Tag Archives: personalization

Personalized e-learning is on its way and we should be prepared


Personalization

With the rise of digitalization has come the rise of digital personalization. Personalization has been existent for a couple of years now, in different kinds of industries, such as retail, cars and even perfumes (Randall, Terwiesch & Ulrich 2005). This means companies and researchers also already had quite some time to learn about both the benefits and the drawbacks of personalization. However, the drawbacks are harder to overcome nowadays, since the use of personalization has already been implemented on such a big, global, scale. Think, for instance, about privacy concerns that could have at least partly been prevented if legislation was set in place in time. However, we can all understand that it is hard to act upon potential drawbacks in advance if there is no prior experience whatsoever.

Nevertheless, it is always a good idea to be cautious and critical about upcoming trends such as personalization, before blindly implementing them without thinking about any potential consequences, either negative or positive. This means, foreseeing any potential drawbacks, as well as keeping in mind what you would like to reach as a goal by pursuing a trend such as personalization.

Personalized e-learning

Ashman et al. (2014) have presented a detailed discussion regarding personalization, but not in the field of, e.g., e-commerce, where it is already widely implemented, but rather in the field of e-learning. Personalization in e-learning is still in its beginning phase and therefore not yet widely implemented. Thus, the authors act in advance on warning e-learning providers and educational institutions on the potential drawbacks of the personalization of e-learning, including recommendations on how to overcome them, before it is too late. Especially since educational institutions are increasingly using such models as a way to gain as much as new students as possible, to increase their income, risking to lose their initial, most important goal out of sight: to enhance the quality of education. (Ashman et al. 2014)

But why is personalization of e-learning initially needed, then? The authors acknowledge where institutions’ interest in personalization of e-learning is coming from. E-learning is an upcoming trend on its own already to overcome the lack of time and resources to facilitate an increasing number of students globally. However, students might feel disenfranchised and their individual learning needs might become neglected by the use of e-learning. To overcome this issue, educational institutions are starting to implement the personalization of e-learning. However, then again, personalization comes with its setbacks.

Setbacks

The three main setbacks discussed by Ashman et al. (2014) are privacy concerns, serendipity issues and deskilling problems. The authors discuss these three setbacks in great detail. Privacy concerns is a recurring issue surrounding the topic of data gathering in general, which is also needed for personalization. Serendipity issues are about the reduced ability to learn and understand different beliefs, cultures and lifestyles, or to learn ‘out of your comfort zone’, as personalization leads to the targeted student to only be presented information that fits within his/her field of interest. Lastly, students can be deskilled in the sense that they do not learn how to critically assess and evaluate the information that they are given, as with personalization they are presented the results that most closely fit their needs, so they stop looking further very quickly. The authors emphasize, in order to overcome these issues, it is important to inform students about what and how data is gathered about them, and to give them the opportunity to control what information is presented to them. Additionally, they advise a clear and thorough understanding by e-learning providers and educational institutions of why personalization in e-learning is needed and what can be achieved by it, for which thorough experimentation is required.

In their paper, several universities, such as Harvard, St. Gallen and Ontario, are used as an example, from which data is analyzed very extensively by Google Analytics. Google Analytics tracks staff and students on the websites of the universities. This enhances the concern of privacy, as the user ID’s were visible.

Opportunities

Despite these discussed setbacks, the authors do see great value in personalized e-learning as “the system is genuinely able to interact with users, recognize when they need assistance and guide them to the appropriate information or educational activity” (Ashman et al., 2014). Unfortunately, the authors of this paper focus solely on education in well-established economies, which is only a small part of the world. It would be interesting to see the possibilities of personalized e-learning being enforced globally, and thus in poorer areas, too. Interestingly, founder of Facebook, Mark Zuckerberg, and his wife, are planning to donate 99% of their Facebook shares to invest in, amongst other things, personalized learning. He mentioned:

“Students around the world will be able to use personalized learning tools over the internet, even if they don’t live near good schools. Of course it will take more than technology to give everyone a fair start in life, but personalized learning can be one scalable way to give all children a better education and more equal opportunity.” (Strauss, 2015)

pexels-photo-267399.jpeg

Let’s see what the future holds for us and the upcoming generations regarding a transformation in education, not only in well-established, advanced countries, but also in countries limited in access to good education. Although the negative consequences should not be forgotten and be acted upon well in advance…

References

Ashman, H., Brailsford, T., Cristea, A. I., Sheng, Q. Z., Stewart, C., Toms, E. G., & Wade, V. (2014). The ethical and social implications of personalization technologies for e-learning. Information & Management, 51, pp. 819–832.

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

Straus, V. (2015). A primer for Mark Zuckerberg on personalized learning — by Harvard’s Howard Gardner. The Washington Post. 

