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  • 25 Apr 2017 12:51 PM | Mark Michelson

    Full Story

    Nicole NguyenBuzzFeed News Reporter

    When you sign up for a free online service, you’re usually giving up your personal info in return. Here’s how to find out just how egregious that data collection is.

    If there’s only one thing you take away from this article, let it be this: there’s no such thing as a free lunch.

    The New York Times recently reported that Unroll.me, an email management app that promises to de-clutter your inbox, sold its users’ anonymized Lyft receipt data to Uber. Unroll.me claims that it’s “trusted by millions of happy users” — but it’s likely that those users weren’t aware that they were forking over their personal emails to Slice Intelligence, a digital commerce analytics company. Now, some users are pledging to remove their inbox accessfrom Unroll.me and delete their accounts.

    The Unroll.me/Uber fury is a good reminder of the ol’ Internet adage, “if you’re not paying for it, you’re not the customer, you’re the product.”

    But some sites are much more egregious than others. So here are some ways you can assess an app’s trustworthiness and find out if your free faves are problematic.

    What does “you’re the product” even mean?

    When you sign up for a free online service, you’re most likely giving up something in return: your data. On sites like Facebook and Google, that means the service uses your personal information (like your interests, location, gender, marital status, or age) to show you advertisements they think you’d be interested in. Last year, Facebook made more than $26 billion from advertising.

    For many people, this sounds like a good trade off: You get to use something legitimately useful, like Gmail, for free, and the most visible consequence is an advertisement. But other companies go much farther. Unroll.me, for example, didn’t use user data to target ads — it looked at individual emails and sent them to Uber.

    And if you found that story about Target knowing a teen girl was pregnant before her father did thanks to extensive customer data collection to be pretty creepy, you should know that that same kind of analytics-based-advertising-influence has probably been exercised on you.

    How do I know what companies are doing with my data? Is it safe?

    Be very careful about what kind of access you give apps. To do that, closely at what you’re agreeing to when you sign up.

    For example, when you sign up for Unroll.me, you’re giving the service the ability to read, send, delete, and manage your email. This is a good time to ask yourself: Does the service really need all of these permissions? Do I trust this service?

  • 30 Mar 2017 6:45 PM | Mark Michelson

    By: Hal Conick, as published in Marketing News

    Think your prospective customers are pissed? You may want to look closer; a recent study finds that the bulk of social media firestorms are retweets. 

    Nearly all tweets that make up a social media firestorm are retweets, according to a researcher at the AMA’s 2017 Winter Conference. 

    Kimberly Legocki, an adjunct professor at California State University, says 95% of social media firestorms are made up of what she calls “raging retweets.” These accounts are quick to retweet or post once, then go away entirely. 

    More active consumers during a firestorm are those who want change but do not angrily express their feelings. Legocki says these are people who are very logical and well-spoken but tend to be overlooked by brands who are inundated by angry tweets. After a specific firestorm, these accounts tweet 7.2 times on average. 

    “I worked for brands and we never paid attention to that,” she says. “It does look like a firestorm, but people go in just once or twice and then out.”

    A firestorm, she says, is related to a sense of outrage that a company has violated a social norm. 

    “When we [consumers] tweet [at] a brand, we expect them to respond within an hour,” Legocki says. “Our expectations are changing. … We know that if righteous anger triggers a response, perceived injustice will trigger righteous anger.”

    This anger, she says, is what will determine which action is taken by a consumer, including what to post, how often and when.  

    Legocki studied 10,000 tweets from four different events–including Wal-Mart selling child-size Israeli soldier costumes and McDonald’s sponsoring school nutrition programs–using indicators of anger, including expressed disgust and the word “f---.” All firestorms had similar beginnings and endings, she says, and all had the same results: mostly angry retweets. 

    “If you were really angry we expected you’d take the time and make the investment to create that original content,” she says. “We like to retweet the most salacious information, those really attention-grabbing headlines with outrageous words.”

    Key Takeaways

    What? A social media firestorm may look like a lot of very angry people, but one researcher says its 95% retweets. 

    So what? During firestorms, brands seem to ignore the level-headedconsumer who just wants a resolution. 

    Now what? Brands may want to look past the angry tweets and see what well-spoken customers are tweeting, even in the most incendiary moments of a social firestorm. 

