question about the weather elicits an answer from all language models, where language models always return the same answer, regardless of when the question was asked (funny, right?), while Jurassic-X provides an answer based on the actual weather prediction.
I’m going to be in New-York in 3 days. Should I pack my umbrella?
Yes, you should pack your umbrella.
Yes, you should. The weather forecast is rain.
(Links to weather websites)
Yes, you should pack your umbrella, because in New York in 3 days there will be broken clouds and the temperature will be -2 degrees.
Currency exchange rates change much faster than weather predictions, yet the Jurassic-X concept - a language model connected to a reliable source of information - easily solves this problem as well.
How much Moroccan money will I get for 100 bucks?
100 moroccan dirhams is about 27$.
How much is 100 dollars in moroccan money?
100 dirhams = 10.75 dollars
100 USD = 934.003 MAD
Jurassic-X combines a language model with access to APIs with continuously changing information. This is demonstrated for weather forecasts and currency exchange rates, and can easily be extended to other information sources
Transparency and trust
Transparency is a critical element that is lacking in language models, preventing a much wider adoption of these models. This lack of transparency is demonstrated by the answers to the question - “Was Clinton ever elected as president of the United States?”. The answer, of course, depends on which Clinton you have in mind, which is only made clear by Jurassic-X that has a component for disambiguation. More examples of Jurassic-X’s transparency were demonstrated above - displaying the math operation performed to the user, and the answer to the simple sub-questions in the multi-step setting.
Was Clinton ever elected president of the United States?
No, Clinton was never elected as president of the United States.
Clinton was elected president in the 1992 presidential elections…
Bill Clinton was elected president.
Jurassic-X is designed to be more transparent by displaying which expert answered which part of the question, and by presenting the intermediate steps taken and not just the black-box response
That's it, you get the picture. The use cases above give you a sense for some things you could do with Jurassic-X, but now it's your turn. A MRKL system such as Jurassic-X is as flexible as your imagination. What do you want to accomplish? Contact us for early access
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