Spaycial is a Paris based company founded five years ago. We are around 40 people now, and we've raised a seed round of EUR 1.5 million. We provide a SaaS B2B solution which allows brands, shopping places and retailers to mitigate a better relationship with their shoppers thanks to payment data. This refers to data representing shopping behaviours that, if accessed and studied, allows them to have a unique 360° vision of their shoppers.
To operate our service, we rely on a banking license, which allows us to connect a payment method to shopper benefits (a loyalty programme and rewards, a personal shopper, BNPL, etc.). Once shoppers link their account, they are going to get their benefits (cashback, for example), and we are retrieving transaction counts that will be enriched by our data team. This way, we can identify from raw transaction labels who is the merchant, what has been purchased, whether it is online or offline, and so on. If you do this at scale, you are going to map the behaviour of your shoppers in a 360° omnichannel way, which is a game changer.
When it comes to the CRMs of retailers, right now they rely mainly on what we call RFM models1. These models can become an obstacle when they are limited to purchases and behaviours they can track within the brand, while the reality is that shoppers are now more volatile than ever and they tend to be on multiple sales channels at the same time. This paradigm creates a high level of complexity for retailers. Moreover, by relying on RFM models, they have no visibility with regards to what the competition is doing, safe for declarative data and market research.
When it comes to our product portfolio and main areas of interest, we can name three important ideas:
We provide analytics dashboards and insights, which allow our clients to show, if they are Zara, for example, how many of their shoppers are making purchases from Mango and Uniqlo, and with what frequency. We also provide input such as catchment area analysis for dedicated retailers (delimitating, for instance, what are the true competitors a retailer has in a one-kilometre range, thus identifying their true penetration rate within that area).
360-Shopper Profile, which contains data on the shopping behaviour of consumers (all the spending data in their budget, the percentage of the shopper’s total budget, which is dedicated to the retailer’s segment, data related to the average spending basket – how much of his fashion budget is the shopper allocating to Zara and another fashion brand, in our previous example in the fashion vertical, and many more points of data.) We provide insights into how shoppers are behaving across channels. It is convenient to know for a merchant whether their shoppers and members are spending money online vs in-store, especially when they’re thinking about targeting a specific channel, this data can help them maximize the value of their marketing campaign. We also allow the possibility of identifying to what brand they might lose turnover (in the above example with Zara, Mango and Uniqlo, they might be able to identify if they are losing shoppers to Mango or Uniqlo, for instance).
Shopping Scoring. This allows merchants to get insights into the potential and the expected additional revenue they could take from a specific group of shoppers, if the retailer is targeting them in their campaigns. If one shopper spends EUR 500 on fashion, it can be spent on one EUR 50 purchase in Zara and EUR 450 can be spent on one purchase in a premium store. Conversely, the shopper can spend the same EUR 500 a month but spread out over 10 purchases on fast fashion. We communicate to the brand that in one case, the shopper will have a bad shopping score and it will not be a good match for the merchant client’s target group, since they are spending their budget on a small number of purchases in a high-end store. This category of shoppers might not respond to targeting tailored to the fast-fashion segment. On the other hand, if that shopper spends all their fashion budget within the fast-fashion segment, knowing that Zara gets 10% of their budget, while the other 90% goes to Mango or Uniqlo, this makes the shopper more targetable within the same market and the merchant is more capable of getting market share at the end of the day.
The competition from online pure players (such as Amazon), which have been on a constant rise in the past years.
Shoppers are more volatile than ever (incomparable with a shopper’s profile even 10 years ago). We now have a lot more use-cases in the way people buy – options like click and collect.
When merchants try to implement a loyalty programme, they are losing 35-40% of engagement because people are not presenting their card. This reality is paired with the fact that, on average, merchants need a nine-month history of engagement before they can make predictions regarding shopping behaviours.
With payment data, merchants get more than that, however – a 360 vision, as we call it. They are better equipped to identify their competitors and monitor them.
