What is Social CRM?

Let’s start with what CRM or Customer Relationship Management is. CRM involves the management and enhancement of customer relationships with the help of a large quantity of data about individual customers and a set of tools to interpret and make use of such data. Putting a “social” hat on CRM, to me, it means to use information and communication/interaction in social media to enrich traditional CRM practices as described above. In other words, social CRM merges social media with traditional CRM, both to create a more 360-degree view of the customers and to interact with customers in a more proactive and engaging fashion.

What are the Benefits/Opportunities Presented by Social CRM?

Social CRM brings a slew of opportunities to enrich traditional CRM practices, as discussed in the 18 use cases report. I discuss only a few here:

1. A more complete view of your customers via what they say in social media. This is important because customers’ conversations with others in a naturalistic social media environment are more likely to reveal their true preference and opinions than self-reported data. Instead of forcing opinions out of consumers, consumers are volunteering all this information to companies who are willing to listen. This can be very helpful when it comes to offering support to customers, discovering new product opportunities, identifying brand advocates, etc.

2. Faster collection of information. Traditional feedback through the distribution channel is simply too slow in today’s environment, where consumers are fickle and their preferences are changing faster than ever. One advantage of social CRM is that it deals with real-time data. As a result, companies can gauge quickly how consumers are reacting to a marketing campaign, what they think of a newly announced product, etc. Using this year’s Oscar as an example, Jeff Bridges and Sandra Bullock could have predicted their own winning a week early if they had tallied up their online buzz in the social sphere like Nielsen did.

3. Faster support and response time to customers. Let’s face it, the Internet makes us an impatient generation. When we run into problems, we want them solved immediately. This is where social media can help a lot. It’s not uncommon to hear anecdotal evidence of consumers receiving laser-speed response from companies when they complain about problems in social media. My own experience with Delta Airlines is an example. When I twittered one day about some statement confusion, overnight I received an email from them addressing the issue. That is what I call a wonderful/well-managed customer experience.

4. Engaging Customers. Through better and faster understanding of customers and faster response time, social CRM can provide a more engaging experience to customers. As I discussed in another post about ING Direct’s social media practice, the company successfully uses a blog, Facebook fan page, and a Twitter account to engage a large number of frugal savers. As the Top 100 Brand Engagement Report shows, effective engagement with customers is correlated with financial measures, suggesting social media’s potential to affect companies’ bottom line.

What Are the Challenges?

As something still in development, there are plenty of challenges that can prevent effective social CRM implementation. Among them, I see the biggest obstacles as:

Scalability. As the 18 use cases report points out, 1:1 support is simply not doable for most B2C companies. How can a company enhance customer experience through social media but still remain scalable and cost-effective with its operations? This is definitely something that has to be addressed. Of course, there are automated tools to help streamline this process, but technical tools can only go so far. Eventually when it comes to facing individual customers, who do you listen to and talk to? Whose problems do you solve first? These will require smart prioritization and proper organizational structure to allow for agile response and operation.

Churning Data into Information. Data mining is not a simple science. Added to this the large quantity of real-time data available through social media, the challenges associated with mining social data for information becomes even greater. Scientists around the world are still working on efficient algorithms to make this happen, but the techniques are still nowhere near maturity yet. For those interested, a look at this INFORMS OR/MS Today article on social media analytics may offer some clues as to the challenges associated with (and potential solutions to) analyzing/mining one type of social media data — blogs.

ROI. This is something on everyone’s mind, isn’t it?  Earlier, I mentioned that brand engagement with consumers is correlated with financial performance. Note the words used — “is correlated with”, not “leads to”. In other words, the causal effect is less straightforward. How to measure return from social CRM efforts likely will depend on the goal of the deployment, and a single engagement measure is not going to be sufficient. Moreover, while Dell Outlet was able to pinpoint $3 million incremental revenue from its Twitter presence, for many companies, it won’t be straightforward to isolate the impact of social CRM from other strategies.

I hope you find this discussion helpful in getting you started on social CRM. If you want to dig deeper, I strongly recommend you to download and read the 18 social CRM use cases report, and join in the conversation at the Social CRM Pioneers Group.

Why?

• We tend to know or come across people who are similar to us. Therefore, the friends your loyal followers have are likely to already be your loyal follower too or at least user.
• Loyal followers are likely to buzz about you any way. So the incremental gain from a buzz campaign based on these consumers is limited.

Run the Numbers

These researchers conducted a field study and two lab experiments, which showed significant gain by choosing non-customers as buzz agents. In fact, in the case of Rock Bottom Brewery (a restaurant), they estimated an average of $192 gain in sales brought in by each interaction from non-customer buzz agents. Pretty sizable, huh?

Before You Run Away With It

I see two potential caveats that you should consider before you take the results and apply them to your business:

• Intuitively, it would take more to get people unfamiliar with you to buzz about you. So cost is definitely a consideration. In the Rock Bottom Brewery field study, the non-customer sample came from the BzzAgent company panel. Although both the customer group and the non-customer group were offered potential prizes from the campaign, the non-customer group may have received (or expected) additional incentive from the BzzAgent network (although it’s not explicitly stated in the article).
• People who don’t know a brand well enough may have low credibility when spreading words about the brand. Those who consistently buzz about something they don’t really know (for their own personal gain) may eventually lose the trust of those around them.

