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!

Currently, organizations are using social media to interact more and more with consumers. One of such examples is the strong interaction that KLM Airlines has with its customers over social media. When the consumer ask for help or make statements, they are one of the fastest corporations to reply through social media. However, Lui and Gal (2011) investigate whether interactions affects the relationship between consumers and organizations. Therefore evaluating whether different types of consumer input can have varying effects on this relationship with an organization.

Through various different experiments, the authors try to analyze whether different types of input have which influence on the closeness of the relationship with the organization. Closeness is the amount of bonding that is felt in a relationship (Liu & Gal, 2011). Firstly, they experimented with the role of giving advice on the non-profit index and whether individuals are more willing to donate. When individuals gave advice to the organization, they had a higher tendency to donate to the organization due to the closeness they felt. Therefore asking for advice could actually influence the individuals willingness to donate.

Then they wanted to see whether these aspects would change if the consumers’ input changed to expectations regarding for-profit organizations. From the results became apparent that giving expectations had a negative effect on intent for an individual to purchase a product from that organization. This remained positive for the category of providing advice. Therefore, when the consumer provides expectations, the relationship between consumer and organization can actually be harmed.

Due to this interesting finding, they evaluated further under which conditions these aspects deviate. Through their third experiment, the authors found that advice giving resulted in the consumer placing themselves in the position of the organization and look for solutions. Therefore these factors resulted in more subjective feelings of closeness to the organization (Liu & Gal, 2011). Whereas stating expectations only resulted in a feeling of distance between the consumer and organization. When giving opinions, there was a mediating effect on the overall closeness, but leaning more to a feeling of distance.

Since giving advice can have such a strong influence on the empathetic relationship between consumer and organizations, their next step was to evaluate whether paying consumers for advice would further strengthen the closeness. They discovered that when consumers were paid for advice, the positive effect was actually eliminated. However this effect was not there with opinions. Thus organizations looking to create a strong closeness with their consumers should actually make sure there is a tendency for them to give advice to the organization.

Therefore companies such as KLM that trying to build strong relationships with their consumers can improve by asking for advice. Through consumers giving advice they can further strengthen their connections. Thus, they should stop asking for the opinions and expectations of consumers, but rather let them give advice on which aspects should be improved.

References

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.

For a long time business has relied on the well-known Pareto principle for explaining their patterns of sales distribution – the rule of thumb stating that roughly 80% of events would come from 20% of the causes. In business this principle was commonly used in stating that 80% of sales would follow from 20% of clients or 20% of products. Then came along the internet and it became apparent that the 80/20 rule lost its explanatory power for certain online businesses. Due to the lower search and reach costs resulting from an online business environment the, by now well-established, theory of the long-tail was proposed to explain for the newly observed sales distribution. This longer tail of niche product sales naturally meant that a smaller proportion of sales came from the ‘head’ of the distribution graph, as is shown in the figure below.

Now we have quickly refreshed your memory on sales distributions, let’s have a look at what this study did. The authors of the study were interested to see how the shapes of the distributions were affected by the electronic word of mouth present in the product group. In this, a distinction between goods rated according to more objective criteria and goods rated according to more subjective criteria was made. The authors reasoned that consumers may apply similar evaluation standards to products with objective attributes such as USB sticks. In this sense people would show high tendency to follow the eWOM evaluation, driving consumers collectively to the most popular products. As a result, the distribution tail would be shortened while the head would be thickened.

Alternatively, for products with high levels of subjective attributes such as books or movies, positive eWOM does not necessarily mean that you as a consumer would personally like the product as well. Finding a product that may fit your personal preferences is difficult, for such highly subjective products the authors reasoned eWOM would help you find products you otherwise wouldn’t. As a result, the distribution tail would get longer while the head would get thinner. These different effects of eWOM were indeed found when studying Amazon.com sales of products with both objective as well as subjective selection criteria. In addition, it was found that for complex products with multiple attributes, eWOM had a similar effect on sales distribution as for products with subjective criteria. This can be explained as consumers’ preferences will start to diversify the more attributes have to be assessed.

Resulting from this study we can conclude that eWOM can show two very different outcomes on sales distribution, depending on the product type you are looking at. Sellers should be aware of this and can optimize their online shelf sizes based on the product type they are offering. Stimulating eWOM as a firm selling simple products with objective selection attributes could for example decrease the need to keep a large product portfolio in stock. Saving costs by letting consumers chat to each other, now who would have thought!

Lee, J., Lee, J. N., & Shin, H. (2011). The long tail or the short tail: The category-specific impact of eWOM on sales distribution. Decision Support Systems, 51(3), 466-479.