Too much power?

How many times are you approached by peoples at the train station or at a restaurant with surveys asking on your opinion about the service and the company? Can you imagine that maybe in the future your answers to such surveys may influence the employees’ salary?

It’s so that HTM, the public transport company in The Hague region, has announced last week that they want to adapt their salary system such as that their customers (the travelers) could help on deciding the salary of the HTM employees. Their main idea is to empower the crowd in such an extent that the answers of the yearly “customer-satisfaction” survey (questions based on friendliness of employees, the vehicles, travel speeds etc.) will define the results: the quality of HTM’s customer service. This result will mean that for every 0.1 point increase on the customer satisfaction; will lead to a 0.2% salary increase. As for now the labor unions rejected HTM’s proposition, but they are open for negotiations.

As discussed during the lectures of customer centric and digital commerce, we can relate this kind of ideas to a huge amount of different reasoning. One of them could be the diversity trumps ability expressed on page 258 of (Majchrzak & Malhotra, 2013).  This means that a large diverse crowd of independent strangers may perform better on certain types of challenges than a small number of experts (Majchrzak & Malhotra, 2013). In addition, we can also relate it to the company goals which are for example, the relation to reduction in costs as mentioned by Fuchs & Schreier (2011).

Another nice practical example of where empowering might be going towards, is Incentro. Contrary to HTM who wants to empower their customers to some extent, gave Incentro their employees themselves the power to adapt their own wage.

This type of may be going to the direction of the name-your-own-price as expressed by Hinterhuber & Liozu, (2014). In this case I can name it: “Name-your-own-wage”.

Concluding, in the lecture about Crowdsourcing we discussed several risks and benefits for companies, employees and the customers when empowering the customer/ employees (the crowd). In addition to this last and to relate it to this post, I want to know from you, my readers: Do you think that organizations are giving the crowd too much power? Or do you think the crowd has the right to influence other decision than just the product design, aspects and applications? Would you fill in a survey on a different way when knowing that it may increase or decrease someone’s salary?


Fuchs, C., & Schreier, M. (2011). Customer empowerment in new product development. Journal of Product Innovation Management, 28(1), 17-32.

Hinterhuber, A., & Liozu, S. M. (2014). Is innovation in pricing your next source of competitive advantage? Business Horizons, 57(3), 413-423.

Majchrzak, A., & Malhotra, A. (2013). Towards an information systems perspective and research agenda on crowdsourcing for innovation. Journal of Strategic Information Systems, 22(4), 257-268.

Tsekouras, D. (2015). “Lecture 3:  Ideas & Design”, Consumer-Centric Digital Commerce, Erasmus University Rotterdam, 01-04-2015.

How to maximize your revenue?

The availability of real-time data on customer characteristics has encouraged companies to personalize operational decisions for each arriving customer (Golrezaei, et al., 2014). For instance, has found that Mac users spend on average $20 to $30 more per night on a hotel than Windows users . Therefore the online travel agency can show different and more expensive hotels to Mac users (Mattioli, 2012).

The key question in this paper is: Given the complexity of coordinating real-time, front-end, customer-facing decisions with the back-end supply chain constraints, what policies should companies use to take advantage of real-time data?

Once a customer arrives on the website, his or her customer type will be revealed. This can be based on computer type , zip code, gender or on any available information that is relevant for the company. Based on the customer’s type and the remaining inventory, the firm offers an assortment. An example of a website that shows different assortments based on customer type is Zalando. When I visited the website last week, I was automatically directed to the ladies department without logging in. Associated with each customer type is the probability of purchasing each product under each assortment. This means that the company calculates, for example, the probability that a 40 year old woman who lives in a high-income neighbourhood purchases a certain dress which is showed between other dresses under Assortment A but also the probability that she purchases the same dress that is showed between different dresses under Assortment B. The authors of this paper want to design an revenue-maximizing algorithm that determines the assortment to offer to each arriving customer, taking into account the customer type and current inventories.

In the paper, the authors propose a couple inventory-balancing algorithms. An inventory-balancing algorithm makes use of a discount factor that depends on the fraction of the product’s remaining inventory. This means that when the inventory of a product drops, the discount becomes higher which results in a lower discounted revenue. Upon the arrival of each customer, based on the customer’s type, the algorithm offers the assortment that maximizes the expected discounted revenue. By adjusting the revenue of each product according to its remaining inventory, the algorithms hedge against the uncertainty in the types of future customers by reducing the rate at which products with low inventory are offered. For example, a pair of red leather boots (low inventory) that normally would be showed at page 1 to a 40 year old woman who lives in mens-inner-real-leather-western-glossy-red-side-zip-high-heel-ankle-boots-made-in-koreaa high-income neighbourhood (in case of enough inventory) might now be showed on page 4, because it is likely that she is willing to buy another, more expensive, pair of shoes. When a 35 year old woman who lives in a low-income neighbourhood arrives at the website 5 minutes later, these red leather boots will actually be showed at page 1 because this maximizes the revenue.

These inventory-balancing algorithms work very well in cases with significant uncertainty in the market size, yielding 5%-11% more revenues than reoptimization methods. Reoptimization methods work extremely well and yield nearly optimal revenue because they can effectively ration the inventory to all customers. This means that inventory-balancing algorithms are more suitable for situations with a lot of uncertainty. Also, the inventory- balancing algorithms have a strong performance under both nonstationary and stationary demand processes. This implies that, even when there are sudden shocks in the customers’ arrival patterns, for example in case of seasonality, the algorithm maintains a strong performance guarantee. Another advantage is that this inventory-balancing algorithms are simple and flexible which makes it possible to combine them with reoptimization methods.

Golrezaei, N., Nazerzadeh, H. & Rusmevichientong, P. (2014) Real-Time Optimization of Personalized Assortments. Management Science. 60 (6), 1532-1551.

Mattioli, D. (2012) On Orbitz, Mac Users Steered to Pricier Hotels. Available: Last accessed 23-4-2015.

Auction: iPhone 6 sold for $12.58 to ‘mrcuddles’


In session 4, Dealdash has been brought up by Dimitrios and he explained briefly what this site is about. In hindsight: Dealdash is an auction site that sells stuff that is very appealing to bargain hunters. For example; you can win a brand new Macbook Pro for just an unbelievable price of $ 18.52 (RSP: $ 1299.00). How can Dealdash make this happen? In contrast to Ebay, a bidder on Dealdash can only increase the price with $0.01 per bid. After each incremental bid, the end time extends with 30 seconds. Basically, the price increments with $ 0.01 until there are no new bids within the bidding time. The winner is obligated to buy the product for the final price. Sounds great, right?