Moreover, as new brands emerge each day, shoppers become increasingly difficult to track, especially across more and more sales channels. Finally, what is also extremely important for the sales process is that retailers are struggling to get a clear overview of the impact of the POS over the ecommerce channel. The reality is that we have that data, since we operate the same programmes for shopping apps as well, so we could track if someone went to a store, yet three days later, they purchased an item from the same brand, but online, from a third-party shopping app.
In this larger context, instead of thinking in terms of ecommerce eating brick-and-mortar stores, it is important to identify what is the use of a store nowadays. The store is not only a logistical outlet, but it also has the possibility of offering a whole experience that an ecommerce website cannot compete with. The information regarding all these trends is interesting to study and learn from, and we can see that retailers have a hard time harvesting it.
To launch this product and operate in Europe, we have a banking license. We are being audited every six to seven months by the Central Bank of France. Apart from this, since we are part of the European Union, our solution is being passported to 29 countries in the EU.
The way we retrieve data is based on PSD2, a regulation that made payments data to become personal data and regulate the channels through which it can be shared and used by channels outside the banking system.
Lastly, until two years ago, retailers were tracking users on ecommerce websites and social media. Now Google, Apple and such platforms are removing third-party cookies. Because they do that, it became much more difficult to track someone during website navigation. With Spaycial, since the consent is given by the shoppers, the retailer has access to the entire spectrum of shopping behaviours without having to need access to the website cookies and without having to share this information with anyone in GAFA (Google, Apple, Facebook, Amazon).
We are currently live in ten European countries (Germany, the Netherlands, UK, Poland, Czech Republic, Spain, France, Sweden, Luxembourg, and Austria). We are integrating with around 3-400 banks, seeing first-hand how PSD2 three years ago is not the same as today and how the shopper experience changed drastically.
In the case of Spaycial, our shoppers are on mobile, in a percentage of over 90% and when you validate yourself on this platform it becomes as easy as validating a transaction on your phone. This is because you are redirected to your bank application, where you only have to use your credentials and just like that, you are logged in.
Payment orchestration has helped us in scaling the business. In the backdrop of Open Banking and PSD2, as a young company, we had huge ambitions and within 18 months from our launch, we integrated in ten countries. However, even though PSD2 is a European regulation, this creates huge challenges, as each country’s banking ecosystem has its own specificities. Payments orchestration, provided by our partner Spreedly, allows us to comply with local regulations faster.
In Sweden and the Czech Republic, for example, we don’t use the same technology that retributes users through cashbacks. Instead of doing a bank transfer, from the PSD2 data we trigger, we are asking users to fill in a credit card. This way, we are able to tokenize that card and reuse it in multiple ways. Overall, our system allows us to provide a seamless payments experience throughout multiple countries, reuse card details for multiple use-cases, and provide a better user experience for shoppers (by not having to ask for their details every time we want to onboard them into a new product or even in a new country we launch in).
Right now, we aim to deliver more value for people and brands. For shoppers, it is going to be through new benefits and making sure they are not as dependent as right now on cashback. Apart from our constant international expansion, we are looking into upgrading our analytics service, allowing our clients to gain better access to their hyperlocal competition based on payment data. We want to improve our AI models through activation, churn prevention, next-purchase analysis, and other use-cases. These upgrades are aimed at providing added value for our merchant clients while generating incremental revenue. For the shoppers, in turn, implementing services such as these will allow them to have a better relationship with their favourite brands as they are going to get a better experience online or in-store. This is why we call ourselves Spaycial, as we hope to navigate the special relationship between these two sides.
Benoit is a long-time experienced Product leader, as he was CPO of HelloTrip for 3 years, a tech start-up in the world of travel search engine, before becoming Head of Product at Wynd, a unified commerce platform, for two years and ultimately joining Spaycial more than two years ago in May 2020.
We make it easy for brands, retailers and shopping places to access payment data, the ultimate shopping behavior knowledge for them to have better relationships with shoppers. We understand and analyse shopper behaviours to deliver marketing activations and engagement, helping our clients have cutting edge insights to offer personalized and automated benefits, unattainable until now, to regular members and new shoppers, and ultimately boost their revenues.
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1The RFM model is a behavioural segmentation method that allows you to segment and analyze customers based on three variables in your historical data: Recency (R), frequency (F), and monetary value (M)
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