Red Door Speaker Series: Digital Measurement

Posted on 2011-04-26 by Michele Kiss

On Tuesday 4/12 (San Diego) and Thursday 4/14, Red Door Interactive held our regular Speaker Series. This time, the topic was one dear to my heart: digital measurement.

We had some fantastic speakers agree to be a part of our San Diego and Denver panels, and a huge thanks goes out to them:

San Diego panel:

Katie Grisko, Web Strategy Manager, Petco Animal Supplies, Inc
Matt Lillig, Web Analytics Strategist: Americas, Yahoo!
Michael Whelan, Manager of Marketing Analytics, Qualcomm
Rudi Shumpert, Senior Director Technology Solutions Group & Principal Consultant, Keystone Solutions

Denver panel:

Alison Kane, SVP & Chief Digital Officer, The Denver Post
Rudi Shumpert, Senior Director Technology Solutions Group & Principal Consultant, Keystone Solutions
Nancy Koons, Senior Web Analyst, Vail Resorts

Topics

Our two panels discussed a wide range of issues related to digital measurement, including:

Their definition of “success” and focus of digital measurement practice in their organisation
Challenges and frustrations of digital measurement
Measurement and planning strategies for new channels (for example, mobile and social have been key of late)
How they cope with the feeling of “drowning in data”
The challenges of data silos and getting a more holistic view of the customer
How to sell a data-driven idea internally
Educating business users as to what the analytics team can provide
Where to focus additional investment in digital analytics. For example: Tools or people? (Hint: the resounding focus was on people!)
Online privacy concerns and the impact to digital measurement

Some great takeaways from @reddoor on Twitter:

The biggest challenge we’ve seen is lack of talent out there – @RudiShumpert #reddoor
It has more to do with the people than the tool. We do alot of training and mentoring. – @RudiShumpert #reddoor
Know what you need to focus on before measuring. – @mowineguy #reddoor
The executive level backing is key. – @mattlillig #reddoor
You have to go to the executives with a story. Charts and data aren’t enough to make a business case for analytics. – @RudiShumpert #reddoor
RT @nataliewardel: The takeaway I didn’t expect: it’s all about the people on your team. Utilize quality personnel #reddoor
the tools are only as good as the people analyzing #reddoor #measure
one of the challenges is connecting the dots between social and site – ‘the last mile’ #reddoor #measure
there’s a paradox of drowning in data, and discovering you need another signal #reddoor
Privacy concerns? Review the WAA code of ethics:http://www.webanalyticsassociation.org/?page=codeofethics #reddoor

A “Thank You”

Thank you to our great speakers for finding the time to come and contribute to the event, and a special thanks to Rudi Shumpert for traveling so far to be with us!

And while you’re at it, check out Rudi’s recap on the Keystone blog.

Pictures

Speaker Series Panel, San Diego

Speaker Series Panel, Denver

More photos are available at the Red Door Flickr account

A heartfelt thank you

Posted on 2011-03-17 by Michele Kiss

On Tuesday, 15 March 2011 the WAA held the inaugural Awards Gala. (For the record, it’s pronounced gah-la. Not gay-la.) It was a wonderful event – a chance to spend time with the lovely people in this industry, and make new friends. The Gala was, in my opinion, a hugely successful experience – I can’t wait to attend the next one.

As a part of the Gala, the WAA handed out the very first Awards of Excellence. The categories were:

Client/Practitioner of the Year
Most Influential Agency/Vendor
Most Influential Industry Contributor
Web Analytics Rising Star
Innovator/Technology of the Year

I myself was so incredibly humbled just to be nominated for the award of Web Analytics Rising Star, let alone become a finalist. Those two alone seemed to good to be true.

Imagine my shock to win …

So, from the bottom of my heart, thank you.

Thank you first and foremost to the kind soul who even thought of nominating me. Thank you for the WAA members who kindly voted for me to be a finalist. And thank you to the Awards judges, who made such a difficult choice from an amazing list of finalists, who are all so deserving.

I have been working in web analytics for a few years, but really only got involved with the community last May. I have loved every minute I’ve spent getting involved with the WAA, the Analysis Exchange and #measure. I learned more in six months than I had in years prior.

