KDPaine's PR Measurement Blog

Why you should never trust a computer (or at least Nielsen's)

In case anyone was wondering just how inaccurate Nielsen's automated sentiment analysis is, just read this!

Another sign that it is then end of the world as we know it..

One more word the next generation won't know the meaning of: "Channel" according to Nielsen

Is it the end of Market Research as we know it?

Pat LaPoint has an interesting perspective on the future of Market Research.  I've wondered the same thing myself.

Since I started working in measurement and market research, we've essentially seen phone surveys come and go as viable sources of information, and my prediction is that we will see the demise of the email survey as an effective measurement tool within the next five years. And, while I agree with Pat's point about biometrics and marketing mix modeling, I wonder what will be available for the small to midsize companies?  Maybe that's where content analysis will provide the answer.

There are more important metrics than ROI

Love this quote from the new CMO at Dell from her interview in Adweek

Can social media scale and be measurable?
I think it can and has to. We have 2 billion conversations online a year. If I can't scale to the requirements, I can't continue to be a thriving business because that's increasingly how customers are shopping and engaging. It's not even an option not to scale, but it's to figure out how to do it around the customer. We're less focused right now on attributing ROI to everything we're doing. We're focused on "does it enhance the customer experience?" Does it look like revenue and profit are following? I'm less interested in measuring cost per contact. I know engagement with a fan on Facebook is a really good thing. I don't measure if that shortens the sales cycles. The things I can measure I feel great about. Those I can't I'm not losing sleep about.

via www.adweek.com

Social Media ROI Has Come of Age, Says Society for New Communications Research| Tekrati Research News

Measuring results from social media has come of age. Not long ago, social media ROI was little more than counting eyeballs, blog RSS readers, FB fans, and Twitter followers. Today, smart companies are measuring social media ROI against clear business objectives such as customer engagement or revenue, according to Katie Delahaye Paine, a senior fellow and advisory board member of the Society for New Communications Research (SNCR) and the legendary founder of KDPaine & Partners LLC. She'll share actionable measurement strategies and reference cases during NewComm Forum 2010 this week in San Mateo, Calif. Seats are still available; register with Tekrati discounts now.

via industry.tekrati.com

If you can't make it to New Comm Forum, this is pretty much what I'll be saying

Measuring Word of Mouth

We all know that Word of Mouth is important  -- influencing between 20 and 50% of all decision, depending on whose research you're reading. Probably the easiest way to measure your "Word of Mouth" campaign is to use coupons. A coupon can easily go viral on Twitter and be spread out to thousands of people in less than a week. If you're using network mapping software, you can evenly see the connections and influencers that are helping spread the coupon around.

Another perspective is offered byJacques Bughin, Jonathan Doogan, and Ole Jørgen Vetvik

in a recent McKinsey newsletter:

While word of mouth is undeniably complex and has a multitude of potential origins and motivations, we have identified three forms of word of mouth that marketers should understand: experiential, consequential, and intentional.

Experiential

Experiential word of mouth is the most common and powerful form, typically accounting for 50 to 80 percent of word-of-mouth activity in any given product category. It results from a consumer’s direct experience with a product or service, largely when that experience deviates from what’s expected. (Consumers rarely complain about or praise a company when they receive what they expect.) Complaints when airlines lose luggage are a classic example of experiential word of mouth, which adversely affects brand sentiment and, ultimately, equity, reducing both receptiveness to traditional marketing and the effect of positive word of mouth from other sources. Positive word of mouth, on the other hand, can generate a tailwind for a product or service.

Consequential

Marketing activities also can trigger word of mouth. The most common is what we call consequential word of mouth, which occurs when consumers directly exposed to traditional marketing campaigns pass on messages about them or brands they publicize. The impact of those messages on consumers is often stronger than the direct effect of advertisements, because marketing campaigns that trigger positive word of mouth have comparatively higher campaign reach and influence. Marketers need to consider both the direct and the pass-on effects of word of mouth when determining the message and media mix that maximizes the return on their investments.

Intentional

A less common form of word of mouth is intentional—for example, when marketers use celebrity endorsements to trigger positive buzz for product launches. Few companies invest in generating intentional word of mouth, partly because its effects are difficult to measure and because many marketers are unsure if they can successfully execute intentional word-of-mouth campaigns.

What marketers need for all three forms of word of mouth is a way to understand and measure its impact and financial ramifications, both good and bad.