Personalized offers without compromising the privacy of personal information


Searching on the internet for information is one of the most common activities these days. This behavior varies from searching for the closing time of your favorite shop, to finding out how cognitive processes work. Consumers are generating and sharing data when they search on the web. The knowledge of this process is not always present. Even if consumers knew their steps were being followed, would it change their daily behavior? Privacy is not a new term. Yet, it disclosed itself more than ever the last couple of decades, together with the introduction of the Internet. The general definition is the right to be let alone. It is based on the principle of protection of the individual in both person and property (Warren & Brandeis, 1890). However, this definition is too broad. Informational privacy fits more in the context of these days. The definition is the right to control of access to personal information (Moor, 1991).
It is clear that privacy concern is an issue for companies. Companies want to use personal information to offer personalized adverts which is often considered as something positive by the consumers. A business example is Amazon.  The well-known E-commerce company continually updates the user’s personal page to create more tailored experiences. This is done based on past purchases and browsing history and the objective is to stimulate impulse buys (Reverte, 2013). However, personalization has some concerns.

Amazon

 

Sutanto, Palme, Tan and Phang (2013) wrote an article in which they study the so called personalization-privacy paradox. This is the tension between how IT developers and marketers of applications exploit personal users’ information to offer personalized products or services and these users’ increasing concern regarding the privacy of that information. Eventually, this may restrain the use of these applications. The purpose of this paper is to study whether a personalized privacy-safe application works. This application stores and processes information within the user’s smartphone, but does not transmit it to the marketers. This way, personalized information can be offered, without compromising the privacy of personal information (Sutanto, 2013).

Personalized privacy-safe application
To understand the personalization-paradox, the authors build on two theories; the use and gratification theory (UGT) and information boundary theory (IBT).  UGT suggests that consumers use a medium either for the experience of the process or for the content it offers. These two dimensions are called process gratification and content gratification (Sutanto et al., 2013). While the latter refers to the messages carried by the medium, the first one relates to the enjoyment of using the medium.

Next, IBT gives a better understanding in which factors influence process and content gratification.  This theory suggests that consumers form so-called physical or virtual information spaces around themselves. These spaces have boundaries and an attempt by third parties to cross these boundaries will be considered invasive which makes consumers uncomfortable.   In case of an intrusion, consumers will apply a risk-control assessment, weighting the risk of disclosing personal information and the benefits they gain when doing so (Stanton, 2003).

This study conducted a field experiment with three mobile advertising applications. The first mobile application broadcasts adverts generally (i.e. non-personalized application). The second application filters and displays adverts based on the profile information of users, stored in a central server (i.e. personalized, non-privacy-safe application). The last application filters and displays adverts on the profile information of users, stored on their smartphone (i.e., personalized, privacy-safe application). In this context, process gratification is measured with the number of application launches.  On the other hand, if a user is interested in the content offered by the application, they are more likely to save the advert with the purpose of retrieving it later, thus content gratification is measured in terms of the frequency of saving adverts (Sutanto et al., 2013).

Personalized application

The results of the field experiment showed that there is indeed a difference in process and content gratification between the three different applications.  Process gratification increased by 64.5% when the adverts were personalized compared to when adverts were general. However, there was no significant difference in content gratification. This may be explained by the fact that saving adverts explicitly indicates interest in a specific product, thus it requires the user to reveal deeper levels of information than their own boundaries allow. It is likely that this situation causes an uncomfortable feeling and which eventually will lead to a hesitation to save adverts.  Next, the local privacy-safe personalization design increased both process and content gratification. Application use increased by 9.6% compared to personalized, non-privacy-safe application and by 79.1% compared to the non-personalized application.  Respectively, advert saving increase by 24.5% and 55.1%.

However, there is an important limitation in this paper. There is a possibility that some users launched the application, but already were interested in a certain ad. This makes it more difficult to disentangle process gratification from content gratification.

Concluding, this article proposes a personalized privacy-safe application. The results show significant differences between the three applications in favor of the local privacy-safe personalization application. Thus, offering personalized adverts without compromising the privacy of personal information is possible.

References:

Moor, J.H. (1991) ‘The ethics of privacy protection’, , pp. 69–82

Reverte, C. (2013) Personalization Innovators: Amazon, Netflix, and Yahoo! | Available at: https://www.addthis.com/blog/2013/08/28/personalization-innovators-amazon-netflix-and-yahoo/#.WomB3OciHIU. [Accessed 18 February 2018].

Stanton, J. M. (2003). Information technology and privacy: A boundary management perspective. In Socio-technical and human cognition elements of information systems (pp. 79-103). Igi Global.

Sutanto, J., Palme, E., Tan, C. H., & Phang, C. W. (2013). Addressing the Personalization-Privacy Paradox: An Empirical Assessment from a Field Experiment on Smartphone Users. Mis Quarterly, 37(4).

