All posts by 373396fk

Making Money in Making Playlists


In the current streaming industry, it is very hard for startups to fight against the giants like Spotify and Apple Music. Moreover, it is almost impossible to maintain a music streaming company. This is also what the founders of Kollekt.fm ran into. These two entrepreneurs started their business in 2013 while they were studying and founded a platform that transformed links to music into playlists. They soon found out that they did not have an efficient business model; their revenue model did not work properly. It was too hard to offer the service in a profitable way.

When the founders hit rock bottom, they encountered one of the frequent users of their service. He told them that he was a dj and made money from developing the playlists for coffee rooms in Amsterdam. This gave the founders new motivation. They went from shop to shop in Amsterdam and did market research about the shops’ music facilities. Eventually, the founders came up with the idea of Atmosphere, which is a service that connects shops and hospitality businesses to musicians. For a 15% commission, Atmosphere makes sure that the shops get personalized playlist which match the brand image of the companies. In this way, the company was able to make money from making playlists. However, the requirements to join the platform are strict. Only unique music curators are allowed to develop the playlists, since the company wants to maintain its high quality service. The main goal for the coming period is a collaboration with an establish jazz musician or dj, in order to raise more brand awareness.


Nowadays, approximately 20 firms are already using Atmosphere. The customers vary from supermarkets to restaurants, from clothing stores to work spots spread over more than 100 locations. The fee for the service is €30,- on a monthly basis which can be extended with €15,- for an offline streaming cabinet and for €300,- extra the company develops a custom-made website for their clients which contain all of their playlists.

Efficiency criteria

  • Currently, retailers are trying to distinguish themselves from competition by creating a clear brand image. The in-store experience is very important for customers. In developing customer centric playlists, the companies establish their brand image. This is an important strength in the business model of Atmosphere.
  • Streaming music in shops is not free, the retailers have to pay a licensing fee for copyrights. Atmosphere is aware of this legal aspect and even anticipates to this by offering their clients the service to arrange this for them to avoid difficulties.

In conclusion, I think that Atmosphere has a bright future ahead. The perseverance of the founders seems to pay off and as the company raises more awareness, the customer base will continue to grow. The streaming business will keep rising and Atmosphere must try to rise along. If the company succeeds to eventually found a community to collect playlists, the possibilities are endless.

Sources:

Tsekouras, D. (2016), Lecture 1: Introduction to value co-creation, Rotterdam School of Management.

https://getatmosphere.com/

https://fd.nl/morgen/1191244/geld-verdienen-met-playlists-maken-dat-idee-sloeg-aan

Healthcare at the heart of the digital revolution


Nowadays, we are used to make appointments for the doctor, dentist or other healthcare specialists through the phone. We dial the number of the practice, we dial our way through a phone menu and in the end, after waiting in line for a couple of minutes, we reach the assistant who tells us when our desired specialist is available for an appointment. This appointment scheduling is a daily struggle for doctors and clients. There are 8 billion European doctor appointments booked annually and doctors spend 18 hours a week on average scheduling these appointments.

Doctolib
Doctolib is a French startup that aims to make this process more efficient. The SaaS (software as a service) doctor booking platform is a service that finds nearby health professionals at whom the client can book and track appointments online. Doctolib uses cloud computing and the internet to improve healthcare access for patients in France.

The startup consists of a community of over 17.000 specialists such as dermatologists, dentists, surgeons, gynecologists, urologists, ophthalmologists and allergists. Doctolib collaborates with 435 healthcare centers, such as hospitals, clinics and other establishments.
The service is able to help reduce booking costs of healthcare specialists up to 30% and simultaneously help decrease no-shows up top 75%. Furthermore, Doctolib puts specialists on the map and make them more visible to clients. Through this, specialists get around 20 new clients per month. For the Software, healthcare specialists pay a monthly fee of €99,-.

