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Translate Word of Mouth With Social Media Monitoring Tools

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A comprehensive measurement plan should consist of three parts—gauging the audiences’ reactions to a brand before, during and after a campaign | Social Media Monitoring Tools

A comprehensive measurement plan should consist of three parts—gauging the audiences’ reactions to a brand before, during and after a campaign | Social Media Monitoring Tools

The abundance of content that is easy to access and consume makes launching and sustaining noteworthy online projects challenging. As social media matures, the need to measure online word of mouth and demonstrate success becomes indisputable.

A comprehensive measurement plan should consist of three parts—gauging the audiences’ reactions to a brand before, during and after a campaign. The first step in measuring online word of mouth is to listen and monitor audience chatter across blogs, forums and social networks. This effort helps uncover existing issues, attitudes and behaviors. It marks the starting point for a campaign. The second step requires tracking the campaign’s progress and studying the interaction between message senders and receivers. During this phase, marketers can take note of attitudinal and behavioral changes among their target audience. The third step involves comparing final campaign results with benchmark scores to demonstrate the momentum and change the campaign generated.

When setting benchmarks and tracking online word of mouth throughout the course of a program, marketers can use the following measures to show how their initiatives generated buzz, changed brand perceptions and led consumers to take action.

Volume of discussion: Using blog search engines such as Technorati, Google Blog or research firms’ proprietary software tools, count the number of posts that mention key words or messages related to your program. The numbers of unique Web site, blog and forum posts that reference the brand, product, service or issue indicate online word-of-mouth reach.

Influencer mentions: When writers quote and reference a source, they deem that information outlet reliable and useful. Similarly, every link that points to a social media address boosts that source’s authority. When a blogger refers to your program, enter the blog’s address into the Technorati search engine and note the authority score the search engine calculates for that blog. Some monitoring tools also measure the number of inbound links to blogs from brand sites, news sites, forums and other blogs. The higher the score, the more influential and authoritative the source will be.

Stickiness: To show the full impact of word-of-mouth programs, we must account for those who received and shared a message. Impressions and unique visitors are metrics that speak to the broad universe of people who may have been exposed to a message. However, not everyone passes along every bit of information they receive. Stickiness is based on the percentage of people who pass along a message among those who are exposed to the message.

The Echo Factor and Tone: When reviewing the overall volume of mentions, analysts often distinguish between positive and negative tone. Marketers can take this assessment a step further and measure how their messages echo through consumer conversations. They can calculate the total number of positive and negative messages generated through at least one cycle of word of mouth. Tonality Index, which is based on he ratio of positive to negative mentions, indicates the dominant tone of word of mouth and gives brands a pulse check.

Engagement: There are popular ways of quantifying engagement such as measuring the amount of time spent on a Web site and counting the number of comments online posts garner. Yet, online media engagement can be a qualitative measure that gives directional information about consumers’ online experience. To understand the nature of users’ interaction with the blog content, marketers can study comments’ tone and length. They may find a detailed, positive review more meaningful than a neutral or negative monosyllabic comment. Furthermore, they can classify the topics commentators discuss and analyze the quality of information these social media agents share.

Advocacy: Differentiate between online conversations that are descriptive and those that contain recommendations or warnings. To identify those networking agents who are advocating for a brand, product or a company, look for those who are making solid recommendations, telling others what to do, and potentially influencing others’ opinions and decisions. For instance, “online promoter score,” distinguishing between mavens who are generating much of the volume on an issue and advocates who make recommendations

User Action: Online word-of-mouth campaigns yield recommendations, votes and purchases. When organizations engage word-of-mouth agents and infuse networks with their messages, they hope to see an increase in sales and public support. To connect such outcomes with their marketing initiatives, communication professionals need to document their audiences’ online behaviors and show that online buzz can lead to posts, clicks and downloads, or offline actions such as votes, coupon redemptions and in-store purchases. Marketers can review sales trends during and after the campaign and note any increases that correspond with online buzz volume. Political strategists can explore how visits to online information hubs affect votes, signatures and donations.

the less you respond to negative people the more peaceful a life

the less you respond to negative people the more peaceful a life

Source: PR NEWS

How To Use Social Media Monitoring Tools To Aid Product Development

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To many, the process of developing a successful product can be a mystery. Sometimes companies will spend months of development time to create a product that doesn’t reflect the needs or the scope of its intended market. And other times, successful products are developed completely on accident. Because of this, it can often seem impossible to develop successful products. However, if one takes the time to listen to their marketplace and plan the development process accordingly, they are more likely to succeed.