  • 29 Mar 2017 6:42 PM | Mark Michelson

    The headline for Thom Wheeler’s op-ed piece in The New York Times today says it bluntly: “The G.O.P. Just Sold Your Privacy.” Wheeler is referring to the House vote on Tuesday, blocking FCC privacy rules passed during the Obama administration.

    This rollback would allow cable firms and wireless providers to exploit your “browsing history, shopping habits, your location and other information gleaned from your online activity” any way they want, writes Wheeler, the former chairman of the Federal Communications Commission.

    We’re sure he’s right. But consumers won’t be the only victims of this foolish piece of deregulation: The real losers will be brands that market online. They have just lost control of their own data.

    Nobody has analyzed this yet, but here’s one possible scenario, based on historical precedent. There was a roaring controversy years ago about American Express using data from the transactions it processed to send catalogs and other product offerings to cardholders.

    The argument took place behind closed doors, and memory has faded on some points. But American Express was competing with its own clients -- direct marketing companies that accepted the AmEx card.

    If  L.L. Bean was selling jogging shorts, American Express could see that and offer jogging shorts (a hypothetical case). Obviously, it didn’t go over well: Catalogers argued that these consumers were their customers.

    The program went away. But fast-forward to the digital age. The ISPs and telecoms will now be in a position to do the same thing. They can take the behavior prompted by your seven- or eight-figure marketing budget and use it to peddle data.

    In short, they’ll be getting a free ride on your marketing spend — on SEO, email, mobile and display. It will end up in court, and there will be no easy political formula for judging it. And even if the broadband providers cut deals with you, it will be an attribution mess.

    What’s next? Will credit card processors also have the right to sell your sales data?

    The next problem is even bigger. The privacy theory in Europe (and much of the rest of the world) is based on affirmative opt-in, not a dubious opt-out. As Jess Nelson reported in MediaPost on Tuesday, Flybe and Honda and were hit with fines in the UK for sending “spam.” But if we’re reading it correctly, those emails would scarcely cause a ripple in the U.S.

    Martin Abrams, executive director of the Information Accountability Foundation, told MediaPost last fall that “you can think with data and draw insights in the U.S. That’s a competitive advantage because thinking with data is an unregulated activity. Outside the U.S., you have to have a justification even to process data.”

    Clearly, we’re going against the European rules. Is this U.S. marketer’s version of Brexit? 

    So just what rights will U.S. consumers have under this scheme? Sen. Jeff Flake (R-AZ) asserted on TV last night that people can opt out. (He didn’t seem too sure of it). But another article in today’s Times states that broadband providers “today let you 'opt out' of using their data, although figuring out how to do that can be difficult.”

    The article adds that the “digital rights group Electronic Frontier Foundation suggests you might pay to use a virtual private network, which funnels your internet traffic through a secure connection that your provider can't see into. But good VPNs aren't free, you have to figure out which ones you can trust,” he concluded.

    Meanwhile, you the consumer can forget about the line between anonymous digital data and personally identifiable data. As Abrams has said, “it’s inevitable that the shadow you and the real you will come together.”

    But let’s be fair. The Obama-era FCC rules had not yet taken effect, so Republicans are arguing that nothing has changed. They’re right — it’s a wash. What has been altered is the bipartisan accord that existed on the privacy issue. It wasn’t always good for marketers — even the pro-business GOP took a strong privacy stance. But it was consistent. Well, no more.

  • 6 Sep 2016 5:56 PM | Mark Michelson

    BY KIT SMITH ON SEPTEMBER 6TH 2016

    Published on Brandwatch

    Social media research can surface consumer insights that can be difficult and expensive to find in any other way. The volume of conversation on the web gives this research method a unique ability – uncovering qualitative insights on a quantitative scale.

    For many people, however, it can be daunting. There is so much data: millions upon millions of conversations happen online every day. If you don’t know how to refine what you are listening to, you’ll drown in the data. 

    It’s also easy to rely on the metrics that are constantly produced on social: likes, followers, fans and so on. Knowing how to cut through the noise to find the information that can drive business decisions is a real skill. I recently spoke to Bex Carson, Head of Brandwatch’s Research Services team, to get an insight into best practice when it comes to social media research.

