All posts by 459622sk

Introducing the future of fashion with Coded Couture

“We’re about to change the fashion industry by bringing the customer’s personality into the design process through data technology”. – Aleksander Subosic, co-founder of Ivyrevel.

Finding unique clothing can be difficult; custom-made clothing is usually not affordable and designing your own piece requires design experience, which most people lack. Ivyrevel, part of the H&M Group and the world’s first digital fashion house, partnered up with Google to solve this issue by combining couture with data technology. Together, they created the Data Dress: “A personalized dress designed entirely based on a user’s context signals” (Brook, 2017).


How does it work?

First, install the app and select an occasion e.g. party, gala or business, and a style for the dress. Then, simply carry your phone wherever you go (which you probably already do) and the app will use Snapshot API to learn from your daily activities, with your permission. During seven days, the app will capture context signals and ask you to confirm certain the data to ensure that it corresponds with your lifestyle. By doing all of the aforementioned, you become part of the value creation process. Finally, the data is passed through an algorithm that creates a virtual custom-made dress, ready for you to purchase.

Will customers buy the Data Dress?

According to a survey by Bain & Company, +/-30% of shoppers were interested in designing their own clothing. Furthermore, they found that unique products lead to lower return rates and create a deeper connection between shoppers and retailers (Wiggers, 2017). Thus, there is a potential market for the data dress.


Efficiency criterion

  • This video, shows that Kenza lives in Stockholm where it is -2°C and that she visited a fancy restaurant. Therefore, the app made her a black velvet dress with crystal details, reflecting her lifestyle. This shows that consumers will get a truly unique, on-trend, and custom-made dress. Additionally, consumers become part of revolutionizing the way we look at fashion. For fashionistas interested in technology this is the ideal combination, and it allows them to be early adopters within their community.
  • Ivyrevel aims to ‘merge fashion creativity with technological innovation.’ ( Thus, by introducing the data dress, Ivyrevel will achieve this goal. Regarding costs, Ivyrevel is not dependent on designers and won’t need to invest much in production, since it already has a clothing line and production facilities. According to, the dress will cost €93.
  • I expect that Google will receive financial resources by allowing Ivyrevel to use its API technology. Additionally, Google enhances its positive reputation regarding technological innovation and receives positive WOM.

Feasibility of Required Reallocations

Currently, the app has launched in closed alpha stage and is being tested by selected global fashion influencers (Brook, 2016). Since not much information is available regarding the specific institutional arrangements and –environment, I will propose a few.

Ivyrevel must consider:

  • The protection of consumers’ privacy. Consumers are responsible for granting tracking permission and Ivyrevel will not share their information with other parties.
  • Safe payments within the app.
  • Production processes that are carried out under ethic conditions.

It is important that Ivyrevel makes clear arrangements for these kinds of issues to prevent problems from arising.


Google blog:

Brook, J. (2017, February 06). Fashion gets a digital upgrade with the Google Awareness API. Retrieved March 6ƒ, 2017, from

Business Insider: Wiggers, K. (2017, February 07). Google partnered with H&M-backed fashion startup Ivyrevel to build customised ‘data dresses’ Retrieved March 6, 2017, from

Adformatie: H&M, Google en MediaMonks personaliseren jouw kleding. (2017, February 9). Retrieved March 6, 2017, from

Polarsteps – Automatically track and share your trip

Imagine you just came back home from a long trip abroad and you want to share your experiences with your friends and family by writing a blog. Travel blogging, however, is time-consuming. So, you decide to post your photos on Facebook. Not a bad way to share your journey, but it does not really capture the essence of your awesome experiences either..

Polarsteps – Why was it created, what is it and how does it work?

Just before leaving for his sailing trip, Polarsteps co-founder Niek wrote a small script that read off his coordinates from a GPS tracker and sent them to a server via satellite phone (Ho, 2016). Niek’s website went viral among travelers, who loved the idea of telling your travel story on a beautiful map and sharing it with their loved ones back at home. This inspired Niek to further develop the idea (Ohr, 2016).

Polarsteps is an Amsterdam-based app that automatically tracks travelers’ journeys. Travelers don’t need to worry about internet costs, because the app uses offline GPS-tracking. Once Wi-Fi-connection is available, the app seamlessly transfers all tracked information to the traveler’s Polarsteps webpage. Here, the trip is displayed on an interactive map showing the traveler’s routes, key locations and photos. Polarsteps tracks a ton of other statistics, such as kilometers traveled. Furthermore, travelers can keep their friends and family updated as to where in the world they are by sharing their trip real-time. Users can instantly add photos and locations to the interactive map or do it afterwards. Thus, no need to worry about battery drain either. Travelers can also follow other travelers and explore their trips. Finally, Polarsteps offers printed travel books, which includes all travel routes, statistics and photos, turning travel moments into lifetime memories.


