Dr. Brent Coker of the University of Melbourne recently published findings indicating that web users tend to trust web sites 20% more today versus 2007, but are 30% less loyal to ecommerce sites versus 2007.
1. Why He Believes Trust Increased
Dr. Coker said the increase in online consumer trust is largely linked to the visual appeal of websites. “As aesthetically orientated humans, we’re psychologically hardwired to trust beautiful people, and the same goes for websites. With websites becoming increasingly attractive and including more trimmings, this creates a greater feeling of trustworthiness and professionalism in online consumers.”
Anyone interested in web credibility should also visit the Web Credibility Project at Stanford University.
2. Why He Believes Loyalty Decreased
“The biggest source of frustration is the inability to find relevant information on a website. The best way to stop defection to other websites, and increase loyalty, is to be interesting. Being pretty, but with nothing to say, is not enough.”
The research found that if a website has poor navigation or access to information, or is slow (i.e. more than two seconds to download), web surfers are more likely to opt against purchasing and navigate to an alternate website. (No surprises there.)
However, it is interesting that, in the last five years, the frequency of referring others to websites has increased by 32%. Largely due to social utilities, such as Facebook and Twitter.
Forrester’s excellent new report, “Five Ways Interactive Marketers Should Use Social Data,” is a must read for brands interesting in effectively integrating social media intelligence into their organization. Translating social intelligence into action is pehaps the largest impediment to widespread enterprise adoption of social media. But we’re working hard to change that.
One item that caught our eye — and is dear to our heart — was this:
Most listening platforms are ill-equipped to inform marketing strategy. While many listening vendors promote their ability to assist marketers, most simply don’t know how to translate social data into effective marketing programs. In fact, we found that just two of the nine best-in-class vendors in our most recent Forrester Wave™ evaluation of listening platforms were able to provide deep strategic marketing insight.
The full report is here.
How Converseon Turns Social Data into Action
As an increasing number of brands are finding, simply having social data pumping through a dashboard to a few analysts simply cannot drive the real value of social intelligence across the organization for competitive advantage. The next generation of social intelligence is fusing deep levels of intelligence with business knowledge and experience to unearth those game-changing insights and get them deeply embedded into the parts of the organization that can take action on them. That is why we say social intelligence is increasingly becoming the impetus for redesigning business processes.
So how does Converseon so effectively translate listening into effective marketing programs? WIth five not-so-secret ingredients:
As part of our ongoing efforts to help companies understand their employees through conversation mining, Converseon recently analysed online conversations around the #lovemyjob hashtag, and below are some of our findings:
Men and Women Use the Hashtag Differently
- Women tweet #lovemyjob three times more often than men
- Nearly 20% of #lovemyjob tweets from women discuss working with children
- When women tweet #lovemyjob, they tend to discuss interacting with co-workers and customers
- When men tweet #lovemyjob, they tend to discuss job perks and slacking off at work
Artists, Teachers and Lifeguards Love Their Jobs
- Artists, teachers and lifeguards use #lovemyjob more than any other occupations, as follows:
- Artists: 9% of tweets
- Teachers: 8% of tweets
- Lifeguard: 6% of tweets
- People who tweet #lovemyjob more frequently work in hospitality (12% of tweets), creative (11% of tweets) and education (9%) industries
- 5% of the #lovemyjob tweets name the person’s employer
- The most frequently named company was a retail-clothing company
Email Converseon to see how we can enhance your HR and organizational development initiatives.
I just finished participating in a Digiday panel on big data management in beautiful Deer Valley, UT where I warned about an over-infatuation with technology, to the exclusions of people. Indeed, many conversations today make it seem as if humans are simply a tangental voyeur to the vast processing intelligence of our evolving algorithms.
That is far from the truth.
Machines and technology do some things wonderfully well. They churn through vast amounts of data — searching for anomalies and patterns — and machines help to filter the signals from the noise. But the signals have to be interpreted by humans to spark the insights that can change a business. No machine has yet developed much of a creative streak.
We see this every day in our Conversation Mining technology. Instead of using algorithms to interpret the meaning of human conversation on the web, we use machines for what they do best: to find and identify the obvious conversations, and look for patterns of meaning that go beyond what humans can generally perceive.
Nearly one third of Chinese citizens went online as of June 30*, and most of them are young. A few facts:
- Nearly 3/4 of China’s 420 million internet users are under 30.
- The 20-29 age group is the largest online age group in China.
- 79% of 10-19 year olds in China are online
- While the 30-49 age group accounts for 37% of the population, 27% of of them are online.
Data Source: China Internet Network Information Center (CNNIC), a Chinese governmental agency.