In this post, I would like to discuss how to use social media monitoring tools to aid in product development and market research.

There are many steps to developing a successful product. But the first step is always concept creation. Here we are thinking about broad-based ideas. Using social media monitoring at this step can help form a direction and scope for the rest of the development process. For example, if we want to develop a product focused on online video, we might monitor such terms as “video”, “video sharing”, or “video rating”. During this first stage of monitoring, we will want to focus on what aspects of online video people are talking about most.

How To Use Social Media Monitoring Tools To Aid Product Development

How To Use Social Media Monitoring Tools To Aid Product Development

Sniffing user needs out of social media

Identifying trends and audiences is extremely important to defining the scope and direction of your product. With our example, we might find that the largest demographic for video consumption are young adults and predominately focus on music and entertainment.

After we have used our monitoring tools to identify trends and audiences, we now begin to monitor scope and direction. Understanding how your target audience is using products is important in your planning process. With our example above, we might monitor conversations to determine where and when video content is being viewed the most. Questions such as “are the users using handheld devices or traditional desktop machines?” can be helpful when determining the scope and direction of your product.

While observing how the market uses similar products, you can begin to make a potential features list. For example, you might observe some users prefer video playlist and some prefer video sharing. Making a features list based on actual user conversations/engagement can be extremely powerful when deciding how to delegate resources during the development process.

Prepare your competitive position

After you’ve completed your features list, research other companies and products that meet the needs of your target audience. Use this list of companies and products to begin brand monitoring to aid in competitive analysis. Here, we will be looking at users reactions and sentiment towards competitors in your marketplace. Pay attention to any gaps between your target’s dialog and what your competitors are offering understanding these gaps can help develop a strong point of difference with your product.

At this point, you should now have a direction, feature list, and definitive point of difference that is all reflective of your marketplace. Now its time to send your ideas off to the engineers! But wait, don’t stop monitoring social media! After you have launched your new product, you are going to want to continue to monitor social media to identify flaws and improve with extended feature sets that are now more apparent after you have launched.

Understanding your marketplace and target audiences are important to product development. Whether it is concept creation or refining your feature list social media monitoring can help with the necessary research in building the perfect product.

Source: www.theseohelpblog.com

Customer experience and sentiment analysis

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The notion that listening to your customer’s voice is important is firmly established. Companies have depended for a long time on data from customer surveys, call center transcripts and focus groups. This data was captured in a structured format and visualized via charts or processed with the help of business intelligence applications, to help identify how to improve customer service, develop or improve products and pinpoint competitor vulnerabilities.

Companies face a very real need not just to acknowledge the impact of unstructured social media on brand and product perception, but to understand and filter it sensibly, and to integrate it with structured customer data and get it into the hands of the right people to make it actionable.

Companies face a very real need not just to acknowledge the impact of unstructured social media on brand and product perception, but to understand and filter it sensibly, and to integrate it with structured customer data and get it into the hands of the right people to make it actionable.

But the veritable volume of the customer voices in the Web 2.0 age more often than not leaves companies struggling to keep up. Now people have a voice and a tendency to express their opinions with blunt honesty via blogs, tweets, e-mails and forums about products and services that they find gratifying or disappointing. And those opinions hold weight. A 2007 study by Jupiter Research (now Forrester), called “Social Networking Sites: Defining Advertising Opportunities in a Competitive Landscape,” found that 30% of frequent social networkers trust their peers’ opinions when making a major purchase decision, compared to the 10 percent who trust advertisements.

As Andreas Wiegend, former chief scientist of Amazon.com, predicted in a blog post for the Monitor Talent Group, “In 2009, more data will be generated by individuals than in the entire history of mankind through 2008.” Companies face a very real need not just to acknowledge the impact of unstructured social media on brand and product perception, but to understand and filter it sensibly, and to integrate it with structured customer data and get it into the hands of the right people to make it actionable.

For many companies, the burgeoning text analytics approach of sentiment analysis is becoming a critical component of their overall strategy, giving them a much-needed assist to stay responsive to customers, market opportunities and trends.

What is it?

In his white paper “Text Analytics 2009,” Seth Grimes, analytics strategist at Alta Plana, describes text analytics as “the software and the transformational steps that discover business value in ‘unstructured’ text.”