More Information

You can hear Professor Godes and Professor Mayzlin talk about their research in this Science of Better podcast. Or if you want to read the article yourself, you can find it in the July/August 2009 issue of Marketing Science (subscription or pay-per-view required).

Frankly, I am not impressed. With so many social media solutions already out to “revolutionize” the way we communicate, I am starting to feel indifferent.  Just count the sheer number of social networks out there, and we see how fragmented online social networking has become. While I am a strong believer in the value of social media, the number of competing solutions is suggesting that this market is getting to a more mature stage.  Just like the hundreds of car brands in the earlier part of the last century, we are bound to see a shakeup of this marketplace, and the ones that eventually survive will be the ones that offer differentiation that appeal to a large enough network of consumers.

So with this idea of differentiation, I thought of doing an exercise.  In market research, there is a technique called brand personification which is used to explore in-depth a brand’s meaning to consumers.  The technique asks consumers to imagine a brand as a person and to describe what this person would be like.  Here I took the liberty and brand personifies some of the best known or “buzzed” social networks we see today.

• Facebook: A child prodigy who won the heart of America almost overnight. But now at a mature adult age, he has not quite found who he is and who he wants to be. As a result, he does face lift or nip/tuck every so often, and he is hopelessly addicted to Farmville, Cafe World, Happy Aquarium, and the like.
• Twitter: A geek with an entrepreneurial soul.  He is fun loving, quick to act, and can get really passionate about a topic. But because of interest in a large number of topics, he also shows symptoms of ADD and can have a fairly short attention span.
• LinkedIn: A businessman dressed in a suit and tie. He is ready to do a sales pitch at any time, and he always carries with him a briefcase no matter where he goes.  In that briefcase, he never forgets to bring with him a copy of his resume in case some better job becomes available.
• foursquare: An overly social person who thrives on attention to such an extent that he literally lives in the public’s eyes.  His smartphone is his prized possession that indulges his need for constant connection to the outer world.
• Google Buzz: Someone in his 40’s or possibly 50’s.  He has gotten onto the Internet late, after his son, daughter, and friends have enjoyed it for quite a while.  Once online, he cautiously explores around and finds email as a safe haven for staying connected. He hangs out with @mashable often, who admires him for his infinite wisdom.

Not hard to see that I am a little cynical in almost all of these.  Perhaps we haven’t found the winning formula yet?  Or perhaps I am just an outlier in the sample.  If I were to pick one from this list as my friend, I would choose the geeky Twitter, which is also the service that I find myself gravitate toward the most nowadays.  What would your choice be?

To do this, we collected a random sample of slightly more than 100 videos from YouTube over the course of a week. These are all fresh new videos just uploaded onto YouTube, so that we can study their rise to popularity from scratch. We traced each video for a period of two months, recording the number of views and the average user ratings each day. We also collected a large number of characteristics for each video (see the figure below), including those related to the video content, to the video author, and to the network of users connected to the video author. We further recruited a group of individuals to rate each video on its production quality, educational value, and entertainment value, which are the three components of what we call “innate content quality”.

Equipped with all these data, we then used a technique called recurrent events analysis to see how these video characteristics affect the popularity of a video. Below are some of the main things we found:

• Authors with a large number of subscribers who each has only a handful of friends are in a better position than authors with a small number of subscribers who in turn may have a large number of friends.
• Once past an author’s direct network of subscribers, influence rather than simple reach becomes critical. We attribute this to the large amount of user-generated content (UGC) being passed around everyday and as a result our tendency to ignore most sharing unless they come from someone we are really close to or someone whose opinion we respect (opinion leadership is not dead!)
• What proportion of an author’s subscribers know each other also matters. A totally segregated set of subscribers does not help get the words out, but a group of subscribers where everyone knows everyone else is not optimal either. We found that the ideal connection ratio (termed connectivity or density in network analysis) was about 38%. Below this ratio, diffusion rate increases with connectivity up to the maximum, and then decreases after this threshold.
• Of the three innate quality components, entertainment value and educational value are equally important in determining a video’s popularity. Production quality, on the other hand, did not matter.
• But the biggest impact did not come from innate quality but from what we call “manifested quality”, which is quality information publicly available through other users’ ratings (i.e., the little stars underneath each YouTube video). Increasing the average rating by 1 star can lead to as much as 13.5% gain in diffusion rate.
• Age has a negative effect on diffusion rate, meaning that younger users’ contributions are more likely to be popular.
• An author’s past experience and success also carry over to the new content. More prolific authors and authors whose past contributions were more popular are more likely to see their new content popular.