So all I can say is thank you. I really, truly love this community of amazingly smart people. Thank you for welcoming me so generously into it. Thank you for letting me learn with you and from you. Thank you for the time we’ve spent discussing, debating and encouraging each other.

Congratulations to the award nominees, the award finalists and the award recipients. You are what makes this community such an amazing thing to be a part of. I am humbled and grateful for the award, but more thankful still to be a part of such a wonderful community.

Statisticians + Web Analysts = Awesomeness

Posted on 2011-03-11 by Michele Kiss

One thing I have found can work very successfully is a hybrid team of web analysts and statisticians. When you combine the business and website knowledge that the analyst has with the “mad stats skills” that the statistician brings, you can create some truly powerful work.

There are a lot of different things that a web analytics team can leverage a statistician’s help for. This is by no means an exhaustive list, merely a place to get started.

1. Significance Testing

So you’ve run an A/B or Multivariate test. While your testing tool will likely also advise of you the statistical significance of your results, a statistician can dive deeper into this, and help you to measure significance outside of your tool. Perhaps you noticed shifts in site areas that weren’t one of your test success measures – a statistician can help you decide if these are merely interesting, or statistically significant.

Or perhaps you’ve tested in more of a time-series fashion. A statistician can try to tease out whether the change had an impact, or whether changes are due to seasonality. (This relates closely to the idea of an Impact Analysis.)

2. Impact Analysis

You make a site change, and you notice an increase in visits to a site area, or some key metric. You’re tempted to attribute this entire shift to the site change. (“Woo hoo! We’re up 5%!”) However, what about changes in marketing spend? Seasonality of your site traffic? Social initiatives? Are you taking those into account before reaching your conclusion?

A statistician’s analysis can attempt to tease out those additional variables to estimate the impact of the actual site change, vs. these confounding variables.

This same approach can be used to measure the impact of industry events or company changes (outside of the website) – anything, really. The benefit here is a better understand of the actual impact of events or initiatives, but a nice perk should be presenting your findings to the business and not having to freeze like a deer in headlights if someone says, “Yes but we spent another million dollars in paid search last week – did you factor that in?”

3. Standard reporting automation

Statisticians can use tools such as SAS to fetch data from FTP, combine and compute it, and deliver outputs to your system of choice (for example, Excel, if that’s somewhere you’re comfortable working.) This can allow you to schedule FTP delivery of SiteCatalyst reports, Discover reports, ad server reports (etc) – basically data from multiple sources – have SAS do the work of fetching multiple data sets, combine them and output to Excel.

That, however, doesn’t mean you need to deliver a huge scary data sheet to the business. On top of the data, you can build a more user-friendly view (preferably formula-driven, so that you’re not manually updating!) in Excel to present the data.

This allows you to take a lot of the manual part (copy-paste, copy-paste) of standard reporting out the equation, and focus your time on explaining the shifts you might be seeing in the report. e.g. Perhaps traffic to a specific content area is down – start digging in. What traffic sources are driving it? Are there particular pages experiencing a more dramatic shift?

In addition, once the business sees the value of this work (the time it frees up for analysts to actually analyse!) it may actually help argue for further automation and investment in further tools. So make sure you provide those insights, and use this work to prove why you shouldn’t spend your time copy-pasting.

4. Forecasting

Statisticians can build forecasting models to predict your site traffic, sales, ad impression volume – pretty much anything. You can go short-range, or long-range. Perhaps a simple “forecast through end of month” will suffice to start, or maybe you want to start forecasting three or six or twelve months in advance.

So why would you do this? Well, good analysts know that data needs context. That’s why we have KPIs, or compare month over month, year over year – to understand whether “2.6%” is “good”. Comparing to a forecast can be another way to get context for your data. If you’re diverging from your forecast, you can start digging in to see why. This divergence might be good – perhaps you saw a better than expected responses to your marketing initiatives. But on the flipside, you might also need to frantically search for why you’re suddenly down -10% compared to forecast …

Even a through-end-of-month forecast can be helpful here. An EOM forecast will tell you where you’ll likely end the month, based on current performance – even though you’re only on day 9. This will allow you to course correct throughout the month, rather than waiting till end of month to realise you didn’t match your forecast.