Warren, S.D. and Brandeis, L.D. (1890) ‘The right to privacy’, Harvard Law Review, 4(5), pp. 193–220. doi: 10.2307/1321160

FlavorPrint: Personalizing your recipes through your tastes


How amazing would it be if you knew every meal you cooked would fit your tastes? McCormick & Company, a major player in the flavor industry, is reinventing traditional FMCG business models through its data-driven, customer-focused offerings. While the company generally manufactures and distributes spices, seasonings, and other products over 125 countries and territories (Amazon Web Services, n.d.), a shift has occurred from a product-centered company to a business model in which the entire customer value is achieved through a comprehensive consumer journey.

McCormick is continually moving towards innovative solutions to reach customers relative to competitors or FMCG companies in other sectors. The expected sales target of $5bn by the end of 2019 will come from e-commerce, innovation through platforms, and acquisitions of other companies (Nunes, 2017); evidently, digitization is driving the company’s growth. In 2014, McCormick created a spinoff company named Vivanda, through which a transformative product called FlavorPrint was developed (Nash, 2015).

FlavorPrint

FlavorPrint is ‘a technology that matches people with food they love’ (FlavorPrint, 2017). When users sign up to McCormick’s recipe platform, they are asked to fill out initial questions about their food preferences. Their recipe search behavior on the platform will continuously adapt the user’s ideal taste palate to recommend recipes that fit the user perfectly. FlavorPrint ‘combines sensory science and culinary science’ to ‘offer personalized recommendations for recipes, meals, and eventually wine pairings’ (Amazon Web Services, n.d.). FlavorPrint is able to change a person’s cooking habits by offering exciting alternatives that are customized to the user (while promoting McCormick’s products) (FlavorPrint, 2017).

Value to Consumers

Vivanda’s FlavorPrint follows a number of mass customization (MC) drivers while requiring little to no investment by the consumer, and consumers participate in the service because it offers them significant product utility. The extra costs for consumers are low; the quality of recommendations is high, no financial investment is necessary to use the service, and the effort of signing up to the platform is relatively low (Tsekouras, 2018). Furthermore, the FlavorPrint service works automatically, meaning that the consumer does not have to take any specific action to use the service, other than signing up to the platform. In short, FlavorPrint’s predictive analytics technology has made recipe selection much easier and more likeable, while demanding little time and effort from consumers.

Efficiency Criteria and the Future of Predictive Analytics in Food

In 2013, McCormick initiated a small beta program for its new technology. While a 1% increase in sales is very large in the industry, FlavorPrint quickly grew to 100,000 participants (while still in beta mode) and drove sales up by 4.9% (Amazon Web Services, n.d.). This was a sign that the company needed to ensure scalability for its platform, to allow millions of users to participate.

While financial data and statistics regarding platform usage have not been published, Vivanda has officially spun off from McCormick. In 2016, Vivanda announced a strategic partnership with and investment from German software giant SAP. This collaboration will ‘help our food industry partners to grow profitably by delivering increasingly personalized experiences and outcomes directly to customers’, according to E.J. Kenney, SVP Consumer Products Industry at SAP (SAP, 2016). The partnership indicates that Vivanda has shifted its strategy from focusing on McCormick customers to delivering its service to various players in the food and beverage industry; by targeting a wide range of food and beverage customers, Vivanda’s growth seems inevitable.

Drawbacks

It will be interesting to see what the future will hold for Vivanda and the use of predictive analytics in food. McCormick evidently derives great value from the technology, but one has to wonder if the technology has its criticisms pertaining to a possible lack of understanding of consumer behavior or privacy issues. For example, while the technology takes into account various contextual factors such as consumer budget and nutritional objectives while recommending foods, changing lifestyle situations may prove it difficult for the technology to adapt fully to consumer’s lives.

Conclusion

Although FlavorPrint does not directly offer a new revenue stream, the new possibilities for consumer packaged goods firms to reach customers indicate a potential for significant impact on future sales for Vivanda clients. Customization/personalization lies at the heart of the service, which is why the business model provides companies with a way to target consumers much more directly than through traditional marketing.

Will you use FlavorPrint to find new recipes? Does the company have a bright future? Let me know in the comments!

 

References

Amazon Web Services. (n.d.). AWS Case Study: McCormick. [online] Available at: https://aws.amazon.com/solutions/case-studies/mccormick/ [Accessed 18 Feb. 2018].

FlavorPrint. (2017). FlavorPrint. [online] Available at: https://www.myflavorprint.com/ [Accessed 18 Feb. 2018].

Nash, K. (2015). Tech Spin-off from Spice Maker McCormick Puts CIO in the CEO Seat. [online] WSJ. Available at: https://blogs.wsj.com/cio/2015/04/01/tech-spin-off-from-spice-maker-mccormick-puts-cio-in-the-ceo-seat/ [Accessed 18 Feb. 2018].

