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Personalized retailing is not a new concept. Ten years ago, Amazon and Netflix were typical representatives of personalized retail. Since then, Amazon has been able to present personalized homepages to consumers based on their browsing history or historical shopping history. This practice was highly praised at the time. In contrast, the personalization of other retailers is much simpler—identify customers who have browsed their web pages by name, and say hello to them, or allow them to save site preferences; then there are pages for subdivided visitors. View effects this one-to-many web page rendering.
And now, these most basic methods have become a bargaining chip for retail marketing. But consumers are smarter than they were a decade ago. They not only want personalized services, but also want you to predict their preferences! Artificial intelligence (AI) applied to the retail industry makes this demand possible.
With the explosion of big data and the application of machine learning in distribution platforms, the personalized retail industry is undergoing a dramatic change.
| AI successfully used in the retail industry
Some retail organizations have used AI to build successful business cases. Take the example of Stitch Fix, a US-based personal exclusive fashion style shopping website for women. The style order service can use AI to tailor clothes and matching accessories to suit their taste, budget and lifestyle. Fashion editors regularly post 5 pieces of costumes for customers to choose from. They can buy clothing that customers are satisfied with. If they don't like it, they can package it and send it back. Stitch Fix's fashion editor works with more than 60 big data analysts to analyze each item for the right user. The machine recommendation is mainly to collect user's various traces on the network to analyze user preferences, while the editor predicts and even based on Personal ideas provide users with suggested clothing styles. As machine learning is used in this process, more and more data will make computer computing more intelligent.
This strategic marketing that combines human and machine intelligence has been a success. According to statistics, more than 80% of customers place orders on the site again within 90 days. About one-third of customers spend half their clothing budget on subscription services.
Although we see that many companies also want to catch AI trains to accelerate their retailing process, the Stitch Fix approach does not apply to all companies. On the other hand, experienced retailers can apply AI to different business units. Macy said its consumer service department is testing an AI-based "mobile companion." The consumer can tell the mobile partner to buy the item from the physical store, ask questions, and get an answer. This allows the AI ​​to interact with the customer in real time. That being the case, how to use this technology to expand new users?
| What should a marketer do?
The use of AI in marketing not only enables retailers to acquire new customers, but also enables them to replicate patterns. As merchant recommendation functions and supply functions are increasingly tailored, customer loyalty will continue to deepen. Every day, consumers will see hundreds of advertising and marketing information, and want their products to stand out from the information, AI-based marketing is essential.
With the help of AI, marketers can use powerful algorithms to find patterns within websites and within third-party data, and then look for duplicate points in these patterns. The key to turning artificial intelligence into personalized retail services is machine learning. This sounds very simple, machine learning is the use of data and probability to solve problems. However, in the context of personalization, machine learning can continue to adjust data settings until the most appropriate marketing information can be presented to individual users through the most effective channels.
For personalized marketing supported only by AI, marketers need to keep the following in mind:
Don't be overwhelmed by big data
Many retail organizations have a lot of data, but they don't know how to organize the data together, let alone make them meaningful. If you don't have internal resources to use these separate data, outsourcing this work to other technology companies can be an efficient way.
Use as much of the third-party data as possible
From POS machine swipes to loyalty data measurement, retailers have a wealth of consumer information. However, if you want to have a 360-degree understanding of every consumer, you must get a lot of third-party data. I do not mean simple demographic data, although it does have some effect. Your data should incorporate factors such as location, time, social activity, price sensitivity, etc., because it is this data that lets you understand how, why, and how consumers shop, and find trigger points that are effective in stimulating consumer shopping. So that merchants can show them the marketing information that best evoke their interests.
Decide the best output channel
As shown by Stitch Fix's "mobile companion," mobile terminals can be used to enhance in-store service while it can be used as instant promotions and instant offers. In addition, retailers should also focus on chat bots powered by AI technology.
Of course, you don't have to offer personalized ordering services to customers like Stitch Fix. As long as you apply AI in a certain business area, you can make great achievements, whether it is marketing, consumer services, or sales. Personalized technology has long been known and applied by people. Now, with the arrival of AI, it will also become the future of the retail industry.
Via: retailcustomerexperience
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