Of course, with only one study, we are far from completely answering our initial question.  But what we found here suggest that there are indeed systematic differences among videos and authors that can help predict the success of future content. Carrying this over to other types of user-generated content such as tweets and consumer blogs, these findings and findings from future studies should help companies pour through the overwhelming amount of user-generated content available online and selectively invest effort in the ones that are most likely to become popular.

What do you think?  I’d love to hear your thoughts.  Is there anything important that we are missing? If you are interested in more details about our study, you can download our working paper at http://www.yupingliu.com/files/papers/liu_rogerson_ugc_diffusion.pdf.

1. Emergence of “Individual” Corporate Identity

As social media give companies an opportunity to step closer to their end customers, this new strategy also puts individual faces onto what used to be a collective corporate identity.  Two cases in point: Peter Cashmore for Mashable, Scott Monty for Ford, to name just a few.  While such corporate spokespersons have existed before, now they have a much more personal face that interact with consumers day in and day out. This new corporate “individual” identity can have important implications for corporate branding and even companies’ hiring practices.

2. Mobilization of communication via geo-enabled services and mobile devices

Foursquare, need I say more?  Undoubtedly this represents great business opportunities for many companies. But more than that, this finally arriving mobile market is going to create new consumer privacy concerns and will require new types of policy to regulate how consumers’ geographic information can be used and protected.  Coupled with the buzz on real-time search and interaction, business practice and public policy in this area can be complicated.

3. Real-time verbal and textual translation

Google recently added a real-time translation service that can be integrated into an online chat session or used via its new Google Wave service.  When the precision of this type of services improves, its impact on cross-cultural communication will be tremendous. With the help of the Internet and social media, individuals already collaborate in many areas such as R&D, open source applications, and cause advocacy.  Now only imagine magnifying this many times to a global scale.

In the spirit of this blog, I’d also like to refer my interested readers to the predictions made by a few other web and social media experts:

• Social media prediction for 2010 by Scott Monty (Ford social media expert)
• Trendspotting’s expert trend predictions in 140 characters
• Social marketing trends from reviewing 2009 by web strategist Jeremiah Owyang

What about you?  What do you expect to see in 2010? Whether you agree or disagree with all these predictions, I hope everyone has a happy and productive year!

http://www.flickr.com/photos/teofilo/ | CC BY 2.0

How to target ads to consumers without sacrificing their privacy?

The recent controversy surrounding Facebook’s privacy setting changes shows us that privacy issues are still very much on people’s mind these days, especially with a large amount of very personal data now available through online social networks. To advertisers, the increasing amount of social and personal information represents a great opportunity to offer very targeted ads to consumers.  But as we get closer to consumers’ personal domain of interests and friend networks, advertisers are also treading a very dangerous water of consumer privacy. This is why I find New York University Professor Foster Provost’s research to be particularly interesting, as it allows target advertising toward consumers while still protecting their privacy, or in the researchers’ term, “privacy-friendly” target advertising.

The basic idea is quite simple, although the actual implementation can become more complex and mathematical.  The underlying premise of the approach is that consumers who are more similar to each other are more likely to buy the same brands and share similar consumption habits. This is why social network information can be very powerful, because we are likely to buy the same things as our friends or at least have a good deal of influence on each other.  The problem with using such explicit social network information is the privacy issue. To circumvent this problem, Professor Provost’s approach uses anonymized browsing data instead.  It builds on two key sets of information: (1) a set of consumers who are considered brand actors; and (2) browsing data for these brand actors and other consumers whose brand affinity is not yet behaviorally demonstrated.

For the first set, one can use criterion such as having visited a brand’s website or fan page on Facebook to identify consumers who are brand actors. Notice that advertisers do not need to know who these consumers actually are in terms of names or demographics, but just that they are entities who have demonstrated certain desired behavior.  Then with this information, the brand proximity/affinity of other consumers can be calculated by analyzing how closely the content (brand and non-brand related) visited by those consumers resemble that of the brand actors.  Potential consumers can then be ranked based on this similarity to identify the ones that have the closest brand proximity. Professor Provost’s research shows that consumers picked in such a fashion have a much higher concentration of potential brand actors than random picking and that these consumers are much more likely to be linked to known brand actors.  A paper from this research project is available from Professor Provost’s website.

To me, the beauty of this research is two-fold. First, because the only data needed are browsing logs without personally identifiable information attached, it allows advertisers to selectively target consumers without having to worry about privacy issues. Second, because the approach is defined in a sufficiently general fashion, it allows for much tweaking and customization. For instance, various brand proximity measures can be used (this research itself suggests five measures), and different measures can be combined to most accurately gauge brand affinity. Moreover, the criteria used to spot brand actors can be customized based on an advertiser’s needs (e.g., visit to awareness page vs. conversion page depending on the goal of the campaign).  Such flexibility makes the approach applicable to a wide variety of situations.

What do practitioners want to know?

The conference organized a few industry panels to talk about their own experiences and their unanswered questions. Out of these industry participants, Mr. Gary Spangler, E-Marketing Manager from Dupont, spoke the most systematically about a set of research questions that need to be addressed from a practitioner’s standpoint. Many of these questions were echoed by other industry participants.  I list them here for the benefit of academics who are in search of practically relevant research questions.

