12 Practical AI Use-Cases For Your Online Business to Gain Competitive Edge in Profitability and Higher Probability of Matching Market Needs

AI supported by big data already ignited the so called 4th industrial revolution. Two years ago, industry magazine Business of Fashion proclaimed that no area of life or business will be insulated from AI and today these technologies, as well as easy access to their utilization, are disturbing every aspect of any industry, like never before.

Nowadays, AI is often being communicated as a black box – you feed it data and you get great results, fast. Most current AI algorithms are making predictions and choices not unlike humans. They learn from historical data, and as we will be able to provide AI with higher quality training data, its precision could dramatically improve.

Marketers working in online businesses are excited about the prospects that are brought upon by plugging in artificial intelligence to their e-commerce sites, but we are still scratching the surface of what’s currently possible as marketers and e-commerce managers are actively looking for ways to best apply the benefits that AI promises.

According to a study by IMRG & Hive, Three quarters of fashion retailers will invest in AI over the next 24 months and e-commerce giant, heavily involved in fashion retail, Alibaba, already invested $15bn in R&D labs in a push to become the AI leader.

Impact of AI on Online Businesses:

The ever-increasing scale and granularity of personalization in online businesses is impossible to manage without the assistance of AI and related automated processes. Gartner predicts that by 2020, customers will manage 85% of their relationships with an enterprise without interacting with a human.

A growing number of companies who have adopted these new technologies are raising the bar on service and personalization, which customers have learned to expect.

McKinsey says that the top 20 percent of fashion businesses generated 144 % of the industry economic profit and unless you become one of these top performing companies, you are highly unlikely to make any profit. That’s why finding ways to implement AI is crucial as it helps companies streamline their costs and provide a better customer experience.

Today’s customers expect from their ideal online store that it won’t “waste their time”, that they will find the products that fit them, in stock and in a large variety to choose from. With that in mind, customers yearn for a personalized experience, which is exactly what AI can help them achieve, using deep content personalization and thus, AI is slowly, yet steadily transforming the way customers shop for fashion online.

All the most recognized brand marketers are utilizing recommendation engines to engage their customers with content that is relevant and valuable. Personalized content is currently expected by the majority of e-commerce customers. Let us look at 3 companies which are using AI successfully for personalized recommendations

1. Netflix – Suggests movies and shows based on the content you’ve previously watched.
2. Spotify – Recommends songs based on your listening habits.
3. Amazon – Personalized product recommendations on the website and via email.

So if you have an online business or working in one, read this carefully.

Today, there are three main models which are fuelling most recommendations.

Content-based

Recommendations are based on the similarity between items (e.g. Similar Items). Products are recommended using visual or contextual similarities.

For example, if you are browsing green winter jackets, this algorithm will suggest other jackets sharing the same properties (e.g. category: winter jacket, color: green). The advanced technology of Natural Language Understanding can be used to recommend products sharing similarities in description (unstructured text).

Lastly, Convolutional Neural Networks (CNNs) can extract various visual features, such as shapes, colours, or textures from images and use this representation for finding similar looking clothes.

Collaborative filtering

Recommendations are based on the similarity between users, taking into consideration their view/purchase history (e.g. Customers who bought this item also bought).

For example, if user A viewed items 1, 2, 3 and user B viewed items 1,2, this model will recommend item 3 to user B.

Neural Networks

The convolutional neural network makes the applications of recommendations in e-commerce feasible by learning to recognize image representation and find similar images. Based on this ability to learn images and search for similar products, the Convolutional Neural Networks (CNN) can make applicable recommendations. It is an algorithm that was inspired by the complexities of the animal visual cortex. It is comprised of several modules whose sole purpose are to identify and analyze visual imagery.

If the network is designed to work within facial recognition, then some modules may be activated when they spot various facial features (ear, nose, etc.). Convolutional Neural Networks are viewed as being among the most modern advances when working with pixels. To get a better idea, go to Google and type “blue jeans” into the search field and click “images”. From the results that you see you should have a pretty good idea of what Convolutional Neural Networks (CNN) is capable of doing.

