Chinalytics Longmarch Blog

For the last Web Analytics of the year we have a special treat for you. The folks from Millward Brown are going to introduce Dynamic Logic , a solution that they hope will help to answer a key questions all brand advertisers face when doing online campaigns: How did a branding campaign impact the perception of and attitudes towards the brand for those users who have been exposed to the ads online? A question that is more important to many brand advertisers than understanding “traditional” website and banner KPI’s

Reader Tai Te, posted an interesting comment on the “The third party online metrics controversy” post, that I used to argue that 3rd party metrics are unnecessary to plan online marketing campaigns and and to evaluate campaigns success. This post generated a lot of interest due to Kaiser Kuo’s mention his the Digitalwatch blog.

Tai Te writes:

I highlight this comment because it echos common concerns about using web analytics metrics for campaigns planning and evaluation.
I can agree with this criticism for “one off” campaigns. It becomes mute though when you regularly or
continuously run campaigns (which is the case for most online advertisers) since you can build on experience. It also leads to best practices that accommodate media testing and continuous media optimization.
Testing and ongoing optimizations are critical for other reasons too. While reliable 3rd party
metrics will be able to tell you the number of impressions and clicks
you can expect for your campaign given a dollar number and media, these metrics will not allow you to predict the results of
the campaign.
Real campaign results (outcomes) depend on actions users
take on your landing page (e.g. buy, register, play video, learn more
etc.) I would argue that its these outcomes you should optimize your
media buying for.

My colleague Song Xing (Sidney) is gearing up to translate not only my
posts, but also posts of global web analytics thought leaders on his blog. If you prefer to read about analytics in Chinese, go there! Also tell your less English inclined friends about this great resources. Its a big step forward in our industry.
To take a look at what other thought leader write about analytics in
English, take a look at my blogroll in the sidebar or subscribe to my shared items feed. Or as a comprehensive references, buy

• Avinash’s book “Web Analytics: One hour a day”, which provides much more than the title suggest
• and Eric T. Peterson’s “The big Book of KPI’s” which remains an amazing reference.

While both “guru’s” are engaged in a vigorous debate about how “hard” or “easy”
web analytics is, I recommend you to read them both. They offer amazing
insights and have been my guide on this interesting ride since the
beginning.

One of the questions that regularly comes up in my discussions with friends like Kaiser Kuo and Bill Bishop,
is the lack of reliable 3rd party data for web traffic in
China.  Their beef is that data from sources like Alexa and iResearch
is unreliable and open to abuse.
My answer is usually quite simple: It really doesn’t matter that much for advertisers.
An effectively implemented web analytics program will provide all the
data that is needed for a comprehensive online advertising optimization effort.

1. Your media plan will tell you how much you spend on each media and each banner.
2. Campaign tagging will tell you how many visits came form each banner and each media. It enables you to calculate cost per landing page visit (CPV).
3. Goal tracking and conversion tracking will tell you how many
   visit from what banner actually ended in the desired end action on your
   site. This enables you to calculate the cost per end action (CPA).

These data sets enable effective optimization of media
spending, without relying on any data from the publisher or the ad
tracking vendor. All data is owned by the advertiser.

• For campaigns that emphasize brand awareness, CPV  is a good key
  performance indicator (KPI). It tells you how many times your ad has
  been seen by a visitor to the publishers site that found it interesting
  enough to click on and interested enough in your content to actually wait until your landing page was loaded.
• I find optimizing for CPV more effective than optimizing for CPM (Cost per 1K impressions) or CPC (cost per click).
• CPM is sub optimal, because an ad impression just means the banner has
  been loaded from the ad server (or the tracking pixel has been loaded
  from the tracking server to be exact). It does not mean that the ad has actually
  been seen or that, if it was seen, had any impact on the observer.
• CPC is
  sub optimal because in China many clicks on banners seem to be
  accidental and users will abort the loading process once they notice
  that are actually leaving the publisher’s site
• For campaigns with a specific end action (or a set of specific end actions) as campaign goal, CPA is the most
  relevant metric, since it allows you to directly link your ad spend
  with the desired return.

There are two drawbacks to this approach

1. It required continuous testing and regular optimization. (which you should do anyway)
2. It does not help investors or VC’s, since their main interest in
   tracking publishers traffic is gauging revenue potential by projecting
   its revenue potential. Well, maybe it helps to talk to advertiser who
   actually spend money on the site. The advertisers ROI could be a metric to understand if a publisher has the potential to grow its ad revenue.