Now follows the tricky part:

All products start at a price of $0.01 and the price increases with one cent per bid. After each bid, a timer starts ranging from 30 seconds to a few minutes. If the timer expires without a new bid, then the last bidder gets the product. Usually the bidder pays a price which is much less than the original retail price.

So how does the owner of the site make money? The revenue model reveals that the site uses ‘bidding rights’ to let participants pay for each single bid. Yes, ‘bidding rights’. This involves an amount of $ 0.99 or more. Furthermore it is not possible to apply any skills or knowledge.

Institutional environment

Therefore, Dealdash’ Dutch alternatives (a.k.a. ‘centveilingen’) are considered illegal in the Netherlands. Two main factors are attributed to the reason why Dealdash is considered to be illegal: (i) you do not know who other participants are and (ii) it falls under gambling according to the Dutch law and penny auctions do not obtain such gambling license. Kansspelautoriteit monitors the gambling market in the Netherlands. They observe the market and penalize illegal activities.g5

PROS: The site owner makes a lot of money with the bidding rights. Furthermore, the winner gets a product for a bargain.

CONS: All other participants lose their investment. Because only the last participant gets the auctioned product and earns back his/her investment.

Instead of asking you to participate, I do NOT recommend to click on Dealdash or any other penny auctions at all.


The Inner Circle: An Exclusive Dating Community

Nowadays, a lot of apps can be downloaded for free in the App Store or similar shops. Most of these apps try to get a large user base, so that they can sell data and the more users they have the more income they generate through advertisements. There are also a lot of dating apps available on the market. Tinder is a famous app that can be used by everybody looking for a date, and there are several variations for niche markets available. One of the dating apps that is expanding worldwide now is The Inner Circle. In contrast to most other apps, they do not try to get as much users as possible, as you can only become a member if you get an invitation from another member. And even if you have an invitation, your social media profiles will first get screened to check if you would fit in The Inner Circle community.

The Inner Circle members are mainly young, inspiring, and motivated professionals in the age of 25 to 40. The community is attractive for other people because through this app like-minded people can find each other for a serious relationship. In contrast to other dating apps members are pre-screened which gives a more reliable image to other users. It also takes away the effort for members to google other members. If a member meets someone new they know that the other person is most likely successful in life. In the Netherlands The Inner Circle even attracted a famous football player and actor.

Members of The Inner Circle can make use of an app and a website where they can chat with each other before they decide to meet in real life. Besides this, users also get access to exclusive parties organised by The Inner Circle to meet other members. Special games are organized during these parties so that the members get in touch more easily.

There are several plans to expand the app to other countries. The growth of members will not be very fast because of the invite-only policy. After a free week of trial, members have to pay for a Full Membership, and therefore the company is financially healthy. The app was founded in Amsterdam in December 2012 and is now active in London, Paris, Stockholm, Milan, and Barcelona, with 50.000 members so far. The Inner Circle wants to upgrade in these cities through growth hacking and further expand to Zurich, Berlin, Copenhagen, and New York in 2015. In the US there is already a similar app, called The League, which is also a dating app with an exclusive network.

The business model of The Inner Circle sounds like a paradox: they do not want everybody to become a member, however without members the app would not be able to exist. Exclusive networks exist in the real world, but so far it seems that they can also be successful and even profitable in the online world. Would you like to be part of this exclusive community?


Missing person?

Saturday the 25th of April, a disastrous earthquake hit Nepal. Homes have collapsed, century-old heritage sites have been destroyed, phone communications are still down, but most importantly of all – nearly 2,500 people are reported to have died during the disaster and thousands are still missing. People with relatives living or travelling in the area struck, often have no way of directly contacting them due to the damaged communication infrastructure. In an attempt to provide information and to aid rescue efforts, Google once again enabled its Person Finder.

Schermafbeelding 2015-04-26 om 22.11.36

Google first launched the Person Finder after the 2010 earthquake in Haiti and has since opened the Person Finder in response to every other major calamity. The Person Finder is a missing persons database that allows people looking for their relatives and loved ones to search for their names by clicking on “I’m looking for someone”. The database relies on individuals who have information on someone’s status in the areas struck to add a record to the database by clicking on “I have information about someone”. The record contains the name, physical characteristics, and a description of the person as well as information on his or her current status and contact details. In addition to user-added information, the database uses People Finder Interchange Format (PFIF) to aggregate missing persons information from registries of other organizations in an attempt to centralize the information.

Schermafbeelding 2015-04-26 om 22.28.12Person Finder depends on its users to update and remove records when no longer relevant. Users can also report spam, offensive content, or incorrect information. Records added to the database have a limited expiration date – after a certain number of days (minimum of 30), they are removed from the database unless manually extended by a user. As such, Person Finder is a fully crowd-sourced platform as it relies on user’s input of information.

As of Sunday, a day after the earthquake, the database already contains 4700 records and is still growing.


Person Finder: 2015 Nepal Earthquake (2015) Available at:

Smart customer, Smarter business

Firms knowledge about its’ customers over the last years has skyrocketed. With the rise of the internet came revolutionary ways to differentiate customers. The use of different tracking mechanism allows for a company to track the customer. The combined information that is gathered can be converted into a customer profile. Nowadays it is essential for shops to determine groups within those profiles. Easy ones are age, gender and location, but as more data is analyzed more profiles can be distinguished. This blog post will focus on a few of these customer profiles that are a bit harder to determine than age or gender.

Strategic pricing is nothing new, segmentation between customers has been done for ages but it is time to bring it to the next level. Consumers are smarter than ever. Searching costs have dropped with increased ease of use of search engines. Reviews and technical information are for most product categories widely available, this relatively new development leads to improved product informedness; the consumer is better aware of what is in the market. Same goes for the price of products. Comparison sites make it much easier to quickly determine the average industry price, leading to a better price informedness; a customer that knows the value of the product. These factors together lead to a higher consumer informedness.

With all this information available to the consumer, it becomes more and more difficult to define the perfect pricing strategy for your products. Research at the Erasmus University Rotterdam in collaboration with the Singapore Management University suggest that there is no perfect pricing for a product group. First of all the research made a difference between two types consumers. A commodity Segment and a differentiated segment. The first consumer group has a strong preference for choosing the product that offer the best price. The second group is willing to pay more if that means that the product fits better to their needs.

