Nowadays, consumers are using the internet as a source of information on products and prices before purchasing a product offline. Price comparison sites (PCS) provide and compare numerous retailers on the most detailed level. These sites can possibly influence price evaluations by consumers in the offline setting. When all this information is available to consumers, it gets extremely important for retailers to ensure consumers buy their products since it is relatively easy to find the better option quickly. Therefore, retailers need to revolve their business models around the consumers, in order to stay ahead of the competition and prevent to lose clients due to the internet.

Literature suggests that consumers may prefer higher priced, well-known retailers which have the impression of being able to fulfill non-contractible benefits such as delivery time (Smith and Brynjolfsson, 2001). Moreover, price comparison sites usually show ratings for retailers to signal quality. Commonly, when there is consensus in user feedback, this can reduce consumer suspicion and thereby increase purchase intentions (Benedicktus et al. 2010). However, it is unclear whether this also applies to the ratings provided by PCS.

This paper investigates how offline price evaluations are affected by price comparison sites by conducting three different studies, of which study 1 will be discussed extensively and study 2 shortly. In the first study, it is investigated if consumers’ price evaluations are affected by reference prices on price comparison sites as well as the retailer ratings. The authors also consider price validity and retailer quality inferences as mediating factors. Price validity means how genuine and obtainable a certain price is in the market. To test these questions, consumers were shown the search results from a price comparison website regarding heart rate monitors. Information included a list of multiple retailers, their prices for the heart rate monitor and the corresponding retailer ratings. Next, participants had to rate the attractiveness of another offer price for the same product and their opinion on price validity and retailer quality. Besides, participants were informed that ratings are composed from customer reviews and that ratings thus reflect customer experiences. The findings suggest that consumer’s subsequent price evaluations are particularly influenced by retailer ratings from price comparison sites. There is a mediation effect from price validity, but that is not the case for retailer quality perceptions. Study 2 finds that consumers are able to gather important information from the PCS search results and can assess distribution characteristics (price level and frequency), which shows that the use of these PCS prices as reference prices is relatively complex.

The study thus finds that consumers use PCS prices as reference prices when they evaluate prices in stores. Retailers that have favorable ratings on PCS serve as a measure in price evaluation for highly rated offline retailers. Offline retailers should consider the prices that occur frequently on PCS searches when setting in-store prices, since these prices highly influence offline price evaluations.

 

Benedicktus, Ray, Michael Brady, Peter Darke and Clay Voorhees (2010), “Conveying Trustworthiness to Online Consumers: Reactions to Brand, Consensus, Physical Presence, and Suspicio,” Journal of Retailing, 85 (4), 310–23.

Bodur, H. O., Klein, N. M., & Arora, N. (2015). Online price search: impact of price comparison sites on offline price evaluations. Journal of Retailing, 91(1), 125-139.

Smith, Michael and Erik Brynjolfsson (2001), “Consumer Decision-Making at an Internet Shopbot: Brand Still Matters,” Journal of Industrial Economics, 49 (December), 541.

 

 

 

 

 

We have most likely all had this thought once: how does this website know these things about me? Well, basically every website collects information on its users. However, users are not always aware because not every website informs its user on this data collection process.

Personalization is concerned with delivering the right content to the right person at the right time, which makes it a customer oriented marketing strategy. Data is needed to be able to provide this content delivery to the right person. When websites make a conscious effort to inform users on data collection, they engage in overt information collection (Sundarand Marathe 2010). It is assumed that users atomically agree on data collection after they have received such an information effort. However, when users experience ads that contain personal information without being informed that their data is collected by the website, this will lead to negative reactions and can harm businesses (Milne, Bahl and Rohm, 2008). This sense of vulnerability is more like to be accepted by users when the website is marked by trust (Urban, Amyx, and Lorenzon 2009).

The result is a personalization paradox , where personalization effectiveness depends on the context. It would be beneficial for online retailers to understand how they can prevent or minimize the negative effects of personalization. Practitioners claim it is more user friendly to not inform users because informing them would result in users to take longer in performing online tasks. Moreover, when users do not know their data is collected, they might act more naturally and thereby delivering richer data (Verhoef et al. 2010). For the customers themselves it is important that they feel conformable during website visits. Therefore, retailers need to make sure customers feel safe, while still being able to provide them with optimal personalization marketing to enhance customer experience.