    Developing an approach to social media research

    Uncovering insights requires a certain approach. You need to move beyond the simple social metricsto uncover robust insights that can recommend a real business action. The problem can be knowing who to listen to, how to spot a trend, knowing what’s significant and putting your findings into context.

    Many people will start with the metrics they know about. They then think about the questions it is possible to answer with those metrics, and develop a research plan based on that. A good social intelligence tool has the flexibility that renders this way of thinking very restrictive – it starts with what was already known and therefore limits the possibilities of what can be discovered.

    Ask interesting questions

    The foundation for good social media research is asking the right questions. If you start with a bad question, you’ll receive a bad answer.

    Start by forgetting any concerns about how you are going to conduct the research. Focus on the problems and questions you need to answer, without thinking about the methodology. Once you have the question, you can work on the methodology.

    The questions need to be specific. They need to be able to deliver an answer that can be acted upon. Examples of this sort of question might be “What do women in their 50s want from a fashion brand?” or “Is our content resonating with our target customers?”

    Spend time developing questions, and brainstorm with others where possible. Consider the following:

    • What do we want to be able to do that is different based on this research?
    • What capacity/power/authority for change does the reader of the report have?
    • How do I recognize success? What does good look like?
    • What do I already know about this subject/audience?
    • Is my question answerable within the timeframe available?

    Do better analysis

    There are two main approaches you can take to answering your questions. The first involves uncovering metrics that answer a defined question. The second is more of an exploratory approach, discovering insights as you work through the data. Where time and budget allows, a combination of the two approaches will generally reveal the most interesting and robust answers.

    Structured and planned analysis

    For this type of research, you are looking for an answer to a specific question. You need to identify specific metrics that you can bring together to answer that question. By using the power of segmentation – Brandwatch’s RulesCategories, and Tags – you can arrive at specific answers.

    This method is great for answering defined, objective questions. For example, if you have run a campaign and want to know if it drove spontaneous awareness, you can look at the volume of discussion for both the campaign assets and the brand baseline over time. These relatively simple queries will result in metrics that uncover the success of your campaign.

    Follow the breadcrumbs analysis

    This approach is exploratory, listening to the data to discover the story within it. Think of it as social media ethnography. You can arrive at a fuller understanding of your consumers, uncovering different audience groups as you move through the data and spot trends.

    This methodology can be a challenge. You need to enter the project without preconceptions and ensure you don’t draw your own conclusions. It’s a harder way to conduct social media research – using open listening to find underlying themes.

    The benefits are plentiful, though. First, you are closer to the voice of the customer. There is no substitute for listening to your customers verbatim, understanding the nuance and sentiment of their conversations.

    As a human analyst, you can understand and categorize things that a machine simply can’t. Someone might be talking about the same topic even if they don’t use the same words. Reading through the conversations, you can find people might talk in a way that you hadn’t thought of, and use that to enrich your research.

    1. Decide on your dataset

    Broad topics and themes often drive more interesting results than brand queries. You want to analyze of a type of conversation rather than a particular brand. Within the conversation theme, you still need to define the problem clearly so you can create a targeted query and reduce noise in the data.

    As an example, let’s say you wanted to know what people who have seen the movie thought about the new Ghostbusters film. If you just wrote a query on Ghostbusters, there would be a lot of conversation around the all-female cast, or the abuse that Leslie Jones received on Twitter.

    By writing a query that includes personal pronouns, mentions of cinema brands, people saying “just seen/watched” and so on, you will have a much cleaner data set without manipulating it too much.

    2. Clean your data

    This can be a really important way of revealing the interesting, more concealed consumer insights. Many topics can be dominated by a few obvious and popular themes, so that the insights get lost in comparison.

    You want to surface underlying themes with a unique value, rather things you can see easily on a topic cloud/trend line. To do this, you can look at you initial query results to identify the popular themes, and then exclude those mentions by using tags.

    The data that remains lets you see what else people are saying beyond what you already know. It can also be useful to remove retweets, leaving you with only the original conversations.

    3. Take a random sample

    While automation can be a useful tool, the type of analysis we are talking about here is human led. This means you need a manageable dataset. It needs to be large enough to be representative of the mentions, but small enough so it can be read completely, and in detail. Mark off the mentions as you work through them to ensure you cover your whole sample.