Efficiency criterion – Joint profitability

  • Polarsteps automatically generates users’ travel story on a beautiful map which can be shared real-time with their loved ones back at home. Polarsteps also functions as a platform where users can follow other travelers’ trips and share theirs. Additionally, users can buy a personal travel book, so they can look back on their journey.
  • The founders of Polarsteps are passionate travelers who focus primarily on helping other passionate travelers log their travels and tell their stories. Polarsteps wants its platform to enable travel organizations to create branded accounts where they can share their routes and tell their stories. The company considers adding features such as sharing the content on social media and incorporating booking reservations. Eventually Polarsteps wants to become THE app for discovering, logging and sharing beautiful routes. Other sources of income may come from selling travel books and gift cards. However, this is not the core of the business model (

Required reallocations

Polarsteps has an extensive terms and privacy statement that users must agree to. It includes the following:

  • Users are responsible and liable for all activity on their account.
  • Polarsteps collects personal information provided by the user, information collected automatically and information it receives from third parties.
  • Users can choose between 3 privacy levels: Only me, Only my followers or Everyone.
  • Polarsteps urges users to think carefully about the consequences of using the Trvavel Tracker functionality, when Everyone can view their account.


Ho, J. (2016, April 25). A Dutchman sails across the Atlantic and innovates travel logging. Retrieved February 22, 2017, from

Ohr, T. (2016, March 17). Thomas Ohr. Retrieved February 22, 2017, from

Startup pitch: Polarsteps automagically tracks and plots worldwide jaunts. (2015, April 2). Retrieved February 22, 2017, from

Assessing the impact of recommender agents on on-line consumer unplanned purchase behavior

Recommendation agents (RAs) are often used in modern applications that expose the user to a large array of items (Shani & Gunawardana, 2011). They provide the user with a set of personalized items, tailored to their preferences (Knijnenburg, Willemsen, Gantner, Soncu & Newell, 2012). Hostler, Yoon, Guo, Guimaraes & Forgionne (2012) tested the impact of RAs on online consumer unplanned purchase behavior. Furthermore, customer satisfaction with the website and other variables were included, namely:

  • Product promotion effectiveness: The ability of the RA to recommend products that attract participants’ attention and interest them.
  • Product search effectiveness: The ability of the RA to reduce the extent of product search by providing quick access to relevant information.

Hostler et al. (2012) conducted an experiment in which they created a shopping simulation of ‘online purchase of home movies’. Participants were randomly assigned to a control or treatment group and filled out a pre-test questionnaire in which they rated movies and provided information on their online shopping experience. Second, participants listed the movies they considered purchasing. The control group did this without the RA’s assistance, while the treatment group received personalized recommendations from the RA. Hereafter, subjects filled out a survey on impulse buying, the effectiveness of the product promotion and their satisfaction with the website.


Figure 1. Conceptual model of the results

The results (figure 1) show that the effect of product promotion effectiveness on customer satisfaction is the highest, due to effective product promotion through the use of an RA. Furthermore, customer satisfaction and search effectiveness positively impact unplanned purchases.

In the domain of e-commerce, little research has been conducted on how consumer behavior and website satisfaction are affected by RAs. The findings confirm the importance of using RAs to positively influence online consumers’ purchase behavior and fill a gap in the literature. Furthermore, the findings are also relevant for managers, since they indicate that RAs should be designed in ways to allow greater search effectiveness and satisfaction. Letting users answer more questions about their preferences is one way to increase search effectiveness and might also increase product promotion effectiveness.

A business example that effectively uses RAs is Netflix. Approximately 75% of viewer activity is based on recommendations (Vanderbilt, 2013). Netflix’s recommendations are based on i.e. movies users played/ searched for, including the time, date and device. When it comes to giving recommendations, Netflix aims to do this within 90 seconds, because it knows that after 90 seconds users will abandon the service (O’Reilly, 2016). This shows that search effectiveness is important for Netflix.

To improve this study, marketers should identify other sources for generating customer satisfaction and search effectiveness in the use of RAs. The extent to which RAs and websites are user-friendly might affect these variables. The boundary conditions for the positive effect of search effectiveness on unplanned purchases could also be tested, by examining possible moderators such as product knowledge. Lastly, although this study provides insights in the use of RAs, it is limited to the one product category, namely online purchases of home videos. Since this is a relatively simple product, it would be interesting to see whether the results are generalizable to other product categories, such as complex digital products.


Hostler, R. E., Yoon, V. Y., Guo, Z., Guimaraes, T., & Forgionne, G. (2011). Assessing the impact of recommender agents on on-line consumer unplanned purchase behavior. Information & Management, 48(8), 336-343.

Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4-5), 441-504.

O’Reilly, L. (2016, February 26). Netflix lifted the lid on how the algorithm that recommends you titles to watch actually works. Retrieved February 14, 2017, from

Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems. In Recommender systems handbook (pp. 257-297). Springer US.

Vanderbilt, T. (2013, July 8). The Science Behind the Netflix Algorithms That Decide What You’ll Watch Next. Retrieved February 14, 2017, from