For information on how Converseon can help your company research and engage through social media in China, contact firstname.lastname@example.org.
While studies have shown that positive online ratings correlate with higher sales, a study at the University of Maryland using data from BazaarVoice found that brands maximize their long-term benefits from online ratings when they attract a balance of positive and negative ratings from customers.
If you want to increase sales in the long run, you need to attract a balance of negative reviews, in addition to lots of positive reviews.
In The Value of Social Dynamics in Online Ratings Forums, Wendy Moe and Michael Trusov of the University of Maryland found the following:
- The rating of your product today has the biggest impact on sales today.
- However, the number and diversity of reviews that you have today affects how the rating will change over time, as follows.
- A 5-star rating today will most likely go down over time because people will post negative reviews in response to the existing positive reviews.
- Reviews containing only 4s or 5s attract negative reviews because new users often want to add a sense of balance.
- Products with an average rating of 3 stars based on a wide range of ratings tend to improve their ratings faster than those with only 3-star reviews. As a result, the more diversely rated products tend to improve sales more quickly.
Social CRM – the use of social media monitoring together with customer care outreach – has become big business. Gartner group projects it will be a $1 billion industry in 2010. And with good reason. Increasingly, consumers are taking to their social networks to express dissatisfaction with brand customer care experiences. Brands ranging from Comcast to Dell, Delta and others are being increasingly deluged with customer complaints, questions and concerns. No longer are complaints kept within the communication corridors of customer to brand channels, but are being broadcast and reposted/retweeted across the social media landscape. At times these can turn into search engine results, damaging significantly a brand reputation.
The response? Brands have increasingly been putting PR and Communications staff into positions to reach out and address concerns on the premise it’s better to defuse early. But all too often the PR and Comms staff are outnumbered by the volume and, perhaps more critically, they’re not in a situation to address the root cause of the problem. Instead they’re put in position of apologist.
The problem is three fold:
1) Scaling social media into the customer service infrastructure is difficult. Most of the time the social conversation data is not integrated withother customer profile data. In the vast majority of cases, the social listening data is segregated from critical customer data that would help resolve an issue. The promise of social data integrating into a single customer profile database is still a hazy mirage on the horizon. So brands are often focused on the perceived influence of a complainer – using tools such a Klout scores. The problem with this is clear: your best customers may not have high – if any – klout scores. In fact, most senior executives – for example, are probably spending more time doing business deals than tweeting their lunch plans.
Anyone running affiliate marketing or influencer outreach programs requires a mixture of automated and human analyses to design and operate their program. Automated algorithms are great when you need a quick decision in real-time, but when you are choosing 5, 10 or 50 influencers for long-term relationship development, you need to be sure that they are the true influencers in your category. That requires human analysis.
While Klout can now include LinkedIn connections and activities into the calculation of Klout scores, brands should be very careful about using Klout scores for affiliate marketing or influencer outreach, for the following reasons:
- The only way to identify influencers within a category is through a combination of automated and human analyses.
- Klout can not measure influence within a category. For example, Ariana Huffington has a high Klout score, but she is not relevant to most brands. For example, if you sell baby diapers or desktop virtualization products, Ariana is not an influencer. And Klout is not capable of telling you who influences the conversation around baby diapers or desktop virtualization products.
In recognition of the need for category-specific influence scoring, Klout recently launched a +K button, which lets Klout users tell Klout when someone else influences them, but the method has two weaknesses:
- +K is subject to significant self-selection bias. The inputs come only from Klout users who choose to contribute, and
- +K does not capture the extent to which one person passes along another person’s messages. Therefore, the scoring does not adequately allow a brand to choose influencers based upon the extent to which the influencer will drive messaging into the market.
- +K does not associate the influence with a category. For example, I might say that Seth Godin influences me, but does he influence my decisions about car purchases, laptop purchases or the foods that I consume? No.
Converseon recently launched a collaborative research project with comScore, the Advertising Research Foundation (ARF), Communispace, and Firefly Millward Brown to explore the roles of social media in the purchase process. The project brings together a range of research techniques to understand how and when people turn to social media as they make purchase decisions.
Converseon will provide insights based on social intelligence for the project, helping the ARF to establish an expanded understanding of online conversations around purchase decisions for items in the CPG and other categories. Converseon analysis will uncover where in the purchase process social media conversation is taking place, who is taking part in that discussion and where it’s happening.
Our research will include psychographic and demographic analysis, in addition to a range of other metrics based on Converseon’ unique technology + human methodology, giving the ARF the opportunity to cross-tab the analysis and extract the most meaningful information to support its research goals.
We all look forward to the results of the joint project.