Stitch Fix’s value co-creation

In the past it was easy to make a choice due to the lack of options among products. However, this has changed drastically. According to the NY times consumers see around 3,000 to 20,000 marketing messages a day. Moreover, we are bombarded with choices. Choice overload, which happens offline as well as online, exists for all kinds of products and services that we need.

Choice overload also exists when we shop for new clothes. Offline overload could exist when we go into a store and are presented with too many choices. Online overload happens when we browse a web store and experience choice overload. Source overload happens when too many websites and or platforms exist that makes it difficult to choose where to shop.

In 2011 Katrina Lake launched Stitch Fix. Stitch Fix is a fashion retailer that combines expert styling and technology to deliver a shopping experience that is personalized for the customer. It does so by having you first fill out a survey so that your personal stylist knows your preferences. Then it will send you five clothing items or accessories from various brands tailored unique to your taste. You can try all the items at home. You can buy what you want and then return the rest. Shipping and returning items are for free. Hence, Stitch Fix partially solves the choice overload that customers experience when shopping, by filtering the endless amount of choices into five choices.

Online recommendation systems used by companies such as learn from your preferences by tracking your searches and clicks in order to provide you with products that you might want. However, depending whether the algorithm and machine learning works well, this could lead to undesirable outcomes. For example, you could end up getting unwanted recommendations after you buy a gift for a friend or your mother who have different tastes and preferences in products than you have.

What makes Stitch Fix different than other online shopping experiences is that there is a human personal stylist involved in the item recommendation process. Your personal stylist handpicks five items just for you, based on your set preferences. Stich fix charges $20 styling fee for each item that you buy. But if you decide to buy all five items, you will get a 25% reduction on the total price of the shipment. The company gathers data and your feedback on the items that you decide to buy and the items that you decide to return to update your preferences. This will be then used to make better recommendations in the future. Hence, as stated by Stitch Fix, the more you make use of the service the better your personal recommendations will be and greater your satisfaction.

The success of Stich Fix proves that there is indeed a need for personalized recommendations and active customer participation. Furthermore, it shows that customers are willing to pay a premium on top of store prices for services that makes things convenient and or saves time.


Burberry World: Co-Creation in the Fashion Industry

A good place for companies to start with co-creation is beginning to connect their internal sales people with their customers’ communities on social media. An example of a successful implementation of such a strategic approach is Burberry: its CEO, Angela Ahrendts, decided to act upon a grand vision of the company as a social enterprise where all employees, customers, and suppliers share the same experience of the brand, whether through stores or digital platforms. The digital platform that Burberry, the iconic British fashion brand, is called Burberry World (Harvard Business Review, 2012). Burberry is a British fashion brand that produces clothing and accessories. Besides that, Burberry produces their own fragrance line and owns franchise stores all over the world.

On the 13th of September 2012, Burberry announced their most digitally-advanced brand experience for the first time. Part event space, part innovation hub, part store, Burberry Regent Street blurs the physical and the digital to create ‘Burberry World Live’. Highlights of this digitalized store include (Burberry, 2012):

  • Immersive audiovisual experiences with nearly 500 speakers and 100 screens;
  • Innovative use of radio-frequency identification technology;
  • Digitally-enabled gallery and event spaces;
  • Increasing personalization as online insights meet offline interactions to create the most progressive luxury customer service.


Angela Ahrendts states: “Burberry Regent Street brings our digital world to life in a physical space for the first time, where customers can experience every facet of the brand through immersive multimedia content exactly as they do online. Walking through the doors is just like walking into our website. It is Burberry World Live. We call Burberry a young, old company: forever moving forward while never forgetting our 156-year heritage. The fusion of history and innovation in Burberry Regent Street is the brand’s most comprehensive creative and commercial expression.”

Beyond the above mentioned vision, Burberry world is a collection of applications that was developed by that allows stores’ sales and service people and customers to re-invent their interactions as a mini-community. Employees that work in the store can engage with the customers: through a software program called Chatter, the employees can not only have access to traditional CRM-related transactional data, they can see an aggregation of their customers’ social media posts and activities as well.

Moreover, customers can also engage with the brand on their terms. They can create their personal Burberry portal and start conversations on a variety of lifestyle issues, such as music and fashion. Customers also use these portals to make store appointments to view a new collection item or repair their previously bought items. Using both the engagement from the employees’ and customers’ side, the platform unleashes mutual emotions and generates useful data to both parties.

Furthermore, the scope of Burberry’s co-creation strategy is not limited to the sales and service interaction. It is also possible for customers to remotely participate in fashion shows and to order items directly off of the runway. Additionally, they can suggest designs for the next trench coat (my personal favourite).