The groups were analyzed using data that was collected through a series of stated choice experiments in two different contexts. Results were clear, one pricing strategy for a product just isn’t enough. A company needs to develop different pricing strategies depending on the kind of customer it is facing. The research found that different levels of informedness amplified different consumer segments. Consumers of the commodity segment, who highly valued price, where more influenced by a product offering high price informedness. Whereas the differentiated segments behavior was stronger amplified by an increase in product informedness. This means the firm needs to make sure the type of information available of the product matches with the type of customer that is interested in buying the product.

For the company this leads to a necessity to use advanced tracking tools to determine the kind of consumer it is facing, and adjusting not just the price, but all the information available about that product. This will have as a possible result that customers see a totally different price and description compared to their friend or family member, even though it is exactly the same product. And for you: As a consumer you will get product and price information that is ‘tailor made’ to fit to your preferences, which sounds nice but it could for example mean that you pay a much higher price because the company knows you are using an expensive laptop.


– Li, T., Kauffman, R.J., van Heck, E., Vervest, P., and Dellaert, B. (2014). Consumer Informedness and Firm Information Strategy. Information Systems Research 25(2) 345-363.
– Tsekouras, D. (2015). ‘Consumer Centric Digital Commerce session 4’. Business Information Mangement. RSM Erasmus University.

Bro’s before Co’s?

Everyone (at least we hope and assume…) is part of a certain community and there are infinite examples of communities that can be found in daily life. But how many brand communities can you think of? Are you a member of one yourself… or do you want to become one?

Lugnet, My Starbuck IDEA, ORACLE community, Being girl are only some examples of communities that are based upon a certain connection with a brand. A brand community can be defined as ”A brand community is a specialized, non‐geographically bound community, based on a structured set of social relations among admirers of a brand” (Muniz,JR & O’Guinn, 2001) Having an active and involved brand community can be seen as one of the greatest assets of a company, but only if it managed correctly.


An example of how it could benefit both the community and the brand is HOG: the Harley Owners Group. Their relationship goes beyond affection or a simple connection with a brand, because Harley Davidson is embedded in their routine, in their daily life and especially in their heart. However, due to the social interactions among individuals, people start to build up relationships with each other NEXT to having the affinity with the company and companies are afraid that the communities are becoming more focused on the social connections and events inside the communities than about  about sharing their passion of the brand.

One of the questions that could follow out of this information is: Are you loyal to the company or to a brand when you are a member of a brand community? Well, that’s the same question that Marzocchi et al. (2013) had when they started their research about the effect of identification with the company and/or with the community to build up loyalty in a brand community. Since the digitization and adoption of the Internet individuals could get more easily in contact with others to share their passion (or sometimes their envy) about companies and start up or join brand communities. Marzocchi et al. (2013) used an experiment in a setting where the sample which shows similarities to the population of the HOG; motorcyclist at the World Ducati Week and therefore the results of this research can have valuable information for HOG and Harley Davidson.

Marzocchi et al. (2013) tried to answer the research question: “how important in building loyalty in a brand community are identification with the brand owner and identification with the community, respectively? The authors try to provide a better and deeper understanding of the identification-loyalty dyad in a context, where there is a broader system of relationships.

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This research shows that there is evidence that consumer’s identification with a brand community has a positive relationship with two constructs: attitudinal and behavioral loyalty. Furthermore, it has a positive influence on the favourability and constructiveness of comments about the company. A higher identification with a brand will then lead to a longer time commitment (attitudinal) and more sales (behavioral) with as an extra even more positive (e-) WoM! The research found no evidence of a direct impact on resilience to negative information or propensity to comment. Another finding is that the strongest influence of cohesive communities is related to a stronger affective response and trust in the brand, but that effect is mainly indirect. Next to that, the research confirmed that having a positive relation with the brand is very relevant for the creation of the loyalty related constructs. Due to the nature of the sample for this research, this findings can only be generalized for products or services with a hedonic and emotional content, luckily this is the case for HOG.

A company like Harley Davidson, which community can be seen as a very specific and intimate should embrace their community and treat identification as an antecedent of brand trust and brand effort. By building up relationships with their community it is a way to increase brand equity for the company and should therefore be an important agenda topic for managers that are involved in the marketing section of a company. By getting in detail, the needs and wants of the company aswell as for the community, it should be possible to decide which investment portfolio is the most suitable for companies who already have or planning to invest in managing brand communities and are striving to increase brand equity.


*Marzocchi, G., Morandin, G., & Bergami, M. (2013). Brand communities: Loyal to the community or the brand? European Journal of Marketing, 47(1/2), 93-114.

*Muniz, A.M., & O’Guinn, T., C., (2001). Brand Community, Journal of Consumer Research, Vol. 27, No. 4 (March 2001), pp. 412-432

Turn customers into brand advocates by using participation marketing!

Consumer value creation is hot and happening! The successes that can be achieved when crowdsourcing production processes and relying on consumers to create value are plenty: Threadless’s users create and vote on clothing designs that eventually will be produced, Nike offers consumers to design their own pair of shoes, Lay’s challenged its consumers to come up with a new flavor, et cetera. One area in specific – marketing – is interesting when looking at how consumers could create value for a company.

“Participation marketing” or “engagement marketing” refers to a marketing strategy that encourages consumers to participate in the evolution of a brand. This marketing strategy treats consumers not solely as passive receivers of messages, but views them as actively involved producers and co-creators of marketing programs. Two big players are using it with success: Coca-Cola and Yoplait.

With the average person in the United States drinking the equivalent of 275 cans per year, there is no need for Coca-Cola to focus on increasing their immediate sales transactions and acquiring new customers. Coca-Cola is shifting towards creating a more long-term emotional connection with their customers. One successful example is their recent “Share a Coke” campaign, where they replaced their product logos with popular names. This invited consumers to start a big wave of referrals on social medium websites, which resulted, for instance, in a crazy 341,000 posts on Instagram with the hashtag #shareacoke. This is one of the ways Coca-Cola uses to build loyalty and engage customers.

Another example of participation marketing can be found at Yoplait. Yoplait’s annual “Save Lids to Save Lives” program donates 10 dollar cents to a breast cancer foundation for every pink foil yogurt lid that customers mail back to the company. Since 1997, around 35-50 million dollar has been donated by Yoplait and their parent company! This translates into hundreds of millions of customers mailing their yogurt lids to the company! When customers actively engage with the campaign in order to support the cause, they are more likely to purchase Yoplait’s products and encourage others to do it as well. Customers are becoming so-called brand advocates. This way, Yoplait is building brand loyalty whilst also increasing sales.