If your business sets site goals, forecasts can be the first step. First, forecast where your business will be for the next twelve months without any major initiatives. Simply assume the status quo. Then, look at the initiatives you want to add on top of that, and assess how much of an impact they may have. Forecast + specific initiatives = your goal. A statistician can also help you look back over time at previous initiatives and analyse their impact, to make sure that you’re not overstating how big an impact something new may have. (How many times have you heard “This is a game changer!” and found it barely moved the needle?)

There are still things you need to keep in mind when forecasting, but even starting small can bring value to your business.

Group Hug!

Still, analysts and statisticians may sometimes face some hurdles. Analysts need to learn the language of statisticians, and statisticians need to either learn the business, or be guided by the analysts. A statistician exploring data with no understanding of the business, the website, or what any of it means normally doesn’t reveal great insights. On the flip side, the analyst really needs to start learning and at least dabbling in the world of statistics, and be able to translate complex concepts for the business users you support.

However, a cohesive team that learns to work together and leverage each other’s strengths can do amazing things.

Don’t have access to a statistician? Students often need real-life data for school projects. Consider seeking one out! (Who knows – you might find yourself a great future employee.)

Social Media Analytics: Moving From Engagement to Measurement

Posted on 2011-03-06 by Michele Kiss

[Originally published at The Review]

It’s no mystery that social media has been the new buzzword of the past few years. However, companies are quickly moving from “Gee, we really should be doing social” to “Now, how do we measure it?” There are a number of ways a company can begin measuring their social media efforts and those include:

1. Measuring the effect of social media efforts within the network itself

2. Tracking social media links back to your website

3. Understanding social media in the context of other initiatives

Measuring within the network itself

Analysis of Impact and Engagement

Step one of measuring your social media initiatives is to measure success within the network. While there are a wide variety of social networks, we’ll focus on Facebook and Twitter as the primary two.

Measurement of Facebook might include monitoring number of fans, fan demographics, fan interaction with posted content (comments, likes), organic fan posts and traffic to Facebook page, or use of a Facebook app. Facebook analytics can come from Facebook Insights, but there are also options to add the code from your web analytics solution to your Facebook pages.

Twitter has its own set of tools. Two popular ones are Klout and Twitalyzer. Klout combines thirty-five different variables into one “Klout” score: a measure of social influence. While the variables behind Klout score are intentionally hidden to avoid “gaming” the system, one downside is that the lack of visibility makes it hard to understand what’s driving your Klout score – or how to increase it.

Twitalyzer, on the other hand, provides transparency into all their calculated metrics. For any compound metric, a user knows exactly what is going into this score. For example, “Impact” is based on number of followers, mentions, retweets and post frequency. What’s more, Twitalyzer provides users with data-driven recommendations for how to increase their scores.

Other Twitalyzer measures include number of followers, number of lists you are on, number of mentions or retweets, plus calculations of both potential and effective reach: how far your tweet may reach within the network. Twitalyzer also offers users the option to tailor their report to see only metrics of interest to them, as well the ability to set goals. Other benefits include a visual network map to explore your connections, a comparison tool to compare your scores to other Twitter users and customizable sentiment analysis.

So why would you measure your impact within Twitter or Facebook? Social media is more than just a broadcast network – engagement matters. By measuring more than just fans or followers, you can begin measuring your success in engaging with consumers.

Search or Hashtag Analysis

Tools like The Archivist and Twapper Keeper allow you to build an archive of a particular search or hashtag. The Archivist even provides you a dashboard view of top contributors to a hashtag, tweet volume by day and top words used. However some tools (Twapper Keeper and Tweetake) will actually allow you to export full Twitter content, for offline analysis in Excel, SPSS, SAS or any other data analysis or exploration solution. This offline analysis allows for rich time/date and textual analysis of Twitter conversions.

These types of analyses can tell you what time of day a community tends to tweet, and allow off-line, more robust sentiment analysis. These insights allow for tailored posting schedule and contents, to best suit the audience.

Measuring social media back to your website

Measuring your social engagement within the network is a great start. But if your social efforts don’t result in traffic, sales or leads, it’s hardly a justifiable effort.

The easiest way is to leverage the analytics that your URL shortener provides. When social media links are posted, they are typically shortened (to save characters) through services such as bit.ly. These services provide data about how many clicks you received to each link. However, that’s where it ends. A click tells you only that: that the visitor clicked the link. It doesn’t tell you what they did after that. Did they close the browser window before it even loaded your site? Did they see one page of your site then leave? Or did they actually engage with your content, and perhaps funnel through into an online sale?