Nunes, K. (2017). Innovation central to McCormick’s growth strategy. [online] Food Business News. Available at: http://www.foodbusinessnews.net/articles/news_home/Business_News/2017/04/Innovation_central_to_McCormic.aspx?ID={CD115D1F-0E2B-4AE5-8295-8ED5DD8C1516}&page=1 [Accessed 18 Feb. 2018].

SAP. (2016). SAP and Vivanda Serve Up FlavorPrint Technology. [online] Available at: https://news.sap.com/sap-and-vivanda-serve-up-flavorprint-technology/ [Accessed 18 Feb. 2018].

Tsekouras, D. (2018). CCDC Lecture 3.

Helix: How Your DNA is Choosing Your Wine


Imagine that you really like pizza. You probably have a favourite pizza – and a favourite place to get it – right? Let’s say your favourite pizza is a margarita. When you get the pizza and eat it, you will probably like it. However, do you not sometimes think something could have been done differently? Maybe there should have been less cheese, maybe it’s too greasy, or maybe the temperature is just off? Does getting the perfect pizza every time sound like a dream to you?  Well, it’s time to wake up then, because consumer genomics start-up Helix is very close to realizing this concept.

But first, let’s back it up a bit.

What are Human Genomics?
The whole concept of human genomics started off in medicine. A patient’s DNA would be sequenced, which means that “the exact order of the four bases in a strand of DNA” would be determined (yourgenome, 2016). Why does this matter, you ask? Well, with the exact order of the composition of somebody’s DNA, doctors could tailor their treatment, medicine, and pretty much every factor that would impact a patient’s health (Farr, 2016). Probably the most popular case of DNA sequencing is that of Steve Jobs, who paid $100,000 in 2011 to sequence his DNA in an attempt to let doctors gain more insight into his sickness and try to help him more effectively (Farr, 2016). Next to the value of DNA sequencing in medicine, Illumina – the company whose supercomputers are behind 90% of DNA sequencing ever done – has identified a use for DNA sequencing outside of the medical field (Farr, 2016).

The Birth of Helix
Helix – an Illumina spin-off – is said to “democratize genomics” (Farr, 2016). Illumina has managed to bring the costs of DNA sequencing down tremendously – partly due to decreasing lab costs and more lenient regulatory decisions in the US (Farr, 2016; Teo, 2017). Where Steve Jobs paid $100,000 in 2011, a comparable procedure would now cost less than $1000 (Farr, 2016). According to helix, DNA sequencing can – next to provide more insight into diseases – discover other personal matters like your lifestyle, personality traits, taste senses, and much more (Farr, 2016). See where I am going with this?

Helix provides many different products. They – for now – offer six different product categories (Helix, 2018).

  • Ancestry: These products help you find out where your ancestors stem from, to hundreds of thousands of years back;
  • Entertainment: This is the fast-moving consumer goods section, if you will. Here, you can get for example a wine tailored to your taste perfectly;
  • Family: These products are mainly meant for families that want to grow, offering them fertility information;
  • Fitness: Here, Helix wants to help you to “reach your full potential” by designing the perfect workout routine;
  • Health: This is the more traditional use of DNA sequencing as explained in the previous section;
  • Nutrition: Lastly, the nutrition products let you design your perfect nutrition plan that suits your metabolism the best (Helix, 2018).

Source: Helix.com

The Business Model
Helix has a new and unusual business model. As they work closely with Illumina, they have many valuable resources that help them analyse consumers’ whole DNA spectrum, whereas similar companies are able to only analyse part of it (Zhang, 2017). Consumers pay a one-time $80 fee to analyse their DNA and the rest is subsidized by Helix (Zhang, 2017). The consumers then choose what kind of products they would like to purchase, and Helix lets third-party companies create those products based on the genetic information Helix provides them (Zhang, 2017).
Helix has, in that sense, created an online platform with customers – on one hand – who gain access to the platform by letting their DNA be sequenced, and on the other hand the product developers (Molteni, 2017).
The business model is efficient in the sense that its platform brings together companies that offer very specialized, personalized products and consumers that are seeking such products and cannot find them in conventional retail channels. Customers benefit as they receive products that are tailored to their individual tastes to the maximum extent, and companies benefit as they cater to the customers. Also, as the companies get to know more and more about individual customers, they could use this information to develop tailored product recommendations. However, as will be explained in the next section, the efficiency of the business model might suffer from regulatory decisions and consumer privacy issues.

Talk About Personalized Products
Basically, Helix takes product personalization to the next level. Personalizing products has many advantages, for example customers’ craftsmanship is emphasized, and customers form a connection with the product if they have put effort into designing it (Nagle, 2017). However, writing your name on a wine label and getting the wine tailored to your DNA are two completely different things. Because DNA is pretty much as personal as you can get, there are potential drawbacks of the Helix business model. The first and most obvious issue is privacy concerns. If people are already freaking out about the cookies that are gathered on websites, why would they send their DNA to a company to get a product of which they could by a similar version in the supermarket?