The lesson companies should take from these two examples is to shift their focus from viewing customers solely as receivers of marketing and buyers. They should engage with them to create value together. This way they will become lifelong loyal customers and brand advocates. Don’t think only big companies with enormous marketing budgets can pull this off: the ALS association created the “Ice Bucket Challenge”, which went extremely viral. This led to increased customer engagement and more donations.

What do you think? Do you know other great examples of companies that used participation marketing?

Beginning era of Online Health Service

There are so many business idea opportunities out there, but none of them is more challenging than online health consulting service. Health is a not just a matter of preference, everyone cares about it. For that reason, people are very selective and careful in selecting type of health services they can get. On the other hand, medical professionals are also in need of better tool to increase their capacity in delivering health services to more people, especially health consultancy.

In 2013, there were around 15000 medical apps (David Lee Scher MD, 2013) but only few of them were successful. Apple created a healthcare app called iDR 24/7 with the purpose of providing health consulting service at any time, however consulting with professional doctor (in USA) charges user some amount of money. Other examples are HealthTap and iTriage that are available in iOS and Android. Both of them allow you to reach high number of professional doctors and yet provide other advanced features such as daily health tips, smart symptom diagnosis, or even searching for nearest hospital. Likewise iDR 24/7, there is small fee for a doctor’s advice. Last example of famous health-related app is Urgent Care (Android and iOS) which is focused on delivering health services from professional medicals at any emergency moment.

The next question is: why so many health apps fail? In providing health advice and consultancy, good enough is never enough, patients always demand the best possible service when it comes to their health, whereas doctor only wants a tool that helps them simplifying all the health service delivery process such as prescribing medicine, setting up new arrangements and archiving the records of the patient. Beautiful design and sophisticated feature are merely additional as long as the basic functionality works. Thus, involving medical experts in the platform development itself is very important.

In the era of internet, people have access to medical information on internet even without seeing the professionals (doctors). However, for more complex situation, they still need assistance from the experts and thus the implementation of online health consultancy service will help. Not only doctors can provide more timely assistance, they can also reach people who cannot afford medical cost, especially for consultancy. Ball and Lillis (2000) predicted E-health services may educate normal people with basic medical information (general information, disease management, and clinical decision support), support the communication between doctors and patient, and increase administrative efficiency (e.g reduced paper used for medicine prescription, form, charts, etc).

In conclusion, doctors and patients are the main customers of this digital platform. Doctors benefit from the increased capacity in delivering consultancy services whereas customers will have more access to health advice service easily in timely manner. Through collaboration with medical professionals, developers (for website or mobile app) should be able to create a platform in which interaction between physicians and patients can be supported flexibly in lower cost. As health is particular concern of everybody, the advancement of internet technology will drive high demand for online health service in the next 5-10 years.

Ball, M. J., & Lillis, J. (2001). E-health: transforming the physician/patient relationship. International journal of medical informatics, 61(1), 1-10.

Belle, Mika (2013). No waiting rooms, no copay: 6 apps to get a doctor’s advice. Retrieved from:

David Lee Scheer, MD (2013). 5 Reasons why mobile health apps fail. Retrieved from :

How Electronic Word of Mouth can be a critical success factor in e-business

This blog is based on the following study: Yoo, C. W., Kim, Y. J., & Sanders, G. L. (2015). The impact of interactivity of electronic word of mouth systems and E-Quality on decision support in the context of the e-marketplace. Information & Management.

EWOM is defined as “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet (Hennig-Thurau & Walsch, 2003). One of the best examples of EWOM systems and indeed a prototypical EWOM system is the customer review system. Customers can post text-based comments, insert video reviews, and even respond to other customers’ opinions on the product or service in question through EWOM systems. The emergence of these EWOM systems has changed the way that businesses engage the customers as well as other businesses.

To investigate the impact of the interactivity of EWOM systems and E-quality of a website on decision support satisfaction, Olivers (1997) cognition-to-action loyalty framework is adopted as an overarching theory. Oliver argues that consumers build loyalty toward a brand cognitively first, then affectively, next conatively, and finally behaviourally. Adopting interactivity theory and E-quality is appropriate to represent the cognitive aspect of loyalty phases. When decision support reflects customer needs and preferences, customers feel satisfaction with this support. Hence, adopting this construct, decision support satisfaction is useful in describing the emotional phase of the loyalty framework. Finally, E-loyalty is employed to illustrate the conative phase of loyalty. Based on this theoretical framework the authors explore the relationships of interactivity of EWOM systems, E-quality, decision support satisfaction, and E-loyalty by proposing four research questions that can be found in the figure below.


The interactivity of EWOM systems and E-quality are the strong predictors of decision support satisfaction. Therefore, H1 and H3 are supported. The effect of the interactivity of EWOM systems on E-quality is significant, validating H2. Decision support satisfaction is found to influence E-loyalty, thus validating H4. See figure below.


These findings indicate that customer perceptions regarding the interactivity of EWOM systems are very influential on their evaluation of an entire website and their level of satisfaction with decision-making support. This study illustrates that EWOM systems and websites with E-quality help customers enhance their decision making process.

When the four aspects of EWOM system interactivity (reciprocity, responsiveness, nonverbal information and speed of response) are well managed, users are likely to experience decision support satisfaction with the e-commerce site. This result indicates that e-commerce sites should be encouraged to provide a better EWOM environment for reciprocity and advanced EWOM system functionality, which enables multiple channel communications as well as quick and proper responses to customer requests.

The authors believe that EWOM has become an important part of the online shopping experience. Understanding the phenomena is essential for the success of electronic commerce systems.


Hennig-Thurau, T., & Walsch, G. (2003). Electronic word-of-mouth: motives for and consequences of reading customer articulations on the internet. Int. J. Electron. Commer. , 8, 51-74.

Oliver, R. (1997). Satisfaction: A behavorial Perspective on the consumer. In M. Sharpe, Satisfaction: A behavorial Perspective on the consumer. NY: Armonk.

Yoo, C. W., Kim, Y. J., & Sanders, G. L. (2015). The impact of interactivity of electronic word of mouth systems and E-Quality on decision support in the context of the e-marketplace. Information & Management.

What the consumer wants, the consumer gets.

When a company’s vision is to offer “Earth’s biggest selection and to be Earth’s most customer-centric company”, they’ve got some big shoes to fill. Due to the popularity of the term “customer-centric”, everybody’s been claiming they’re dedicated on the customer, however, are their real-time activities supporting this claim?