1. There are more and more ways to reach/touch consumers. Is there a way to analyze the value of each electronic touch (e.g., email, social network, etc.)?
2. When lead time is relatively long (e.g., 1 year or more in the case of B2B marketing), how does one measure the ROI of online marketing investment?  (We all know that ROI has always been an issue, but longer lead time apparently posts an even greater challenge.)
3. How can a company use information from web queries (similar to the browsing information used in Professor Provost’s research described above) to identify potential sales leads?
4. When potential leads abound and resources available to respond to those leads are limited, can we develop a lead scoring system so that a company can properly filter out more important vs. less important leads?
5. Different online marketing approaches use different types of content as input. For example, a company’s website and its social network presence most likely require different content.  How can one measure the value of each content type to different segments and different industries?
6. Demonstrate the ROI of social media efforts to help marketers argue the value of social media participation to upper-level managers.

In us academics’ constant quest for new knowledge, questions such as these are very useful in guiding our research effort toward being more relevant and applicable to practice.  Here I send out a call to practitioners out there to supply us with more of these and to tell us the question marks in your head.  Please feel free to leave your comment here.  As the overarching goal for my blog, I would like to make Ping! an intersecting spot for practitioners and academic researchers.

This is going to be my last blog before Christmas. So here’s happy holidays to all my readers. Wish everyone a warm, safe and love-filled holiday!

A major question addressed by quite a few presentations at the conference was the impact of user-generated content. So in Part I of this two-part conference report series, I would like to highlight three presentations that I found particularly interesting with regard to this topic.

Does consumer chatter about a product affect stock return?

The answer is yes, according to the research presented by Professor Gerard Tellis from the University of Southern California. In their research, Professor Tellis and his doctoral student Seshadri Tirunillai looked at six diverse product categories with rich consumer reviews: data storage, footwear, toys, personal computers, cellphones, and PDAs/smartphones. They gathered consumer reviews in these product categories from three sources: Amazon.com, Epinions.com, and Yahoo! Shopping. These reviews were then analyzed for the overall rating, review volume, and valence (positive or negative) of review associated with each product. Using a mathematical approach called vector autoregressive, the researchers tied these review characteristics to each company’s stock return and volatility. They found that consumer reviews lead stock performance by a few weeks (meaning that consumer reviews can help predict stock performance a few weeks ahead). Specifically, the volume of review (after controlling for the valence of review) has a positive effect on stock return.  The overall rating (e.g., 3.5 out of 5) did not have any significant impact on stock performance.  But the number of negative reviews and the average percent of negative expressions in the reviews negatively impact stock return and increase stock volatility. In contrast, positive reviews did not have a significant impact.

Lessons for marketers:

• It is justifiable not only from a marketing perspective to monitor consumer opinions in social media but it makes financial sense as well. Research such as this can help make an argument to financial managers why a company should invest in such monitoring activities.
• Although positive reviews may make one feel warm and fuzzy, it’s much more important to pay attention to negative reviews.  In general, negative information is much more diagnostic in conveying market sentiment.

Lessons for investors:

• Consumer reviews may seem far removed from the complex mathematical modeling that goes into stock picking and performance prediction. But this research suggests the value for investors to monitor this social space.
• The researchers further recommended a few investment approaches. For example, as a short-term strategy, buy a stock when its product review enters top 20% and sell the stock when it drops out of the top 20%. The recommended holding period for this strategy is 6 weeks.

Do bloggers affect product sales?

Bloggers like me probably would all like to know that we are making a real impact after the time and effort we’ve put into our blogs. Some companies also invest heavily in the blogosphere and want to know whether that’s a wise thing to do. The research presented by Professor Sriram Venkataraman from Emory University found that blogger influence is geographic-specific depending on the demographics of a market.  Using movie industry data, this research finds that a movie’s first-day national sales is not associated with blog variables.  However, when looking from the DMA (designated market area) level, strong geographic influence emerges. Not surprisingly, markets with a larger portion of young people are more likely to be affected by blogs and at the same time are more likely to discount the influence of company-sponsored advertising.  For markets with a higher proportion of female consumers, the research found that they tend to be more forgiving to negative blogs.  These consumers could read quite negative blogs about a movie but still feel and act positively toward the movie.

Lessons for marketers:

• Consumer blogs can be a worthwhile tool to integrate into a company’s marketing strategy.
• Selectively using these tools based on each market’s demographics may be more effective than a blanket strategy.

What about user contribution in new product development?

This research first struck me as using a very clever data source to address an important question.  Partially based on Professor Matthew O’Hern’s doctoral dissertation, this project uses the well-known open source community SourceForge.net to examine if user collaboration and contribution truly lead to better and faster product development. The answer is mixed. O’Hern and colleagues classified user contributions on SourceForget into three categories: (1) user reports: reports of bugs and issues found in a piece of software; (2) user requests: requests of new functionality or modifications to be added to future software releases; (3) user revisions: user-submitted solutions (i.e., codes) for fixing certain problems or adding new functionality to a software release.  They found that:

• User reports of problems increase release activities, indicating a positive impact on software development.
• At the same time, such problem reports alert other users of issues with the software and reduce the download volume for a software release.
• User requests have the most negative impact, both reducing download volume and release activities.
• Most surprising to me, users submitting their own solutions did not have any significant impact on release activities.  The only impact it had was on increasing download amount for a given month.

