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82% of consumers will not give a brand another chance after just one unpleasant experience and over 90% will look to other brands for a more satisfactory experience” – Mary Meeker
There are about 2 million fashion brands in the world – and growing. The retail industry is growing at an incredibly fast pace and customers are finding themselves increasingly spoiled for choices. On the flip side – brand loyalty and recall are dropping fast.
Customers are increasingly choosing brands that connect with them, brands that offer personalized experiences, and talk to them on the channels that they are using.
Why every retailer should adapt to Artificial Intelligence (AI)?
The new generation of shoppers expect bespoke shopping experience in real-time and at the lowest price. They expect retailers to know them as well as their local grocer. They do not want to be flooded with thousands of irrelevant products. Instead, shoppers want shorter wait times, quicker order fulfilment, personalized product recommendations and seamless checkout experience. This new generation came about in a digital world where multiple options are available with a click of a button. This means retailers today have to adopt Artificial Intelligence (AI) to thrive in a competitive environment.
Research shows that when shopping experience is highly personalized, customers are 110% more likely to add additional items to their baskets & 40% more likely to spend more than they had planned.
It is also increasingly clear that while retail stores are not exactly dead, they are undergoing a metamorphosis. There is still value in offline. In fact, Doug Stephens, The Retail Prophet points out that stores today need to see themselves as an extension of their media platform.
Is your brand on the right channel, at the right time?
The most important use case for the store is now to deliver a high level of experience – sensory and emotional in a way that is memorable and trust-building. Online is where sales are moving.
Approximately $5T of the $28T retail market is now online.
Retailers are finding their biggest competitors online. Platforms like Netflix, Instagram, Snapchat, and the likes have achieved the level of individualization and experiential content needed for a platform to become habit-forming – something that retail has not achieved yet. Shopping too is moving to these platforms slowly.
Today, it is easy to spot an item of interest on a platform, get recommendations, and opinions from friends and family, purchase the item of choice all within the same app and have the entire experience be tailored to the individual.
The data challenge in retail and e-commerce
A presence online is not going to solve retail’s problems. Retail’s bigger issue is the lack of accurate data. Even the data that is available ends up being used ineffectively or in a way that does not provide a singular view into the customer across multiple channels.
If records are to be believed, bad data has cost US businesses $3.1T every year, & data workers waste 50% of their time finding and correcting errors.
The challenge for brands in this industry is two-fold:
- Know the customer well
- Use the customer data and build operational efficiency into their systems
Accurate data can help businesses bring optimization, automation, and competence. The best way to use data to bring about this hyper-personalized experience is through Artificial Intelligence.
AI in Retail 2020 and beyond
AI is retail’s saviour. Retail is, in fact, one of the few industries where adoption of Artificial Intelligence (AI) is being seen in a meaningful and actionable manner. Every step of the retail process has the ability to be automated by AI in a way that would increase accuracy, efficiency, and scaling of operations. From customer acquisition using reliable data to catalogue and inventory management to post-purchase experience – AI has the ability to impact retail in a holistic end-to-end manner.
With the help of Artificial Intelligence (AI), retailers can create data models to glean insights and in-turn build prescriptive or predictive decision engines. This can help retailers with things like demand forecasting allowing them to make better data-driven decisions.
AI in retail can also be used to structure and transform data from a semi-structured or an unstructured format to the one used by the marketplace. Algorithms can be used to identify and transform attributes from tables of data or even paragraphs of text
Let’s look at a few use cases of AI in retail.
8 Use Cases of AI in Retail
1 – Data creation and labelling
The problem of inaccurate and inconsistent data can be solved by AI. AI-powered data creation and labelling can happen right at the start of the digitization process. AI can create rich metadata for each product, eliminating human fatigue and error.
This can be done using Computer Vision algorithms which can automatically identify various attributes of a product and tag them accurately. The same algorithm can also be used to generate titles and descriptions for the product resulting in SEO-ready rich metadata which is easily searchable.
SEO-ready tags, titles and descriptions can incredibly boost product discoverability on search engines like Google, Bing, and the likes. Image recognition product digitization results in 90% accuracy and more than 20% conversion through better search, browse and SEO.
2 – Data Structuring and Transformation
Data, once created or extracted from the manufacturer’s catalogue, now needs to be structured and transformed so that it remains consistent across several products, product types, and brands. A marketplace that sells products from hundreds of brands cannot manually structure data.
Lets elaborate this using an example. Brand A might call the colour of a dress orange and brand B might call it coral. While searching for orange dresses, brand B’s dress will not be shown resulting in low discoverability. And if this problem occurs for hundreds of thousands of products across attributes like size, colour, neckline, dress length, sleeve length, pattern, the result is messy along with low revenue.
AI in retail can help structure and transform data from a semi-structured or an unstructured format to the one used by the marketplace. Algorithms can be used to identify and transform attributes from tables of data or even paragraphs of text. Once it is trained with enough data, patterns can be identified, and auto-suggestion of attributes can be used to quickly digitize products across brands and categories. This can help in cleaning up, grouping, categorizing, searching, sorting, and even filtering data on the marketplace.