References in order of appearance:

Minecraft aka Digital Lego

“It isn’t like other games. There are no instructions, no levels, no mission structure, no story, no lives, no points, no clear goal. The only aim is to survive”

User design and co-creation have emerged as a mechanism to build brand loyalty, to fit products to the heterogeneous needs of a market, and to differentiate the offerings of a manufacturer. More than ever, customers want to make their own choices and demand uniqueness. Minecraft was born in the context of our increasingly individualized and digitalized world.

You start Minecraft in the middle of a randomly generated, “blocky-looking” world about eight times the size of Earth and are completely free to do what you want. You can go exploring, or you can get creative.

Minecraft allows users to create their own environment and build new structures with the building blocks provided by the game designers. At the start of the game, the player is placed on the surface of a procedurally generated and virtually infinite game world. The game world is procedurally generated as players explore it. Minecraft also enables multiple players to interact and communicate with each other on a single world.

The game was created by Markus Persson and was recently sold to Microsoft for $2.5 billion. Without any advertisements, Minecraft had 1 million purchases, less than a month after entering its beta phase.

In addition to Minecraft, there are several games that allow users to generate content. For instance, Sim City lets players build up their own city within the game constraints. The novelty of Minecraft is that it offers infinite creativity and control!

Like in mass customization, in Minecraft users are active co creators and are the beneficiaries of their creations. However as some of the best Minecraft creations can be shared, they might benefit others (game developers, architects etc.) But why do customers benefit from playing Minecraft? Firstly because it’s fun, second because it enables users to create experiences that are tailored to their needs and finally, because it may fulfill their social needs, in particular when publicly showing their creations.

Check out some of the best creations:

“It offers infinite creativity and control”

By giving so much freedom, is Minecraft able to maximize users’ satisfaction? Given that Minecraft players may not fully understand their needs or may have different levels of skills, the game bears the risk of a “design defect”: a choice of design parameters that does not maximize user satisfaction (Randall, T., Terwiesch, C., & Ulrich, K.T. (2005). This design defect concept reflects a misfit between the game designed and the one that might have been designed, despite the fact that the user is in control of all of the design decisions. Minecraft mitigates this risk by including some default options that enhance customer satisfaction. For instance, gameplay by default is in first person, but players have the option to play in third person mode.


Randall, T., Terwiesch, C., & Ulrich, K.T. (2005). Principles for user design of customized products. California Management Review, 47(4), 68.

Consumers vs. Company Professionals in Idea Generation

(This post is based on the article: ‘The Value of Crowdsourcing: Can users really compete with professionals in Generating New Business Ideas’ by Poetz, Marion K. & Schreier, Martin 2012)

Involving consumers in generating new ideas though crowdsourcing is a relatively novel alternate source of product idea generation, previously being the exclusive domain of designers, engineers and marketers. Where some are wildly enthusiastic about outsourcing ideas to the “crowd”, others have been skeptical. The writers of this article join the debate and have both users and professionals from the respective company solve a relevant problem in the market for baby products.

The main question arising for this article is simple: who can generate better ideas for new products: the professionals or the potential users/consumers?

Theoretically, the findings of this study demonstrate that user ideas score on average higher in terms of novelty and customer benefit, whilst scoring somewhat lower in terms of feasibility. This shows that professionals are more capable of coming up with ideas that can more easily be developed in a marketable product. However, the average values for feasibility among all users were relatively high, contrasting greatly with average values for novelty and customer benefit.

More interestingly, however, the study revealed that the best ideas overall tended to be more heavily concentrated among users compared to the company’s professionals.

Practically, the findings suggest that crowdsourcing among users might complement the work of a firm’s professionals in the idea generation stage of NPD (New Product Development). Hence, the authors claim that the aim of this study was not to question the general importance of professionals in idea generation. Rather, that an “optimal” approach in practice might more often than not lie in a combination of the two groups, where professionals collaborate with users in some way. Concluding, the findings of this study constitute an important contribution to justify the more active involvement of users in idea generation.

In what situations can we expect similar results so that firms might derive commercially attractive new product ideas from users?

It can depend on the following 3 things:

  • The underlying industry or the respective product category, as well as the nature of the specific problem for which the firm wishes to innovate
  • The users’ motivation may be strongly related to their willingness to invest in generating new product ideas and/or to share them with firms.
  • The amount of ‘qualified’ users attracted. In the above case, the average quality of user-generated ideas was relatively high, indicating that in this specific case highly qualified users were attracted. This needn’t always be the situation

Summarizing, this study shows that the positive effect of users compared to professionals in idea generation is moderated by factors related to the users’ capabilities and motivations, as well as the design of the search and attraction process. The aforementioned factors hence determine when the findings could be replicated.