Some companies using DNA sequencing store consumer data for “unspecified research” and might sell it to third parties (Niemiec & Howard, 2016: p.23). If consumers get suspicious about this, and privacy concerns rise through the roof, it might negatively impact Helix as well. Also, ethical issues such as discrimination based on DNA information are surfacing, too (Farr, 2016). Imagine that your life insurance gets to know your DNA information, this could highly impact the price you pay.

All in all, although customers like personalized products, the safety of information security measures – or even international regulations – need to be established before customers can completely trust the businesses.

The Future
In the future, Helix aims to create an “App Store” for their genomics products and services (Farr, 2016). They want to create the platform in such way that consumers can access their DNA information, browse the “App Store” to discover products that they like (Farr, 2016). The consumers just need to let their DNA be sequenced once – just like you create your Apple ID once – and can then browse the “App Store” as they wish (Farr, 2016). Helix compares their platform to the App Store rather than to Google Play, as they aim to review each seller, which is what Apple does do each app created, whereas Google takes a more lenient approach (Zhang, 2017). Right now, Helix already has 14 employees whose task it is to get to the bottom of the products developed by their featured companies (Zhang, 2017). The buzzword of the platform is that it is “dynamic” (Molteni, 2017). Helix wants to evolve and widen its platform as the research improves, resulting in more products and services to offer to their customers (Molteni, 2017).

So, if you ask Helix, the next time you eat a margarita, you will love it so much that you will feel it in your genes, literally.

References
Farr, C. (2016). Genetics Startup Helix Wants To Create A World of Personalized Products from Your DNA. Retrieved from: https://www.fastcompany.com/3065413/genetics-startup-helix-wants-to-create-a-world-of-personalized-products-from-your-dna [Accessed February 16th, 2018]

Helix (2018). How It Works. Retrieved from: https://www.helix.com/howitworks/ [Accessed February 16th, 2018]

Molteni, M. (2017). Helix’s Bold Plan To Be Your One Personal Genomics Shop. Retrieved from: https://www.wired.com/story/helixs-bold-plan-to-be-your-one-stop-personal-genomics-shop/ [Accessed February 17th, 2018]

Nagle, T. (2017) How Personalized Goods are Shaping the Economy. Retrieved from: https://www.forbes.com/sites/theyec/2017/05/05/how-personalized-goods-are-shaping-the-economy/#b6bca33a1cce [Accessed February 17th, 2018]

Niemec, E. & Howard, H. C. (2016). Ethical Issues in Consumer Genome Sequencing: Use of Conumer’s Samples and Data. Applied & Transational Genomics, 8, pp.23-30.

Teo, G. (2017). The Second Coming of Consumer Genomics With 3 Predictions for the Future. Retrieved from: https://medcitynews.com/2017/07/second-coming-consumer-genomics-3-predictions-2018/?rf=1 [Accessed February 17th, 2018]

YourGenome (2018). What is DNA Sequencing? Retrieved from: https://www.yourgenome.org/stories/what-is-dna-sequencing [Accessed February 16th, 2018]

Zhang, S. (2017). How Do You Know When a DNA Test is B.S.? Retrieved from: https://www.theatlantic.com/science/archive/2017/07/helix-dna-tests/534402/ [Accessed February 17th, 2018]

Consumer driven pricing and personalization in the airline industry


There are several ways for companies to distinguish themselves in the way they price their products and services. They can choose for group pricing, which segments customers in groups that tend to behave similarly towards prices. For example, customers can be grouped based on age (such as student discount), gender or living area. Another option is to use versioning: to offer a product line and let customers decide on the trade-off between quality and price. The last form of differential pricing is perceived as difficult to achieve, namely personalized pricing. This means each individual customer receives a personal price for a specific product or service (Schofield, 2018). You may think that, in an offline world, no customer would accept personalized pricing. Can you imagine buying bread and cheese at a grocery store, and the person in front of you pays less for the exact same groceries? However, in an online world, this method has become a lot more feasible. Actually, there is a large chance you have already experienced personalized pricing online. One of the most obvious examples is eBay: one of the first companies to implement personalized pricing with their worldwide market place platform. However, it is important not to interpret personalized pricing as dynamic pricing. The main difference between these two forms of pricing is the variables that determine the final price. In dynamic pricing, the variables that are taken into account are, for example, time of the day, available supply or competitors’ prices (Baird, 2017). Personalized pricing has a customer focus and is interested in a specific customers’ behavior. Companies use data analytics to identify characteristics of the purchase environment or the customer’s profile and behavior that impact their willingness to pay. Bertini and Kounigsberg (2014) argue that the success of personalized pricing depends on at least the following three factors. First, abundant, high-quality data is needed. Also, the companies need to overcome various organizational challenges that come hand in hand with dedication to advanced analytics. Last, companies should be prepared to deal with customers who claim that the pricing approach is not fair.