The research includes three studie. Study 1 and 3 had the most interesting findings. In the first study, the personalization paradox is tested by comparing situations in which websites did and did not inform users on data collection, and how more personalized advertisements affected click-through rates. Facebook is used as the setting of the research because promotions through social media are important. Participants first had to review the advertisement. Hereafter, they could fill in their click-through intention and their perceived vulnerability on a 5-item scale. The results suggest that click-through rates after personalized advertisements increase when websites overtly collect data and decrease when websites covertly collect data due to the vulnerability it causes to users, meaning that vulnerability mediates the effect of personalization and information collection on click-through intentions ( see Figure 1). In the third study, the findings suggest that negative effects caused by collecting data covertly, can be mitigated by providing information and control to users at the moment that personalized advertisements are shown to them. Probably because this enhances reliability and trustworthiness.

 

 

The paper convincingly addresses the personalization paradox and provides valuable ways in which negative effects of covertly data collection can be mitigated. The result is that retailers can make careful decisions as to what information should be used and how sensitive this information is to users. Furthermore, retailers need to be transparent on their data collection methods, which will benefit their business. Lastly, this will probably enhance customer satisfaction, since customers will feel like retailers act in the best interest of the customer.

Milne, George R., Shalini Bahl and Andrew Rohm (2008), “Toward a Frame-work for Assessing Covert Marketing Practices,” Journal of Public Policy& Marketing, 27 (1), 57–62.

Sundar, S. Shyam and Sampada S. Marathe (2010), “Personalization versus Cus-tomization: The Importance of Agency, Privacy, and Power Usage,” HumanCommunication Research, 36, 298–322.

Urban, Glen L., Cinda Amyx and Antonio Lorenzon (2009), “Online Trust: Stateof The Art, New Frontiers, and Research Potential,” Journal of InteractiveMarketing, 23 (2), 179–90.

Verhoef, Peter C., Rajkumar Venkatesan, Leigh McAlister, Edward C. Malt-house, Manfred Krafft and Shankar Ganesan (2010), “CRM in Data-RichMultichannel Retailing Environments: A Review and Future Research Direc-tions,” Journal of Interactive Marketing, 24, 121–37.

 

Imagine you are moving into your first apartment. At that moment, it is finally possible to furnish the apartment to your own preference. However, when you start orienting for furniture, you discover that there are no options to be found that resemble the image of how the new apartment should look preferably. Moreover, the furniture is often expensive as well. That is why Made.com decided to create a community in which its customers can vote for the designs they want Made.com to produce.

Business model

Made.com is an online furniture retailer from England without warehouses and inventory. This allows the company to save costs. Instead of warehouses and inventory, they use crowdsourcing. The website allows members to submit designs. Whether the design will get produced, is determined by the number of votes it gets from members of the website. This is also a great opportunity to draw attention for designers who lack reputation (Goldsmith, 2010).

When a design is proved to be popular, Made.com makes it available for pre-order. The pieces are then shipped directly to the customer, which cuts costs (Graham, 2010). In the past years, Made.com has started opening showrooms that allow customers to view the products available on the website in real life.

Efficiency criteria

Made.com benefits from joint profitability because designers get paid 5% royalties on successful designs and members benefit from being able to vote for their preferred pieces. Value is therefore co-created by involving customers actively in the process of deciding which pieces should be produced. A situation is created in which customers will not tend to switch to competitors because they have more options with Made.com and because they buy the exact pieces they want from Made.com. Made.com, on its turn, benefits from knowing which pieces they will probably sell (Graham, 2010).

Made.com did, however, get negatively affected after Britain voted to leave the EU in 2016. The feasibility of required allocations is therefore far from optimal at the moment. The Brexit was a huge setback to technology founders who are very dependent of foreign developers and engineers. Hiring these skilled employees will now be even tougher than before, since it was already hard to find expertise within the United Kingdom. Moreover, 35% of the employees that are located in London, are European. There is a huge uncertainty whether these employees will be allowed to stay in London (Olson, 2016). It could be decided any moment that Europeans need certain visas, which would affect the positions of the current employees and would make hiring skilled workers from outside the UK even harder. It could take two years for Britain to leave the EU, which means years of uncertainty where Made.com will not know regulations will impact their business (Meyers, 2016). The political institutional environment has created this situation, and people involved with Made.com are also socially impacted. Brent Hoberman, co-founder of Made.com, has stated that people feel rejection and that the atmosphere at the headquarters is very depressing (Olson, 2016).

The above illustrates that companies can be heavily impacted by its institutional environment, and that companies sometimes have little power to prevent such threats.

 

http://www.bbc.com/news/business-11437839

http://www.cbsnews.com/news/madecom-crowdsourcing-furniture-stocks/

http://www.forbes.com/sites/parmyolson/2016/06/24/u-k-tech-startups-stunned-by-brexit-vote/#25959a18a5b1

http://fortune.com/2016/06/24/brexit-tech-impact/