Poetz, Marion K. & Schreier, Martin (2012), The Value of Crowdsourcing: Can users really compete with professionals in Generating New Business Ideas, Journal of Product Innovation Management , 29(2), 245-256

A rational perspective on the privacy issues when considering using location-based services

Big data and data collection are often seen in a negative daylight, as public attention to big data gathering usually results in unwanted attention for organizations. The other side of the story is that such data collection is usually the result of organizations wanting to deliver personalized services more effectively. In cases where the user becomes skeptical when asked to share their personal and private data, organizations provide an (additional) incentive to mitigate their perceived risk. In the case of recent developments in mobile shopping services, there is a balance between the perceived value and the perceived risk of sharing private information of the customer Xu et al. noted [1]. An easy example of this the case of a customer of having an empty stomach, an empty fridge at 10pm and a connected smartphone. Will he decide to give out his location-based information to a mobile service in order to look for food ordering opportunities or will he not? Will he value the potential to find food less than his location-based information at that hour? You decide.

Furthermore, Xu et al. found that the usage of location-based services is correlated to monetary incentives. Individuals are more willing to disclose their locality to location-based services when offered a financial incentive, Xu et al. have found in their research [1]. The financial incentive is often given in the form of some future saving, implying that there is money to be gained in future expenses. These incentives often take the form in discounts on related services or rebates. Some skeptics have been in agreement with having their personalized data shared in trade for an additional incentive. When asked about their rationalization, some skeptics claim that ‘the risk is worth the gain’ while others state that they have ‘serious concerns’ about sharing their information. If you think that the former is non-existent, please consider the example of the guy with the empty stomach again.

The location-based are services that require more personal and private information in order to function better. The so-called personalization privacy paradox is the epitome of the previous statement; the better services an individual wants or requires, the more willing he has to be to share his personal information. Xu et al. have found that using personalized services could help individuals in superseding their privacy concerns. When addressing the paradox, the authors imply that if customers are more knowledgeable of the service that they require, they make a more rational decision. If the customers have high privacy concerns towards the use of personalized services, they are less inclined to consider using the service and will automatically consider alternate opportunities (in case of the hungry customer, he could use the ‘service’ of asking his physical neighbor for information) and therefore are not part the targeted demographic Pappas et al. imply [2]. In addition, if the location-based services give the option to the consumer to control the use of their personalized information, the mitigated effect might tempt critics to use the service after all [3], although future research would have to investigate this in more detail.

In the end, the rule of thumb is: “when (information) services are offered for free, you are paying with your personal data”. Some people are okay with this, and that is… okay.

Disclaimer: Although largely based on the article of Xu et al. [1], the opinion presented in this article does not portray the sentiment in the paper itself. The opinion presented in this article rests solely by the author and by none of the authors cited in this article. Critics are free to comment below, and are encouraged to do so.

[1] Xu, H., Luo, X. R., Carroll, J. M., & Rosson, M. B. (2011). The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision Support Systems, 51(1), 42-52.
[2] Pappas, I. O., Giannakos, M. N., & Chrissikopoulos, V. (2012, June). Personalized services in online shopping: Enjoyment and privacy. In Information Society (i-Society), 2012 International Conference on (pp. 168-173). IEEE.
[3] Schwaig, K. S., Segars, A. H., Grover, V., & Fiedler, K. D. (2013). A model of consumers’ perceptions of the invasion of information privacy. Information & Management, 50(1), 1-12.


Customers hold knowledge about their own needs. Companies want to benefit by offering products or services that match these needs. Value co-creation tries to bridge this information asymmetry gap by engaging customers into the creation of value. The internet facilitates companies’ turn towards this direction by enabling the evolution of existing business models that traditionally excluded customer engagement, or by allowing the creation of new ones.

Skillshare, a company lunched in 2011, is classified in the second category. Numerous business posts do not hesitate to describe the company as a game changer in the education sector. Skillshare’s co-founder, Michael Karnjanaprakorn states: “The problem of education today is that is no longer about learning”. “All I’m doing in college is drinking, eating and memorizing things for exams that have nothing to do with real life.” As a graduate student himself, Karnjanaprakorn knows firsthand what it means to enter the job market without practical skills.

The missing link between education and learning is what initially motivated Karnjanaprakorn to create the company. Skillshare is an online platform for learning anything from anyone. Doers from all over the world introduce themselves and share their skills with anyone who is interested in them. Skillshare brings world’s diversity into a single platform resulting to dozens of different categories of online courses such as design, entrepreneurship, programming, culinary and the list goes on. Unlike other massive open online courses (MOOCs), Skillshare focuses on learning by doing. Thus, learning is not only about watching prerecorded videos. Interaction is a major part of the learning process and it is carried out by the completion of specific projects. Furthermore, PhDs are not a criterion for joining instructors’ community. The team believes that the best teachers are among people with no formal education at all.