The main goal of Doctolib is to help healthcare specialists with their appointment scheduling with online reservations. Doctolib strives to deliver a seamless healthcare experience for healthcare specialists and patients.

doctolib

Competition
That all sounds very impressive, but there are several players on the market that offer online reservation systems. The main advantage of Doctolib over its competition is that it uses cloud computing. Doctolib tries to offer an innovative, integrated experience through the cloud, to give doctors a clearer, up to date overview of their schedule. This requires large amounts of human effort and the dedicated employees of Doctolib get this done. That is why Doctolib has a competitive advantage, higher customer satisfaction and a market share of 80%. 

Efficiency criteria
Doctolib can be seen as efficient business model since patients are better off, doctors are better off and there are no other firms on the market that offer similar integrated solutions. Doctolib creates value for clients and doctors, if more participants of both groups use the software, both parties maximize their profitability. 

Future
The software is now only available for doctors in France, but Doctolib is currently working on an expansion towards Germany. The long-term purpose for Doctolib is to take over the entire European market.

All things considered, Doctolib has a bright and promising future. I am very curious what Doctolib will bring to the healthcare industry and if the Netherlands will soon be conquered by this tech startup.

schermafbeelding-2017-03-05-om-11-02-05

Sources:
Tsekouras, D. (2016), Lecture 1: Introduction to value co-creation, Rotterdam School of Management.

http://tech.eu/brief/doctolib-series-c/

Doctolib Grabs $20 Million For Its Booking Platform For Doctors

http://vator.tv/news/2017-01-27-doctolib-a-booking-platform-for-doctors-raises-26m

Recommendation networks and the long tail of e-commerce


Nowadays, we almost can’t imagine online shopping without recommendations systems. Popular electronic commerce websites like Amazon, Bol.com, Asos.com and so on all have a section with products they personally recommend to their customers. This is often displayed as: ‘You may also like…’ showing multiple products related to the ones you have recently viewed.

Integrating social networks like Facebook and Instagram into the world of electronic commerce is on the up and can contribute to the personalized recommendation systems of online retailers. In this way, customers get personalized recommendations based on what friends in their networks bought. This makes the less popular products, which customer normally not have looked for, more visible and stimulates consumers to buy products that they normally wouldn’t have found. These products are known as ‘the Long Tail’ products and are often presented as ‘Customers like you also bought…’.

To put it differently, if consumers get e-commerce recommendations based on their networks, the level of awareness for less popular products will increase. This means that the distribution of revenue and demand is influenced and shifts more towards a long tail distribution and away from selling primarily the most popular products. Simply by peer-based recommendations, customers will buy more and different products than they would normally have.

 

Research done by Oestreicher-Singer & Sundararjan (2012), investigates the impact of peer-based recommendations on the demand and revenue distribution. They research the influence of network-based recommendations on the online sales of 250.000 books from online retailer Amazon.com. The research shows that by recommending books based on what friends in customers’ networks bought, the distribution of demand and revenue is highly influenced. The researchers focused on the top 20% most popular and top 20% most unpopular products.

Categories of unpopular books that were displayed based on peer-recommendations experienced a 50% increase in revenue whilst the commonly unpopular books experienced a 15% decrease in revenue. This meant that the unpopular books suddenly became more visible to customers which led to an 50% increase in sales.

That all sounds quite impressive, but one could not say that this 50% increase was only caused by the visibility of products through recommendations. Different other contemporary factors also contribute to the redistribution of demand and revenue of consumers. Lower search costs and higher product variety for instance, have a great influence on the long tail of e-commerce.

 

All things considered, e-commerce is highly influenced by the power of social networks. The influence of recommendation networks positively affects to phenomenon of the long tail demand. Selling less of more rather than more of less is going to characterize the e-commerce demand curve in the future. The implementation of ‘what other customers like you bought’ will continue to impact our online shopping behavior. If companies implement the right recommendation systems to influence consumer demand, the opportunities are endless!

 

Sources:

Anderson, C. 2006. The Long Tail: Why the future of Business Is Selling Less of More. Hyperion Press.

 

Brynjolfsson, E., Hu, Y., and Smith, M.D. 2006. From Nichees to Riches: Anotomy of the Long Tail. Management Review 47(4) 67-71.

 

Oestreicher-Singer, G., & Sundararajan, A. (2012). Recommendation networks and the long tail of electronic commerce. MIS Quarterly