How Generative AI Is Transforming the Retail Industry
In this post, we will discuss the major opportunities for the retail and ecommerce industries in 2023, and how companies in these sectors can apply generative AI to scale operations, drive more sales, automate workflows, boost productivity and gain an advantage in an increasingly competitive landscape.
Although Generative AI is at its early stages, its potential to disrupt industries across the board has already become apparent. Many industries are already experimenting and beginning to innovate with this technology, including the retail industry, which is showing incredible promise for growth fueled by generative AI.
Let’s explore some of the ways we’ve already seen generative AI benefit retailers, as well as some high-potential opportunities for future exploration.
82% of shoppers feel product descriptions are extremely or very influential to their purchasing decisions, according to a study by FieldAgent.
However, writing and managing effective product descriptions takes a great deal of time, effort and money, especially for large brands selling thousands of products.
Using AI to improve descriptions
Large language models (LLMs) can be used to generate product descriptions, titles and taglines that incorporate the product’s attributes, the style of description, the brand’s unique voice, and more.
By filling in a few simple details, such as the name of the product and its main features, the writer is able to generate the first draft of a product description with the click of a button.
See example below:
The generated description:
The product description can also be customized according to length, style and tone of voice, so that each product description is generated in a consistent format, and speaks the same language.
GenAI can also create new, or adjust existing, product descriptions at scale, making them more relevant in terms of seasonality or upcoming holidays and events. This is especially helpful for SEO purposes.
For example, let's consider a product description for a sweater. The original description may look something like: "Stay warm and stylish with our cozy women's sweater, made from high-quality materials. Perfect for any occasion."
Generative AI can make this more relevant for the upcoming holiday season by writing something like: "Embrace the holiday spirit with our cozy sweater. Whether you're attending a family gathering or enjoying a winter stroll, this sweater is the perfect blend of comfort and holiday charm."
This entire process frees up writers’ time, allowing them to focus on more complex, creative and high-value work, best suited to their skill sets, with the end result being acceleration of time to market.
As an ecommerce brand grows, so does its need to give a higher level of customer service.
Customers expect instant, professional and personal online and phone support, which is a costly expenditure for companies, and is challenging to scale.
Using AI to improve support efficiency reduce costs
Using large language models, retail organizations can instantly respond to customer inquiries online, using pre-existing information such as documentation, policies, FAQs and knowledge base articles. This can also help to personalize the customer’s experience, and allow them to have a human-like conversation, and get answers to their specific questions.
In addition, companies can incorporate an “ask me anything” search bar, which can provide contextual answers on return and exchange policies, and present customers with relevant information available on the website. This provides a seamless customer experience, making it easier, more accessible and personalized for each customer’s requirements.
For example, if someone wants to know how long the standard delivery time is, they can simply type their question in the search bar and receive a clear and concise answer, without reading any other irrelevant information.
With generative AI, customers become more self-sufficient, improving their experience and satisfaction, while saving businesses time and money.
Manual review reading and classification play a crucial role in helping businesses identify successes, failures, upcoming trends, and potential market changes. However, this is a time consuming task, which can be extremely burdensome for brands.
Using AI to streamline the review analysis process
LLMs have the ability to process information from each review. They can track sentiment analysis, which determines the emotional tone behind the text, whether it is positive, negative, or neutral, providing real-time, accurate insights into reviews.
LLMs also allow for multi-dimensional classification, meaning they can extract and classify various aspects mentioned in each review, enabling comprehensive categorization and analysis. This multifaceted approach allows brands to gain a deeper understanding of specific aspects of their products or services that are being praised or criticized by customers. This ultimately enables more accurate and efficient insights into customer reviews, providing brands with real-time feedback that can be acted upon quickly.
For example, let’s take a look at this review of bed sheets:
This is not a completely straightforward review, because it discusses the high quality of the sheets, while also conveying the disappointment because of a misleading product description. A LLM will be able to classify both aspects of the review, and categorize them accordingly so that none of the information gets lost.
Start your AI retail journey
Artificial Intelligence (AI) is becoming an essential tool for the retail and ecommerce industries, helping to boost productivity and gain a competitive edge, and we are already working with major players to actualize the vast opportunities that exist for these sectors.
The use of large language models, like those offered by AI21 Labs, can provide retailers with the ability to automate repetitive tasks, offer a high level of personalized customer support, and produce high-quality content that resonates with their target audience. With the continued development and adoption of AI in the retail industry, we can expect to see even more innovation and optimization in the near future.
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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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