Artificial Intelligence is all the rage and everyone is talking about it. We will explore whether AI can create intelligent eCommerce or if this is a fad.
Artificial Intelligence, or AI, is a topic that you can't ignore today. PwC reports that AI technology will result in improvements in labor productivity, product quality, and consumption.
This could lead to $15.7 trillion in the global annual gross domestic product (GDP), by 2030.73% of GDP US In 2019,
In 2035, AI will have an impact on industry profits.
The AI revolution will not be limited to a handful of industries, but it is expected to have a significant impact on a wide range of them. Accenture's study concluded that profits in the Wholesale & Retail sector alone will rise.59% more than the baseline Profit levels in 2035, among other things.
Artificial Intelligence seems to have a bright future, but to be honest, 2030 and 2035 seem far off. It is more important to ask what AI technology can do for us today than in the future.
Intelligent eCommerce - overhype or reality?
Many retailers are turning to eCommerce to save themselves from the wrath of COVID-19 and other global lockdowns. The eCommerce sector is already booming, with online retail sales expected to exceed $6.5 trillion by 2023. Online shopping has taken off since the outbreak.
Even the biggest retailers can't keep up with the demand.
As people increasingly embrace online shopping as their preferred method of purchasing goods, the COVID-19 pandemic clearly has impacted eCommerce's reputation. Online shops that offer the best customer experience and retention will be able to retain customers who are increasingly shopping online. A seamless shopping experience is essential.
AI can help.
AI has much to learn before it can hurt us.
We have found that there is often a disconnect between the value we can deliver with AI and what customers want. This is not surprising given the hype surrounding AI technology that has led people to believe it can solve all their problems.
Does this mean that you should accept every AI opportunity presented to your company? No, not at all. However, it is important to fully understand the potential benefits and drawbacks of AI technology when it comes to eCommerce businesses.
How can AI be used in eCommerce?
To be effective and efficiently implemented, every AI solution must meet the following conditions:
- Data must be labeled for the task at hand. Simply stated, no data, no Artificial Intelligence. Important to remember that the data label does not have to be strictly from an online retailer.
Sometimes datasets closely match the distribution of data found in online shops. These datasets can be used to optimize AI models, or they can be augmented with data from online shops if necessary.
There are AI algorithms that do not use labeled data, but they are often part of larger AI systems that still require labeled data.
- The benefits of AI technology far outweigh their costs.
- An external or internal team can help take an AI product from its experimentation phase into production.
Instead of explaining each condition in detail, we will instead focus on concrete examples that show how they can be applied to the best AI-powered eCommerce solutions. The third condition is described below.
- Visual Search
Visual Search, an AI-powered technology that allows users to search through millions of items to find visually similar items, is called AI-powered technology. The search takes only a fraction of a second after the query image is given and returns only visually and semantically relevant results. Let's now look at how these conditions relate to this product.
Visual Search makes it easy to find similar items.
Visual Search can use labeled data
Visual Search requires product images as well as their categories. This is because most of the products in an online shop can be categorized into appropriate categories.
Visual Search results depend on how granular the categories are. The more precise results you get, the finer the categories (iPhone 11 vs.
just "Mobile Phone") will be. Specific attributes of products can also be used to improve the granularity and make results more precise.
Although it may seem strange to require categories, product categories do not have to be related to the visual perception of the products.
However, AI models use product category information differently from humans by using something called embeddings. Embeddings represent abstract representations of images that correspond to the visual perception of identical images in humans.
Visual Search offers benefits for the business
Online shops with large product catalogues will enjoy the greatest benefits from Visual Search. The ability to search for a product is available without the need to describe product attributes or use complicated filters. Visual Search has been used by many established online retailers like ASOS and eBay for some time and has had strong results.
- Automatic product categorization
eCommerce websites often have large categories of goods. Adding new items to them can be a hassle. Automated product categorization allows retailers to quickly find the correct category while reducing the chance of products being placed in incorrect categories.
AI models will always attempt to return the most precise category, but if they aren't confident enough, they will return the less specific product categories as shown in the figure below.
Categories tree. The categories tree.