That’s where campaign tracking comes in. Using the same methods of campaign tracking used for other online initiatives, you can track your social media behavior back to your website.

Each web analytics tool does campaign tracking a little differently, so it’s worth touching base with your marketing or web analytics team to see how to set this up for your solution. For Google Analytics, campaign tracking involves appending campaign variables, in a specific format, at the end of the URL for Google Analytics to read. (Note of course that this is only an option when your social post contains a link.)

Without campaign tracking, you might post a Tweet and link back to:

http://www.mysite.com/

Campaign tracking would involve adding variables at the end of the URL:

http://www.mysite.com/?utm_source=twitter&utm_medium=social&utm_content=nutrition&utm_campaign=freedietbook

source=twitter tells you the source of the traffic. In this case, this link was posted to Twitter.

medium=social tells you that this was a social media post (vs perhaps an online media or PPC link.) Think of this as representing the “channel”.

content=nutrition tells us that we’ve categorized this post as a “nutrition” related. Content allows you to group “types” of posts. (For example, quizzes vs nutrition vs recipes.)

campaign=freedietbook gives you a short description of what the post was. It should be short, but enough for you to recall the post.

To create these campaign codes, you simply use Google’s campaign tracking code generator. Based on your inputs, it will auto-generate the URL with campaign tracking. This new URL is then fed into your bit.ly or other URL shortener, and carries through campaign information into your web analytics solution.

So why do you need this?

This campaign tracking will allow you to compare different mediums (for example, Twitter vs Facebook) and the quality of traffic they drive. By categorizing posts into different “content” groups, you can analyze how different types of posts drive traffic and behavior, and even look at how visitors driven from one particular post behave. This includes looking at whether these visitors leave your site, stay and engage, how many page views they see, how much time they spend, and whether they convert into a lead or a sale.

This information can help to optimize future posts. For example, if recipe posts convert well into sales of a book, a business may focus on more of these posts. You can even compare multiple of elements: for example, do recipe posts perform better on Twitter or Facebook? Add in day of the week and time analysis, and you have a rich analytics opportunity to provide insights for future posts.

Campaign tracking even allows for calculation and optimization of ROI. If you know the time that is spent on social media efforts, and the actual sales driven by traffic through those posts, you can calculate that return on investment.

Understanding social media in the context of other initiatives

So by now you’re measuring your impact and engagement within the Facebook or Twitter community, as well as the behavior of social media traffic back to your website. However, couldn’t social media have an impact even without someone engaging with your brand or visiting your website?

Coupons are a clear way of tracking a social media promotion back to sales, as a specific coupon code can be used to distinguish between different channels.

Loyalty cards may be leveraged, if you can entice consumers to couple their loyalty card with their social media identity.

Another approach is to simply ask. Customer surveys can help here. The “Where did you hear about us” may help tie at least a portion of your offline sales to social media, though there will likely remain a segments of customers who choose not to answer, or who don’t necessarily recall when or why they first decided to purchase your product.

You may also analyze the correlation between social media efforts and sales to tell you directionally whether your efforts are working.

Finally, don’t forget that it can often be a culmination of different initiatives that result in a visit to your site, or a customer walking into your store. A customer may engage with your brand through social media, then later visit your site through a paid search link to research a product, and ultimately purchase offline. Television, radio, print or banner ads may be further mixed into this, requiring some pretty serious multi-channel analytics efforts.

Get Started

The next step in social media is to move from simply getting involved to measurement. While social is a new channel, it is not so uniquely different that we can’t leverage learnings and best practices from other channels to help us understand its impact. Leveraging social media analytics tools, campaign tracking, and multi-channel efforts can help you understand the impact of your social media efforts.

Want to grow the analytics workforce? Go out and get ’em

Posted on 2011-02-17 by Michele Kiss

Yesterday, Red Door Interactive held an intern event. We have four paid internship openings for this summer, in four fields (marketing communications, email marketing, SEO and web/digital analytics.) The purpose of the event was two-fold:

1. Tell the interns a little bit about the different positions – What is web analytics? What is SEO? – and give them a chance to learn, ask questions, and figure out what they’re interested in.
2. Get to know the interns, to start the interview process.