Airline industry

One of the largest industries that divides consumer groups and price accordingly, is the airline industry. Different fares are charged for the exact same product, based on a market segment’s perceived ability to pay. For example, business travelers tend to pay more for their ticket as compared to leisure travelers, even when they fly the exact same route (Sumers, 2017). The key success is working to learn what the customer needs. Lufthansa, the largest European airline in teams of fleet size and passengers carried in 2017, is testing various approaches to better understand their customers. For example, they have deployed Bluetooth beacons and sensors, to be able to send out real time messages to their customers. When a targeted customer goes through security and has Bluetooth enabled on their phone, the personalization process is started. Or as Lufthansa calls it, the “Big Data Engine”. This program checks a traveler’s mobile boarding pass and looks at how much time the traveler has left before departure. If it is more than a set amount of time, the system examines the traveler’s profile in order to determine whether the customer would be interested in the “Miles and More” program, a discount for access to the airport lounge. This information is combined with the data from the sensors in the lounge, that register whether and how much space is left in the lounge, in real time. This lounge promotion program is part of SMILE., a companywide program that is dedicated to personalizing travel (Lufthansa, 2018). Companies can also use traveler data to offer two or more products or services as a package, increasing profits as it allows companies to appropriate a larger share of customer surplus, known as bundling (Hinterhuber and Liozu, 2014).

Future chances

Although airlines have quite an advanced personalized pricing and recommendation system, there is more potential to be revealed in the future. Lufthansa is working on larger projects that try to develop a Netflix-style algorithm that seeks to guess where its most frequent flyers would like to go to next (Sumers, 2017). The airline then offers a personalized price and ticket to this customer, and further develops its algorithm using customer data. For airlines to stay competitive, they need to keep a close eye on the current and future changes in the market. First of all, airline companies should fully embrace innovation. Data should be used not only to cut costs and to be able to deliver the cheapest flight tickets, but also to facilitate new customer experiences and deliver more personalized services. This leads to an increase in importance of brand loyalty, as consumers are more closely connected to the airline that is best at personalizing their prices and services. Last, the mobile wallet should be seen as the central hub for the digital consumers. Mobile transactions are a lot richer in terms of data collection and analysis, and it provides access to end-consumers, which can drive more sales (Popova, 2016)

 

Sources:

Baird, N. (2017) “Dynamic vs. Personalized Pricing”, https://www.rsrresearch.com/research/dynamic-vs-personalized-pricing, accessed at 13th of February 2018.

Bertini, M. and Koenigsberg, O. (2014) “When Customers Help Set Prices”, MITSloan Management Review, accessed at 14th of February 2018.

Hinterhuber, A. and Liozu, S. (2014) “Is innovation in pricing your next source of competitive advantage?” Elsevier Inc, accessed at 14th of February 2018.

Lufthansa (2018) “Official website”, http://www.lufthansa.com, accessed at 14th of February 2018.

Popova, N. (2016) “Has Personalization of Passenger Experience Entered a Critical Stage?”, https://skift.com/2016/12/29/has-personalization-of-passenger-experience-entered-a-critical-stage/, accessed at 14th of Febuary 2018.

Schofield, T. (2018) “Price discriminations: definition, types, and examples”, https://study.com/academy/lesson/price-discrimination-definition-types-examples.html, accessed at 13th of Febuary 2018.

Sumers, B. (2017) “Airlines Become More Sophisticated With Personalized Offers for Passengers”, https://skift.com/2017/02/03/airlines-become-more-sophisticated-with-personalized-offers-for-passengers/, accessed at 14th of February 2018.

Managing Consumer Privacy Concerns in Personalization: A Strategic Analysis of Privacy Protection


In the digital age that we are living, one of the major concerns is the protection of our privacy. Research shows that 90% of all online consumers either do not disclose any personal information to companies at all or choose to disclose only to the ones committed to fully protecting their privacy (Taylor, 2003). On the other hand, companies need as much data regarding their customers as possible, in order to be able to provide them with effective personalized product recommendations.

The study of Lee, Ahn et al. delves into this topic by following a very interesting method of research. By implementing game theory, the authors studied the impact of autonomous privacy protection decisions, by firms, on competition, pricing and social welfare. Additionally, this research sheds light on the impact of a regulated environment, regarding privacy protection implementation, on social welfare.

The three main findings:

  1. Asymmetric protection mitigates competition. In simple words, when there are differences between the privacy protection measures that firms implement in any given market, the firm with the strongest privacy protection policy is able to increase its profitability by getting access to a wider pool of consumer data. This simply happens because this firm inspires customers to feel confident to share their personal data with it.
  2. The strategies that firms implement regarding privacy protection should be based on two criteria:
  • Investment cost of protection. This factor introduces the notion that firms in order to implement a privacy protection policy incur some costs such as, infrastructure, personnel and training costs.
  • Size of the personalization scope. This perception regards the pool of the customers for which companies possess personal information and thus are in a position of offering them personalized products or services.
  1. Regulation is socially desirable. According to the research, this holds true since, although the autonomous decisions of firms improve social welfare in general, they redistribute the benefits between firms and customers with firms enjoying the benefits and customers becoming worse-off.