Automatic product categorization can be done using labeled data
This task requires data similar to Visual Search. In product categorization, this task relies on product description and product title-category pairs.
Product descriptions and titles are more detailed and contain less noise than product images, making them more useful for categorizing. Some categories can be difficult to identify from just images (e.g. iPhone 11 Pro and iPhone 11 Pro max).
To resolve these cases, the AI model uses product descriptions and titles.
Images can still contain valuable information that is difficult to convey in text, such as unique visual patterns.
This is why images are used if possible, resulting in better categorizing accuracy.
Read Blog 5 Ways AI is Revolutionizing E-commerce Experience
What are the advantages of an automated categorization?
This solution has many benefits. It will reduce the time required to locate the right category for new products.
This is what our experience has shown.Reduced by 70% for a limited timeThis saves retailers valuable time. The second reason is that mismanaged product categories can cause severe irritation to buyers. Automatic categorization can help reduce such errors.
The first thing shoppers will see when they arrive at an eCommerce site is the internal search engine. Proper product categorization ensures that the search results are relevant and seamless.
- Automated Product Tagging
An online catalog contains product descriptions, prices, suppliers, and other details. To ensure customers are directed to the correct product, they must be able to provide precise details such as color, style, pattern and neckline.
Unstructured and inconsistent tagging of product descriptions or facets can lead to poor product discovery for retailers and end shoppers.
Manually tagging so many attributes can be time-consuming and prone to error.
The labeled data allows for AI-powered product tagging by retailers
Product attributes are not mandatory. They can be optional. In this case, AI models cannot perform optimally due to insufficient data.
Transfer learning, an AI technique that allows for the reduction of this problem, is possible. Transfer learning is a technique that allows us to use larger public attributes datasets, such as this one, to create a more generalizable AI model in the context of attribute tag. The generalized models can then be fine-tuned using the retailer's existing attributes data, making the final product tagging solution even more precise and accurate.
Even more exciting is the possibility to apply the transfer learning process To almost any AI-based solution Data isn't always available, but it doesn't mean that everything is lost.
Automated product tagging has many benefits.
- Relevant results from searchesBuyers are more likely to be annoyed by irrelevant search results, which leads to higher bounce rates. A simpler search also reduces the time it takes to shop, which means more money for the retailer.
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Reduced time required to tag catalogsManual tagging is tedious, slow, and easy to make mistakes.
It can be done on any size catalog.
- Keyword-ready, accurate and SEO-ready descriptions, titles, tags and descriptions Google searches can increase discoverability and people would love to see their website at the top.
- eCommerce Personalization
eCommerce Personalization is a term that online retailers use to describe the practice of creating personalized interactions and experiences through eCommerce sites.
It involves dynamically displaying content, media or product recommendations based upon browsing behavior, purchase history, demographics, and psychographics.
Personalization can be done in many ways in eCommerce. It could include suggesting clothing based on the weather or offering complementary products.
Do you fancy a red jacket? The personalization algorithm can help you find exactly what you're looking for.
What type of data are you looking for?
There are many types of personalization available for eCommerce. One type of data may not be appropriate for all use cases.
Personalization often uses user interaction data. This data includes items from abandoned carts and products recently viewed, items from abandoned baskets, past emails interactions, loyalty program memberships, and past purchases. Context (type of device, time and place of access visually matching styles or even data from an external user) are other types of data.
The goal of all this data is to be used for one purpose -Unique interactions and experiences that are tailored to each customer.
Customer personalization has many benefits
AI eCommerce Personalization has the obvious advantage of delivering relevant content to the buyer. This results in increased time on the site, greater engagement and ultimately more sales.
Online customer data can be used by retailers to personalize offline stores. This includes informing clerks of online preferences. If you are still skeptical, the fact that online personalization is a poor choice has made it possible to change your mind.
- Voice Search Optimization
Voice search is the use of spoken language to interact with search engines rather than typing into a search box. Experts predict a rapid rise in voice-driven digital interactions, given the introduction of Google Assistant, Siri and Alexa, Cortana and similar virtual assistants.
This prediction is supported by solid evidence.