Amazon’s Vice President of External Payments, Patrick Gauthier is not in agreement with the statement that everybody is truly focused on the customer, as too many are obsessed by the industry lingo, too fixated on generating the next big cash cow, and repeatedly overlooking who’s voice it is that they are representing, namely that of the consumer and the merchant. Payments and commerce leaders should analyze the situation with a customer-first mentality in this industry in order to enter into true innovation.

“Start with the customer and work backwards.”

Most companies claim they begin their processes with the consumer in mind, however in amidst of the translation, the lingo which is used in the industry – that of the insiders, in essence keeps innovation away from the vision, as the true message appearing in the lingo of the consumer is lost in translation. Gauthier perspective, also shared by payments, commerce and retail experts at Innovation Project 2015, proposes a backward motion of customer-first mentality in seeking what a business model needs to solve. For example, every Amazon product manager is known to write and internal press release, which focuses on the problem of the customer and the current solution they are offering and how this offering fails. Following this, the managers write down every single benefit that the new product will provide to this problem, and will not stop until the benefits will be of interest for the customer. They stay that the money will follow once the company focuses on what the customer truly want. In this new era, having the perspective of a customer-focused company is surpassing having a financial perspective. Starting with the consumer in the back of your mind and going backwards, thus focusing on what a company believes the business model is intended to solve, as this will deliver diverse methods of serving those customer needs. Innovation can occur quickly, but more importantly, instead of focusing on the speeds, it is more significant that innovation happens right as well as for the right target audience.

How to approach this according to Gauthier:

  1. Being part of more dialogues with outsiders
  2. Connecting to the merchant and consumer
  3. Having more conversations that are focused on actual consumer needs
  4. Conversing less regarding payments
  5. Conversing more about the commerce experience

Drop the industry jargon

When the industry jargon is dropped, a focus can be given to consumer identity and true innovation, and in turn enables a full customer experience. Companies can only provide this by forming an open setting of transparency, enabled through conversations. Listen and acknowledge who the consumer is, and it will answers how the consumer prefers to pay will come to the surface. This in turn has the potential to open gateways to a richer commerce experience for the company as well as the customer.


Botch the customer and the merchants in essence want be part of a commerce experience, in contrast to a payment experience. Gauthier states that companies need to focus less on payments and more on the individuals that are making the transactions. He discusses identity and economics in the same sentence, as he believes it is pivotal.

“Who I am [as a consumer] is pretty key to what I’m going to do. Today, the economics is very centered around how I pay. And maybe at some point the economics should be centered, or certainly migrated, not with just so much just how I pay, but who I am. The who I am, and the willingness to share who I am — what I choose to share — should potentially change the economics of a transaction.”

– Patrick Gauthier, Amazon’s VP of External Payments


Both identity and payment experience are part of the commerce experience. Yet, from the merchants’ side, the occurring conversations focus on the payments side. The downside of this occurrence is that it diminishes the key role of the retail and commerce experience during the transaction stage, loosing out on the important achieving merchant-centric experience.

The industry is battling with conflicts due to having opposing viewpoints, on how payments and commerce ought to be engaged into the consumer experience. This tension of sort is a result of commerce innovation is completely being overtaken by payments innovation, similar to the example of a current successful business model Uber.

“The [retail] industry needs to embrace customer-centricity in order to really get to what it can be in the next 10 years.”

– Patrick Gauthier, Amazon’s VP of External Payments

Many views, and opinions from payments-, retail- and commerce sectors as well as managers with innovative perspectives are opening up discussion points. What is essentially needed though is a dialogue regarding what and who these individuals are actually chatting about.


Pymts (2015) “Amazon’s customer-centric Focus.” 24 Mar. 2015. Web. <;.

McAllister, Ian. (2012) “What Is Amazon’s Approach to Product Development and Product Management?”, 18 May 2012. Web. <;.

Bulygo, Z. “Becoming a Customer Centric Company” 9 June 2014. Web. <;

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Infographics: Web. <;

Bringing us together or driving us apart

Nowadays it is common for companies to ask your customers for advice via their online platform, and I am curious if this is the best way to engage your customers. Currently I work at a marketing and communications department, which is at the moment very active and involved in social media. One of my main activities is to manage the social media of our brand, and the last couple of months the focus lies on creating content on the different social media websites to inspire the audience. The question that popped into my mind is if it would not be a better idea to focus on engaging the audience and thus make it a two-way direction communication platform? To summarize our company is still doing it the old way, inviting customers for focus-group meetings in order to collect suggestions and ideas. Isn’t it a much easier and more efficient way to ask for input from customers via your Facebook and Twitter account?

Liu et al (2011) investigated the effect of soliciting consumer input on customers’ tendency to transact with an organization. To better serve the needs of their customers, nowadays more and more businesses are welcoming comments and suggestions from their customers and are trying to build a relationship with them. In the paper the authors take a closer look at relationships and the closeness degree.

Clark et al. (1993) theoretically distinguish exchange and communal relationship. Examples of the latter are friendships, which can be defined as a type of relationship in which one feels a special sense of responsibility for the other’s welfare as if it were it’s own. Exchange relationships can be defined as the interaction in which benefits are exchanged with the expectations of receiving something in return. An example of an exchange relationship is the relationship between a store owner and a customer. Liu et al. (2011) assume in reality relationships are a mix of both.

Besides the concepts of exchange and communal relationships, closeness is a closely related to customer relationship and often implicitly refers to the communal aspect of a relationship and the degree of bonding. The closer the relationship, the bigger the chance the consumer will embrace the business (Liu et al., 2011).

The findings of Liu et al. (2011) led to the conclusion that asking consumers for advice improves the relationship with the accompanied business and tendency to transact. Another conclusion is a decrease in perceived relationship distance between consumers and businesses is an effect of spending time or thinking of spending time with a brand. This in turn leads to changes in one’s engagement with the business.

Concluding, it is a much easier method to ask for input from customers via online platforms and to engage with them, but do watch out for achieving exactly the opposite. As Liu et al. (2011) conclude, soliciting advice tends to have an intimacy effect whereby the customers will feel closer to your business. Soliciting expectations from customers tends to have a contrary effect, as it will drive your customers away from your business. So be careful with what you ask from your customers.

Clark, M.S. & Mills, J. (1993), “The Difference between Communal and Ex- change Relationships: What It Is and Is Not,” Personality and Social Psychology Bulletin, 19 (6), 684–91.