Lessons for marketers:

• Wiki-type efforts by users may not always be beneficial to a company’s new product development.  When not properly managed, it can actually prolong the development process and reduce the speed-to-market.
• Caveat: SourceForge is a community of mostly volunteers who do not have a strong commercial interest. Therefore, the proper utilization and integration of user revisions may be limited due to the lack of human power and resources. I would not be surprised that user submission will have a more positive impact in a more closely managed environment.

* * * * * * *

Plenty of information to digest for a while.  So I’m gonna stop here for Part I of the series.  What do you think of these research insights?  I’d love to hear back from you.  If you find any of these projects particularly interesting and would like more information, I encourage you to contact the presenter.  Whenever possible, I tried to provide a link to the presenter’s homepage so that you can find his/her contact information.

In Part II of this series, I will discuss another project on a privacy-friendly target advertising approach based on social network data.  I will also share with you a few high-priority topics related to social media and Internet marketing that were identified by practitioners at the conference.  So stay tuned!

Start with the Brand
As a brand, ING Direct stands out with its distinctive orange dot logo, like the way Apple stands out with its half-eaten apple logo. The color came from its parent company ING Group’s logo — an orange lion.  Considered the most “edible” color, the warm tone of orange conveys a sense of energy and caring. The dot further adds the touch of fun and rebellion.  Together, the orange dot is a significant departure from the usual uptight image of suit and a tie associated with most banks.  This is perfect for ING Direct’s target audience of younger and tech-savvy consumers.

Social Media Engagement

ING Direct’s social media portfolio consists of a fairly standard set of tools: Facebook page, Twitter, Flicker, YouTube, and a blog called “We, the Savers“.  While any company can establish a presence on these social networking sites, not everyone is able to build them into interactive channels that truly engage the customer. I am especially impressed with ING Direct’s Facebook page (see the screenshot above).  Here are a few things that I think they are doing particularly well:

1. A clear mission: consumers. Instead of trying to sell their financial products, the page has a clear mission: to serve “We, the Saver”.  As the little blurb in the left side bar puts it concisely: “Proud to be a Saver? You’ve come to the right place. It’s where we inspire our fellow Americans to save their money.” This goal of “inspiring” rather than “selling” brings consumers much closer to the company, uniting the savers into one big family. In today’s economic environment, this likely resonates with a lot of consumers.

2. Content from third-party sources that has nothing directly to do with the bank but very relevant to consumers. For example, a recent post on the page is an article from the website getrichslowly.org on how to use a flowchart to evaluate potential purchases. This is information that savers would really be interested in reading. ING Direct then adds a little personal touch to the content by adding in the comment: “We learned about flow charts in 7th grade English class. This is the first practical use we’ve seen since then:” This again reflects a consumer-centered approach and comes cross to consumers neither as pushy nor condescending as one might expect from a commercial bank.

3. Plenty of opportunities for interaction. Oftentimes ING Direct posts little fun questions to consumers on the page (and via Twitter).  Here are a few questions asked recently: (1) “The library, visiting a pumpkin farm & going to the park are three great free fall activities. What are your favorite fall must dos?” (2) “Our favorite “money” movie quote may be from Swingers- ‘You’re so money and you don’t even know it!’ What’s yours?” (3) “Simple tips from everyday people on how to save money on everyday expenses: Got any others?” (4) “Will your next car be new, or pre-owned?”  These questions are things that consumers can easily relate to and answer, which motivate them to participate in the conversation.  I always see at least a dozen responses and comments on such posts, which also translates into potential better understanding of consumers by the company.

Take Away for Marketing Practitioners

Social media give power to people, and to win in this arena, focusing on the people is a must. Traditional mindset of selling is not going to work in this setting. If your business does not have the courage and culture to adapt to this new collaborative environment, it may be worse to pretend to be a social media participant when nothing you do is “social”. The end results will be skeptical consumers and wasted resources. ING Direct’s social media strategy offers good ideas on how to do this properly. For those who want further guidance, I recommend this Smashing Magazine article: “Social Network Design: Examples and Best Practices“. You may also find this report on the top 100 brands’ customer engagement level and its business impact useful.

Take Away for Marketing Academics

While customer relationship management and relationship marketing have gained strong hold in the academic marketing discipline, I think there has not been enough attention paid to customer engagement, especially through the use of social media.  I suggest a few research questions below that I believe are relevant to both marketing theory and practice.

