Swogo is the world's first automated bundling solution for e-commerce retailers. We increase average order value and margin for Europe’s largest e-commerce retailers including Tesco and BCC.
As the e-commerce market becomes increasingly competitive, profit margins are falling and the cost of acquiring a customer is rising. The best way to combat this is to sell high margin complementary products alongside the host product.
However - making these recommendations is difficult, especially when taking into account compatibility, stock levels, profit margins, and more.
Our solution aims to resolve it for them. Our technology matches the best complementary accessories to a host product, based on compatibility, purpose, and more.
Our unique promotional algorithm then optimises these bundles for sales, taking into account profit margins, average order value, stock levels, and more.
This allows us to help e-commerce retailers increase average order value and margin.
Retailers face three major problems which make bundling ineffective:
Manual Work: Retailers are forced to manually match extras to each product. This takes too much time at a high opportunity cost. BCC.nl, a subsidiary of the Darty Group, faced this problem. As an example, they had bundles on just 40% of their laptops. Darty is Europe’s third largest electrical retailer, with annual revenues of over €3.5 billion.
Compatibility: For recommendations to be effective they have to be compatible, such as the right sized case for a new tablet. Incompatible matches lead to high return rates and reduced customer satisfaction. When products are matched manually, mistakes are often made due to human error. MediaMarkt, Europe’s largest electronics’ retailer, face this problem - 12.5% of their laptops have incompatible recommendations.
Optimisation: In order to optimise each bundle for sales, retailers are forced to run through spreadsheets of data. To effectively optimise, they need to take into account attachment rates, profit margins, and more. They then have to manually adjust each bundle based on these findings. It’s impossible to keep up.
This becomes even more difficult when product ranges refresh every quarter and retailers have to deal with a full new set of products. Decide.com found that a new camera is released every 45 hours, a new TV every 15 hours, and a new laptop every 4 hours. Manual bundles go out of date fast.
Enterprise retailers have long development roadmaps, often 18 months in advance. They don’t have the time or resources to solve this problem in-house.
Swogo aims to solve this - as the first automated bundling solution, starting with consumer electronics.
Substantial accomplishments to date
We currently have four paid pilot agreements, in different countries across Europe. Major European retailers including Tesco and BCC, part of the Darty Group, have agreed to paid pilot agreements.
Last year, we were chosen out of 500 startups to join European accelerator Startupbootcamp. We were based in the Netherlands for three months, allowing us to gain a wide network across Europe. We are focused on finding customers within the UK, Netherlands, Germany, France, and the Nordics, due to a combination of our connections and market size. From here, we will expand across the rest of Europe.
If our pilots are successful, we will continue to invest in our technology in order to expand across a wider product range, beginning with consumer electronics.
We based our business model on that of successful social commerce platform Reevoo. They work with brands worldwide, including Dixons, Acer, and Toshiba.
We intend to take a 33% fee on any revenue we generate for a retailer. This means that for every £3 in gross profit margin that we generate for a retailer, we will take a fee of £1.
By working on a success fee, we’re able to grow alongside the retailer. Furthermore, as our technology continues to improve and optimise, we will be able to benefit from it.
Use of proceeds
We have already raised £250,000. We’re now looking for a further £17,500, as part of a £70,000 round, the majority of which is being raised offline.
With this, we plan to:
- Expand our reach across Europe. At the moment, our focus is on the UK and Netherlands, although we recently expanded to major markets such as France, Germany, and the Nordics.
- Reach profitability. Once we have done this, we will also enter the US market.
- Expand our development team to cope with the demands of our large enterprise clients.
- Expand our product range, maximising the revenue we can generate per client.
Our target customer is the large enterprise retailer. This is for three reasons:
1. Traffic: A typical retailer will have a ~2% conversion rate. This means that for every 100 people who visit their website, only 2 will buy. We have found that retailers with well executed manual bundles achieve an 18 - 80% attachment rate, which is what we expect to deliver as well.
For us to successfully test and improve our solution, we will be focusing on retailers with large amounts of traffic. This allows us to collect more data to optimise our technology.
As such, we will also be able to make more revenue for the retailer, and receive a higher fee in return. With this, we can continue to invest in our technology and grow our business.
2. Development Roadmaps: Large enterprise retailers often have long development roadmaps of up to 18 months. This makes it incredibly difficult to find the resources to build a technology such as ours. Without this technology - they’re losing money every day.
3. Paid Traffic: Large enterprise retailers have the capital to compete against other large retailers for paid traffic, such as through Google Adwords.
However - the market is becoming increasingly competitive. Profit margins are falling, which means that the cost of acquiring a customer (CPA) through these platforms is increasing. In order for a retailer to “beat” their competitors, they need to find a way to increase their margins and decrease their CPA.
Our technology will allow them to make more revenue and profit per customer. As such, they’re able to better compete in the paid traffic landscape.
Characteristics of target market
The e-commerce market in Europe is incredibly large. In 2012, the e-commerce economy in Western Europe reached €160.8 bn, a growth of 15.8% compared to 2011. The UK (€96.2 bn) is the largest e-commerce country in Western Europe, growing by 14.4% in 2012.
As such, we believe there's a great potential for sales starting with Western Europe.
For electronics such as laptops and TVs, profit margins are often slim. Some of the most profitable items in the market come from additional accessories, such as laptop bags and mouses. 35% of Amazon’s sales can be attributed to cross-sales such as these. As such, online stores are actively seeking ways to push these peripherals. By understanding a customer's needs and budget, we're able to effectively cross-sell these items.
We reach our market primarily through direct sales.
Due to our network at Startupbootcamp, we have been able to connect to major retailers across the Netherlands. We continue to expand our network, allowing us to reach the market with greater ease.
There are a number of companies within the “recommendations” space. However, all of them have one of two major issues: they’re manual or they’re ineffective.
Manual Our closest competitor is CNET’s Intelligent Cross-sell (ICS). ICS has a set of “merchandising rules”, which determine which products are compatible with other products. However, in order for these “merchandising rules” to work, the retailer must manually set “attributes” to their products.
Moreover, ICS focuses solely on consumer electronics. In the future, we plan to expand our technology to a wider range of categories, including “Home”, such as furniture and homeware, and white and brown goods.
Ineffective There are a number of solutions on the market which rely on customer data, “data driven solutions”, in order to provide recommendations. These include RichRelevance and Peerius.
Data driven solutions are great at recommending alternatives, e.g. “Customers who viewed X also viewed Y”.
However, they are ineffective at recommending accessories for three reasons:
1. Not enough data: In order for an accessory to be recommended alongside a host product, the two products need to have been bought together by multiple visitors. However, a retailer often does not have enough data on each individual product to provide these recommendations.
2. Compatibility: Data driven solutions rely on customer data. As such, they see products only as “IDs”, not by their technical specifications. This means that they cannot guarantee compatibility.
3. Merchandising: When new products arrive, such as an updated Operating System, it makes sense for retailers to promote these products. They often have higher price tags and are more popular. However, as these products are “new”, data-driven solutions have no data on them. This means that they can’t recommend them.
By understanding the category and specifications of each product, we’re able to provide the most relevant and compatible recommendations at all times.