X SEO

Social Media as a Performance Channel with Google Analytics – SESNY was originally published on BruceClay.com, home of expert search engine optimization tips.

Speakers:
Phil Mui, Group Product Manager, Google Analytics
Ilya Grigorik, Lead Engineer, Google Analytics

This morning at the keynote, Avinash unveiled this: Google Analytics Update Connects Social Marketing with the Bottom Line.

How do marketers view social media? A MarketingSherpa social marketing report shows that social marketing is mostly considered a promising tactic, but ROI is not understood. “Since everyone is doing it we’ll look foolish if we don’t” is a common approach.

What about number of followers or number of shares? Isn’t that social media measurement? This isn’t really understanding the value of social.

Content consumption today has moved off the browser to applications. A marketer needs a 360 view of actions, which is hard to pull together. Number of tweets or followers is comforting because it gives a sense of measuring. But it doesn’t address economic value.

Assigning Value to Social: The Problems

1. Upper funnel impact is hard to capture and understand.

2. Social data silos are hard to aggregate and compare. No single system aggregates various activities across social silo.

3. Off-site engagement is hard to identify and measure. There are ambiguities of language and when conversations occur offsite it can be hard to be sure the keywords are relevant to you.

Social Web Made of Actions: Across multiple sites and networks

• Actions on-site
• Actions off-site

Google Analytics wants to bring the two together. That’s what social reports is geared toward.

Social Reports in Google Analytics

Your business:
site visits -> on-site social engagement
-> conversions (business outcomes I care about)

Measure the last interaction and the assisted (upper-funnel) and find the economic value lies somewhere in the middle.

Now a demo (using Avinash’s blog). Traffic Sources > Social

In December, Google announced an initiative called the social data hub. Google is offering an open standard to let other data collectors to import into a hub. Integrating all the data into Google Analytics allows for easier comparisons of data. Expanding the scope of understanding of all content sources.

Once you know the overall metrics, you want to know what people are actually saying. The Conversations tab let’s you see actual comments off-site.

Conversions

Data broken down by network

Assisted vs. Last interaction analysis: the economic value is somewhere in the middle (complex attribution models)

Social Plugins: On-site activity of share buttons can be measured. An API lets you measure what happens and on which pages with these buttons. Placement of button, which buttons work for the audience and that type of content can be optimized for.

A visualization displays the flow of traffic through a site from a social source. You can see drop off points and rates at each point on site.

These tools are meant to understand social media from a value perspective. Now for questions.

Q: Can you give me a high-level breakdown of conversions vs. assisted social conversions vs. last interaction social conversions in the circle diagram?

The blue is the bottom level of the conversion funnel, assisted are those who interacted with social at some point (in the last 30 days, touched by social media – the upper-limit of what social is worth, and you determine what exactly that means to your business), and the large outline circle is conversions generally across all channels.

Q: How current is the activity stream?

The actual activity stream is to the minute. A rule of thumb metric they use is that the half-life of content online today is 1 hour.

Q: Do you plan to have something like Ripple for other networks besides Google+?

Ripple is something we can do since we own Google+. We can’t give you ripples on networks we don’t own. The Social Data Hub is with partners who choose to give us ability to provide in-depth reporting. We’ll leave it open ended about what might come in the future.

These reports will start rolling out tomorrow and to all users in the next few weeks.

SESNY Keynote: Business Optimization in a Digital Age with Avinash Kaushik was originally published on BruceClay.com, home of expert search engine optimization tips.

Mike Grehan welcomes the crowd. He offers apologies for the intermittent problem with the microphone. Every once in a while it causes the speaker to sound like he has a British accent.

He mentions it’s the first day of spring! He says today’s Google doodle features a zebra. This connection he does not understand.

Before our featured speaker, let’s hear a presentation from Dr. Edel Garcia, Sr. Researcher, Minerazzi Project, Microsoft Innovation Center of Pureto Rico. He’s going to explain  new research: the Self-Weighting Model (SWM) for conducting meta analysis and applications to SEO/SEM correlation studies.

The Hunter-Schmidt and Hedges-Olkin models for meta-analysis can fail. This new model (SWM) has no sample size issues, n oadditivity issues, no statistics type issues and no distribution issues. Unfortunately, this is all over my head so I’m unable to explain the diagrams and illustrations that helped explain this research. So you’re going to have to read the research paper yourself:

Garcia, E. (2012). The Self-Weighting Model. Communications in Statistics – Theory and Methods, 41:8.