• How do we properly gauge customer engagement?  Is this a state of mind?  Or is this measured by the number of conversations and/or followers?
• How should models of persuasion in traditional advertising being modified in the context of customer engagement?
• Do consumers undergo different processes to form their attitude and satisfaction judgment when they are engaged with the brand vs. when they are not?
• What metrics should be used to measure the outcome/ROI of customer engagement?
• With these metrics in place, can we show that customer engagement does lead to visible business benefits?

What are your thoughts about customer engagement?  I’d love to hear your stories and questions about customer engagement.

Managers are often hesitant to invest in encouraging word-of-mouth, however, as its effects are notoriously difficult to measure.  This is because word-of-mouth behavior is often unobserved, and it is difficult to tease out the concurrent impact of traditional marketing.  These are the exact problems a recent article by Michael Trusov and colleagues in Journal of Marketing tried to tackle.  Entitled “Effects of Word-of-Mouth Versus Traditional Marketing: Findings from
an Internet Social Networking Site”, this article offers a clear answer to the relative effectiveness of word-of-mouth vs. traditional PR and marketing.

What did they look at?
The impact of word-of-mouth, event marketing, and media appearance on the sign-ups for an undisclosed online social network.

Some intuitive findings:
More new sign-ups resulted in more word-of-mouth; event marketing led to more media appearance, and vice versa;  word-of-mouth was not affected by previous event marketing or media appearance, however, suggesting consumers’ relatively independent opinions and actions.

Some not-so-intuitive and very important findings:
The 3-day elasticity of sign-ups with respect to word-of-mouth was .17. In layman’s terms, this means that doubling the amount of word-of-mouth increases sign-ups by 17%. The corresponding impact from event marketing and media appearance, in contrast, was only 1.7% and 2.2%. The gap became even bigger with regard to long-term effects.  In the long run, the effect of word-of-mouth is 20 times that of event marketing and 30 times that of media appearance.  While doubling event marketing or media exposure led to 1.7% and 2.6% respective increase in sign-ups in the long run, doubling word-of-mouth increases sign-ups by a full 53%. Financially, an outbound word-of-mouth referral translates into 75 cents/year increase in advertising revenue.

What does this mean for marketing practice?
Word-of-mouth is a powerful tool for customer acquisition.  With the help of more powerful tracking tools provided by social networks and websites, it is possible for managers to measure the return from word-of-mouth activities. The mathematical approach used in this article (vector autoregressive modeling) further helps tease out the impact of other marketing and PR activities so that the true effect of word-of-mouth can be accurately measured. Together, this should reduce the hesitation to incorporate word-of-mouth into a company’s overall marketing strategy. The findings from this article also provide a strong motivation to better utilize word-of-mouth channel of communication.

Cautions
Readers should be cautioned from taking the results from the above research too literally.  Two things should especially be taken into consideration.  First, the data came from an online social network.  Customers on such websites are usually highly motivated to invite their friends, and those invited by their friends are also very likely to sign up.  If we were to change the context to, say, online banking, both the level of referral and the impact of referral are likely to be lower.  Second, the word-of-mouth activities studied in this article are all organic referrals initiated by consumers themselves. If the word-of-mouth had been stimulated by the company (say, with financial incentives), the referrals may not have been considered as genuine to other consumers and therefore may not have created as strong of an effect as reported in this study.  Although these are real limitations, the findings from this study are still quite powerful indicators of word-of-mouth effect. It is a tool managers should not ignore.

Reference
Michael Trusov, Randolph E. Bucklin, and Koen Pauwels (2009), “Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site,” Journal of Marketing, Vol. 73 (September), p.90-102.

1. Video Games

Pew Internet and American Life reports 97% of teens play video or computer games, and half of these teens play daily and typically for at least an hour each day.  In a separate report released by Nielsen, video gaming is also found to be one of the major media activities that teens engage in, and teenagers spend an average of 25 minutes each day on console gaming.  While some may see video gaming as an isolate activity, the Pew report suggests otherwise.  It shows that teenagers often engage in video gaming as a social activity, with 65% playing games with others in person and 27% with others over the Internet.  The most popular games?  Guitar Hero, Halo 3, and Madden NFL.

2. Mobile Devices

Mobile devices as a popular teen hideout came as no surprise to me as it has always been a challenge getting my undergraduate students not text message in the classroom.  Nielsen reports an average of 96 text messages sent or received by a teen in merely one day. But the story does not end with text messaging.  Nielsen also finds a higher than average consumption of mobile video by teenagers. The popular genres? Music, comedy, and user-generated videos.  Perhaps as even better news for businesses, an article from ClickZ offers evidence that teens are more receptive to mobile advertising than their adult counterparts.

3. TV and movies

TV is by no means a new medium, but it remains surprisingly popular among teenagers.  The same Nielsen report mentioned earlier tallies the time teens spend watching TV on an average day to be more than 3 hours. In a more in-depth rather than representative look at teenager media consumption, the 15-year-old intern Matthew Robson from Morgan Stanley says that teenagers’ TV watching is often driven by seasonality and scheduling of shows. When popular shows are on, there could be a big spike in TV consumption.  Popular shows among teens?  Nielsen lists Family Guy, American Dad, and American Idol.  Related to TV watching, teens also frequent movie theaters, seeing an average of 10.8 movies per year.  But despite high levels of interest in TV and movies, teens actually trail behind those 18-to-44 year-olds in online video consumption, watching just over 3 hours of online video in a month.  This does not mean teens are completely uninterested in online video, however, as both the number of teens watching online video and the time spent on online video are still increasing pretty fast. Only time will tell where the consumption level will settle eventually.