As it can become easily understood, there are many stakeholders when it comes to privacy protection decisions. This research provides a robust foundation regarding the factors that managers should take into account while making decisions concerning their firm’s privacy protection policy. As far as academia is concerned, it connects privacy, in a personalization setting, with equilibria points regarding competition in a market setting. Finally, regulators have one additional source of guarantee that the introduction of privacy protection legislation will be beneficial for society.

In order for all the interested parties to be able to evaluate the findings of this study, it should be underlined, that the authors, in order to calculate the equilibrium in the market, used the notion of firm and customer privacy calculus. This notion advocates that both consumers and firms are perfectly capable of calculating the profits and costs of disclosure of their personal information and the decision of the implementation of a protection strategy respectively. However, this might not always be the case since a lot of biases take place in these processes, as research has already proven.

 

References

Sutanto, J, Palme, E, Chuan-Hoo, T, & Chee Wei, P 2013, ‘ADDRESSING THE PERSONALIZATION-PRIVACY PARADOX: AN EMPIRICAL ASSESSMENT FROM A FIELD EXPERIMENT ON SMARTPHONE USERS’, MIS Quarterly, 37, 4, pp. 1141-A5, Business Source Premier, EBSCOhost, viewed 16 February 2017.

Taylor, H. 2003. “Most People Are ‘Privacy Pragmatists’ Who, While Concerned about Privacy, Will Sometimes Trade it Off for Other Benefits,” The Harris Poll #17, Harris Interactive, New York, March 19 (available at http://www.harrisinteractive. com/harris_poll/index.asp? pid=365)

Will the global village fracture into tribes? Recommender systems and their effects on consumer fragmentation


Netflix, Spotify and Amazon and many other companies recommend personalized content to best suit the customers’ preferences and increase consumption or sales (De et al., 2007). Recommender systems are used for different types of media such as movies, books, music, news and television. Moreover, they strongly affect what customers view and buying behavior. Many positive effects of personalization are known such as reduction of information overload, increasing relevance and loyalty (Tsekouras, lecture 2017).

However, personalization may also have drawbacks. As the internet becomes more and more specific to our interests, this “ hyperspecification” may fragment users, reduce shared experience and narrow media consumption. For example, fully personalizing news sites may mean that we no longer see the same news articles. Therefore one could argue that recommender systems will create fragmentation and will cause users to have less in common.
On the other hand, recommenders may have the opposite effect as they share information among customers who otherwise would not have communicated. The paper presents empirical evidence on whether recommender systems fragment or homogenize customers.

In an observational two-group experiment the behavior of the participants before and after recommendations is compared. Recommendations are provided in iTunes and the user experience is personalized through mostly content-based recommendations and a small part collaborative data.

Interestingly, contrary to the author’s expectations, recommendation systems increase purchase similarity (homogeneity among customers) and therefore do not increase fragmentation. Customers tend to purchase more after being exposed to personalized recommendations due to the volume effect, which in turn increases the chance of customers purchasing the same product.
Furthermore, customers buy a more similar mix of products after recommendations due to the product-mix effect. Consequently, customer networks become denser and smaller. However, it is important to note that the recommender system does not recommend the same items to many users but that the diversity of items consumed increases.

Within business, the findings of the paper confirm that recommendation tools can help marketers to increase sales but also alter the product mix customers buy. Off course, these are some of the the reason why numerous retailers (e.g. Netflix, Amazon, Spotify) use these recommendation systems.
However, what I find more interesting about the paper is the application of recommendation systems in the context of society. As we increase to spend time online and recommendation systems become more advanced, I think this is an interesting application. Similarly, I think it would be interesting to research to what extent the proposed homogenization of customers has implications for society and (business) cultures.
Fragmentation may have negative implications for society as when people share little information filter bubbles and echo chambers may exist. However, in business context, I think there are situations in which fragmentation of customers may provide better results than homogeinity . For example in the context of innovation, crowdsourcing and generating new business ideas, a variety of views and opinions may yield to better and more refreshing ideas from different anchors. In this case, it would not be desirable if every customer listens to the same music and reads the same news articles and books, as different point of views are needed. I think for companies who want to generate new ideas from their customers, the homogenization of their customer base might be something to take into account.

References

De P, Hu YJ, Rahman MS (2010) Technology usage and online sales: An empirical study. Management Sci. 56(11):1930–1945.

Hosanagar, K., Fleder, D., Lee, D., & Buja, A. (2013). Will the global village fracture into tribes? Recommender systems and their effects on consumer fragmentation. Management Science60(4), 805-823.