Liu, W. & Gal, D. (2011), “Bringing Us Together or Driving Us Apart: The Effect of Soliciting Consumer Input on Consumers’ Propensity to Transact with an Organization”, Journal of Consumer Research, 38(2), 242-259

Android vs Apple 2.0?

When browsing the internet you normally encounter dozens of news items, blogs and other content. It is no exception that a catchy title usually makes you decide to click through and see what’s out there. And of course when the item ‘Android takes a piss on Apple on Google Maps. Seriously’ popped up on my Facebook news feed I decided to take a look at it.

If you would have searched on Google Maps on the 24th of April for certain coordinates just south of Rawalpindi, Pakistan, a giant Android could be seen urinating on the Apple logo. First thought was that it was an Android developer fuelling the old rivalry again, although later Google released that it was user-created content which was slipped through the approval filter. Because of this item I decided to dig into Google Map Maker, the tool that allows you to edit Maps, which was unknown to me since I had read the blog.

The official goal of Google Map Maker is to share information about places in user’s neighbourhood, like companies or university campuses. Places which are inaccessible with Google street view cars can thereby be edited by Map Maker users. It is actually even more elaborate, because users are also able to add roads, railways or other places and add new languages. Once an edit is sent to Google, it can be reviewed by other users by giving a thumb up or thumb down. This score is considered by the Google algorithm to accept or reject the edit. As we can see with this case users can, with enough peer user support, fool the algorithm.

If we look into the business model of Map Maker more closely we can link it to the first phase of consumer co-creation, namely recommend and develop products. Instead of only browsing through Google Maps to find by Google pre-defined places users can now develop new elements and recommend them to each other. The wisdom of the crowd is the most prominent reason for delegating (crowdsourcing) products. Best known comparable platform that uses this is Wikipedia. At Wikipedia every edit is implemented immediately and can be removed by higher ranked users if they incorrect, offensive or silly.


During our course we learend that controlling quality of submissions can be done by having specific terms and conditions, clear guidelines peer evaluation of content and punishment or public shaming (Tsekouras, 2015). However, the first thing that teenagers do when joining Wikipedia is to make a page about themselves or another try to edit a celebrity’s page. We now have seen that this can also be the case with Google Maps. Google claims that ‘the vast majority of users who edit Maps provide great contributions’, however internet users show that a manipulation is easy to make. What do you think of user generated content on the internet? Should it be better monitored or are we allowed to have a joke every once in a while?


How do you find consumers to create value with? Try automating!

Automated Marketing Research Using Online Customer Reviews

When shopping online, consumers often read several reviews of products they discover and many times base their decisions to purchase on the information provided by reviews. Research covered in class focused on the elicitation of ratings and reviews from consumers and ensuring they are valuable to the consumer. Reviews are not only beneficial for the consumer and there are distinct benefits that companies can extract directly from the information in the reviews.

In their study Lee and Bradlow (2011) use text-mining techniques to automate the analysis of customer reviews, forming valuable information for use in market research. Previous studies have not covered the analysis of market structure through reviews to describe the environment surrounding a business. Market structure analysis is an important part of the market research process, as many of the marketing decisions rely on information about existing the existing market; the potential substitutes and complements for the product. In order to form these market structure analyses, attributes of products are commonly mapped to represent different brands.

The study utilizes simple methodology to suit capabilities of marketers better; by combining techniques commonly used and less complex language processing. The techniques chosen do not require predefined product attributes to be tested which is how current market research commonly approaches eliciting these attributes. The authors’ rationale is to allow the methodology to be used repeatedly, so that analysis “can be done (unlike traditional methods) continuously, automatically, inexpensively, and in real time.”

The authors’ collected all digital camera reviews on the platform between  July 2004 and 2007. By clustering attributes detected in product reviews into common attributes, the authors’ were able to compare these attributes to attributes found in expert buying guides. What they found when comparing the opinions of experts, interestingly, was that they have no consensus in what attributes of a product are seen as important. In their comparison study they found attributes from analyzed reviews to be more valuable to the respondents and discovered new attributes.

To prove the use of their methodology in forming overviews of market structure repeatedly, the authors’ ran the analysis on a parallel data set they collected from reviews in between 2005 and 2007. This showed interesting results as the changes in attributes matched the changes seen in the market in terms of company strategies and consumer tastes. When Nikon changed its marketing from promoting technical specifications to a more product benefit focused approach, the attributes used in reviews reflected the change.

Managers can use the findings of Lee and Bradlow to support marketing strategy decisions. By mining customer reviews, the company can see how its brand aligns with competing brands in consumers’ minds. Tracking how the attributes mentioned change over time can be valuable information in determining how successful campaigns have been. New segments can be found by clustering characteristics detected with semantic analysis. Spotting attributes associated with competitors’ products is valuable insight in how competition is performing.

The study showcases how big data can be used in marketing research and brings to light the great value customer review data has when finding the customers to involve in the value creation process. With the current popularity of social media analysis it would be fascinating to compare the effectiveness of analyzing reviews and social media postings.

Thomas Y. Lee, Eric T. Bradlow (2011) Automated Marketing Research Using Online Customer Reviews. Journal of Marketing Research: October 2011, Vol. 48, No. 5, pp. 881-894.

Youtube, Facebook or Twitter?

Which is the most valuable social media channel in terms of user-generated content?

Firms like to engage with customers these days and social media strategy seems to be key in the success of customer involvement. They want to receive comments, likes, tweets, and review vlogs. This gives firms the option to co-create value with customers, but what many are not aware of is the differences in such content across the three major social media.


Smith et al. (2012) tried to determine what aspects of user-generated content (UGC) would be different across Youtube, Facebook, and Twitter. They collected data from two retailers (Lululemon and American Apparel) on the three social media. The main research question evolved around the differences in UGC on six dimensions, which will be discussed in the following paragraphs.

Promotion self-representation
The authors found support for the fact that self-representation actually occurred more often on Youtube compared to Facebook and Twitter. They attribute this finding to the fact that Youtube actually promotes users to be the main character in videos with their “Broadcast Yourself” slogan.

Brand centrality
Brand centrality was found to occur in the highest degrees on Twitter and the lowest degrees on Youtube. Twitter is designed differently where posts have limited word count, thus, the brand centrality is likely to be higher. Youtube highlights the individual self where brands are often only a detail of a video.

Marketer-directed communication
The authors only found partial proof for the fact that marketer-directed UGC would be less likely on Youtube compared to Facebook and Twitter. The first case, Lululemon, indeed showed the lowest market-directed communication for Youtube but American Apparel did not support this hypothesis. Marketer-directed UGC was thought to be lower on Youtube because consumers need to put more effort into that platform (time, resources, technical skill) than in Facebook or Twitter.