When interns came to the event, they had previously applied and indicated which of the positions they thought they might be interested in. Not surprisingly, a large percentage were Marketing Communications – because who knows what web analytics or email marketing is if you don’t already work in the field?

What was amazing was how many spoke to me after the event or emailed to say, “I put down Marketing Communications, but now that I know more, Web Analytics sounds really interesting to me!” or furiously wrote down my web analytics book recommendations.

So here’s where I got to thinking. Those of us in the industry, especially those of us in a manager, director, VP level (etc), lament the lack of smart, qualified people, and how hard it is to hire. There are too many positions and not enough good people to fill them.

Well you know what? We need to do something about that.

So here’s what I am going to do. I am going to reach out to local universities and colleges, and see how I can start getting in front of students. I am going to tell them about our field, what we do, what the work is like, what a “day in the life” involves. Some students will shrug, but I guarantee that some students’ eyes will light up, just like they did yesterday.

We’re not going to encourage more smart young people into our field by hoping they stumble upon analytics. We’re not going to grow our industry by random chance. We need to go out and get them – and there’s no time like now.

Who’s in?

Twitter Analytics: Presentation from Social Media Masters

Posted on 2011-02-13 by Michele Kiss

For anyone who is interested, my presentation from Social Media Masters is available for viewing on Slideshare:

Twitter Analytics: Michele Hinojosa, Red Door InteractiveTopics include: Twitter research for competitive intelligence, hashtag and network analysis, download, export and backup tools, Twitter account measurement using Twitalyzer and Klout and tracking Twitter links back to your website. Originally presented at Social Media Masters Twitter workshop in San Diego (2/11/2011)

View more presentations from Red Door Interactive.

It’s not just about the race, it’s about how you train

Posted on 2011-01-28 by Michele Kiss

This is one of those things that has stuck with me ever since I read it. It’s an account from a Colombian rider, Santiago Botero, in the Tour de France, about being overtaken by Lance Armstrong in a particularly tough mountain stage.

We all get so busy managing the day to day of our work, and the crises that come up. But the truth is, we need to find time for the long-term efforts , to think forward as to what initiatives we can start on now. It is tough to prioritise these when they won’t ease the present pain points, but they’re crucial to our development in the future.

It’s not just about the race, about that present moment. It’s about what you did to train, to prepare, to be ready for future challenges.

There I am all alone with my bike. I know of only two riders ahead of me as I near the end of the second climb on what most riders consider the third worst mountain stage in the Tour. I say ‘most riders’ because I do not fear mountains. After all, our country is nothing but mountains. I train year-round in the mountains. I am the national champion from a country that is nothing but mountains.

I trail only my teammate, Fernando Escartin, and a Swiss rider. Pantani, one of my rival climbers, and the Gringo Armstrong are in the Peleton about five minutes behind me. I am climbing on such a steep portion of the mountain that if I were to stop pedaling, I will fall backward. Even for a world class climber, this is a painful and slow process. I am in my upright position pedaling at a steady pace willing myself to finish this climb so I can conserve my energy for the final climb of the day.

The Kelme team leader radios to me that the Gringo has left the Peleton by himself and that they can no longer see him. I recall thinking “the Gringo cannot catch me by himself.”

A short while later, I hear the gears on another bicycle. Within seconds, the Gringo is next to me – riding in the seated position, smiling at me. He was only next to me for a few seconds and he said nothing – he only smiled and then proceeded up the mountain as if he were pedaling downhill.

For the next several minutes, I could only think of one thing – his smile. His smile told me everything. I kept thinking that surely he is in as much agony as me, perhaps he was standing and struggling up the mountain as I was and he only sat down to pass me and discourage me. He has to be playing games with me. Not possible.

The truth is that his smile said everything that his lips did not. His smile said to me, “I was training while you were sleeping, Santiago.” It also said, “I won this tour four months ago, while you were deciding what bike frame to use in the Tour. I trained harder than you did, Santiago. I don’t know if I am better than you, but I have outworked you and right now, you cannot do anything about it. Enjoy your ride, Santiago. See you in Paris.”

