Tối ưu hóa và hành động

Cho đến tận bước này, sau cả 3 bước trên thì phân tích web vẫn chưa đem lại 1 lợi ích nào cả (buồn và chán nhỉ?). Cho đến tận lúc này các khoản đầu tư đều là mất chứ chưa sinh lợi. Vì thế, bước thứ 4 – Hành động mới thật sự chứng tỏ được sức mạnh của phân tích web. Nó biến tất cả các kiến thức mà chúng ta thu thập được trong cả quá trình trước đó thành hành động thực tế, đóng góp vào sự phát triển của sản phẩm và công ty. Do đó dù ta có làm tốt các bước trên bao nhiêu, tốn nhiều tiền của bao nhiêu cho công cụ và nhân sự để phân tích, nhưng nếu thất bại ở bước 4, toàn bộ các khoản đầu tư đó đều xem như đổ xuống biển.

Với các insight (sự thấu hiểu – ở 1 mức độ nào đó) và cơ hội nhận ra ở bước 3, bạn có thể lập ra 1 kế hoạch hành động mới, điều chỉnh chiến lược, tối ưu các khu vực còn kém hiệu quả, … Tất cả những ‘hành động’ thiết thực đó cần phải được thực hiện, càng xuyên suốt (các bộ phận có liên quan) bao nhiêu, hiệu quả sẽ càng lớn bấy nhiêu. Và đây cũng là điểm phát sinh cho các công cụ, phương pháp phân tích và tối ưu hóa tiếp theo được đưa vào sử dụng như A/B & multivariate testing.

Kết quả và chu kì mới

Phần cuối của quá trình phân tích web rõ ràng là tuyệt vời nhất : kết quả. Một khi bạn đã tối ưu hóa các khu vực trục trặc của website hay của chiến dịch marketing, bạn có thể ngồi xuống và yên lòng rằng bạn đã làm tốt? Thật ra, còn 1 bước cuối cùng nữa: kiểm tra lại các Business Metrics 1 lần nữa và xem bạn đã được gì sau cả quá trình.

Chắc chắn các nỗ lực của bạn có những kết quả đáng kể và đó là thành công! Một trong số những thành công đó là bạn đang đưa công ty mình đi theo hướng data-driven organization (tổ chức hướng thông tin). Đừng quên thông báo cho tất cả mọi người trong có liên quan trong công ty về các kết quả đạt được. Để nhờ đó, họ hiểu tầm quan trọng của phân tích web, sự hợp tác của họ có ý nghĩa như thế nào trong thành quả của công ty và của bạn.

Lưu ý rằng sơ đồ quá trình 4 bước phân tích web là 1 sơ đồ tuần hoàn. Điều đó rất quan trọng cốt lõi! Chắc chắn là nó không bắt đầu hoặc kết thúc với chỉ 1 áp dụng đơn lẻ. Nó là 1 quá trình tiến hóa, trưởng thành thông qua hàng loạt các chiến dịch, thử nghiệm, phân tích nối đuôi nhau, cái trước bổ sung cho cái sau, giúp cái sau lớn mạnh hơn nữa, giúp cho công ty thành đạt thông qua hiểu rõ hơn khách hàng của mình và tối ưu hóa hơn nữa các hoạt động. Do đó, sau mỗi 1 chu kì như vậy là lúc để bạn nhìn lại các Business Metrics. Bạn có thỏa mãn với các kết quả? Công việc kinh doanh của bạn có thay đổi nhiều đến nỗi bạn cần tập trung vào các KPIs mới? Thế thì phải xác định các BMs mới. Và nó lại bắt đầu …

Bài viết liên quan:

Bước 1 phân tích web – Xác định KPIs (Business Metrics)

Bước 2 phân tích web – Báo cáo

Bước 3 phân tích web – phân tích

Các báo cáo gửi đi cho các bộ phận đều đặn (hoặc không đều đặn) cơ bản là để cho các ông bà bên đó nắm tình hình chung và phản ứng nhanh. Chứ chưa phải là sâu và insightful. Bởi vì các số liệu thu thập được nhìn thì nó là thế, nhưng thực chất “tại sao” nó lại như vậy thì không thể ngay lập tức hiểu được mà còn phải đào sâu qua nhiều lớp dữ liệu khác nhau.