Now to the keynote with Avinash. Avinash is Google’s digital marketing evangelist. If that doesn’t earn him the designation of guru, then what would?

“Information is powerful. But it is how we use it that will define us.”

He’s going to explain his perspective of how to do more with what we have access to. We don’t have to be in a hording mindset of getting more data.

There are overlapping circles: influence, experience and value. He tends to live in the intersection of the three. Some people like the venn diagram to be labeled: marketing, magic and money. He’s okay with that too.

Embrace Economic Value

…understand and solve for 100%.

Of the top 50 e-commerce websites, conversion rate is 2%. We measure everything for this 2% and end up optimizing for this 2%. What about all the rest of the people we work hard to get through the door. We need to solve for the rest of the people and let them give value to the company.

Obsess about the one big thing your boss cares about – conversion, direct revenue. This is the macro conversion.

But also think about the other possibilities available. What are they? If people didin’t jump into bed with us right away? Is there anything we can extrapolate and use 2 weeks later? These are the micro conversions.

Rarely do we understand the complete view of what digital contributes to our value.

Rock: The Strategic

None of us optimize for just one thing or the other. We invest in many things: social, search, display, email…

Don’t cut out channels because you don’t see them driving macro conversions. It’s like cutting off your legs to run faster.

Intelligent Attribution Modeling

…understand, be less wrong.

Some users may come in through Google and buy. Straightforward attribution.

Some may search Yahoo, leave, find an affiliate code, come back and buy. More complicated attribution.

How can you optimize digital marketing budget with a more sophisticated approach?

1. Understand How You’re Doing Today.

The problem you face is that there’s a lot of data. How do you understand 18k rows of data. It’s not the fault of people who use data? It’s the fault of vendors who create data pukes.

Core diagram lets you see how any one particular channel creates an outcome for your brand. First and last touchpoints are visualized along with lines connecting interactions between channels.

How to make that diagram: create custom grouping of channels, dump it into Excel, then boom.

http://zqi.me/vizd3 <- Create these visualizations yourself!

You can see there are channels more likely to convert after last click. Other channels’ jobs are for pushing toward other channels.

2. Apply Optimal Attribution Model.

There have been attempts at giving credit to channels, like the last click model, equal credit model, the time decay model. The “personalized to your business”, which Avinash calls the MCU (Make Crap Up) model, he sees growing in popularity with agencies. If you create your own custom model, remember that these are just a handful of many variables to consider.

Measure –> value –> test (and back around)

Understand, test, be less wrong – that’s the best model you can do.

Influence, Evolves!

People are talking amongst themselves. The arrows have moved from one direction (traditional marketing is like yelling over a megaphone) to bi-direction when we listen to who users are (Internet marketing) to multi-directional where users are talking to each other, too (social web marketing).

Social sucks today because we’re measuring dumb things like what time you tweet.

Instead, do 3 crucial things.

1. Ground your understanding in: So What? What’s the economic value delivered in social channel. Find the indirect value of social. There will be no debate over the value of a fan, a follower on Twitter.

2. From where are all these things happening? Discover your off-site activity. Correlate overall traffic and social traffic and identify what you’re doing on social that’s causing traffic to come back.

3. Why?! This will bring you back to the people causing economic value to be created. Why are people +1ing and liking…

New social analytics section inside Google Analytics as of today! It grounds actions to discussions outside your site.

Multi-Channel Portfolio Optimization

…controlled experiments rock!

This is the only way you can become really sophisticated. Send only a catalog to some users, only emails to others and emails and catalogs to others. This is an online/offline test, but it can be used between any channels.

Do online searches drive in-store conversions? There was a test.

Test details: 4 weeks

Structure: 11 test markets (128 stores)

39 control markets (621 stores)

Outcomes: $1 spent in paid search drove $15 purchase

22% contribution margin (this is mindblowing – you’re usually lucky to crack 3%)

1. Move away from being data pukers, to hiring ninjas with these skills: scientific method, design experiments, data analysis

2. Always on – constantly experimenting. Use 10% of budget to test.

Line of Sight Framework

What are we doing to improve the 4 important things:

Price, cost, marketing share, market size

Evaluate your strategies and KPI in those areas. Without this line of sight about why you’re solving for what you’re solving for, it will be very hard to find success.