Word-of-mouth equity

A starting point has been to count the number of recommendations and dissuasions for a given product. There’s an appealing power and simplicity to this approach, but also a challenge: it’s difficult for marketers to account for variability in the power of different kinds of word-of-mouth messages. After all, a consumer is significantly more likely to buy a product as a result of a recommendation made by a family member than by a stranger. These two kinds of recommendations constitute a single message, yet the difference in their impact on the receiver’s behavior is immense. In fact, our research shows that a high-impact recommendation—from a trusted friend conveying a relevant message, for example—is up to 50 times more likely to trigger a purchase than is a low-impact recommendation.

To assess the impact of these different kinds of recommendations, we developed a way to calculate what we call word-of-mouth equity. It represents the average sales impact of a brand message multiplied by the number of word-of-mouth messages. By looking at the impact—as well as the volume—of these messages, this metric lets a marketer accurately test their effect on sales and market share for brands, individual campaigns, and companies as a whole (Exhibit 2). That impact—in other words, the ability of any one word-of-mouth recommendation or dissuasion to change behavior—reflects what is said, who says it, and where it is said. It also varies by product category.

What’s said is the primary driver of word-of-mouth impact. Across most product categories, we found that the content of a message must address important product or service features if it is to influence consumer decisions. In the mobile-phone category, for example, design is more important than battery life. In skin care, packaging and ingredients create more powerful word of mouth than do emotional messages about how a product makes people feel. Marketers tend to build campaigns around emotional positioning, yet we found that consumers actually tend to talk—and generate buzz—about functional messages.

The second critical driver is the identity of the person who sends a message: the word-of-mouth receiver must trust the sender and believe that he or she really knows the product or service in question. Our research does not identify a homogenous group of consumers who are influential across categories: consumers who know cars might influence car buyers but not consumers shopping for beauty products. About 8 to 10 percent of consumers are what we call influentials, whose common factor is trust and competence. Influentials typically generate three times more word-of-mouth messages than noninfluentials do, and each message has four times more impact on a recipient’s purchasing decision. About 1 percent of these people are digital influentials—most notably, bloggers—with disproportionate power.

Finally, the environment where word of mouth circulates is crucial to the power of messages. Typically, messages passed within tight, trusted networks have less reach but greater impact than those circulated through dispersed communities—in part, because there’s usually a high correlation between people whose opinions we trust and the members of networks we most value. That’s why old-fashioned kitchen table recommendations and their online equivalents remain so important. After all, a person with 300 friends on Facebook may happily ignore the advice of 290 of them. It’s the small, close-knit network of trusted friends that has the real influence.

Word-of-mouth equity empowers companies by allowing them to understand word of mouth’s relative impact on brand and product performance. While marketers have always known that the impact can be significant, they may be surprised to learn just how powerful it really is. When Apple’s iPhone was launched in Germany, for example, its share of word-of-mouth volume in the mobile-phone category—or how many consumers were talking about it—was about 10 percent, or a third less than that of the market leader. Yet the iPhone had launched in other countries, and the buzz accompanying those messages in Germany was about five times more powerful than average. This meant the iPhone’s word-of-mouth equity score was 30 percent higher than that of the market leader, with three times more influentials recommending the iPhone over leading handsets. As a result, sales directly attributable to the positive word of mouth surrounding the iPhone outstripped those attributable to Apple’s paid marketing sixfold. Within 24 months of launch, the iPhone was selling almost one million units a year in Germany.

The flexibility of word-of-mouth equity allows us to gauge the word-of-mouth impact of companies, products, and brands regardless of the category or industry. And because it measures performance rather than the sheer volume of messages, it can be used to identify what’s driving—and hurting—word-of-mouth impact. Both insights are critical if marketers are to convert knowledge into power.

What this says is that more than ever, what we do and what we measure isn't just "PR" or "Social Media" -- but customer opinion, customer behavior and its impact on business.

SAS now free to higher ed

This little announcement may have gotten lost in the hubub surrounding SAS Social Media Analytics launch, but in many ways, it is no less significant.  This is a huge benefit to anyone who loves data and research in higher ed, and I strongly urge my colleagues in the Journalism schools to take advantage of it.

KDPaine finally finds an automated analysis tool she can love..

My involvement with the SAS Social Media Analytics tool,  began with a simple question asked on the roof of the Kennedy Center in Washington, DC at your typical  post-presentation cocktail party.  Diane Lennox, in charge of PR measurement at SAS, asked a simple question: ‘Would you consider working with us to use our product in our measurement system?’