Until 2014, instructors were able to set their own price for each course and a 12% fee was charged by the company. The average price that could be found was $20. In 2014 the revenue model altered to monthly subscription. “Hardcore” students were happy to see such a change, since they can save more than $75 per month according to company’s research.

But let’s return to the disruption. Do Skillshare and other types of MOOCs threaten traditional universities? According to Laseter (2012), universities do not provide their students with the necessary accoutrements for improving their chances when they apply for a skill demanding position. Education applicants will realize more and more this weakness of the conventional university and will focus on education that meets their expectations and guarantees future recruitment. Given the disruptive potential of the online learning, which Christensen (2011) also underlines in his book The Innovative University, in combination with the continuously increase of the tuition fees in traditional universities, it is expected that online educators will attract more and more students through MOOCs. Universities that consolidate rather than change the situation will lose large proportion of their market share in the future. What is your opinion?


Christensen, C., Eyring, H. (2011). The Innovative University: Changing the DNA of Higher Education from the Inside Out. John Wiley & sons

Laseter, T. (2012). The university’s dilemma. Booz & Co

Are cross-platform social recommender systems the future?

(This blog post is based on the research article ‘A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship’ by Li, Wu and Lai, 2013)

In a world where there is not only a massive selection of different products but also the internet that enables us to theoretically choose among all those offerings without the high search costs that have hindered us from an informed choice in the past, the problem of information overload is a challenge for both the consumers and the companies offering those products (Li, Kauffman, Van Heck, Vervest and Dellaert, 2014). As explained by Murray and Häubl (2009), especially in the e-commerce environment, good recommender systems (RS) that aid the consumer in finding the right product are becoming increasingly important, ensuring a higher consumer satisfaction and increased sales for the offering companies (Li, Wu and Lai, 2013). Even though good RSs exist in the market (such as Amazon’s collaborative item-to-item filtering mechanism or Netflix’s hybrid model of collaborative and content based filtering (Jones, 2013)), it seems that today’s RSs fall behind what should be technically possible. Thus, Li et al. (2013) suggest a new RS that does not only take a customer’s past behavior into account but adds a social component that would greatly increase the information available to the system and thus improve its prediction accuracy. An important drawback of today’s RSs mentioned by Li et al. (2013) is that the platforms utilizing RSs are independently operated and only use the data obtained within the boundaries of their respective platform. Real value, however, could be obtained when integrating the data of various different platforms and adding the social component of the social network of a consumer to the recommendations shown on an e-commerce platform. In real life, after all, we also tend to ask our friends for advice when shopping and especially in cliques of close friends, the shopping behavior of individuals potentially influences the shopping behavior of the others (Li et al., 2013; Shang, Hui, Kulkarni and Cuff, 2011). Continue reading Are cross-platform social recommender systems the future?

Customer Loyalty and Recommendation Agents

Recommendation agents (RA) are giving online customers recommendations for the past few years. Although the first main function of RAs was to reduce information overload, now it’s also used to increase sales.  More and more information is gathered through the internet and especially social media, to improve personalized preference-based recommendations. At the same time, these systems show success measured by online sales and user satisfaction.

Customer loyalty is considered to be a source of competitive advantage and is useful for long-term business success. Research has shown that there is a strong relationship between customer loyalty, firm’s profitability and stock returns. Returning customers are more profitable than new customers and thus good for business. The aim of the study is to identify the effect between various independent variables (e.g. RA Type, Recommendation Quality, Customer Satisfaction, Product Knowledge, and Online Shopping Experience) and on the dependent variable customer loyalty.

Recommendation quality is based on the preferences of the user and the perceived value of the recommended products. This is the outcome of the type of RA, which could be either content-filtering or collaborative-filtering. Also, the impact of the moderating variable Product Knowledge and shopping experience will be measured. When having expertise in a product, this could negatively affect the customer satisfaction when being advised by a recommendation agent. Shopping experience is also hold in account because the more shopping experience a customer has, the more likely the customer is familiar the interaction with RAs, and the more likely the customer is able to use a RA effectively.

The main reasons for the study is that from marketing perspective, the adopted cognitive-affect-conative-action framework of customer loyalty has not been empirically tested in the context of RAs. This framework states that customers become more loyal when going through multiple stages. Every stage represents some sort of loyalty. There has also been done little research assessing the effect of increasingly higher customer expertise on customer loyalty in the presence of RA usage. Thus, central in this research are the moderating effect of product knowledge on the relationship of Recommendation Quality and Customer Satisfaction.