The final lessons:

1. Measure holistic success

2. Figure out intelligent-er attribution

3. Rethink social (so what, where, why)

4. Stop guessing – controlled experiments

5. Insane focus – have a clear line of sight

Multi-channel funnels (MCF’s) are a Google Analytics (GA) feature which gives more insight into conversions where the user visited a website via multiple channels (PPC, organic, direct, etc). PPC Hero wrote a great article guiding users through MCF. After analyzing some features along with Aaron Levy, Michelle Noonan and Rachael Gerson, there are some great applications of MCF’s for PPC and some things to avoid. Here are some recommendations on how to use (and how not to use) MCF for PPC.

Time Lag Reports

MCF time lag reports can be great for figuring out how to long to remarket to people for. If time lag reports show that 98% of visitors will convert within 5 days, why set your remarketing cookie to target people on day 6? If they were willing to convert, they probably would’ve converted by day five. Use MCF path length reports to figure out how long you should remarket to people. The same logic can also be applied to how long you show remarketing via 3rd party banners or other sources.

Recommendation #1 – Use time lag reports to set remarketing time windows.

Multi-Channel Attribution

AdWords & adCenter cookies track on the PPC visitor’s browser. If a PPC visitor returns later by another channel, AdWords & adCenter will still record a conversion in their respective PPC interfaces because the user’s browser is cookied for AdWords and adCenter. Adding more tracking cookies (like for example if someone got sent a follow up email with tracking after clicking on a PPC ad, or saw a banner ad after clicking on a PPC ad) doesn’t disable the original AdWords or adCenter PPC cookie. Clicking on an SEO listing or directly typing in the URL doesn’t remove the AdWords or adCenter cookie either. Any data in MCF about that later conversion is redundant; we’re already counting that via AdWords and adCenter codes. If we’re using the AdWords or adCenter codes to track and we add a conversion recorded in MCF’s that started with PPC but ended with SEO, we’d be double counting.

If you’re using GA to track conversions via utm_ parameters or auto-tagging, those cookies won’t be removed in the regular GA interface because someone came back via direct traffic. However, the GA cookie will remove organic, email, etc (only direct doesn’t get overridden by the last visit). Using last channel attribution via conversion path reports is only useful if you’re using GA, not AdWords or adCenter codes, to track PPC. This is one reason why regular GA data and GA multi-channel funnels don’t match. The second has to do with sampled data which we will get to later.

Recommendation #2 – Don’t add last click conversions which occurred via SEO, direct or another channel into your PPC numbers if you’re tracking PPC with AdWords or AdCenter codes.

Assisted Clicks and Impressions

If you’re using AdWords or adCenter codes, MCF’s may not be helpful for multi-channel attribution because AdWords and adCenter cookies aren’t removed or broken when someone returns via another source. However, if someone clicks on multiple PPC ads AdWords counts the last click as a conversion. In this situation, looking at the sequence of multiple PPC clicks is useful. AdWords already has search funnels and I haven’t found a reason to use MCF’s in GA for AdWords. However, assisted clicks and impressions can be useful for adCenter or any second tier search engine, as adCenter and most second tier engines don’t have a search funnel feature.

Recommendation #3 – Look at assist clicks and impressions for GA MCF for adCenter and second tier engines. To analyze AdWords, use AdWords search funnels rather than GA MCF’s.

Reporting

MCF’s use sampled data. Sampling in Google Analytics or in any web analytics software refers to the practice of selecting a subset of data from your website traffic to make predictions about overall traffic. Sampled data is just an estimate, it might not be good to use in reports. If MCF’s show that lots of people who come to a site via SEO later return as direct visitors and convert, that is useful data which can be used to build a business case for ongoing investment in SEO.

Some have hypothesized that Google created MCF’s only to explain why GA data didn’t match AdWords data. They might have a point.

Recommendation #4 – Don’t use MCF data for reports.

Path Reports

In my experience, long conversion paths are common. If you 40 different sequences each of which has 3-8 different PPC keyword visits in the sequence along with 6-10 organic visits (all for different keywords) and possibly more, it’s hard to optimize all of it. Also, if low volume keywords are involved, you won’t have enough data for statistical reliability. Path length reports can be useful for determining if channels are important to the conversion path, but deeper analysis can become practically difficult to implement.

Recommendation #5 – Stay at the basic channel grouping level for path reports.