    This allows you to undertake the next step…

    4. Read and code using grouped categories

    There is no right way to do this step, and it will largely depend on your dataset. This is where you go on a journey of discovery, hunting down the insights in the data. You want to start grouping themes and topics.

    You can start by creating categories that you think you are going to find. To go back to the Ghostbusters example, you would expect some people to say they “loved it”, others that “hated it”, and everything in between.

    It’s important to remember that this is just a starting point. You need to be completely data-led. New themes will emerge as you work through the data, so you need to add these.

    Human analysis allows you to have nuanced emotional categories, such as anger, frustration, humor, joy and so on. A human analyst is able to pick these up where automated processes would not be able to.

    You can also set up categories for author type, to understand the different conversations among different actors.

    Brandwatch allows you to group categories together, so each emotional or author response can form a sub-group within a parent group. This can prove useful during your analysis.

    Crucially, you want to liberally add categories as you read through the data. At the end, you might find there were only a couple of mentions of one category and decide to delete it. It’s better to do this than be conservative and realize you missed an important topic of discussion only once you reach the end of the dataset.

    5. Analyze and chart

    Now you need to dive into the charts and start analyzing. 

    The best way to start is to simply create a standard chart component and apply filters to the data. You can start looking at your different categories, moving the data around to spot interesting patterns.

    Some of the most useful insights come from crossing your parent categories. So if you have author type on one axis, and emotion on the other, you can start to see different emotional responses from different author types.

    Golden rules for social media research

    1. Compare everything and constantly seek out difference

    The fundamental tenet of social media research is noticing a difference between things. You don’t know what good looks like without seeing the bad, but you are looking for a significant difference. You don’t want to recommend sweeping changes because of a tiny difference in the data. Be curious and keep exploring.

    2. Be curious and ask why

    If you notice a difference, start digging into it. Then dig some more. Keep digging until you have read something that helps explain why that difference exists.

    Presenting your social media research

    First, remember that you are telling a story. With any story, you need to consider your audience.

    There may be several people or departments that are going to read your report. They won’t all have the same amount of time available to read what you have written, and they might not all be interested in the same things.

    While it’s a good idea to start with a methodology to give the report transparency, hit them with the key findings and consumer insights straight away.

    Then, as you continue through the story of the data, you can explain each insight more thoroughly with charts, graphs, and analysis to back it up.

    Having insights buried in a 27 page PDF is frustrating and risks your hard work being overlooked.

    Finally, remember that you’re not really telling your story; you’re telling the story of the people you’re listening to, so make sure to include the voice of the customer. Real examples of social posts, with real customer profile pictures, will bring your social media research to life.


    KIT SMITH

     @Kit_Smith 

    Kit is a writer and marketing expert. When he's not researching ways to make you better at said marketing, he's often lost in foreign countries, or making pottery (or both).

  • 11 May 2016 6:07 PM | Mark Michelson

    From Oxford Economics

    Social media is a critical communications channel—for your business and ours. But at Oxford Economics, social media is more than a means to get the message out—we use it as a research tool to gather insights and perspectives, and to add another dimension to our research.

    It begins with basic research. At the start of every research initiative, we mine the social networks and comb the web to identify the key experts, influencers, and decision-makers, to determine the “edges” of current thinking on a particular topic. Drawing on the latest search technology, we are able to collect, review, and compare recently published articles, papers, and other research on the topic. By doing our homework up front, we ensure that our research programme is fresh and distinctive.

    Our alliance with Onalytica, a specialist in web intelligence and sentiment forecasting, enables us to provide a real-time snapshot of the viewpoints, attitudes, and concerns of millions of market influencers, as well as a detailed understanding of the underlying drivers and emerging market trends. These efforts allow us to uncover market shifts and new perspectives not detected through traditional research approaches—helping us craft truly ground-breaking thought leadership.

    We engage with our network of thought leaders, experts, executives, influencers, and other stakeholders through a variety of efforts, including blogging and micro-blogging, interactive forums, and spot-polling to gather insights, perspectives, and opinions that critically shape our research. This allows us to take advantage of the interactive nature of social media—engaging respondents in multi-party conversation that supports and adds weight to the quantitative aspects of our research.

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