As a nice follow up to my post on online metrics in the world of user generate content, I had the chance to have lunch with Sam Flemming of CIC / IWOM fame. While I have been somewhat critical of CIC’s reports, I do admire Sam as a visionary and Chinese internet pioneer. CIC almost single-handedly popularized Social Media marketing and measurement (they call it Internet Word of Mouth or IWOM) in China, a market that
has not even started to measure online activity on company websites effectively.
Most of our discussion focussed on how to integrate social media measurement, an area where CIC has patent pending technology, with web analytics data, CIC’s positioning towards agencies, using social media effectively and the Chinese Social Media environment in general. But one thing after the other (or Step by Step, as NKOTB said, yes I still can sing that song)

Chinese Social Media environment

• China has a very vibrant social media scene that is driven by BBS culture
• Social networks are growing out of successful BBS’ and this history makes them unique and uniquely different form popular western networks
• These networks are populated mainly by young netizens, high school and university students
• The discussions hosted on these networks cover a wide range of topics, many of them relevant to major consumer brands
• Currently there is no equivalent content rich network for business users (while there are LinkedIn clones they do not feature discussions), although companies like Alibaba and Xing are moving into this space

CIC’s positioning towards agencies

• Sam sees online agencies as partners for CIC and has positioned the company as a compliment. They will measure social media conversation and consult clients on a IWOM strategy, but it will not execute or manage IWOM campaigns or seed posts.
• In reality both agencies and clients don’t see this distinction as clearly and advertisers do position CIC and agencies as competitors. More communication is needed to create a win-win situation. Our  discussion was good step into this direction.

Effective usage of social media

• Once marketers understand that their brand is talked about online and how this influences their brand equity, their natural response is asking “how can I influence the ongoing discussion”. While this is a valid question, the easy answer (seed your own posts or hire a 3rd party vendor to control the debate) raises serious ethical issues and will, in the long run, destroy the trust and effectiveness of internet word of mouth. Especially multinationals should be aware of the risk to their reputation that comes along with faking posts.
• Sam believes that this traditional media approach (just shout larger than everyone else to make your message heard) is too simplistic and recommends advertisers to listen first then be understood. (Wise words if you ask me) Understanding the discussion going on online can inform advertising campaigns, since it works like a giant focus groups (free of charge). In a next step advertisers are also able to get almost real time feedback about users take aways from ongoing marketing efforts, and can adjust their campaigns to better resonate with their audience.
• IWOM tracking can also be an early warning system. Brands can learn about problems with their products, communication or distribution by listening to the unfiltered voice of the customer
• For brands to participate in the discussion (note the difference to ‘control the discussion’) Sam recommends a more sincere approach that centers around ‘real people’ sharing their personal opinion in a casual language.
  He notes the example of Dell, a company that effectively used the ‘personal’ blog of senior managers to enter into an open discussion with users that previously voiced their frustration on sites like Dell
  Hell. An interesting question will be how to translate such blog based effort into the BBS culture in China (Any ideas? Let me know in the comment section). I do strongly agree with Sam that in the
  long run only sincere and real participation will be rewarded. But China being China, many a company will look for quick fixes that will in the end harm their brand.

Integration of social media measurement & web analytics data

• To enable a better integration of IWOM data into regular web analytics reports, along the lines of Dennis Mortensen’s Online Business Media Quadrant Model, I highlighted some challenges of CIC’s current offering to Sam

1. Timeline: My main gripe with CIC’s “CIC data” report is timing. It usually covers a 1 month time frame, while ad campaigns usually start sometime in the middle of the month and end 4,6,8 or 12 weeks later.
   Aligning these timelines is almost impossible. In addition CIC’s report arrive with 2 weeks+ time delay after the month is over (most of this, I am sure, due to the insight mining that takes place), that is too long for kind of near time reporting I am looking for. Sam assured me that their “CIC Alert” product provides more flexibility in terms of timing (daily, weekly or monthly) and can be adjusted to follow the campaign duration. This type of reporting would integrate with Web Analytics efforts more effectively. To take that one step further we talked about access.
2. Access: Currently CIC delivers its reports in PDF formats by email. I would love to have a web interface, much like my web analytics tools, or IResearch’s IUserTracker (link in Chinese). That way I could download the quantitative info (number of posts, share of positive, negative, neutral) whenever I need it (I can wait for the qualitative insights by PDF), manage the keywords I would like to have tracked (product names, campaign names, competitors etc.), organize them into campaigns, specify the timeline I want to analyze and then export all this data to an Excel spreadsheet and compare and correlate to my other online data. Doesn’t that sounds like a nice little IWOM vision

• While I am not at liberty to share more details, I can say that Sam was certainly listening intently and I am curious what they have in the works. Stay tuned. I certainly will.