Response to online marketer action
Evidence was found for Youtube to have the lowest UGC in response to online marketer action. Again, this difference is attributed to Youtube being a more complex social media.

Factually informative about the brand
Although the authors hypothesized that brand-related factual information in UGC would be equal across the three social media, it was only found that Youtube and Twitter were close. Facebook scored significantly lower on factual information. This difference could have to do with the different levels of actions that firms undertake on social media. For example, American Apparel does not respond to consumer inquiries meaning that other consumers may provide non-factual information.

Brand sentiment
Sentiment in brand-related UGC was also expected to be similar across Youtube, Facebook, and Twitter. Unfortunately, no support was found for this hypothesis. For both firms, sentiment differed across the three platforms and each company had their own particular pattern in this difference. The authors could only conclude that brand sentiment differs per site but that this difference was not predictable.

This paper provides some primary insights concerning the usage of social media by companies that want customers to generate content. The main difference is that Youtube’s videos or comments are less likely to primarily focus on the brand, thus, will not respond as much to marketer action. This does not mean that companies should not invest in Youtube, as this platform might be more useful for other information such as the association with other brands. Twitter is most distinctive from Youtube where brand centrality tends to be higher and tweets can be rather critical. Facebook was found to lie somewhere in between. Companies need to take these differences into consideration when analysing the user-generated content.

A criticism of the paper is the rather limited data, which was collected. The paper only compared two companies in a similar type of industry. Furthermore, the targeted customer segment may also show different types of behavior on social media and this was not included in the study. Lastly, as mentioned in the discussion, the user may perceive their audience on social media in certain ways, which could influence the decision on the type of content to upload.

  • Smith, A. N., Fischer, E., & Yongjian, C. (2012). How does brand-related user-generated content differ across YouTube, Facebook, and Twitter?. Journal of Interactive Marketing, 26(2), 102-113.

Information overload? Use the RA!

When you are browsing the web in order to buy a certain product, you have got to process a lot of information. As we have been taught in class extensively, a website’s Recommendation Agent (RA) can help consumers to make purchasing decisions. Shoppers are loyal to e-stores that enable them to function efficiently.

Before M. Aljukhadar et. al (2012) conducted their research on consumer’s use of an RA to cope with information overload, little was researched about a consumer’s likelihood of using an RA or conforming to its recommendations. Therefore, they investigated several approaches to measurement of product information load; the relationship between delivered information load and perceived information overload; using an RA to indicate occurrence of information overload; effects on information overload on decision strategy while accounting for consumer’s need for cognition; and information overload and decision strategy on several performance measures.

The researchers created a fictitious retailer website that offered laptops. They chose three levels for the number of alternatives as well as for attributes. As consumer’s normally consider many attributes when shopping for complex search goods such as a laptop, the researchers included a high number of attributes. 466 participants were asked to choose a laptop they would seriously consider buying and needed to rate the importance of each laptop attribute. Afterwards, the participants could choose to click on a link to the recommendation according to their preferences.

Participants also were asked whether there was too much information to make a choice on two seven point scales and on two additional scales on how satisfied they were with the choice they made, in order to test perceived information load and choice confidence respectively. The need for cognition was measured by asking to fill in an 18-item scale, e.g. “I find satisfaction in deliberating hard and for long hours”.

The researchers found that overload was actually experienced by consumers and that the relationship between information load and perceived overload was curvilinear. Participants who experienced high information overload, consulted the RA and accepted its recommendation more often than those who did not experience overload. When overloaded, a consumer with low need for cognition is more likely to consult an RA and vice versa. Moreover, as information overload increases, choice quality improves when consulting an RA. Thus, particularly in complex situations, the use of an RA has a positive effect on choice quality.

While using an RA upholds choice confidence, confidence will decrease for a consumer who contradicts the recommendation. The relationship between perceived information overload and e-store interactivity is curvilinear as well. The researches propose two possible explanations for this: first, the use of an RA makes choice accuracy feedback immediate and tangible. Second, rejecting a personalized and accurate recommendation leads a user to face more choice difficulty and cognitive dissonance, which results in less choice confidence and lower satisfaction with the performance of the webstore.

Given these results, it would be advisable to webstores to proactively show product recommendations when information overload is high. Also, RAs should provide accurate advice. Lastly, apply other measures to increase the number of shoppers that conform to the recommendations.

Aljukhadar, Muhammad, Sylvain Senecal, and Charles-Etienne Daoust. “Using recommendation agents to cope with information overload.” International Journal of Electronic Commerce 17.2 (2012): 41-70.

Crowdfunding getting personal.

This week the multinational Philips announced to stop sponsoring the shirts of football club PSV after being there main sponsor for 34 years. PSV and Philips had the longest sponsor relationship in world history. Philips will only step down from their title as main sponsor and  will continue to sponsor the club on other fronts and the PSV stadion will still be called the Philips Stadion. However, for a lot of fans this news came as a shock. Philips has always been the main sponsor of the club and has caused for a lot of brand awareness as well. A great amount of fans were extremely disappointed but also concerned that no one at this point will provide the club with decent shirts.

As a response a group of PSV-supporters decided to try to become head sponsor of the club, simply through crowdfunding. The group of fans tries to include as many other fans as possible. They are currently verifying if the demand for the idea is sufficient. If it is they will further work out there plans.

For the football industry this might actually be a radical innovative idea. If the crowdfunding idea works out it creates opportunities within other football clubs world-wide.

Currently, the group of fans would need about 600.000 fans to spend 10 euros each in order to collect a sufficient amount for the head sponsorship.

Crowdfunding is becoming more and more of a solution nowadays. Another great example of this is the crowdfunding campaign: Scusa Roma.  A woman from the Netherlands that lives in Toscane decided to set up a crowdfunding campaign to raise money for the damage that was done in Rome by football hooligans. As a response other people started campaigns for the same cause.

The following graph shows the development of crowdfunding volume on crowdfunding platforms since 2009. We see an extreme growth since then as people become increasingly interested in alternative forms of investment capital.

(Statista, 2015)

The examples in the football field are merely two out of a huge amount of examples for what people use crowdfunding nowadays. The most commonly known example is startups that need funding for development of their products. However, as in the Scusa Roma example, there are loads of people that also use crowdfunding platforms for a good cause. Another example is the Hakiki – Fight poverty through social enterprises – project. A group of students want to help villages in Tanzania with developing  and decided to run a lot of events as for instance dinners, parties or benefit nights. However, to double the amount they have already raised they decided to start a campaign on Indiegogo.