Does this picture of teenagers surprise you?  Do you have any additional information about teen media consumption?  Your comments are most definitely welcome.

People do tend to follow the crowd but a more closely-knit crowd carries much more power

We all have hesitations when it comes to novel new things and may consider them risky. Depending on how risk averse we are, we may wait until some or a majority of other people have adopted the new thing before we jump onto the wagon. In my own research project documented in the last blog, we found the median adoption threshold to be 50%, incidentally supporting the “majority rules” mentality. But the threshold reported by our sample ranged across the whole spectrum from 0% to 100%. Consistent with this idea of an adoption threshold, Katona and colleagues found that more people in one’s social circle adopting a social network makes one more likely to join the network. In this context, perhaps an additional driver besides risk is the fact that the utility of a network increases when more of one’s friends belong to it. The story does not stop here, however. The researchers also found that a closely-knit (or high-density in network science terminology) network where everyone knows everyone else is much more influential. If the same number of individuals in a closely-knit network joins a social network, the remaining non-adopters are much more likely to follow suit than if it were a loose (low density) network of sorts.

Social butterflies are not the most influential

In network science, the fact that some individuals have way more friends/connections than most others in the same network has often been compared to the rich get richer phenomenon.  But unlike the richer people who do have solid cash to spend, social butterflies who have tons of friends (think 1000+ or even 500+ Facebook friends) are actually quite weak when it comes to influencing other people’s opinions. Well, at least when it comes to the decision to join a social network any way. This may be surprising on first look. But not so when one thinks deeper about human psychology. We all have limited energy to build and maintain friendships. The more friends we accumulate on a regular basis, the less energy we have to develop a deep and meaningful relationship with each individual, and thus the less we are able to exert a strong influence.

Weak ties may be good for information travel but exert limited influence

The strength of the weak tie has been a well-known phenomenon for more than 20 years, referring to the fact that weak ties that link disconnected networks are critical to the spreading of information. However, for exactly the same reason, the central role played by these weak ties also makes a network formed around such ties more vulnerable.  Referring to these individuals as structural holes, Katona and colleagues found that the adoption of a social network by these structural holes has less of an impact on their friends, perhaps accurately reflecting the fact that these are “weak” ties.

Lessons learned

• Many factors create counter effects when it comes to increasing awareness of a community vs. increasing participation in a community.
• While sometimes it may be necessary to target loosely-knit networks (more weak ties) for increasing the awareness of your online community, closely knit networks are eventually critical to increasing actual participation in your community.
• The same thing goes with highly-connected individuals. While those who have lots of friends may be good for getting the word out, individuals who have a more moderate friend circle may be more ideal for building the community.
• For a business, how these counter effects should balance out will depend on the exact goal for the online community at each stage.

Reference

Zsolt Katona, Peter Pal Zubcsek, and Miklos Sarvary (2009), “Network Effects and Personal Influences: Diffusion of an Online Social Network“. The full paper can be downloaded from Katona’s website at http://www.cs.bme.hu/~zskatona/pdf/diff.pdf

“If my friend shares something with me, I will view it. But don’t really expect me to pass it on.”

We asked our respondents how likely they are to view information shared by someone they know, and over 60% of them agreed that it is quite likely (7 or higher on a 10-point scale).  This is good news because in the case of viral campaigns, encouraging people to share information with their friends is likely to increase the reach of the campaign. The bad news we found, however, is that way fewer of them would further pass on the information to their respective friends.  Less than 20% of them said they are likely to pass on information shared with them by their friends.  Interestingly, when asked the same question about information consumers found online themselves rather than shared by their friends, those who selected likely to pass on information increased to about 30%.  The lesson here is that first-order word-of-mouth (consumers passing on information they found themselves) is more likely to happen than second-order word-of-mouth (consumers passing on information that are found by their friends). Therefore, companies engaging in word-of-mouth campaigns should still try to spread the word to as many “seeds” as possible rather than counting on a few starting points.

“Make me believe that the information is relevant to my friends and I will pass it on.”