What other recommendations do you have for MCF’s?

by Mike Fleming

Recently, I talked about measuring all the important behaviors and outcomes
that happen on your site that directly affect your company’s bottom
line (not just people buying stuff), and giving each of them an economic
value, if applicable.  You do this so that you’ve got the data you need
to get a holistic picture of how your website is doing its job and the
true economic impact it’s having on your business.  Not only that, but
you get to see what’s working and what’s not.  This way you make
informed decisions about how to invest as your journey proceeds.

But, Are You Listening?

As
every good website owner knows, collecting data and seeing what happens
leaves out an all-important piece of the puzzle.  To truly take action
on the data you’ve now collected, you need to know why it is the way it is.

You can dig into your data all day long, but much of the time you won’t find it (the why).  You only find the why by actively listening to your customers
and their experience with your site.  You may think you know what
they’re thinking and how they shop, but you’ll find out that you’re
wrong.  If you really want to know how to make your site experience
better, you can’t just look at your data.  You’ve got to get the voice
of your customer.

But, many don’t know what’s available in the
online world for gathering this qualitative data.  So, here’s a quick
overview of some of your options…

1. Lab Usability Studies

This
involves observing and monitoring how people interact with your website
when you give them tasks to complete.  People are brought in to special
usability labs that allow users to perform the tasks while others
observe.  The environment is set for the users to complete the given
task as they would on their own.  These tests can help you identify problems and create ideas pre-launch or post-launch in relation to what works and what doesn’t on your site.

Did
you involve your customers in the development process the last time you
launched a new site?  Imagine that!  Think about all of the time, money
and energy you’d save from finding out how your opinions about your site might differ from those who actually matter. 
Then, you can take all those ideas, test them and see what the results
are.  I hear it from clients all the time.  I give a recommendation and
they say, “We think it’s better this way.”  Oh really?  That’s nice. 
Let’s hope your customers do, too!

Yes, this option requires more
resources to complete because you have to create the task, decide on
what is success, find test subjects, pay them, conduct the test, etc. 
But, if you have the ability to do this, there’s no better way to listen
to your customers than to actually physically observe them in action.

2.  Remote Usability Studies

If 
full lab studies are not possible, there are a number of solutions that
can help with recruiting actual people who visit your site to do
usability studies remotely.

These solutions will place an
invitation on your site with a value proposition for the user and allow
them to decide to sign up as a candidate.  Once you identify users who
meet your criteria, you can call them and conduct the test through
screen-sharing.  Then, follow the same steps as in-lab usability
studies.  This way you reduce your investment along with the
artificiality of people being in a lab.  The trade-off is that you don’t get to observe them physically.

If you don’t have people to conduct this type of research, you can outsource it.  Companies like UserTesting.com
have panels of people that will go through your test.  You outline the
test parameters, and they will then supply you with a video of the
participant’s experience and a summary of their thoughts.  You can then
take this data, perform analysis and push to implement recommendations. 
There are some downsides to total outsourcing (i.e. artificiality and
lack of unstructured experiences for users), but if you want quick feedback that’s cost-effective, then this works well.

3. Surveys

Surveys are an optimal tool for gaining insights about your visitors.  If you don’t use them, you’ll have a huge hole
in your ability to understand your customers.  Also, they are an
affordable solution that provides great qualitative data to mix together
with your clickstream data.  There are 2 main types of surveys…

1. Page-Level
   – These focus on individual pages or focused tasks on a page.  Although
   it’s hard to get large sample sizes because of the typically subtle
   invitations, you will hear from your most upset or engaged customers
   - which is great.  These are not used to collect site-level data like
   intent or site experience.
2. Site-Level -
   These collect macro-level data about customer experience and are
   increasingly becoming permission-based.  Visitors are asked at the
   beginning of their session if they’d like to participate when they leave
   the site.  You can control which visitors see the invitation, as well
   as control its exposure with cookies.  This type of invitation model
   gives you a more diverse response because it’s more aggressive.

The single biggest survey mistake is asking too many questions. 
You have to balance what is knowable with what is actionable so that
you’re not wasting time analyzing unimportant data and so that you
communicate to your visitors that their time is valuable.  Here are the 3
questions you absolutely must ask people who come to your website…

1. What is the purpose of your visit to our website today? – You might think you know, but you don’t. You will be surprised at these answers.
2. Were you able to complete your task? -  Task completion rate is the most important Web analytics metric.  If you improve this, your business will grow.
3. If you were not able to complete your task today, why not? – The answers to these questions become your to-do list of issues to improve your website experience.