- OC&C Strategy Consultants projects that 55% of US households will have a smart speaker in 2022
- Google reports that 27% use voice search on their mobile devices.
- According to Gartner, voice search will make up 30% of all browsing sessions by 2020 according to a study.
What is the difference between voice and text search?
Different "languages" are used by people when they speak and text, which results in different syntax but the same semantics. Customers are less likely to type "Nike shoes black 42" in textual searches and more inclined to say "find me black Nikes size 42". Site SEO can suffer greatly if it doesn't consider both.
Voice search is the future of eCommerce? It is possible, according to statistics. Let's find out how we can prepare and what benefits voice search brings online retailers.
Voice Search is revolutionizing SEO. It is more important than ever to be the first result.
Data required
This is great news! To reap the benefits of AI voicesearch, you don't actually need any extra data. While you don't require any additional data, voice search capabilities like Google are required by companies.
However, this is beyond the scope of this article.
You do not need to focus on voice search optimization for your online store. Voice Search: The future and its benefits
Voice search is becoming more popular every day. If all of the above predictions are true, optimizing your website for voice search will be a medium-to-long term strategy that will give you a competitive advantage in terms SEO.
Voice search is more convenient than text searching. But, it's not clear how effective it will be.
Common pitfalls of AI and how to avoid them
Artificial intelligence isn't bulletproof technology. We share the common pitfalls that businesses face when using AI, including eCommerce, to help them avoid these pitfalls.
Be realistic about your expectations
AI technology is not suited for every use case. Online retailers need to be aware of all the benefits and limitations of each AI solution before they can plan their actions.
Although it is limited in scope, Artificial Intelligence has quickly become a crucial part of eCommerce businesses.
An expert AI team can provide clear guidelines on what is reasonable and what would drain business resources.
A team of experts working on the AI solution
The team includes data scientists, deep learning and machine learning experts, software engineers and data engineers.
This ensures that clients are satisfied with AI solutions. Although it is a great long-term strategy, you should be aware that creating your own AI team can be costly. a long time and there are many resources Because it is more difficult to find good AI talent than software development talent. External teams with AI expertise can produce results much faster than if they are not qualified.
High-ranking companies invest heavily in AI talent.
Good teams should help with implementation, but also provide you with initial feedback. Setting realistic expectations and exploring the feasibility for AI solutions in light of the conditions described above is valuable initial feedback.
AI enthusiasts are showing a lot of interest in the boom. Although we are all in agreement that AI is now a commodity, our years of experience have shown that it takes a lot more to get an AI model from experimentation into production. 70% of performance in AI solutions comes from 30% effort.
However, to make the solution production-ready you will often require 80-100% performance. This is where the majority of the work is done.
You should plan for the maintenance of AI systems.
Artificial Intelligence technology depends on accurate data in order to function. Data can be dynamic, so AI systems need to be updated regularly with new data.
Consider the automatic attribute tagging. Although the current database contains attributes such as "black", "patterned", short sleeves", and "logo", it does not have "v-neck" since none of your products have a v neck. AI will not detect a v neck product if you introduce it. The AI model should be re-tuned to match the new attribute.
ImageNet benchmark dataset to verify AI categorization accuracy. Models are constantly improving, so it is important to keep them updated.
This will ensure the best possible user experience.
Data isn't the only variable that can change over time. AI technology is still very young. Researchers continue to discover faster and more accurate AI models every month. To ensure that your customers have the best possible experience, AI software must be regularly updated.
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Final Judgement
We've seen what conditions are required for AI solutions, five technologies that can be used to help existing eCommerce businesses, and what to look out for when choosing AI software.
Does your online store require Artificial Intelligence? While AI technology is not essential for most eCommerce businesses, the tide is turning in favor of it. Amazon, ASOS and eBay have all been investing in AI tech for many years and the results have been amazing.
In a few years, AI will be the norm. Your business will be behind if it doesn't have the right technology. Proper data storage is key to AI.
This is why it is crucial to implement proper data storage processes today in order for AI systems that work properly tomorrow.
This will reduce initial costs, make it easier to learn about AI technology, and give your customers a more futuristic shopping experience.
