How brands are leveraging AI for customer acquisition?

As the fashion industry rapidly adapts to new technology, brands are leveraging AI and ML to reach out to existing customers and attract new customers. Leading fashion brand Tommy Hilfiger turned to AI to improve its designs when it tied up with IBM and Fashion Institute of Technology (FIT) in 2019. Their project Reimagine Retail, was directed towards using AI to map out future industry trends and also in improving the design process. Popular sports fashion brand Nike uses AI to keep the customer happy by personalizing customer experiences and improving engagement. As per Nike 60 % people wear the wrong shoe size and to fix this problem, the folks at Nike created the Nike Fit tool and integrated it with the Nike app where customers will be able to find their right size and even see how the product looks on their feet. This is done using a combination of technologies including Computer vision, Data science, Augmented reality, Recommendation Engines, and Machine Learning. 

Nike also has access to a wide range of customer data from its supply chain, enterprise data, and the app ecosystem. In 2017, the company had announced Nike Direct, a direct-to-consumer sales channel. To attract customers using product recommendations, the company has acquired four data science and analytics firms since 2018. Each of these acquisitions contribute to the ultimate goal of taking a step towards better customer experience. While ‘Invertex’ brought powerful 3d scanning technology that creates accurate models of one’s anatomy to Nike, ‘Zodiac’ projects revenue streams at the individual-customer level as it applies predictive behavioral models and customer analytics to target data, and Celect helps in optimizing inventory by predicting demand for the future by applying ML to the current data. So, by integrating varied tech, Nike uses the huge amount of data at its disposal to create customized recommendations and create demand. If financial figures are anything to go by, Nike Direct sales have shot up from USD 11.7 billion in 2019 to USD 16.3 billion in 2021.

Moving on to the luxury segment, Dior too, has used AI to launch its chatbot or beauty assistant called Dior insider. It chats with customers on the Facebook messengers and helps the customer with what they are looking for.

A new study from Juniper Research found that the global spending by retailers on AI services will reach $12 billion by 2023, up from an estimated $3.6 billion in 2019. Juniper expects over 325,000 retailers to adopt AI technology over the period. The future of the fashion industry clubbed with AI looks promising for sure

Role of AI in OTT platforms

That 98% match to your preferred movie types or TV serial genres on your favourite OTT platform is the handiwork of AI. With on-demand streaming fetching more and more users globally, it is only natural for media companies to look to AI to enhance customer experience. Let us look at how they are doing it.

AI in recommendations

AI-backed recommendation engines gather, collate, and extract user data before filtering it out in the form of recommendations.OTT platforms are some of the biggest users of such engines to push out the best content to their users. The more personalized the content is the more likely it is for the customer to remain loyal to the platform by watching more and more. Netflix Recommendation Engine (NRE) has great accuracy as it  filters content on the basis of an individual’s user profile using a mix of algorithms filtering over 3,000 titles at a go using 1,300 recommendation clusters. It is no wonder then that the market size of the recommendation engines is poised to reach $12.03 billion by 2025.

Role of ads and Metadata

As users of OTT platforms you may have noticed videos on these sites have information about the visuals, the emotions, a synopsis, and the genre of the show or film such as horror, romance, thriller etc. This is the metadata on which AI can assess the scenes in the show or movie to generate teasers automatically. Meanwhile, using the same metadata, advertisers find the ideal spots where they can place the products.

Demand forecasting

User behaviour can also be predicted by OTTs using demand forecasting. They can analyze which genres will resonate well with particular audiences. Demand forecasting can thus help OTT platforms find genres for fresh content, determine what is a good time/month/period of the season to release new content, the preferred languages and so on. Some of the most watched shows on Netflix for example are from other languages such as Squid Game in Korean, Money Heist in Spanish and so on.

As the entertainment industry moves towards wider use of AI, it is the early adapters that benefit the most. Intense competition also warrants constant innovation of AI tech to boost efficiency and keep up with the trend of upward growth.

If you are interested in the metrics of OTT and how AI is driving this craze, write to us at contactus@infiniteanalytics.com and do sign up for our weekly newsletter

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