Tsekouras, D. (10 February 2017), Lecture Customer Centric Digital Commerce, “Personalization & Product Recommendations.

Personalized Online Adverstising Effectiveness: The Interplay of What, When, and Wher


If you go to any website, or online store specifically, your behaviour is tracked. Landing page, time spent, clicks, exit page: you name it, it is tracked. But even when you leave a page, a company does not really leave you: they saw what you clicked on, and based on your browsing behaviour, they retarget you: they show you a (sometimes personalized) advertisement on another channel, hoping you will come back and purchase the product you viewed.

Retargeting can either be done during or after a website visit, and is done based on a customer’s visit. When showing personalized recommendations, for example, it is important to take into account the quality of the recommendation, the level of personalization and the timing. This is what Bleier & Eisenbeiss (2015) looked at: what should they show, when should they show it, and where should they show it.

As with any academic article, past literature is analysed and hypothesis are developed. In order to test the what, when and where of personalized online advertising effectiveness, Bleier & Eisenbeiss conduct two large-scale field experiments and two lab-experiments. The first field experiment looked at the interplay of degree of content personalization(DCP), state, and the time that has passed since the last online store visit, at a large fashion and sports goods retailer, who carries over 30000 products. The second field experiment, conducted at the same retailer, looked at the interplay of placement and personalization. Based on the results from these two field experiments, two lab experiments were designed: one focussing on web browsing in an experiential model, the other focussing on goal-direct web browsing.
Within this paper, thus, many things are studied and confirmed. The papers shows the importance of how to determine the effectiveness of online personalization’s, and which one works best when. When a customer sees a personalized ad right after his/her website visit, the ad becomes more effective. This is mainly because preferences are not constant: they can over time. Thus if you liked a shirt 5 minutes ago, you will mostly still like it now. Thus if a company is able to directly respond to a consumer’s behaviour, the CTR is expected to be higher.

While the effectiveness of recommendations decreases over time, the level of personalization plays a moderating role. This means that high-level personalization in later stages of the decision making process have lower effectiveness, because of changes in customers tastes’ and preferences. The personalized ad is therefore not applicable anymore. Thus, the more personalized an ad, the sooner after a website visit it should be sent. Moderate personalized ads are thus more effective over time, as they take into account these changes in preferences. As visual recommendations are often highly personalized, these type of recommendations are more relevant shortly after a visit. Cross-sell recommendation, which is a more moderate recommendation type, performs better later in time

So what does this all mean? When retargeting customers and showing them personalized ads, it is important to keep in mind how long ago they visited a website. Given that this research was performed at a large fashion/sports retailer, it would be interesting to see whether the same conclusions hold for other settings. What do you think? And when do you consider (personalized) ads target to you most effective?


Bleier, A., & Eisenbeiss, M. (2015). Personalized Online Adverstising Effectiveness: The Interplay of What, When, and Where. Marketing Science, 669-688.

“Buy a present for my wife” said Jan to the phone


This year St. Valentine’s Day caught millions of men by surprise, again, leaving them wondering what present to buy for their partners. What if somebody or something could perform this burdensome task in a timely manner? There might be a solution…

Viv

Viv is an intelligent personal assistant introduced to the market on May 9, 216 and acquired by Samsung in October 2016. Similar products such as Siri, Google Now, Microsoft’s Cortana and Amazon’s Alexa can perform some basic tasks but nothing beyond the tasks they’ve been programmed to do. Due to artificial intelligence, Viv can generate code by itself and learn about the world as it gets exposed to more user requests and new information.

This makes it by no means a universal product. Viv is expected to learn and store information about every user, and learn with time how to serve him or her personally. For example, if the owner asks: “I need to buy a present for my life for St. Valentine’s Day”, Viv should be able to predict what a suitable present would be or perhaps book a table for two at a fancy restaurant downtown.

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M-Commerce – Creating new opportunities?


Introduction

Mobile commerce is growing rapidly and at a faster pace than e-commerce (Brohan, 2016). Currently, mobile commerce accounts for one third of total e-commerce sales. This percentage is expected to exceed the 50% mark soon. In other words, mobile commerce optimization is not a competitive advantage anymore, but a competitive imperative for companies (Roggio, 2016). Due to mobile commerce, you can buy everything you want, whenever you want. What would you buy via your mobile device? Do you have any wishes on your shopping list?

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Man versus Machine: Deep learning and its applications


What do the stock market prediction, medical diagnosis, employee selection, electrical demand prediction and personalization all have in common? They are all labor intensive. But do they have to be?

Deep Learning (DL) could be – and to some extent already is – the answer to various human labor intensive tasks. Deep learning is a subsection of machine learning that “focuses on computational models for information representation that exhibit similar characteristics to that of the neocortex”. (Arel et al., 2010).

Continue reading Man versus Machine: Deep learning and its applications