Data Is The Key To Falling In Love With Your Decor Product

  • March 20, 2020 at 9:41 AM EDT
  • By Jing Xue, DecorMatters
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Some years ago, decorating a home or an office required professional help. People trusted this labor to experienced people with critical eyes, and they took some distance from the task. But digital technology has forever changed the way we decorate our spaces.

Today people embark on decorating projects where they can really feel they are putting their personalities in their walls. In addition, in these “DIY” days, people transform old furniture or build new products by themselves.

First, people started to get inspired by mood boards and social platforms like Pinterest. But then new technology like AR arrived and people felt more empowered to decorate by themselves than ever before.

Yes, AR is amazing. You can see how your actual living room will look with that sofa you loved at IKEA, it will match your carpet color and it will fit exactly in the space you have thanks to features like AR rules. But behind a great decor success like this one, there is a unique resource that is making people falling in love with decoration: data.

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Data-collecting technology is becoming more of the norm. Sensors accurately capture choices from different profiles and process them in order to show better options to each customer. Even though sensors need to respect everyone’s privacy, thanks to data brands, they also can define upcoming trends.

Now, retailers can take extra advantage of the information that data and artificial intelligence provide them by focusing on offering products that can match others from different brands. This can sound surprising, but we are in the age of cross-selling, and it is time that we acknowledge that customers can love a sofa from one retailer and match it with a carpet from another one.

Let’s face it, customers love mixing and this is not going to change. Today we need to understand that our clients need a unique place where they can shop for different brands in order to satisfy their tastes. This necessity should not be considered a downfall, but an opportunity.

Because of this, a couple of years ago, new decoration platforms based in high technology like AR, AI and machine learning started to appear. These platforms showcase multiple brands in a unique view and their goal is to improve the user’s experience and deliver unique choices to each customer. Thanks to these platforms, people can shop from different stores with a seamless experience.

What’s more, these modern platforms are based on a thorough knowledge of their customers, and can display a wide range of options from big retailers and independent designers. All these new opportunities transform the shopping experience into a community of decoration lovers that get inspired by new trends and creative designs.

With all of this in mind, if retailers start understanding the value of collaboration and cross-selling, they can improve their sales. It’s not a matter of disregarding the brand or the opportunity to sell a whole decoration space, but it is time to understand that people can fall in love with one product and that retailers can offer great ones that can match others from different brands.

Data has forever changed the way we decorate. Thanks to data sensors and analytics, decoration trends are put together and technologies like AR and AI deliver top-notch experiences to customers. Great products that match requests from decoration communities are more than ever required, but it is the retailers’ job to gather all the data from their customers to offer the best ones.


Jing Xue obtained an M.S. from Rochester University, and a B.S. from Harbin Institute of Technology both on Computer Science. She has overseen AI product development and management at Nvidia and Qualcomm in the past. Currently, Xue serves as Co-Founder and COO of DecorMatters, the first AI-powered collaborative platform that brings together interior designers, shoppers and furniture retailers to make any home renovation project easier and more affordable.

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