There is a free on-exit survey called 4Q from iPerceptions that contains these 3 questions.

4. Competitive Benchmarking Studies
– You pick a task or process you want compared to your competitors and
they get users to execute the tasks and provide you with data and
analysis. (UserZoom.com)

5. Rapid Usability
– You get open-text summaries of what people recalled on your site
after a five-second view of it.  You can even do it with your
competitors’ sites.  It will tell you a lot. (fivesecondtest.com)

6. Online Card-Sorting
– This shows you how your customers would organize the information on
your site.  You create cards and then invite customers to sort them.  (optimalsort.com, websort.net)

Be sure and visit our small business news site.

SMX West 2012: What Search Data Reveals About Customer Needs & Desires – And How To Use It was originally published on BruceClay.com, home of expert search engine optimization tips.

In honor of today being Google privacy policy change day, let’s look at how you can take advantage of how people search and come to your site to better target them and use info to figure out new products and services. Twitter is following this session with #smx #32B.

Moderator: Danny Sullivan, Editor-in-Chief, Search Engine Land (@dannysullivan)

Speakers:

Bill Hunt, President, Back Auimuth Consulting (@billhunt)
Michael King, SEO Manager, Publicis Modem (@ipullrank)
Hamid Saify, Search Marketing Director, Deutsch LA (@hamidsaify)

Michael starts. Find his deck at http://www.slideshare.net/ipullrank/datamining-the-target-way. Google has the holy grail of digital marketing: intent, interest, demographics and network. Facebook has the same with the Bing partnership.

The new SEO process:

Build personas – hypothetical representations of your target audience. Develop personas with social data and educated guessing. Social data validates personas.

First figure out your goal, then look at FB Insights and quantitative analysis tools. FB Ad Planner is the personas version of the AdWords Keyword Tool. Figure out how many people in your audience exist on FB. Then do preliminary social listening. Throw in a kw and find out the syntax and language used when people talk about it to avoid the jargon common of marketer speak. Go to forums to listen, too. Now do preliminary keyword research. See what questions people are asking on Quora. Take those keywords into a KW Research tool.  Protip: Use Scraper for Chrome with the AdWords API plugin.

Now they start creating the personas by defining their age, wishes, their needs and a personality backed by a story. This allows you to create a mental model. Your last persona is always Googlebot.

Eventually you’ll get to a point of which persona owns this keyword. You won’t have to look at all four, but the one that converts.

Keyword Arbitrage tracking keyword demographics allows the attribution of future purchases to the original keyword.

See the story about Target knowing when a woman’s pregnant. That’s how it all comes together for subsequent conversion prediction.

Pzyche.com, Linkedin, and Google+ are other places you can use this on besides Facebook.

Internal search is a gold mine – if you find out a persona is looking for something else, you can change your messaging.

Bill steps up next. Advanced Keyword Modeling: by understanding the search terms and anlysis you can understand the needs, wants and intent of the searcher allowing you to:

• understand the voice of the consumer
• effectively map your content with their query and intent
• identify new products and services
• create and influence PR and socila media opporutnities

You should be using your SEO keywords on your social media campaigns. It can be searching for a needle in a haystack when you are looking for the right data. There are a number of keyword models (missed opportunity matrix, critical kw performance monitoring, high ranking and underperforming, and more). Words that help us isolate a specific opportunity, interest, audience or need are keyword qualifiers.

Look at “girdle”, “shapewear”, “shaping underwear” and compare the searches and CPC. Manufacturers want to use the latter two because they sound good, but “girdle” gets more search and has lower CPC. Moms in the midwest still use girdle. Look to use the consumers’ vocabulary. 75% of queries were “clothing type + shapewear”. Site content did not match keyword category segments. They flatened the pie chart of search terms into a taxonomy.

By looking at search query volume and social media we saw that women talk about shapewear in the following buckets of thought. This translates in to an experience that provides the user with progressive levels of information about shapewear.

Mining for opportunities: Content miss-match

• UK travel site matched kw to top ranking pages.
• Swapped to a page with best chance for conversion.
• $120k incremental revenue in 25 days.

Mining for opportunites: Identify interests and needs

• 58% of pet related searches are dog related, 25% are cat related and 17% were others, but never “pets”.
• 66% of pet food related searches are about dogs.
• Searches on dog food are 2.5 x higher than searches for cat food.
• The company changed their content when they learned this surprising stats.

