Machine learning utilizes big data to understand users' needs and to provide them relevant information. To a retailer, how will machine learning improve the way they interact with their consumes? In this article, I will talk about 3 pioneers that has already been using AI to provide a better user experience and what are 5 areas you, as a consumer, can leverage on the AI power to improve your shopping experience.
In terms of machine learning, currently, most of the implementation is focusing on the awareness/discover stage.
Understand your need and suggest the right products: The North Face shopping assistant powered by Watson through agency Fuild, then you go on the website to search for the perfect garment, all you need to do is to answer from simple questions, and the shopping assistant will find a list of results base on your needs.
Give directions when shopping in store: Macy’s On Call powered by Satisfi and Watson help consumers quickly reach the store they want to go after they answer some simple questions.
You probably don’t see the value of the first and second use cases, after all, The North Face has only 30-40 products under each category and Macy’s 60-70 shops in one shopping centre.
Then, what about SK Planet’s Machine Intelligence Lab AI Algorithm that can Suggest matching apparel based on what you already have. The SK planet engineers get the advice from fashion style experts and build an Algorithm to suggest how a user can best match their cloth to create some kick-ass styles.
To sublease the full potential of machine learning and understand how consumers will interact with them during their shopping journey, let's look at how machine learning will revolutionize the existing shopping experience:
To summarize, the existing consumer journey will transform as existing flows changes and new factors emerge:
1. Blur the Boundary between Discover and Evaluate: yes, Google does crawl the page and show you a list based on what you have opened; but what it does not is to realize that you opened 10+ pages until you found the one you are looking for. While what machine learning does (and products such as Zwoop shopping assistant), is to take aver the discover and evaluate and by doing so, improve the shopping efficiency;
2. Build a Express Lane Between Purchase and Experience; what machine learning also do, is to reduce the delay between purchase and experience by simplifying the payment experience and dispatch.
3. Bust the Myth of Consumer Loyalty: one potential area of machine learning is to actually help consumers return back to the origin on why they become loyalty to a brand in the first place and thus discover new opportunities to spend less, get more using the same or even less time. You think you like brand A, but this is because you trust their quality and brand B is also a trust-worth product that is 30% cheaper.
4. Transform One Way Advocacy to Two Way Interaction: we cannot control where consumers express their opinion (except you can pay Facebook to delete the negative ones from your Official page); and most of the time it is too time-consuming for us to seek a second opinion before making the purchase; machine learning can help obtain what is needed to make an informed decision no matter where the information is.
5. Shift from Institutional Trust to Individual Trust: with the ability to interact with other consumers, trust and even loyalty is created; it matters more to us what others say than what merchants say, and with machine learning, we can “hear” more efficiently.
6. Connect Consumers to Create a Journey to Discover Together: have you bought any music on Spotify through people you follow?; Ever got clothing and food inspiration from people you follow on Instgram? Machine learning helps create community, to connect people, and from these connections we discover new things we may like.
Currently, the focus has been on point 1 and 6 naturally because they have the biggest impact on revenue. Nevertheless, the other use of machine learning will also bring enormous benefit to consumers.
A millennium consumer will utilize these machine learning to access real time information, to make better decision and to connect with each other. With the matching learning enabled tools, our shopping experience will become more efficient and enjoyable. Developed machine learning create new possibilities for customer experience and allow retailers to offer a personal experience with relevant recommendations and experiences.
In terms of machine learning, currently, most of the implementation is focusing on the awareness/discover stage.
Understand your need and suggest the right products: The North Face shopping assistant powered by Watson through agency Fuild, then you go on the website to search for the perfect garment, all you need to do is to answer from simple questions, and the shopping assistant will find a list of results base on your needs.
Give directions when shopping in store: Macy’s On Call powered by Satisfi and Watson help consumers quickly reach the store they want to go after they answer some simple questions.
You probably don’t see the value of the first and second use cases, after all, The North Face has only 30-40 products under each category and Macy’s 60-70 shops in one shopping centre.
Then, what about SK Planet’s Machine Intelligence Lab AI Algorithm that can Suggest matching apparel based on what you already have. The SK planet engineers get the advice from fashion style experts and build an Algorithm to suggest how a user can best match their cloth to create some kick-ass styles.
To sublease the full potential of machine learning and understand how consumers will interact with them during their shopping journey, let's look at how machine learning will revolutionize the existing shopping experience:
To summarize, the existing consumer journey will transform as existing flows changes and new factors emerge:
1. Blur the Boundary between Discover and Evaluate: yes, Google does crawl the page and show you a list based on what you have opened; but what it does not is to realize that you opened 10+ pages until you found the one you are looking for. While what machine learning does (and products such as Zwoop shopping assistant), is to take aver the discover and evaluate and by doing so, improve the shopping efficiency;
2. Build a Express Lane Between Purchase and Experience; what machine learning also do, is to reduce the delay between purchase and experience by simplifying the payment experience and dispatch.
3. Bust the Myth of Consumer Loyalty: one potential area of machine learning is to actually help consumers return back to the origin on why they become loyalty to a brand in the first place and thus discover new opportunities to spend less, get more using the same or even less time. You think you like brand A, but this is because you trust their quality and brand B is also a trust-worth product that is 30% cheaper.
4. Transform One Way Advocacy to Two Way Interaction: we cannot control where consumers express their opinion (except you can pay Facebook to delete the negative ones from your Official page); and most of the time it is too time-consuming for us to seek a second opinion before making the purchase; machine learning can help obtain what is needed to make an informed decision no matter where the information is.
5. Shift from Institutional Trust to Individual Trust: with the ability to interact with other consumers, trust and even loyalty is created; it matters more to us what others say than what merchants say, and with machine learning, we can “hear” more efficiently.
6. Connect Consumers to Create a Journey to Discover Together: have you bought any music on Spotify through people you follow?; Ever got clothing and food inspiration from people you follow on Instgram? Machine learning helps create community, to connect people, and from these connections we discover new things we may like.
Currently, the focus has been on point 1 and 6 naturally because they have the biggest impact on revenue. Nevertheless, the other use of machine learning will also bring enormous benefit to consumers.
A millennium consumer will utilize these machine learning to access real time information, to make better decision and to connect with each other. With the matching learning enabled tools, our shopping experience will become more efficient and enjoyable. Developed machine learning create new possibilities for customer experience and allow retailers to offer a personal experience with relevant recommendations and experiences.