What Your Company Needs to Know About Potential Online Privacy Regulation

Posted on 2011-01-18 by Michele Kiss

[Part 1 originally published in Colorado Biz on 01.18.11.
Blog post updated on 03.06.11 to include Part 2 of the article.]

The internet is a wonderfully measurable place. Businesses are able to use online data to drive strategy and measure return on investment. However, the wealth of data that makes the online world a prime space for analysis is also leading to consumer concerns over privacy. Facebook privacy controls, data capture, ad targeting and mobile applications have all been the subject of privacy discussions in the media.

A recent survey by USA Today and Gallup suggests that only 35% of respondents believe that “the invasion of privacy involved [in behaviorally targeted online ads] is worth it to allow free access to websites”, with younger respondents (40%) being more willing to accept this than older respondents (31%). However, while only 14% would allow all companies to target ads to them, another 47% would be willing to allow the advertisers they choose to target ads.

With so many concerns out there about data capture and privacy, what is a company to do to ensure their behavior and data practices are not called into question – or made front-page news?

What kind of data does your company use?

First, your company needs to differentiate between types of data capture, understand what you are leveraging, and the current climate around different types of data use.

Understanding the current landscape

Recently, the Federal Trade Commission released their draft report on Consumer Privacy. The FTC’s report distinguished first and third party data capture, with different views as to what consent and regulation should be required for each.

First party data includes web analysis done through tools such as Google Analytics, Webtrends and Adobe Omniture, for the purpose of improving consumers’ online experience and a company’s profitability online. First party data use also includes first-party marketing: a company recommending products or services based on a consumer’s previous purchases. The FTC recommended that this type of data capture not require specific consent, as these are considered commonly accepted business practices.

Third party data capture, however, is considered separately. This includes companies that deal in the buying and selling of information. For example, ad networks who buy and sell data to allow delivery of highly-targeted advertising. The FTC’s main concern regarding third party tracking is not banning the practice, but rather, allowing for informed consumer choice. While the FTC declined to declare opt-in or opt-out as the appropriate method for expressing consumer choice, the FTC did call for a Do Not Track mechanism, enforced through either legislation or industry self-regulation.

Legislative vs. Self-Regulatory approaches

The FTC’s recommendations open the door for potential legislation of online privacy and data capture. However, the Commerce Department has recently recommended self-regulation.

The Commerce Department disfavored prescriptive rules, noting the need for an approach that allows for rapid evolution and innovation, while enhancing trust. The Department called for voluntary but enforceable codes of conduct that promote transparency in data collection and use, and recommended enlisting the “expertise and knowledge of the private sector” in this regard.

The web analytics industry in fact recommended this very thing back in September 2010. A voluntary code of ethics for web analytics practitioners was proposed and drafted by Eric T. Peterson and John Lovett of Web Analytics Demystified, in conjunction with the Web Analytics Association, and a second initiative has begun regarding consumer education.

New medium, same challenges

While online data and privacy may seem new and uncharted territory, this is simply a new medium for similar challenges faced off-line. For example, consumer acceptance of tracking and targeted advertising in exchange for free online content is not too different to accepting grocery store data capture via loyalty cards in exchange for discounts. The difference is that online data capture is a newer, without a well-established procedure for privacy safeguards, and a lack of education about what the benefits or exchanges for tracking may be.

What is required in the industry is two-fold:

Finding the appropriate way (not necessarily legislatively) to establish and regulate those safeguards online; and
Educating consumers about the types of data capture and use, and potential benefits, to allow for informed consent.

How can a company protect itself?

So how can a company protect itself, in light of the current uncertainty around online privacy?

Safeguard consumer privacy as if you are already legislated to.

This has two benefits: If enough companies voluntarily safeguard consumer privacy, legislation may not be needed, leaving flexibility for companies to find the right way to protect privacy within their own business model; and
If legislation does occur in the future, your company should not require major changes to your privacy model to be in line with the requirements.

Follow FTC recommendations and integrate privacy considerations into your company’s daily business practice, by:

Taking reasonable precautions to protect the safety and ensure the accuracy of data;
Only collecting data required for a specific, legitimate business need (rather than capturing data “in case” it can later be monetized); and
Ensuring your data retention periods are reasonable.