Mining for opportunities: Paid and organic collaboration. E-commerce site mapped paid and organic co-optimization and found missed opportunities and cannibalization.

Hamid is going to show us how to do things wrong, but stumble into really interesting finds. Before 2 or 3 years ago, laptops were all standard neutral colors. They found people want personal colors – 9 million to 12 million people were looking for a particular phone their client company sold in colors. With this data, they developed a non-black/white/silver phone for AT&T since they had a business case that could convince AT&T there was demand. Search had validated the interest. No focus group or scouting necessary.

A keyword may look the same on the surface but point to a very different consumer. It’s not just about keywords but interests.

A PlayStation Vita comes in 3 options (3G, WiFi and both). They listened to what people were talking about the product on Twitter (buzzmetrics pulling +/- and neutral mentions) and decided to push the WiFi bundles. People were not excited about the 3G only version.

Interest targeting through searcha nd content stimulates other media. They found a game was more popular with the sci-fi fan base and so they upped their commercials with TNT.

The disruptive Google New Media Ad: They’ve been able to test different creative. Compare these 2 videos. The first took months and months to produce. The second is just game footage. The second consistently outperformed – it was hard to break the news.

http://www.youtube.com/watch?v=uaDVsLhYRik

http://www.youtube.com/watch?v=ObDQwh9N4k0

SMX West 2012: Justifying The Investment: Analytics For Social Media was originally published on BruceClay.com, home of expert search engine optimization tips.

This session, #smx #31B, aims to demonstrate social ROI, look at emerging trends and provide an in-depth look at marketing tools and packages.

Moderator: Greg Finn, Chief Marketing Officer, Cypress North (@gregfinn)

Speakers:

Tami Dalley, Vice President of Analytics and Insights, Buddy Media (@DataDivaDalley)
Merry Morud, Account Manager, aimClear (@MerryMorud)
Monique Pouget, Senior SEO & Social Media Strategist, Thunder SEO (@moniquethegeek)
Courtney Seiter, Community manager, Raven Tools (@ravencourts)

Courtney is up first. Whenever she reads a post about what’s the social media ROI, she thinks about the fact there’s no silver bullet. There will be no simple equation that provides a number to quantify ROI.

Only 20% of CMOs think social media marketing produces measurable ROI. Only 13% of marketers think they’re doing a good job of social media marketing. 100% of those that did not define ROI in the calssical definition believe that social media benefits their business.

3 tips for understanding social media ROI:

• know your goals
• align social metrics
• break it down

Types of social media goals:

• brand sentiment
• sales
• share of voice
• customer service
• reach
• product development
• engagement
• marketing insights
• brand advocates
• brand trust
• brand loyalty

Example: Gilt Group

• Goal: Solve shipping costs barrier using insight from online community
• Result: 10% increase in shopping cart conversion

Example: AT&T

• Address negative sentiment and unresolved customer service issues on the social Web
• Result: 21,000 customer issues resolved, 37,500 customers served

Example: L’Oreal

• Goal: Increase awareness and product sales at salons
• Result: Participation by 6,000 salons, 21 million impressions, 2.2 million engagements

[Align social metrics with goals chart]

How can you quantify social media metrics? Get more specific!

Measuring product innovation can be looked at in terms of:

• number of new product ideas
• number of new product ideas built
• researched time saved
• traditional research cost savings

Break it down by goal, by campaign, by time period, by medium, and even by post.

http://gaconfig.com is a free tool designed to help you set up advanced goals in GA to see social media ROI.

Next is Monique. Her company did a sweepstakes for client Bazudo, a community of apartments across a number of regions. Asked people for their secrets of different locations, like best happy hour, theater performances, etc. They wanted to make sure the cost of the sweepstakes was right (from designing and copywriting, email, call tracking, ads…)

Wildfire was a tool they used. They also learned FB requires a 3rd party tool like Wildfire when running a sweepstakes on an FB page. They set up a FB Microsite. Wildfire allowed them to keep a consistent brand image. They also learned people can’t be forced to like the page. Instead they built encouragement into the banner ad that pointed directly to the like button.

KPI #1: Emails

Building up their email list was a big asset. They collected their name, how they entered, whether or not they liked the page, if they were a current resident. Total costs/ new emails = cost per email. Turned out this was lower cost than their regular cost per email.