The results showed that the collaborative filtering RA has a higher recommendation quality than a random RA. The recommendation quality has a positive effect on customer satisfaction and customer loyalty. Also, customer satisfaction is positively related to customer loyalty. The results also show that the impact of recommendation quality on customer satisfaction is negatively moderated by customers’ product knowledge. Thus, product expertise negatively affects the perceived value of the outcome of a RA. Shopping expertise does not have an effect the relationship between customer satisfaction and customer loyalty.  70% of the variance in customer loyalty can be explained by customer satisfaction. This research has shown that an effective use of RA positively influences recommendation quality which in turn positively influences customer satisfaction. When users will have increasing levels of product knowledge, it will negatively influence the customer satisfaction with the website.

The increased knowledge about RAs and how it will increase customer loyalty towards your website is interesting for businesses to retain customers. However, retaining customers are likely to get an increased level of product knowledge. Thus, RAs should always be innovated more and more.


Yoon, V. Y., Hostler, R. E., Guo, Z., & Guimaraes, T. (2013). Assessing the moderating effect of consumer product knowledge and online shopping experience on using recommendation agents for customer loyalty. Decision Support Systems, 55(4), 883–893.

Meet Local Motors

“Dealership customers are doing vehicle-related Internet searches right in front of salespeople during negotiations.” – Darin George in Wardsauto

How will we buy our cars in the future? It is doubtful we will still be standing in the physical locations of dealerships we see today facing a sleazy salesman. Instead, maybe we will experience a virtual test drive before actually ordering the right car via an app or website on our smart devices. Or maybe we will design and build our own cars. Here’s a short clip about the potential future of car manufacturers:

Local Motors (LM) is an Arizona-based car manufacturer running on a business model that stands out within the global car industry. The most unique element of their company must be the inclusion of various stakeholders’ knowledge in designing, conceptualizing, and producing cars. LM allows amateur designers from around the world to send in their ideas for vehicles (the product line also includes a type of bicycle). However, also professionals, customers and partners are allowed to add value to the design and production phase. The crowd of LM is a highly diverse online workforce and together they make one of the most innovative vehicles in the world. Even BMW recognized the hidden talents of LM when they teamed up with the company for an “Urban Driving Experience Challenge”. The crowd of LM was asked to come up with new features and functions to improve the BMW driving experience.

How does LM work exactly?

The business model of LM revolves around the idea of co-creation and open-source. People from all kinds of backgrounds are welcome to join the online car community. They can submit their designs or ideas via the platform and are also empowered to comment or like the works of others. Via a voting system, the most popular design is selected and then the work is opened up for others to start co-creating. It is important to note that all original designs are protected by “Creative Commons”; a non-profit that provides licenses. Once a design is complete, it can be ordered by customers up to 2000 times, meaning the editions are rather limited. The production of a LM car takes place in a local micro factory creating local jobs and sustainability gains. The customer that ordered the vehicle is actually allowed to actively participate in the building process and can get training from the LM community. Lastly, the blue prints become readily available to the LM members who can download them freely and these prints are again protected by “Creative Commons”.

Why would this be the future?

Business models are increasingly evolving around the consumer. In order to remain competitive, it becomes crucial to create an exact fit between a consumer need and a product or service. The consumers of today are much better informed about the alternatives and can set higher demands. LM decided to empower the consumer as much as possible but they also got rid of a tunnel vision on car manufacturing. Part of their success lies within innovation and co-creation, but a major competitive advantage would have to be the fact that they claim to “bring vehicles to the market at 1/100th of the cost and 5 times faster than the traditional vehicular development and production paradigm” as stated in the 2014 report of the European Union on Design for Innovation. Although it must be noted that LM aims for the niche markets where consumers enjoy being part of the car production process, we can admire them for their innovation and co-creation efforts. How can a traditional car dealership and manufacturer battle this unique way of producing and selling cars? How can competitors match LM’s pool of knowledge?


Dervojeda, K., Verzijl, D., Nagtegaal, F., Lengton, M., Rouwmaat, E., Monfardini, E., et al. (2014). Design for Innovation. European Commission, Business Innovation Observatory. European Union.

George, D. (2015, March 15). Car Dealership Salespeople Should Lose the Bicycle Helmet. Retrieved April 3, 2015, from Wardsauto:

Ramaswamy, V., & Ozcan, K. (2013). Strategy and Co-creation Thinking. Strategy & Leadership , 41 (6), 5-10.