Handling Social Media – Part 2: The Understanding

Friday, August 13th, 2010

By Fernando, Lead Evolisten Engineer

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So, you think you can handle Social Media? Great! Now what?

Well, let’s start at the beginning. What specifically is Social Media? Bypassing the fancy definitions, we can say that it’s a bunch of people coming together to talk and stuff, but not in person. The communication takes place through some sort of technological thingy, usually the Internet. The obvious examples are the ones that everyone has heard about, like facebook, twitter and all the other usual suspects (they’re only the tip of the iceberg by the way).

So, you have decided to start with twitter. You create an account and jump in to see what’s going on in there. You’re going to listen to the people, read their tweets and make everything better! After a while, you will find the first problem: there is a heck of a lot going on twitter. Like really, A LOT! You can read tweets for hours, until your eyes hurt, and get nowhere. Just in there, there are thousands upon thousands of relevant tweets to check out, hidden among millions upon millions of other stuff you don’t care about. And this is just one site. What about other popular sites? What about the whole Internet? You’re going to need some help.

Maybe hiring a couple more guys would be enough? Not really. Hiring a dirty dozen? Not really. Hiring a few thousand people and providing them with training, management and equipment? Maybe that would work, but that sounds awfully expensive. This is not going well.

ppdigital from morguefile

And it gets worse. It’s just not a matter of finding the content and reading it, that’s only the beginning. The real work comes after that. What’s the sentiment of the content? What are they talking about specifically? What’s valuable and what’s spam?

And, what if you really want to do things right and go all researchy on it. What are the demographics of these people? What about sampling and weighting? What about other stuff you don’t even know about? This is a lot of work, and you’re definitely going to need some help. Help with expertise.

Luckily, there’s one positive thing going on for you: we’re in the future! It’s the year 2010, the 21^st century! And we may not have flying cars yet or robot butlers as they promised us, but one thing we do have: information processing power and people who know how to use it. If you’re reading this blog, then you know by now that you’re not the first one to think about taming the Social Media beast. People are out there already doing this, already solving all those problems, and coming up with cold hard numbers and data that you can use to improve things. I know it because I’m one of those people.

It’s a whole new ballgame out there. Things are changing fast. Can you adapt and thrive? It is your choice. The help you need is already out there.

Social media research… The final frontier

Monday, August 9th, 2010

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How important is sampling? Well, how important is gay marriage?

Sunday, August 8th, 2010

Are you in favor of gay marriage? Are you against gay marriage? One way or the other, many people have extremely strong feelings towards this topic. It’s a topic that has the ability to quickly divide us and turn normally civil people into very angry people.  There is almost no room for error.

But let’s take a quick step backwards. When a data provider gathers online conversations, or crawls the internet searching for conversations about a topic, it is impossible for them to gather every single conversation. We can’t all be Google but we can gather very large  samples of conversations. We can crawl blogs and microblogs and forums and consumer websites, searching as much as we can for relevant conversations.

Let’s say I gather my sample of gay marriage conversations by focusing on certain websites. You gather your sample by focusing on certain websites. Someone else gathers  their sample focusing on certain websites.  Though we MAY all touch the same websites, we touch them all in very different ways. The internet is one single giant database, but we now have three different completely collections of conversations about the identical topic. What are the consequences?

In a previous blog, we saw that Twitter data is unlike other online data. Twitter has much higher highs and lower lows, likely because twitter more closely resembles mouthing off, spur of the moment, off the cuff remarks.

Twitter is just one of several very popular microblogs represented by the red “Micro” line in the chart. Look at how the positive emotions towards gay marriage range from a high of about 22% of conversations in April to a low of about 1% in May.  If you had gathered a sample of conversations about gay marriage that focused heavily on microblog data, you would think people’s online opinions about gay marriage are all over the place.

(Please note: This blog only shows positive emotions. It does NOT show the % of opinions that fall in the neutral range or the negative range. Do not interpret the 1% positive to mean the other 99% is negative.)

We know that just looking at Twitter or microblog data is not a fair measure of online opinions. So how should we measure online opinions towards gay marriage? Should we let opinions from blogs count a lot more because they are well thought out  (the light blue line) or should we let blogs and microblogs contribute an equal amount toward the overall opinion (green line)? Should we let the data fall however it wants to fall (purple line) or should we make sure that each website contributes an even or consistent amount of results each month (black line).

What is clear from this chart is that the way you gather conversations from the internet determines what your results will be. You can create a more positive or more negative average opinion easily enough through careless or unthoughtful sampling.

We’ll let you ponder which method of sampling produced the correct answer, and whether we’ve even provided the correct answer here. (We haven’t.) But the conclusion to draw from this demonstration could not be more serious or important. If you’re going to tackle social media research from a social policy point of view, you had better be an expert in sampling.
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[Method: Over 30 000 opinions gathered from thousands of websites, processed, cleaned, validated through Evolisten]

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Related links
Quirks Magazine: Thoughts on sampling and weighting in social media research
Tracking the mood of Americans: Use Twitter if you want to prove they’re happy

The official end of the neverending qual vs quant debate

Friday, August 6th, 2010

It’s a question that tugs at the heart strings of many researchers. Are you a quant person or are you a qual person? You must choose only one, and you must choose wisely, because it will define who you are as a researcher for the rest of your professional career.

Your decision will determine which market research methodologies you are permitted to use.