3 – Data Insights to drive better decisions
The data that has been created, labelled, and structured can now be analysed to identify trends and patterns that can help retailers make better decisions. Exploratory analytics is the most basic thing that can be done with the data available to identify trends. While this is fairly simple, it certainly cannot be used to automate decision-making engines.
With AI, retailers can create data models to get better insights and in-turn build prescriptive or predictive decision engines. This can help with demand forecasting, allowing them to make better data-driven decisions. With time, these predictive models can learn to make better decisions taking more and more data into consideration allowing retailers to accurately forecast trends and be better prepared for them.
4 – Product Discovery
Product discovery is the first, and most important step in a shopper’s journey. A simple and straightforward discovery process can immensely help a retailer gain a shopper’s trust. A shopper who finds exactly what they are looking for is way more likely to come back to the same retailer for their next purchase. On a website, discovery begins with the site search.
According to Neil Patel, 59% of web visitors frequently use the internal search engine to navigate on a website.
Search engine users are high intent shoppers and are a crucial segment of any e-commerce site. The results of a search on a website need to capture the intent of the query and show products that match the searched item as close as possible.
With accurate, consistent, and structured data, the problem is no longer about the relevance of the results, but more about the relevance of the results to each individual. In other words, are the search results personalized to everyone’s style preferences?
An AI-powered personalized search in retail can help tailor the search results, by boosting specific affinities for each shopper. Each click can be leveraged to learn more about the shopper’s preferences and in turn converge to a hyper-relevant search bar that understands the customer.
5 – Product Visualization /Visual Merchandising
A large chunk of eCommerce returns today happens because shoppers feel the product looks different in person. This means retailers need to show the same garment on –
- Models of different ethnicities
- Models standing in various poses
- Models of different sizes
Retailers across the globe spend approximately $100 to $1000 per product on photography and digitization. Instead, retailers can use AI to generate automated on-model fashion imagery using Generative Adversarial Networks (GANs) and drape the same dress on models of various sizes and ethnicities standing in different poses without having to photograph all of this manually. This can reduce photoshoot costs and at the same time, increase shopper engagement and conversions, and greatly reduce returns.
6 – Real-time personalization of the shopper journey
Brands need to create a style profile for each shopper to personalize content, offers, and products across all channels. No two shoppers look the same in a product. Each shopper has their own unique style that is bound to influence their shopping behaviours. It is possible to glean the style profile of each shopper from their purchase history and from each click on the website. This helps in offering personalized product recommendations as well as the highest engagement.
7 – Personalized Curation
Shoppers do not want to waste time on irrelevant products. Instead, they are looking for unique collections curated specifically with their tastes in mind. They expect styles according to their own taste and preference. However, personalizing hundreds of looks for each shopper will need a whole army of stylists.
An AI styling assistant can curate looks, mood boards, outfits, and collections for each shopper based on their visual style preferences. Each shopper can get a personalized styling recommendation for every product they browse, increasing the number of items in the cart while at the same time giving a great personalised experience to the shopper.
8 – Cart Abandonment Emails
Cart abandonment is when a high-intent shopper visits an ecommerce website, adds at least one product to the shopping cart and proceeds to exit the website without completing the purchase. Retailers reported to have lost a whopping $4.6 trillion to abandoned merchandise in online shopping carts, according to Business Insider.
While retailers can do simple fixes to prevent cart abandonment, cart abandonment is still bound to happen.
Cart recovery emails are an effective way of dealing with cart abandonment. It is used by more than 62% of retailers around the world and are a proven way to recover carts. Half of all abandoned cart emails are opened and over a third of clicks lead to purchases back on site according to study conducted by Shopify.
Since shoppers get emails from multiple websites every single day, to ensure your brand stands out from the rest, choose an AI-powered cart recovery tool that recommends visually similar products and styling suggestions personalized to each shopper’s visual style profile. These emails add value to shoppers as the content is completely personalized. This not only helps recover the cart but also informs the shopper that the retailer understands them.
Conclusion
Today, intelligent retail is about leveraging the power of data. The amount of data that needs to be processed from every single shopper with multiple touchpoints across several platforms makes automation a necessity and certainly not a luxury. Failing to automate in a data-driven economy can lead to retailers missing out on capturing data completely with dire consequences in the future.
There is a constant need to create, use, examine and distribute data in a timely manner for contextual decision making and value creation. This can only be done with automated systems capturing data from different touchpoints and channels.
Retailers today not only need automation to capture data but to unify this data so as to speak in a single voice to the shopper. A shopper should be able to transition seamlessly from online to offline and back again online. This means, the data captured on a website should be available to the sales assistants in stores and the products bought by a customer in-store should be used to improve the recommendations on the website. Data cannot be isolated when creating a complete story about each shopper.
AI in retail and e-commerce can completely transform how their teams function. Automation can single handedly take care of efficiency, giving retail teams time to think creatively and provide incredible experiences – both online and offline.
When retail truly becomes efficient with AI, shoppers can enjoy the thrill and magic of shopping anywhere, anytime.
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