For first party data capture and marketing, ensure that you have a plain language, non-legalese privacy policy that allows consumers to understand what data you’re capturing, how you use it, and clearly distinguish your first-party data use from third-party data use.

For any third-party data capture, make your practices transparent (this means not burying information behind legal jargon!) and educate your consumers. Advise what data is being captured, the benefits to the consumer, and provide an easy way to opt out. (A hint: if the only benefits are for your company, and not the consumer, you should expect a high opt-out rate.)

Additional considerations

For companies in business overseas, keep in mind that privacy laws may differ between countries. For example, Europe’s privacy laws are already stricter than the United States, and will potentially receive further overhaul in 2011 to modernize the 1995 Data Protection Directive.

Prepare for the future

Online privacy is not likely to quiet down in the coming months. However, by being proactive and considering consumer privacy in your daily and long-term business strategy, your company can set itself up on the right side of proposed legislative or self-regulation.

These are a few of my favourite things (about Twitalyzer)

Posted on 2011-01-16 by Michele Kiss

For those of you who don’t know, Twitalyzer is one of my favourite Twitter measurement tools. Why? Well for one, they let you see the math. As someone in the digital measurement industry, I find it hard to put a lot of faith in a “secret sauce” approach to measurement. (“Just trust us, this is your score.” No thanks.)

I have been using Twitalyzer for months now, first through a free option, and now through a paid subscription. Why did I pay? Because it’s worth it. (And FYI, the individual subscription is cheaper than a cocktail. That was a very easy sell for me.)

So (Von Trapp style) … here are a few of my favourite things.

1. Customisable metrics report

Over the months, the number of metrics included in Twitalyzer reports has grown. Great, but for me, it’s approaches a “TMI” level. Therefore, I love that I can actually customise not only what order different metrics appear, but also whether they appear at all. If I decide something doesn’t help me manage my little corner of the Twitterverse, I can choose to hide it, and simplify the metrics report. To me, this is especially useful in simplifying the world of social media to those who may be newer. Choose the key metrics you need them to see, and perhaps add to those over time.

Now, to be honest, I’m looking forward to evolution of how you customise the metrics report (currently it’s a little more cumbersome than I’m sure it will be in the future) but the customisability alone wins big points with me. (FYI, it also transfers across accounts – so if you have a number of Twitter usernames connected to your Twitalyzer accounts, you don’t have to repeat the process every time.)

So what do I care about?

I’m still playing around with this, but here’s what I am currently showing on my Twitalyzer metrics report:

Impact: My impact is defined by how many followers I have, how many references there are to me, the frequency at which I’m uniquely retweeted and retweet others, and the frequency at which I post. (I call this one the Twitalyzer Ubermetric.)

Engagement: The ratio at which people reference me vs. those I reference.

Influence: Is the likelihood that a Twitter user will either retweet or reference me.

Retweeted: How often I am retweeted. I find this useful to help me understand what content of mine my network feels is worth sharing.

Signal: Whether my tweets contain a link, mentions or retweets another user or include a hashtag. Aka a way to measure how many of my tweets are of the “Ohhhh, my cat is so cute!” or “Yum, I just had pancakes” variety vs. something actually interesting. I’m sure in some networks, depending on your contacts, low signal isn’t a big deal. But I’m conscious of not shoving too much “status” type stuff out there. (I figure general “here’s what I’m up to” is what Facebook is for.)

Generosity: The percentage of time that I retweet others.

Clout: The likelihood that I’ll appear when searched for in Twitter. I won’t lie – this one still makes me go “Hmmm …”

Followers: Number of followers I have.

Lists: Number of lists I’m on. Often it’s a good idea to look at what the lists are (e.g. I’m on some lists of “People who RT-ed me” … probably not one that the list creator even reads!) but it’s definitely a “nice to watch.”

Velocity: The frequency at which I post.

Referenced: The number of times I’ve been mentioned on Twitter.

Potential Reach: The number of followers I have + the number of followers those who retweet me have. This attempts to estimate how far a tweet I send can be viewed.

Effective Reach: Takes my retweeting followers x my influence (the likelihood that I’ll be retweeted.) This attempts a more realistic estimate of how far a tweet I send could go.

Klout Score: Comes in through Klout. (The “secret sauce” tool.)

So why do I care about these metrics, and why don’t I care about others?