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After getting all worked up about JPMorgan’s metrics, my  colleague Sydney restored me to sanity today by pointing out that the table I was referring to, most probably was reporting only to Search advertising data. Can anyone confirm that the numbers make sense for Search? T.R.? Is there similar benchmarks for display advertising in the report? Too bad I was late downloading the whole thing as Kaiser Kuo recommended. But then again I was relaxing my mind and stomach from a stressful Germany holiday

Thanks Kaiser for pointing out this article at the China Web 2.0 Review with a summary of the China section of the new JPMorgan “Nothing but Net” report from Morgan’s Internet analyst Imran Khan and his team.
Apart from the interesting market size predictions (past data curtesy od CNNIC) the most interesting info for me was the average CTR and CPC numbers supplied for the China market (curiously called Price per click). I have been looking for industry
benchmarks for ages, and this could fill this gap. (Insert exclamations of happiness, for frenzy forward to friends here) But looking at these
numbers, they seem to make little sense. Click-trough-rates I have seen
globally (incl. China) are usually less than 1% (not >20%) and CPC
in China varies between USD 0.02 to USD 1. RMB 0.40 seems out of this
world. I checked with CNNIC and they claim innocence. Can anybody with a hotline to JPMorgan clarify?

Google is doing a great job in making Analytics accessible for everyone with Google Analytics. Now the key challenge  is how to use this new wealth of data effectively to drive change and increase the ROI. An interesting resource I recently came across is the "Google Conversion University" playlist at Youtube. Take a look at all these videos, but if you short on time, get the quick fix and watch Avinash talking about "Bounce Rate" my most favorite metric of them all. (note to self: find a topic to disagree with Avinash). For Chinese users, make sure you fire up your proxy before enjoying the wealth of learning available there.
And if you really want to do me a favor, share your experience with bounce rates for campaign landing pages with me in the comments or by mail florian-dot-pihs-at-ap-dot-mccann-dot-com.

It seems Avinash stirred up a lot of controversy with his post on engagement (see Sunday’s post). In a series of thoughtful comment I want to highlight Gary Angels response, which is leading the pack in terms of clarity among analytics experts that defend "Engagement" as a metric. I especially like what Gary responds to Avinash’s last point. He says:

• Engagement isn’t a proxy for measuring an outcome on a site at all. It’s more often a means of aggregating a set of outcomes into a single visitor score or segment. Since a set of outcomes can’t reasonably be described by just appending all of the events into one long name, the analyst is always going to have to pick a name that reasonably represents the overall concept. In many situations, and for many sites, “Engagement” does a pretty good job of carrying that baggage

My conclusion is that measuring Engagement can be an effective tool in the web analytics toolbox. But like every tool, it can used for good and bad purposes (much like nuclear energy, I guess). In order to use Engagement effectively we need to be aware of certain preconditions and caveats.

• Engagement is not a standard metrics that is the same for every company, or even every campaign of one company, which often makes it difficult to compare and improve on it over time.





• Engagement should never be an excuse not to define, track and analyze specific outcomes of a page. Without a clearly defined objective and target action a landing page has no value.





• What Engagement can do however, is summarize a set of outcomes (e.g. Video played, PDF downloaded, link clicked) and the compare this set of outcomes across different visitor segments (e.g. search vs. banner ads, media A vs. Media B, Landing Page A vs. Landing Page B). This can lead to valuable conclusions and actions.

Now the main challenge for non e-commerce sites is to prove beyond doubt, that a higher engagement leads to a better, faster or less expensive achievement of the objective. That again is very unique across companies and will often involve additional surveys.

Due to the Chinese National Day holiday I am little late on this one, but Avinash Kaushik just earned another feather in his guru cap with his post on engagement as a web analytics metric. His main points are:

1. Engagement is not a metric that anyone understands and even when used it rarely drives the action / improvement on the website.
2. Because it is not really a metric, it is an excuse. An excuse for an unwillingness to sit down and identify why a site exists. An excuse for a unwillingness to identify real metrics thatbmeasure if your web presence is productive.
3. It is nearly impossible to define engagement in a standard way that can be applied across the board. Definitions that exist are either too broad (to cover every nuance) or too narrow (hence very unique)
4. At the heart of it engagement tries to measure something deeply qualitative. Yet most efforts to measure it in our world tend to be hard core quantitative

Its great to see someone spelling it out so clearly, so please go ahead and read the whole post.

My own experience trying to implement Avinash’s suggestions for our own clients, suggests a deeper meaning between the lines of this post. An answer to the question “Why do Web Analytics practitioners still use Engagement as a metric, when most of us should share Avinash’s skepticism?”
Its because in most companies Web Analytics is in a silo, where it is not involved in strategic planning and decision making. Following Avinash’s methodology we end up asking strategic questions over an over again, but in many companies web analysts are not expected and / or not allowed to ask questions like:

• What is the objective of your site?
• What is the objective of this campaign?
• Can that objective be measured by analyzing clickstream data?
• What action do you want the user to take on the landing page?

These questions are owned by other silos, so we end up defining metrics that are under our control. We look at the data from our web analytics tools and hope to gain additional insights from “Engagement” however we define it. This may not solve the problem, but it is within our circle of influence.

So Avinash, what I hear your really saying is this: “Web Analysts of the world unite, and increase your circle of influence. Make Web Analytics a strategic function”. Oh, and one more thing: Don’t call your approach Web Analytics 2.0. Call it by its real name. Don’t hide behind a pretty moniker. Call it Strategic Web Analytics