KPI#2: Social referral traffic

Saw how people came to the page from social networks through GA. Built other networks (wildfire, hootsuite) into advanced social segments.

KPI #3: Website traffic from a specific link

Created vanity URLs – easy to remember in ads. Connected with parameters that showed sources, saw how people were coming to the site from specific collateral. Can easily see this in GA.

KPI #4: Leads

Phone calls from dynamic call tracking phone number used on sweepstakes page. Contact form completions tracked with GA goals. Total costs / active leads = cost per lead

KPI #5: Banner and Facebook Ads

KPI #6: Entries

Got cost per entry down with every successive sweepstakes.

KPI #7: Twitter engagement

KPI #8: Facebook engagement

Need more FB and Twitter metrics? Raven, seoMoz, Simple Measured

Beyond KPIs:

• Storify allows you to look at conversations and go beyond KPIs and stats.
• Informed content: Wrote blog posts with info gathered from sweepstakes entries. Pumping information back into overall strategy.

Tami gets asked the question of justifying social media a lots. She’ll look at measuring through social sharing and the value of socila media as a free market research tool.

Value of social = as;ldfkja;sldfjadoiuasdf;lj… Value is specific to you.

The first step in determining SM ROI is what makes sense for your business goals. After that the KPIs fall in line.

Social sharing buttons are embedded on sites and drive referral traffic, etc. Sharing has no variable costs. It doesn’t cost when someone shares, or when their friends click. Social sharing is influential. People are twice as likely to trust info emailed or shared on social network from a friend. It drives traffic to the top of the funnel. On average a single share to a social network drives 6 new visitors to a website.

Sales resulting from conversion buddy shares have 26% higher average order value (AOV) than other channels. It drives both micro and macro conversions. But, to justify the investment it must be tied to conversions. When you can put a graph that lines up shares with clicks, conversions, purchases and sales, you have a case for investing in social.

Merry takes the podium. She’s going to look at Facebook Insights, button and URL analytics, social and open graph site tools and powerhouse tools.

Start with the right questions – what are my goals and what are my indicators? How big is my circle and by how many degrees of separation? Where can we associate convos – find it, tap into it? How do people engage – like, share, comment? What are the demographics of my audience?

Organic social is going away , it’ll all be about paid organic… oxymoron? Check out Marty’s post Undressing the Secret of Facebook: Paid Organic is the New Black.

Insights graph can identify virality of individual posts. Identify referrals to your page – if you’re getting a lot of traffic from Twitter, for instance, that’s where you should be.

Identify your needs from tools:

• page management
• multiple accounts
• social listening
• robust analytics
• reporting

Third party tools:

• Bit.ly (URL tool)
• Share this (button tool)
• Janrain (social and open graph site tool)
• Trackur (social monitoring tool)
• Socialmention (social monitoring tool)
• The archivist (twitter tool)
• TweetReach (twitter tool)
• TwitterCounter (twitter tool)
• WildFire (FB tool)
• PageModo (FB tool)
• Vitrue (FB tool)
• Shoutlet (FB tool)
• Hootsuite (FB analytics)
• MediaFunnel (FB analytics)
• Buddy Media (FB analytics)
• Awareness (FB analytics)
• Lithium (FB analytics)
• SproutSocial
• Sysomos (powerhouse – rep mgmt, listening stations)
• Radian6 (powerhouse – ecommerce integration)
• Raven Tools (powerhouse – SEO, social media monitoring, persona manager, basecamp integration)

I just have to say this: Thank you, Google!

Many users were switched into the updated Google Analytics interface this afternoon and were greeted with some fantastic updates. Not only was the appearance improved, but there are functionalities we’ve all been anxiously awaiting since last March.

We’re still exploring here, but rather than just tweeting every thing we find, let’s go through together! I’m not going to run through any cosmetic changes, just the ones that really matter.

1) Percent Change is Back!

One of the biggest pain points for users was not being able to compare percent change from one time range to another.

2) Advanced Segments Can Be Shared

When viewing advanced segments under Assets in the Admin settings, there is now an option to Share advanced segments with other users.

3) Dashboards Can Also Be Shared

Prior to this update, dashboards were only visible to the account that was used to create them. Now we have the new “Share Dashboard” button.

4) Report Timing & Report Loading

As promised in Google’s recent post, new features make it possible to see how recently a report was generated and to quickly refresh the report.