Surveys – Just for quant people
Focus groups – Just for qualis (like @BenSmithee and @LongoMR)
MROCs – More stuff for qualis
SMR  – Now we’re stuck…

Actually, there is no such as thing as a researcher or a method that is just quant or just qual. When was the last time you analyzed survey data and didn’t read a single verbatim? Reading those verbatims and trying to organize them into meaningful pieces of information counts as qual. When was the last time you put together a focus group without regard for who or how many participated and then proceeded without a carefully designed discussion guide? Counting those people, counting those opinions, measuring the intensity of those opinions counts as quant. Certainly every research method, whether quant or qual, has a leaning, but every method dips its toes into each bucket, sometimes more deeply than other times.

The same holds true for social media research (SMR). Your personal style probably has you leaning on the quant side or the qual side but SMR is almost by definition a beautiful combination of the two. For those with qual leanings, you can individually review hundreds or thousands of online conversations, sort them, categorize them, and treat them as you would any qualitatively focused study.

For those whose personal leanings are in the quant direction, you can take advantage of systems which transform hundreds, thousands, or millions of verbatims into datasets that look surprisingly the same as what you would generate with a survey. Box scores, percentage scores, norms, crosstabs, and t-tests are all just waiting to be admired.

Even better, imagine the insight, yes the dreaded word insight, that you would achieve by letting both your quant and qual sides show. A qualitative approach to quantitative research. A thorough, individualized analysis of social media data subsequently quantified using standardized procedures for maximum generalizability.

How nice it is to lean both ways.

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Related links
A 300 year history of text analysis
Goodbye static, Hello dynamic

Norms: Striving to beat the worst of the pack

Thursday, August 5th, 2010

Norms are among the most commonly used and sought after features of market research. They are a great way to compare how your brand is doing relative to other brands in the same space. You need them to see where the entire market place sits relative to your brand. You need them to see in which areas the entire industry is weak, areas where perhaps you could strive to excel. You need them to see where the entire industry is strong, areas where you must ensure you do well.

Most companies have spent years and decades creating enormous norms databases that allow any brand to instantly determine where their brand fits in without having to expand their research dollars to 10 or 20 competitive brands outside of their own brand. There is no doubt that norms are an essential part of our work.

But here’s something to ponder. What exactly is a ‘norm’?

Norms reflect the average of many brands. It’s a watering down of the fabulous brand in the upper 2% of the normal curve, the great brands in the upper 16% of the curve, and the 2% and 16% of brands at the bottom of the curve, those that desperately need to pull up their socks. Norms reflect the good, the bad, and the average, unassuming, nothing special brands.

Are those really the brands you want to compare your brand with? Is your ultimate goal to tell your boss that your brand is achieving consumer satisfaction scores that are better than all of the really terrible and somewhat terrible brands? Congratulations, our brand is beating all of the brands that went out of business last week! I suspect not.

Instead, is your ultimate goal to be the Coke of soft drinks, the Starbucks of coffee shops, or the Apple of computers? Wouldn’t you rather strive to beat the brands in the top 2% of the curve? The brands that are stealing market share and consumer awareness and consumer engagement? Wouldn’t you rather tell your boss that your customer satisfaction scores are better than the top contenders in your category? I suspect yes.

Perhaps market researchers ought to focus first on the category leader and then see how the losers are doing.

We would love to hear your thoughts on norms! Please share your comments.

Quirks: Thoughts on sampling and weighting in social media research

Wednesday, August 4th, 2010

Social media research has officially burst onto the scene and we are pleased that Quirks has devoted the entire August issue to it. Included in this month’s magazine is an article on sampling by Annie Pettit, our Chief Research Officer. The abstract follows and if you want to read more, simply click here and head on over to page 42.

Article Abstract
When analyzing data from social media, the choice of a sampling plan depends on the purpose of the research and how those results will be used afterwards. The author discusses three options and some of the issues surrounding the use of data from social media.

Article ID: 20100806

Battle of the Brands: Homer Simpson vs Dunkin Donuts

Wednesday, August 4th, 2010

Today’s Battle of the Brands is inspired by @SurveyGizmo

Using only the thousands of social media opinions generated by their fans as their weapons, we have analyzed, samplized, sentimentalyzed, and contentalyzed Homer Simpson and Dunkin Donuts. Whomever wins the most matches will be declared the victor. Let us begin.

Cool factor:
Even though Dunkin Donuts scores 27% positive, Homer proves that wearing the same clothes every day of your life is even more cool. Homer beats Dunkin by a mile with a score of 45% positive.

Favorite:
Favorite cartoon or favorite donut, you simply must choose one. And a 52% positive score for Dunkin Donuts (watch out Tim Horton’s and Krispy Kreme!) means it beats Homer’s 38% positive.

Funny:
Wow. Donuts are funnier than Homer! Dunkin Donuts scores 33% positive while Homer only generates a 26% positive score. Looks like Dunkin needs to keep up their great sense of advertising humor and Homer needs another Spiderpig!

Intelligence:
Though a donut has no brain, the people have spoken and decided that Dunkin Donuts has more business smarts than does Homer. The donut shop scores 22% positive while a cartoon character with a cartoon brain only scores 16% positive.

Reputation:
While Dunkin is a shining example of a retailer with a great reputation, Homer is a shining example of  why reputations don’t always matter. Dunkin Donuts scores a fabulous 55% positive score while Homer scores stunning 61% positive score.

Overall emotions:
Mmmm donuts, I guess we have our winner! Dunkin Donuts scores 30% positive while Homer scores 25% positive. I could sure go for a Boston Creme donut right now!

Though Homer Simpson is usually the one eating the donuts, this time, good ol’ Dunkin showed Homer who’s boss!

Next Battle of the Brands? Completely up to you. Leave your requests in the comment box!

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Related links
Battle of the Brands: Angelina Jolie vs Bacon
Homer Simpson and Dunkin Donuts on Evoplay