Mà có thể sẽ có người hỏi tại sao phải đào? Vì ở đó có ‘cơ hội’ cải thiện, có insight. Chẳng phải ta đã lập chiến lược, dàn binh bố trận ở bước 1, ra trận, chiến đấu rồi thu thập dữ liệu ở bước 2, chẳng qua là chỉ chờ có ngày này – hiểu rõ những trận đồ mình dàn ra thành công ở mức nào, thất bại ở chỗ nào và hơn thế nữa : tại sao thất bại, và cuối cùng thì cái insight của khách hàng nên ‘vẽ’ lại như thế nào? –> để ‘bày keo khác’ hiệu quả hơn nữa ạ!

Tuy vậy, “phân tích” không chỉ dừng lại ở đó. Trong điều kiện lý tưởng thì bạn có thể tập trung nguồn lực đánh các cơ hội mà bạn đã tìm được. Nhưng khi phân tích được thực hiện đúng, thì nó luôn mang lại cho bạn nhiều cơ hội hơn là khả năng (nguồn lực) mà bạn có thể đầu tư để phát triển. Khi điều này xảy ra, bạn cần phải chuẩn bị cụ thể hóa (thành tiền) các tác động về mặt tài chính của các cơ hội trên rồi xác định mức ưu tiên cho chúng.

Bài viết liên quan:

Bước 1 phân tích web – Xác định KPIs (Business Metrics)

Bước 2 phân tích web – Báo cáo

Bước 4 phân tích web – Tối ưu hóa và hành động

Thế mới nói cái bước khổ cực nhất, gian nan nhất, dài dòng nhất, khó khăn nhất là bước đầu tiên kia vì nó dính líu đến gần như cả công ty, những bộ phận ngoài phân tích web, đến các trưởng phòng, đến leader. Còn giờ thì quá nhẹ nhàng.

Khi bạn đã xác định được các business metrics rồi, bạn đã có thể tiến hành thu thập dữ liệu. Công đoạn này có dễ dàng không, khả thi không phụ thuộc vào bước 1. Nếu bước 1 bạn làm tốt và rõ ràng bao nhiêu thì vấn đề ở bước 2 này sẽ dễ giải quyết bấy nhiêu, vì nó thuần kĩ thuật. Bô phận chịu trách nhiệm hiệu chỉnh công cụ phân tích web (có thể outsource hoặc có thể là chuyên gia được thuê của công ty) sẽ dựa trên các yêu cầu, KPIs từ bước 1 để implement vào công cụ phân tích web sao cho nó đo được các KPIs này và các chỉ số đóng góp khác giúp cho bạn hiểu rõ hơn các chi tiết phía sau hoạt động kinh doanh.

Nghe có vẻ khó? Thật ra thì không. Ví dụ đơn giản, giả sử bạn đang cố gắng thu hút thêm nhiều ‘khách hàng tiềm năng’ vào website của bạn. Bạn tạo 1 button trên trang chủ và khắp website của bạn với tên gọi “Yêu cầu thêm thông tin”. Một trong các KPIs của bạn có thể là visitor-to-lead conversion (số chuyển đổi từ khách vãng lai thành khách hàng mục tiêu). Giả sử, 3.2% tổng số khách vãng lai yêu cầu thêm thông tin. Thì dưới đây sẽ là mẫu cho 1 vài thông số dữ liệu hỗ trợ  mà bạn cần biết:

- Trang nào khiến nhiều khách click vào nút “Yêu cầu thêm thông tin” nhất?

-  Phần trăm người hoàn thành đầy đủ form điền thông tin?

- Sau khi họ điền thông tin, họ làm gì tiếp?

- Người ta làm gì sau đó khi họ không điền thông tin?