I of course agreed,  knowing that I’d always lusted SAS's their analytical tools. Little did I know at the time that they were working on an automated text analysis system. But back then, we also didn’t know what social media would do to the measurement business. Since then, we’ve watched the system develop into an incredibly useful integrated measurement system that is truly a game changer in the industry.

So why am I so excited about this particular solution?  SAS SMA offers all the standard features of social media monitoring platform with several  important points of differentiation:

1. It’s integration of traditional and social media and web analytics into one seamless application. So now when someone says "did adding Facebook toour launch mix work? We can now say yes, not only did it impact the pickup of our key messages, but it also increased web traffic by 22%.
2. It’s accuracy level -- over 90% is better than most humans can do, and orders of magnitude over anything I've seen in the industry.
3. It’s ability to deliver “phrase clouds” as opposed to word clouds which are much more useful when trying to analyze a marketplace.
4. The potential to integrate the data with business analytics that SAS is known for like CRM and Marketing Mix Modeling.

I'll be adding to this more as it happens, but got to go answer a few questions live

Update as of 10 am PST.

A few other important point to make about why I'm now such a fan of SAS's social media measurement solution.

To me a big differentiator is SAS itself. SAS is a company that provides data to make better business decisions -- just like KDPaine & Partners. Their philosophy is that data alone doesn't provide answers, it is the interpretation of the data and the degree to which that data is customized to the specific business goals of your organization that makes it valuable. So unlike most other organizations they don't start by talking about the tool, they start by identifying the drivers to business success and the goals and strategies that will drive results. THEN they begin to identify the words, phrases, issues and problems that need to be analyzed. THEN they develop a taxonomy. THEN they test that taxonomy against how humans would interpret it. When they get to a sufficient accuracy level, then they deploy. I know of no other company other than my own,that invests that much upfront time to get things right.

On the back end, they have the tools to help make sense of that data. Either the CRM application (and yes, they already integrate with Salesforce) to help manage your media; the web analytics to deterine the impact on revenue or efficiency (i.e. cost per customer acquisition or cost per download or click thru) or the financial analysis tools to figure out impact on market share and earnings per share.

Then there's the whole competitive intelligence aspect. Most of my clients want to know where they fit in any discussions about their industry. They all know that they need to be tracking topics and issues, not just their brand name. At best they can pull in discussions about the competition, but few have the budgets to accurately analyze the entire industry. Now that is not just possible, but eminently doable.

To sum up, when I started KDPaine & Partners 8 years ago, I had in mind a system that would somehow, (I didn't know how at the time) connect the dots between outputs and outcomes. That's what SASSMA does. I can't wait to share with you the case studies. 

 

Once again, a dream deferred

For years, clients have been asking me for the ability to predict outcomes based on exsiting data. When I read this abstract, I thought that maybe all my dreams were going to come true. But, since I don't have a PhD in research, I checked it out with some of my friends who do. And sadly,  we're along way from any magic bullets here.

Tina McCorkindale who has done some wonderful research into the Fortune 500's use of Twitter and Facebook (stay tuned for a synopsis of her latest)  gave me this perspective:

 

 I appreciate their efforts trying to predict sales based on sentiment, but I found so many flaws in the analysis. First, they generated 2.8  million tweets to analyze using a computer model . There’s all sorts of flaws in using computer analysis especially analyzing sentiment (not to mention the unreliability of even accurately searching for the movie titles). Next, their hypothesis that most of the tweets prior to release of the movie should be promotional and stop, followed by positive and negative tweets I disagree with as well because they didn’t even look at the content of the tweets, but a computer did. Plus, they didn’t have any research to back this up. Some people may say they are going to the movie or they heard it wasn’t very good (there’s all sorts of error entered). Also, the sample size is entirely too small (as far as the number of movies) and there could be other factors as well such as a high holiday time. I can’t imagine there being an enormous amount of tweets about Twilight (or even draw an analysis) because the movie is geared toward tweens and teens, who aren’t really even on Twitter. So I think for some of the movies, Twitter probably isn’t the best source because it’s not the movie’s target audience. I can also see drawing a parallel with the news media analysis, but it’s an entirely different ball of wax. It’s just flawed in the definitions, methods, analysis, and conclusion. I really liked the attempt and I think it’s a great way to measure, but I think they went about it all wrong and drew conclusions when they shouldn’t have.

    Thanks Tina, I couldn't have said it better myself.