The survey contained an open-ended question asking the respondents to list the factors that would make them more likely to share information online.  The dominant reason listed (by 35% of the sample) was relevance to the friends that they are passing the information on to.  This is perhaps not surprising considering that few of us want to jam our friends’ inbox with junk information.  For companies, this means an opportunity to encourage passing-along by demonstrating the content’s relevance to a consumer’s social circle.  Financial incentives offered to friends by some referral programs is an example of this approach. The second most widely listed reason was something funny.  Apparently, we as human beings like to share laughter with others.  Below are the top five reasons the respondents cited ranked by frequency:

1. Relevance to those sharing information with
2. Humor
3. Relevance to oneself
4. Importance/worthiness of information
5. Unusual/unique information


Opinion leaders share more information but are also more likely to seek advice.Studying information sharing is not complete without considering opinion leaders, those individuals that are on the cutting edge and are likely to influence other people’s opinions.  We found that being an opinion leader increases the likelihood to share information with others by 38%, perhaps partially explaining why these people are opinion leaders in the first place.  While this finding seems rather obvious, what is not so obvious is the finding that opinion leaders are also more likely to seek advice from others such as family, friends, and neighbors. Compared with regular individuals, opinion leaders are 25% more likely to seek advice from others. This finding is important because we have often seen the argument that the right way to treat social media is to be social (in other words, interacting with others).  Our study finds concrete support for that.  A true opinion leader does not just broadcast information to others but also listens closely and actively seeks out others’ feedback.

As we move forward to the next stage of the research project, we would love to hear your thoughts.  What makes you more likely to share stuff with other people?  As a company, how do you manage your viral campaign content and seeding process so that it can create the maximum ripple effect?

In recent years, consumer psychologists have made significant progress on alternative influences of consumer decision making.  Instead of treating consumers as cold, rational decision-makers, this research stream reveals that consumers are often driven by automatic processes that they are often not consciously aware of.

Example 1: Which sofa consumers buy depends on your website’s background image

Naomi Mandel and Eric Johnson published a study in Journal of Consumer Research, in which they exposed consumers to the same online furniture store with only one subtle difference: one version had fluffy clouds and blue sky as the background, and the other had coin images on green-colored background.  They found that those who saw the fluffy cloud background were more likely to buy the more comfortable but pricier sofa, whereas those who saw the coin background were more inclined to choose the cheaper but less comfortable sofa. The reason behind such differences is that the different images primed different attributes (comfort vs. money) in the consumers’ mind, therefore increasing the weight of the corresponding attribute in consumers’ choices.

Example 2: Drive by Walmart on the way to shopping to avoid overspending

A more recent study by Tanya Chartrand and her colleagues published in the same journal looked at the subconscious activation of goals through contextual cues. In one of their experiments, individuals were asked to focus on the center of the computer screen to complete a task. In the meantime, the brands of prestige (e.g., Nordstrom) vs. thrift (e.g., Walmart) retailers were flashed randomly on the edges of the computer screen for 60 milliseconds, outside of the individuals’ focal attention area. In a subsequent choice between two brands of socks and between two microwaves, individuals who were flashed the prestige brands chose the more expensive brand than the cheaper brand in each task, and the reverse was true for those who were flashed the thrift brands. Interestingly, the researchers also found that once a specific goal (achieving prestige vs. saving money) is activated subconsciously, it grows stronger until the goal has been satisfied in a subsequent real choice. All of this happens without the consumers’ conscious awareness of what is affecting them.

Lessons Learned:

• There are many more factors that affect consumer decision making than what companies normally pay attention to.
• The graphics, banner ads, and other contextual cues on your website or in your store can significantly alter consumers’ decision related to your product. If your product excels on a specific attribute, you may want to play up graphics and contextual cues related to the attribute to make it more important.
• The fact that these “innocent” contextual cues can affect consumers without their awareness brings interesting legal and ethical questions, similar to the long-debate surrounding subliminal advertising. For example, how far can marketers go in affecting consumers’ mind this way? What if such tactics are used on younger consumers who are less knowledgeable and therefore may be more subject to such influences?

What is your take on this?  Has your company ever tried such tactics? Or as a consumer, would you be offended if you know a marketer tries to influence you in this way?

Further Reading:

Mandel, Naomi and Eric J. Johnson (2002), “When Web Pages Influence Choice: Effects of Visual Primes on Experts and Novices,” Journal of Consumer Research, Vol. 29 (September), p.235-245.

Chartrand, Tanya L. et al. (2008), “Nonconscious Goals and Consumer Choice,” Journal of Consumer Research, Vol. 35 (August), p.189-201.

1. Why Companies Enter Second Life?

Most of the companies we surveyed/interviewed entered SL in 2006. The decision to enter SL varied from an innovative mentality to jumping onto the wagon under competitive pressure. Usually, an individual or a small team of employees were personally involved in SL at first, and they eventually became champions within the company. Interestingly, European companies’ decision to enter SL were driven more by potential for media exposure rather than by the actual functionality or use of the virtual world itself, suggesting important cross-cultural differences in corporate innovation.

2. What Companies Use Second Life for?

Below are six ways the companies have been using SL, ranked by their popularity. The list makes it obvious that communication and learning represent important uses of SL.

3. How Do Companies Benefit from Second Life?

Most companies considered their SL venture to be worthwhile, although only one-third of them acknowledged realizing financial gains. Here is a list of the benefits companies believe they have received from their SL presence, ranked from most-often mentioned to least mentioned:

What do you think about these findings? Has your company ever experimented with Second Life?  I’d love to hear about your experience.