Cùng lúc đó, bạn nên cẩn trọng thu thập thêm cáo thông tin khác ngoài dữ liệu phân tích web để hoàn thiện ‘kịch bản’ mà bạn đang điều tra. Dữ liệu này có thể từ bộ phận Customer Services tiếp xúc khách hàng, các cửa hàng bán lẻ, các khảo sát thái độ người tiêu dùng  hoặc các thông tin về các đối thủ của bạn.

Cuối cùng, bạn cần phải xác định phải đưa báo cáo đi như thế nào:

- Ai cần các dữ liệu nào?

- Họ có thể tận dụng chúng như thế nào?

- Họ cần được báo cáo với độ thường xuyên như thế nào?

- Báo cáo dưới dạng nào?

Bước 1 phân tích web – Xác định KPIs (Business Metrics)

Bước 3 phân tích web – Phân tích

Bước 4 phân tích web – Tối ưu hóa và hành động

This award-winning agency produces beautiful creative communications work across print and web for big client names in the media, charity and corporate world. They have doubled in size over the past two years, securing a strong reputation for producing beautiful yet highly accessible, user-centred web communications and now have a need for a dedicated digital project manager. It’s important that you have worked in a small but busy and thriving design agency environment, and relish the culture, opportunities and challenges that a small company offers. Culture fit will be crucial for this hire, and it’s likely you’ll be very hard working and committed but fun, friendly, confident and collaborative. 

Your digital project experience will be solid – you must have proven abilities in managing multiple projects end to end, including planning and IA, defining and managing budgets and timelines, managing design and build with in-house teams and freelance resources, through to delivery, support and maintenance. You’ll also liaise directly with the client throughout the project lifecycle so must be a confident, empathetic communicator with proven relationship management skills. Whilst you’ll be naturally very organised and methodical, it’s important that you bring a flexible approach to projects rather than being overly process-driven. 

A good technical knowledge and ability to communicate technical concepts to non-technical people is also important, as is a passion for and broader understanding of the digital landscape including web analytics, user experience and web content.

Gill Arnold

0207 420 3538

gill@wearefutureheads.co.uk

Analytics can be very helpful to your website, blog, online communities and the like. Though Google Analytics is widely known, it is not the only way to measure and analyze the visitors surfing your webpage. Nonetheless, it still is a fair way to do so and is free. But times have now changed and marketing has evolved in every aspect. The tools for analytics have become more apparent and expressive in the form visuals. They are here to help you with internet marketing in UAE.

Image credit: www.makeuseof.com

Let me introduce you to one of the best visualization tools called the Heatmap by CrazyEgg. This tool easily shows you what’s hot and what’s not on your web page via a heat map. It lights up those spots every visitor has clicked on the page in colours ranging from red (most clicked) to blue (least clicked).

Basically, the heat map tracks all the information on visitor clicks during the allotted period of time and then transforms the visits visually. While this heat map is fascinating to visualize, the results it squeezes out are even more fascinating.

Like the Heatmap, there are other visualization tools for different purposes available in the market like Fidg’t, Digg’s visualization, Twitter’s visualization, Facebook’s visualization, Debategraph, etc.

Visualization just helps you understand your visitors better and helps improve your site. If other analytics tools are difficult for you to understand, visualization tools are the ones for you. Either adopt one, or let an SEO company in UAE like Online Marketing Dubai inform you about the results.

Sau đây là 4 bước trong phương pháp phân tích web được công ty ZAAZ khuyên dùng – theo Jason Burby và Shane Atchison trong cuốn Actionable Web Anlaytics:

1/ Xác định các chỉ số hoạt động – Business Metrics (KPIs)

Đây là bước đầu tiên, quan trọng nhất và khó nhất.  Dịch ra là “chỉ số hoạt động” thì hơi không chính xác, không ra hết cái nghĩa của chữ Business Metrics. Ý nói rằng các chỉ số này cần phải gắn với mục tiêu của doanh nghiệp, phải đo được hiệu quả hoạt động dựa trên yêu cầu của toàn bộ hoạt động kinh doanh. Khác với Campaign Metircs là các chỉ số chỉ áp dụng cho 1 chiến dịch nào đó.