Additionally, users can now see how much longer it will take a report to load.

5) Control Report Calculation

Users now have some degree of control over how data is sampled in GA. This was also from Google’s most recent analytics post. I think the best part about this is actually knowing what the sample size is, rather than being able to adjust. However, Google does now start sampling at 250,000 visits, rather than the previous 500,000, to make reports load faster.

That’s not all, but that’s all I have time for at the moment! Check back again for updates.

To answer the question we keep getting, no, PDF export isn’t back yet. However, Google previously announced the old version of GA wouldn’t be turned off until the new version is fully functional, so we won’t lose the ability to PDF or schedule reports.

by Mike Moran

I’ve talked before about how Google might have cracked the code on qualified sales leads through search. Somehow, sites are seeing drops in traffic but not all of them are seeing commensurate drops in revenue, because Google is figuring out which keywords drive revenue for each site. So, the question is whether you know which keywords are driving your own revenue. How are you tracking the connection between marketing effort and spending and your actual sales? If you’re an e-Commerce company, it’s easy–just track the sales through to your shopping cart. But for most companies–the ones that sell offline–tracking sales back to the online tactic that led to it is still an elusive goal.

Be sure and visit our small business news site.

Facebook’s Insights tool is reportedly about to become much faster with the addition of real-time stats.  Techcrunch broke the news that the official Facebook analytics program will move towards live (or near real-time) statistics instead of the current 48 hour reporting delay. Insights has…



Please visit Marketing Land for the full article.

by Mike Fleming

Two posts ago, I talked about the importance of laying a web analytics foundation for your company by measuring, valuing and analyzing
the critical few visitor behaviors on your site that have an impact on
your bottom line.  In my last post, I talked about the step after that,
which is determine why the data you’ve collected is the way it is (the why?).
Once you’ve listened to why your customers couldn’t complete whatever
tasks they were trying to accomplish on your site, you should have a bunch of ideas on how to fix it.

Now it’s time to take those ideas and figure out which ones will work the best.  This is done through experimentation and testing.  Lucky for us Web geeks, testing on the Web holds a major advantage over all other marketing channels because of very low cost and fewer limitations. 
This has made setting up, running and controlling tests – as well as
the ability to collect and analyze the data – easier and cheaper than
ever. This in turn makes guessing what you should do with your
site  an even worse proposition than guessing what you should do in
offline channels (which is also bad).  Not doing experimentation and
testing isn’t an option.  It’s mandatory.

With that said, what can you do?  Testing on the Web basically boils down to two options – A/B Testing and Multivariate Testing.

A/B Testing

These tests are the cheapest and quickest.  You test two uniquely different participants
for a predetermined outcome. For example, you make two very different
versions of your homepage and see which one is best at getting visitors
to click through to the product categories. Or, you see if providing a
2-step checkout process versus a 4-step checkout process works better
for your e-commerce conversion rate.

Multivariate Testing (MVT)

With these tests, you create multiple variations of multiple elements of a page and have a testing tool (like Google’s Website Optimizer)
mix and match them to the visitors. In the end, you are able to see not
only which elements contributed the most to a lift for your outcome,
but also which combinations of elements work the best.

While A/B
Testing is much easier and demands less resources, it does not give you
data that is as good as MVT.  Why?  Because after your A/B test is
complete, you may know that version B of your homepage performed better,
but you don’t know why. Was it the headline,
the images, the navigation layout, the color of the call-to-action
button?  Only with MVT can you find out.  So, start with A/B tests to
get your feet wet, but jump into MVT as soon as you’re ready.

Get Creative

You can use these tests to experiment with just about anything. Of course, you want to test those areas that will most affect your bottom line.
But when you’ve gotten comfortable with testing, that can get very
creative. For instance, let’s say you have a $100 product that you are
offering on sale for $10 off.  How would offering 10% off instead of $10
off affect sales? You’re thinking it won’t because it’s the same
amount? You might be surprised .

How about testing more cheaply
and easily on your site what would cost more money to test elsewhere?
Like products you’re thinking of putting in your store, calls-to-action
for paid search ads, or offers in your email newsletter.

If you’re
tracking what’s important and have actively put in place ways to listen
to your customers, now it’s time to find out the best ways to deliver what they want.
So, use what customers give you to start developing hypotheses about
how you could improve their experience. Put your ideas on how to
accomplish the goal into action! If you do, you will start to have a
distinct competitive advantage.

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