There’s special business value in discerning opinion, sentiment and subjectivity—the “voice of the customer”—in text as varied as blogs, forum postings, articles, e-mail and survey responses. That field of “customer experience analysis” applies sentiment analysis and other techniques to understand and help predict consumer behavior via text analysis coupled with analysis of customer transactions, profiles and demographics.

Vendors generally use a combination of statistical analysis of word frequency andco-occurrences with linguistics (involving lexicons, dictionaries and language rules) in an algorithmic approach to understanding exactly what the consumer is saying. Grimes says, “The narrower you can frame the problem and the data you collect, the better, because you can then adjust your approach to match specific business requirements and information sources.”

The technological challenges are not for the faint-hearted or the linguistically timid. Suresh Vittal, analyst at Forrester, says, “For a long time, text analytics was a technology in search of a business need. Now, thanks to social media, the need is there; the question is whether the technology can ramp up fast enough to be commercial.” Early adoption by government agencies, which sought to apply text mining to mountains of classified documents, is giving way to more mainstream commercial demand from industries for whom customer perception is critical: hospitality, consumer brands and high-tech, among them.

Classifying the messy middle

Ours is a world in which online consumer reviews of hotels that might include the phrase “the lobby is baaaaad!” meant in a positive way, or a review of a holiday toy saying, “I would give this to all the children in my life, if I were Scrooge,” meant to disparage. Throw in slang, language evolution and socio-cultural gradations in word use, and you have a mammoth challenge for accurate computational treatment of opinion.

Levy says accuracy remains a challenge in the industry. “The sentiment side is good at the two poles, positive and negative. But the neutrals are difficult. If you give four people in a room 100 neutral opinions and ask them to classify, even they will only agree 55 to 60 percent of the time.”

The level of granularity can also be important. If sentiment is assigned at a document level—that is, each tweet or blog post is assigned a positive, neutral or negative sentiment—how does the hypothetical tweet “I love Marriott’s bathrooms but the beds are lumpy” get classified? A chief executive officer cautions, “Ratings need to be assigned on a subject level at a minimum; a solution that assigns them at a document level is going to miss something.”

Whose opinion is it?

Even if a sentiment analysis tool were always accurate, the opinions don’t necessarily carry equal weight.  Dell has an estimated 8,000 to 10,000 online conversations about its brand each day, which span the spectrum of positive to negative; the company needs to understand whose opinion actually has the power to move brand perception, and keep close tabs on those. Sentiment analysis needs to be connected to social metrics and influence analysis to make sense.

Levy agrees, saying companies understand that listening to social media is important but now need help in filtering. “There is no longer the notion that trusted information only comes from The New York Times,” he says. “Once you get a handle on who is influencing your brand, that becomes actionable.” Influence analysis, analyzing digital breadcrumbs to see which individuals have the highest credibility and widest reach, should be a part of the overall text analytics strategy. By knowing in advance who the influencers are for your brand, you’ll be better prepared to manage crisis and opportunity effectively, reaching out to 20 key contacts instead of 10,000 questionable ones.

Taking sentiment out of the silo

There’s widespread agreement among vendors and analysts that text analysis is only as valuable as the actions it prompts. In a Forrester report from February 2009, called “Obstacles To Customer Experience Success,” a survey of 90 customer experience decision-makers from large North American firms found that 89 percent said that customer experience would be either very important or critical to their 2009 efforts, but a lack of cooperation across organizations remains a major obstacle.

When it comes to sentiment analysis, different functions are listening for different answers. A customer service manager needs insight into customer experience, a product manager wants to hear complaints or praise for features as well as product design ideas, and brand managers may be looking for competitive intelligence.

For customers of online monitoring solutions, acting on the feed of information is the hardest part of the equation. You must have an environment where people are culturally attuned to action.

The challenge to the enterprise is to combine analysis of what is being said, by whom, with more structured customer intelligence data  in order to develop a robust customer engagement strategy. Forrester’s Vittal says that to break sentiment analysis out of the silo, “The platforms must be open and integratable. Customer intelligence data is still siloed, and there is a complexity gap that must be overcome.”

Companies should combine unstructured data from opinions about their brands and products, posted on social media and other traditional online sites, with public opinion as measured through structured survey research, to paint a richer picture of consumers’ emotions and decision factors.

Getting started

For companies that are just getting started with sentiment analysis, the first step is to listen to what’s being said, analyze the information and identify possible root causes behind it—a company can truly begin to capitalize on the promise of text analytics- and use it as input to their marketing strategy.