Consumer Data Intelligence

Luxury Fashion Retailer

In a world where most companies are focused on improving personalization, brands that don't prioritize creating a tailored experience run the risk of leaving behind and leaving themselves vulnerable to brand switching.

Challenge:

71% of consumers feel frustrated when a shopping experience is impersonal. - Segment

Today, customers gravitate toward brands that listen to them, understand them, and pay attention to their specific needs. That's where personalization comes in. Personalization allows brands to contextualize the messages and experiences to each visitor's unique profile. So the question becomes, how does one brand achieve this? That's where Starseed's AI-enabled Semantic NLP solution comes to the rescue.

Solution:

Go Beyond Personalization to Hyper-Personalization

Starseed's sophisticated AI architecture and Semantic Natural Language Processing (NLP) ecosystem uses Semantic analysis, coupled with Behavioral Monitoring, to build a holistic view of your visiting and registered customers with or without historical data. This tailor-made real-time solution allows you to offer advanced and real-time content customization and customer experience at an individual level.

Case Study: Luxury Fashion Retailer - Customer Profiler

When a customer profile is too broad, that means it's being targeted to the general audience rather than an individual profile. Thus, you end up solving a few problems for a few customers.

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Goal:

Our goal was to build a personalized profile for individual consumers to uncover key driving factors behind their purchasing a product, thus helping BrandX offer real-time and advanced content and customer experience at a personal level.

Result:

Using Starseed Semantic Consumer Data Intelligence, we auto-collect, organize, and semantically auto-generate the "Customer Profile" of both gust visitors and loyal customers and find out what they care about. The results were nothing short of a gold mine of meaningful insights into the customers' mindsets at the subconscious level.

Breakdown:

By monitoring just a few interactions, Starseed was able to gather the customer's main topic interests and other metrics such as intent, name, time, value, tone, emotion, impact, color, style, intensity, nuance, and more to build a holistic customer profile for each visiting consumer. Then, using the profile, Starseed recommended appropriate content to be shown to the user.

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