My reasons for Tweeting in the first place, and for tweeting about the topics I do, are fairly simple: to learn via reading what others post, to contribute to the discussion, and to build relationships in the community.

Measuring my signal ensures that I’m not posting junk that might alienate my Twitter contacts. (Sure, my cats are cute, but I’m pretty sure nobody cares.) Monitoring retweets, followers and lists is a good indication of whether what I’m posting is considered of any value by my contacts. Monitoring references to and by me, and my retweets of others, is a good indicator of whether I’m engaged with the community.

Basically, I’m choosing to focus on the things that relate to my goals in using Twitter, and as such I’ve currently hidden some metrics in Twitalyzer.

2. Percentile

This is a small one, but when I first started using Twitalyzer, you could only see “Percentile” by clicking into a metric. Now they have incorporated it on the metrics view, visible at all times. I love this! The percentile basically tells me that my score is the same or better than X% of the Twitter users tracked by Twitalyzer. This can be helpful when you realise that a Impact score might seem low, yet still be in a fairly high percentile. (Aka it makes me feel better…!)

A nice future add-on would be a tailored percentile, based on a subset of Twitter (perhaps those you follow, or a list you’ve created.)

3. Goals

Data in a vacuum is meaningless. Twitalyzer lets me set goals for individual metrics, to ensure that I’m actually measuring something in context.

4. Comparisons

I love the ability to compare myself to other Twitter users on a variety of metrics. To me, this is another way to benchmark myself, and perhaps even help to set my goals. (What’s feasible? Well, what is X’s score? Okay, I’ll aim for something similar.)

However, I’d definitely love to see a future ability to pre-create a number of comparisons, rather than doing this individually each time I log in.

5. Recommendations

It’s pretty standard for a tool to tell you how you measure up. It’s less common for one to tell you how to go about moving the needle. Twitalyzer will actually give you recommendations of what is more and less important for you to do to positively affect your impact scores.

6. Network Explorer

Now, to me this is more informative than necessarily something you’re benchmarking against, but it’s still super cool. Basically the Network Explorer is a visual map of the people and topics you are connected to. (Because who doesn’t like to play Six Degrees of Awesome #Measure Folks?)

7. It’s about the community

So you follow these people on Twitter, or they follow you. So what?

Twitalyzer can help you understand:

When your network is online (e.g. time of day, day of week) and where they are located. (I definitely think there are some improvements that could be made in the presentation of regional information, but that’s an aside.)
This can be very helpful information if you’re trying to decide when the optimal times to post, say, a blog post are.
Who the most influential members of your network are, who your most influential “friends” are (based on your communication with some members of your network more than others), and who appears to to influence you.

Network Activity (Time of Day):

8. It’s about me

Curious about what dates or time of day you tweet? Are mentioned? Retweeted? Never fear, Twitalyzer is here:

But what about the content? What hashtags do you use? What communities and discussions do you tend to be a part of? What of your content has been retweeted lately? Which of your links were clicked? You can find all of that out.

There’s even the beginnings of sentiment analysis. Now, sentiment analysis is a toughie. (Especially if you have folks in your network who are, shall we say, sarcastic? Noooooo….) The reality is that there aren’t many great sentiment analysis solutions out there that don’t involve human assessment of sentiment. So to be honest, I tend to view all sentiment analysis (included Twitalyzer’s) as interesting, but not necessarily something to hang my hat on. That said, I do like the ability to play around with what words you consider to be negative and positive:

9. Integration with Google Analytics

I’m also looking forward to jumping more into the integration with Google Analytics – but that might turn out to be a topic for another time (entirely of itself.)

10. Export options

The ability to export data does not go unnoticed!

Last but not least … (definitely not least!)

11. Customer service

I’m not kidding you. Even before I was a paying customer, Jeff Katz at Twitalyzer was always enormously helpful, and though I’m a now only a meager paying customer (with my little individual account) he still goes above and beyond to help solve problems. He rocks.

And as a little FYI, if there’s something you can’t get working right in Twitalyer, take a hop over to Firefox or (shudder) IE. I’ve had very few issues using Twitalyzer in Chrome, but the rare issue I’ve had has been browser related. (Which I discovered because the aforementioned lovely gentleman Jeff Katz helped me out.)

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