“Artificial Intelligence is about replacing human decision making with more sophisticated technologies.”– Falguni Desai
A few years back, it was not possible to imagine that we have to depend on Artificial Intelligence guidance to make our purchase decisions. However this has become the actual picture today.
Machine Learning Technology or Artificial Intelligence, incorporated in ecommerce apps, have made our shopping decisions easier and much more customised.
A popular post has come up on Twitter that clearly presents our current dilemma-
“The biggest problem we have is not what to buy but what to wear.” -uriminkoff
Fluper believes that the younger generation, today, finds it brainstorming to keep up with the urban culture and the modern fashion trends that come with it. In their attempt to remain updated on the current fashion trend, they search for Shopping Apps that will not only feed them with the ongoing trends but they can custom make their outfits as well. They also expect the Apps to serve as their reminder to make essential consumer durable purchase also.
Based on these popular demands, companies like Amazon, Facebook, Google, Apple and others are accelerating customer expectations and what is technically feasible. Here comes the role of Machine learning technology that is working hard to bring out new and innovative features, thereby making the whole shopping experience totally automated.
Imagine you are about to leave for work and you hear a voice from a particular device on the dining table: ” It looks like you are about to consume your last remaining stock of milk by tomorrow evening and yoghurt is on sale for a minimal price of 50/-. Will you like to make a purchase of milk and yoghurt from the Trader Joe, for a total of 150/-.” You agree with it and the order is made available at the time of your return from office.
The above story sounds similar to something that is featured in a futuristic Sci-Fi movie. However the future is not very far and the Big Companies are finding out ways to harness the AI technology to convert this imagination into a proven Reality.
MACHINE LEARNING IN PREDICTIVE SALE:
To survive in this market, it is becoming more crucial for companies or web retailers to understand and study the purchase behavior of their prospective consumers. Retail Giants have been using machine learning algorithms to forecast their demand-supply curve and decide on pricing strategies as well. AI or MLT can be exploited successfully by the retailers to predict not only to stock their stores but also to dynamically suggest products and set prices suiting to the individual consumer taste and wallet. This function requires a lot of research on the purchase behavior, taste and living standard of its consumer base.
Currently the buyer behavior and expectation has undergone a rapid change. Recent study has revealed that they expect the Shopping Apps to have Artificial Intelligence technology embedded in them. They require the Apps to not only provide them with a wide variety of products to buy from but also to understand their taste and customise the products as per their requirements.
Let us look into the basic features that an Idol Shopping App must have
Customised Styling and Personalised Recommendation:
This system is made of several data-sets, allowing the Shopping Apps to categorise the consumer items in different segments enabling the shopping recommendation more relevant and customer satisfying. Example: Polyvore is a popular fashion community that uses the AI technology to bring out different types of recommendations.
Users also look for style advice suggestions from a community of friends or professional stylists and requires an App which can fulfill their need. Example: WeStyle is an Application which allows users to hear a second opinion before making a purchase or selecting a look. They can also upload two images of their look side by side and request the community to vote for their favorite. The App also has Cropping Tool that enables users to edit the image before posting them.
These apps are helpful with the “Personalised Recommendation Feature” that allows the apps to show users the items they are likely to purchase based on their sense of style. This system is based on machine learning technology.
Filters and Combinations:
It is important for the Apps to remain up-to-date and successful in the competitive market. Hence the Applications require detailed filters that divide search results into groups based on type, style, color, purpose, brand and price range. These filters enables users to make a perfect purchase for themselves and allows the companies’ app to remember what users liked most often and to offer a personalised selection of goods each time they login.
Another feature that attract users is to push notifications like price drop for items, discount offers, lucky draw contests etc. If the user gets a chance to save money on purchase of a particular product, they will definitely use it even though purchase was not the primary concern for the user initially.
The above features are some of the key factors that the Shopping Apps must incorporate in their contents thereby harnessing the benefits of ML technology to its advantage and staying ahead in the cut- throat competitive market.
Machine Learning has opened new venues for the App based retailers and others as well. It has enabled them to generate more revenue and render quality customer service. These tools are available for smaller players as well. The AI technology has not only helped the users in simplifying their shopping behavior but has also enabled the App based e-commerce companies to indulge in personalised selling, reaching the core of customer satisfaction.
This new and innovative revolution in Modern Technology is here to stay.