There are many recommender solutions which are helping businesses to immediately engage their customers with personalized content. The downside of these solutions is that without a proper analysis, it is difficult to establish which recommendation engine is the best for your business. So before jumping into anything get an expert to consult with your team in order to understand your business and design a customized recommendation engine tailored to your needs. 

That being said, here are some recommendation models for starters for your online business that will fetch some instant results.

1. Product Detail - Create a recommendation model specifically tailored for personalized recommendations on a product detail page. This pre-set delivers recommendations which are the most relevant alternatives to the product on the detail page. Although, it is possible to use these recommendations in an email if you want to tailor them to a specific product that you are promoting, they are the best fit for a product detail page.
2. Purchasing Recommendation - One of the worst problems that fashion retailers are facing is overstock issues. AI is being utilized to predict which products should be purchased to meet the upcoming trends and in what volume. This is being based on the purchasing power of customers and even current stock in hopes to reduce overstock. Some algorithms could also be used to predict supplier price changes and recommending the ideal purchasing windows to lower the purchasing costs.
3. Customer Recent Interactions - Create a personalized recommendation tailored to the customer according to items the customer has recently purchased or viewed. These can be easily used in both emails and on the web.
4. Customers who bought this item also bought - Create a sophisticated model, that recommends other items that were bought together with this item. It is ideal for a product detail page, but again could also be used in an email when promoting a specific product.
5. Customer Purchase Prediction - Leveraging aggregated data from all of the customers, AI algorithms could be used to predict whether certain customers are showing signs of making a purchase, such as visits of a certain number of product pages or increasing frequency of newsletter opens. These insights are then being leveraged to ensure the purchase and create a positive customer experience.
6. Trend Prediction - By monitoring social media and other data sources, AI could be utilized to predict trends according to similar behavior in the past and its results.These insights could be used to inform the purchasing department to stock certain types of products or marketing to prepare specific communication campaigns.
7. Ideal Price Point Recommendation - Using freely available data, AI can be used to monitor competitor product prices and recommend ideal price points to maximize revenue. These changes could be automatically applied using a broad strategy, such as keeping the lowest prices, but retaining at least a minimal margin or to maximize profitability even by slightly increasing the price.
8. Customers who viewed this item also viewed - This is the most common one and is essentially the same model as the one above, but instead of a purchased product, it takes in consideration viewed products. It’s also ideal for the detail page or email when you would like to promote a specific product.
9. Popular Right Now - Create a simple business rule recommendation model based on a specific metric. This metric is calculated project-wide and is not personalized. Therefore, if you want to look at best-selling items, you will see the best-selling items for the whole project, not for a specific customer. This model delivers high value for both email and web recommendations.
10. Similar Items - Create a recommendation model of similar items based on catalogue attributes. Therefore, this model considers similarity in text description of items. Although it could be used in an email to show similar products to the one you are promoting, it’s used best on the product detail page.
11. Personalized Recommendations For You - Create the most complex recommendations out there. This uses collaborative filtering to identify similar users and show you products that you would like. While it might be OK for emails, it is best fed from front-end events and is ideal for web deployment. A good use-case for this model would be to just recommend items from the same brand, where you would only have one required attribute - brand. This way you could upsell complementary products of the same brand.
12. Inventory Management - Fashion retailers tend to have significant capital tied up in inventory and artificial intelligence is being utilized to help them increase the turnover of stock by taking into consideration the “need” to sell older stock as soon as possible. This is a crucial AI use case that helps fashion retailers increase their profitability, since the longer you have inventory in stock, the chances to sell it decrease. 

Using these recommendations will keep your business growing, enabling you to sell, up-sell and cross-sell your customers.

Once any item is added to the shopping cart, Use the collaborative filtering model to recommend items which other users browsing the same item are additionally viewing/browsing. This enables you to upsell your customers just before they finish their purchase journey. Growth in your revenue is one side of the coin, the other side is that by delivering only the most relevant content you will keep your customers engaged, loyal and happy by saving their time and acting as a trusted advisor throughout the purchase process.

Hit me up if want to talk more about picking the ideal recommendation for your online business here: [email protected]