These examples show that crowdfunding is not only for actual companies or start-ups anymore and not solely focused on investment. In contrast, they are getting closer and closer to our personal lives.


The Effect of Customers’ Social Media Participation on Customer Visit Frequency and Profitability

As people are spending more time on social media sites, firms allocate more of their resources to social media. Social media could be used to acquire and or retain profitable customers. However, it is difficult to measure the direct effect of social media efforts on firm profits. This could make it difficult for managers to justify their promotional budgets and social media spending (Rishika et al., 2013).

Rishika et al. (2013) study firm’s return of investment on social media efforts. Moreover, in their study they investigate how the overall social media activity and customer characteristics affect the customer-firm relationship, which is measured as the frequency that the customer visits the firm’s shop.

The authors verify that participation in firm hosted social media by focal customer will have a positive impact on the intensity of the customer-firm relationship. Moreover, they find that customer’s participation on a firm’s social media site increases customer’s frequency of also visiting the firm’s shop. Also, the firm’s amount of message postings and responsiveness on social media increases the customer’s participation on the social media site. However, these vary for different customers with different customer characteristics.

The CRM literature suggests that there is a positive association between the consumer’s average transaction amount and satisfaction, which results in better behavioral outcomes, such as increased commitment towards the firm. This brand loyalty contributes to an increase in the customer lifetime value (Crosby et al., 1990). Rishika and al. (2013) argue that high value customers feel that they are more important to the firm and that thus they are likely to value a firm’s relationship investments in social media more than low value customers. Indeed, they verify their Hypothesis 3: The impact of participation in firm hosted social media on the intensity of the customer-firm relationship will be greater for customers with a larger purchase amount.

Customers who purchase premium products are often the most lucrative firms, and hence it is in the interest of firms to retain these customers. The authors find that the impact of participation in firm hosted social media on the intensity of the customer- firm relationship is greater for customers who have a greater share of premium product purchases.

The findings of the study by Rishika et al. (2013) have several managerial implications. Firstly, it is that it is important to nurture customer relationships through social media, since active social media efforts could increase the bond between customer and the firm and lead to long term financial performance, due to increasing customer visit frequency. Secondly, since different customers react differently to certain social media efforts, it is important to segment your customers on social media. Managers can create subcommunities or discussion forums customers who are interested in premium/unique products.


Rishika, R., Kumar, A., Janakiraman, R., & Bezawada, R. (2013). The effect of customers’ social media participation on customer visit frequency and profitability: an empirical investigation. Information systems research, 24(1), 108-127.
Crosby, L. A., Evans, K. R., & Cowles, D. (1990). Relationship quality in services selling: an interpersonal influence perspective. The journal of marketing, 68-81.

Using people to predict the future, is this the future?

What will happen in the future? Microsoft tries to answer this question by launching the Microsoft Prediction Lab. The lab is an interactive platform that is partially built around the basis of a game. Users can invest points by predicting the outcome of certain future events. If a prediction is correct, the user gains points and if the prediction is wrong the user looses his or her points. Not only is the prediction lab a game, it is also a lab (off course). David Rothschild, an expert in data driven predictive methodology and researcher at Microsoft, sees the lab as a great laboratory for researchers and a new social experience. The team behind the lab tries to figure out what is the best way of predicting the future, using more than the typical dataset.

Why is this development intriguing? First of all, this platform shows that the wisdom of crowds can be used for making accurate predictions. The users fill in their predictions based on personal expectations as well as earlier predictions made by the crowd. The user adds value to the lab by making these predictions. This can be explained as value co-creation, where the value is equal to the correct predictions. Secondly, this digital platform uses positive network effects for its own benefits. The more users submit their expectations and predictions, the more reliable the overall prediction is.

I think Microsoft’s Prediction Lab is on the right track, if you consider the fact that it gave an 84% change of a ‘No-vote’ for the Scottish independence, and predicted 15 out of the 15 knock-out games on the FIFA World Cup correctly. For now it will focus on political events (such as elections) and sport, but the tech-giant plans to use the prediction technology for far more than that.

Microsoft has plans to incorporate the outcomes of the predictions into its existing products. Cortana, Microsofts’ digital help, could answer questions like ‘what will be the outcome of the next match?’ or ‘who will be the next president?’. I think this ability to use the lab for products is the most valuable contribution of the prediction lab; not the actual outcomes, but the value it adds to the existing products. Where do you think this technology will lead to?


What happens when a new business model becomes a new standard?

Successful new ventures often do something completely different than established companies in the same industry. Uber was founded in 2009 and their most recent valuation is north of $40 billion dollars. That’s an insane amount of money, but Uber is growing rapidly in a huge global industry (transportation). Becoming the standard for a global industry like that is worth a lot of money. AirBnB is a similar story; renting out your home to make some extra cash and bam, they are becoming a serious competitor in the hospitality industry.

Enabling customers to make their lives easier or generate more income; these new customer-focused companies are changing complete industries.

Innovators like these companies develop new technology to allow people to do things in a new way. However their business models aren’t protected by patent law or something similar. This allows other companies to use these new business models, allowing them to either (a) copy the model and compete with the original innovator (AirBnB, for example) or (b) use the new business model in a different industry or industry niche.

Competing with AirBnB and the likes does not seem to be the most logical choice; AirBnB didn’t become the company it is now by copying an existing business model; they created a new one. 

However, it sounds logical that these new business models can be used in other industries as well, and apparently companies have been doing so. Quite a lot.  

A quick overview of some initiatives of AirBnB’ styled businesses:

What this implies is that the innovations from the famous startups (Uber, AirBnB, BirchBox), create a plethora of opportunities for entrepreneurs and businesses around the world. The technology is often a lot cheaper given you’re not the first mover, which would be the AirBnB’s etc, the business model is proven and there’s probably still plenty of industries/niches that.

You’ll probably won’t end up with an Uber valuation when you create an “Uber/AirBnB/Tinder for X” type of business, however it does create a lot of opportunity.

Rather than seeing this as copying or unoriginal, we should start focusing on how we can use these new business models for other categories by looking at them in a more abstract manner. Tinder is a dating app, however Tinder’s user experience provides opportunity for concepts that need a low-barrier, many-to-many type of interaction where photos or short snippets of info are the standard. This can be implemented for content, professional networking etc.



Have you heard of some cool or unorthodox uses of these new business models? Please share in the comments!