Home Featured Innovative Technologies for Retail Stores; in Conversation with SixSq

Innovative Technologies for Retail Stores; in Conversation with SixSq

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Recently, our CEO & Founder, Mark Harrop, spoke with Marc-Elian Bégin, CEO & Co-founder of SixSq, a company based in Switzerland developing new tools for industrialising edge computing. Knowing the value that video analytics can bring to the industry, the challenge of providing solutions that totally preserve the privacy of current and future customers, and the need for solutions that reduce the number of returns, Mark was interested to know more about the technologies being developed by SixSq.

Data for decision making

Mark Harrop: I understand that SixSq develops new technologies for retail stores. Can you tell me what problem in the industry SixSq is addressing, and the type of solution that you’re proposing?

Marc-Elian Bégin (Meb): Retail outlets are struggling to react quickly enough to new demands and trends. At the same time, they have to respect all-important privacy requirements, and of course speed up the drive to sustainable fashion. Our vision is to use the latest computer technologies to bridge the gaps between Artificial Intelligence (AI), big data and the cloud. By using what is called edge computing, SixSq proposes to process data close to the action, where the decision needs to be taken, instead of moving all the data to the cloud. We have implemented similar solutions for smart cities and with the European Space Agency for big science at the edge and can see huge potential for retail.

Mark Harrop: Today, we don’t have these data insights in time to be reactive and, in my opinion, the fashion sector certainly needs these insights to improve the way we design, deliver and communicate with the end customer. Twenty years ago, we worked on a two-season year in the fashion industry. Fourteen years ago, we worked on a four-season year. Over the last decade we went to twelve seasons per year. Then we dropped the seasons and we now deliver products/collections by weeks or even days. The Supreme brand for example created massive demand based upon limited product offerings: buy it now or it’s gone! And those products can have a tremendous scarcity value and uniqueness. We need to know the answers in a matter of hours, not in weeks or months. Can your solution help to develop new dashboards for a store that provides all useful information on consumer habits?

Meb: Absolutely. Our platform provides a user-friendly dashboard interface that can be customised to deliver whatever data the customer needs. We can bring the digital revolution offline and into stores. In fact, a major IT integrator came to us recently to industrialise face recognition in the luxury brand stores in their portfolio.

Mark Harrop: Interesting. And it makes sense that the luxury sector is now starting to look at edge computing to develop a smarter tailored service offering. Here is another use-case for you. When people come into the store we need to know:

  • Where they are going?
  • What they are picking up?
  • Do they mostly go for the bottom shelf, meaning popular merchandise is not located in the right place?
  • How do they move around the shop?
  • How much time do they spend in the store?

Can we start feeding this information back to the store design teams and merchandisers to improve the customer’s experience, thereby helping our customers deliver more value in the shop?

Meb: Edge computing can certainly do that. Let’s look at the situation where image recognition doesn’t find a match. In this case, we can carry out anonymised behaviour analysis to inform decisions about store layout, content, etc. Their activity in the shop can be observed by AI and the software will analyse the anonymised data to provide insights via a dashboard. Once the system is in place, you can actually update the programs remotely based upon the learned insights, meaning that the system is future-proof.

Secondly, there is the use-case of an existing customer entering a shop. A customer who recently visited a shop in London, for example, enters a shop in Singapore. Since that person can be recognised, and let me stress only once he/she has opted into the programme, we can provide some advice to the staff. Is this a customer that we should put in the VIP salon in the back of the shop, or is it a conversation that should take place on the shop floor? What would be the next potential product, or campaign, or visit to engage on? It’s not just technology push, it’s about combining the latest technology with the strength of the brand AND the expertise and personal touch of the local personnel to provide the best service possible.

Mark Harrop: It makes great sense to me. At this moment in time, it is obviously aimed at the luxury market and possibly elements of the mid-market. I like the idea of enterprise-wide sharing too. As you say, someone goes in a store in London, and then appears in another of the luxury chain in say Singapore, and if we know that they don’t mind being introduced by their name, suddenly they feel part of a community rather than walking in a cold store, with the usual opening statement of, “can I help you?” I believe over time that facial recognition and the ability to understand the customers and their behaviour insights better will come down into the mid-market and beyond – and why not!

Personalisation vs. privacy

Mark Harrop: But how do we make sure that privacy protection laws are respected, as this is extremely important for customers? Consumers want a personal experience, but they still demand and deserve to have their privacy protected.

Meb: One of the challenges is indeed to implement a solution that totally preserves the privacy rights of the customers and fully complies with the new European data privacy regulation (GDPR). Edge computing provides a great opportunity to get rid of many of these privacy intrusion issues because you are processing the video or audio streams in the shop, near the action. You extract the insights, make a decision on that and then discard the data. That’s a key advantage that edge computing brings. By performing face recognition at the edge, that is where video data is being captured, no sensitive data needs to be transmitted to the cloud or stored. SixSq is therefore able to propose secure technologies for enhancing the customer experience at the retail store.

Zero returns – just a dream?

Mark Harrop: Let me offer another potential use-case. In the retail industry there are far too many returns. In fact, I delivered a webinar recently about sizing and fitting and the use of 3D scanners. The biggest concern is that the customer does not know what his/her true size is. So, retailers and brands are making garments based on sizing surveys, rather than accurate data. The problem is that a person’s body size differs from one region to another. And retailers and brands make and send the same sized garments out to cover all regions of the world. So consequently we now have a tsunami of returns. There are nearly 68% of products being returned today. It’s a massive problem for the fashion sector, and for the environment. Collecting data on the actual size of the prospects (that differs from one region of the world to another) and on their taste, this could help to boost sales and decrease the volume of returns. Not only would this be beneficial from an economical perspective, it could also reduce the carbon footprint of the products. Can we use the same edge computing to help ensure our customers get the right fitting garments by using sizing data dynamically fed to the store employee helping the customer?

Meb: Video analytics could potentially be used to determine the sizes of individuals. The beauty of an edge computing platform is that once we have identified a partner with the correct AI software to tackle the problem in hand, we can fully integrate their solution with our secure edge platform and continue to provide updates to the customer without any disruption to in-store logistics. 

Level the playing field for offline stores

Mark Harrop: I am glad to hear that these edge computing technologies are finally addressing the needs of the retail stores. For the last few decades, high-tech companies mainly focused on helping electronic commerce to develop.

Meb: In many ways, video analytics at the edge will give high street stores the information that online stores already have at their fingertips. Online shopping behaviour is tracked and analysed all the time, delivering a different experience to consumers. If high street stores want to get back some of the online business, they need to be able to react in similar ways, with the added advantage of the personal touch. And they can’t do this without data. We want this technology to make retail more efficient and sustainable. Video analytics allow the store to establish a closer relationship with its customers. The technology allows store managers to reconnect with every person walking into that store.

The early adopters for such technologies are the luxury brands, but in the end most stores will want to follow this trend.

Mark Harrop: Thank you Meb for educating me on how edge computing can positively impact the retail industry. This will need to be followed up closely for the best interest of the industry. I wish you good luck with all your projects.

Meb: Thank you. And, many thanks for your insights, and to our growing partner eco-system, without whom we could not deliver this rate of innovation.

Lydia Mageean Lydia Mageean has been part of the WhichPLM team for over six years now. She has a creative and media background, and is responsible for maintaining and updating our website content, liaising with advertisers, working on special projects like the Annual Review, and more.Joining mid-2013 as our Online Editor, she has since become WhichPLM’s Editor. In addition to taking on writing and interviewing responsibilities, Lydia has also become the primary point of contact for news, events, features and other aspects of our ever-growing online content library and tools.