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, Orbitz.com 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 a 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: http://www.wsj.com/articles/SB10001424052702304458604577488822667325882. Last accessed 23-4-2015.

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.

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.

Sources:

• http://www.dealdash.com (Lecture session 4)
• http://www.kansspelautoriteit.nl/nieuws/alle-nieuwsberichten/2014/augustus/nieuw-verschenen/ (PDF-file: ‘factsheet_centveilingen_augustus.pdf)

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.

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.

References

*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

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

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.

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.

Source
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.

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.

References:

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:

• Boatbound (Airbnb for boats)
• Holidog (Airbnb for dogs)
• Outdoor (Airbnb for outdoor gear)
• Hipcamp (Airbnb for campsites)
• Airpnp (Airbnb for toilets)
• 3D Hubs (Airbnb for 3d printers)
• SupperShare (Airbnb for kitchens)
• SPOT (Airbnb for parking spots)

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.

Lists:

“Uber-For-X”
“Tinder-For-X”
“AirBnB-For-X”

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