Có nghĩa là các KPIs (key performance indexs) sẽ thay đổi tùy theo qui mô của hoạt động cần đo đạc. KPIs cũng sẽ thay đổi tùy theo cách nhìn nhận của nhóm về mục tiêu của hoạt động. Hễ mục tiêu thay đổi thì KPIs cũng sẽ thay đổi. Do đó sẽ không thể nào có 1 công thức chung chung cho các việc xác định KPIs, càng không thể nào có 1 bộ KPIs chung của 1 mô hình kinh doanh hay chiến dịch nào, vì hoàn cảnh thực tế có thể khác xa cái mô hình của bộ đó.

Ý ở đây có nghĩa là những ai muốn học thuộc lòng, bê nguyên xi mà không cần hiểu gì hết thì sẽ sai lầm, cần hiểu các chỉ số cơ bản, hiểu rõ web analytics, xong rồi trong từng trường hợp sẽ xem xét lại KPIs là gì. Và do đó, KPIs không phải là các thông số thống kê trên website như visits, visitors, time on site, … Đây là những chỉ số cơ bản – basic metrics.

Để xác định được các Business Metrics (BMs) – chỉ số của toàn hoàn động kinh doanh, bạn cần phải nhìn vào website của mình trong bối cảnh toàn chiến lược của business. Bạn cần phải nhìn các hành vi của người dùng vào website của bạn trên cơ sở mối liên hệ của nó tới các mục tiêu của toàn doanh nghiệp. Điều này nghe có vẻ khó nhưng cũng không đến nỗi. Cách dễ nhất để suy nghĩ về hành của người dùng là tự hỏi chính mình rằng mình muốn người ta vào website của mình để làm gì. Trong phân tích web, chúng ta thường gọi các hành động này là các hành vi mục tiêu – desired behaviors. Các hành vi này có thể là :

- các đường đi mà bạn mong chờ người dùng sẽ thực hiện nhất trên website.

- các sản phẩm bạn mong họ sẽ mua.

- các yêu cầu marketing mà bạn mong họ sẽ liên lạc đến.

- hay chỉ đơn giản là họ chuyển từ trang chủ vào 1 trang nào đó được mong chờ.

- …

Nói chung là có rất rất nhiều các kiểu desire behaviors bởi vì có vô vàn các business objectives khác nhau. Điều quan trọng là phải biết business objectives là gì, từ đó mới có thể giới hạn chính xác các kiểu hoạt động của người dùng mà bạn mong chờ nhất. Cũng bởi vì vậy, nếu ta xác định nhầm business objectives (có thể vì mơ quá cao, hoặc nghĩ quá thấp) thì bản thân chiến lược kinh doanh cũng trật rồi, nên web analytics sẽ trở thành vô dụng.

Sau khi xác định được các desire behaviors, thì phải cụ thể hóa nó thành tiền. Nói cách khác, bạn phải đánh giá cho được giá trị của mỗi desire behavior (thực hiện bởi 1 người dùng) đối với hoạt động kinh doanh của bạn. Ví dụ, nếu bạn đang cố gắng kéo về nhiều khách hàng cho 1 đợt trao quà khuyến mãi nào đó, bạn phải có thể xác định được khả năng mà mỗi món quà khuyến mãi sẽ lấy đi của bạn đổi lại bạn phải đạt được bao nhiêu khách hàng vào mua.

Bước 2 phân tích web – Báo cáo

Bước 3 phân tích web – Phân tích

Bước 4 phân tích web – Tối ưu hóa và hành động

Web of Science is different because it is a “citation index.” That is

“WOS is more like google than anything else, because it gives weight to some results.” – Joe Janes

Citation databases – “one way to go after valuable literature is to find it by subject. Another way, a certain author is really well known lets read her.

“I want to find every article that cites a really good article. If an article cites, i.e. links to, a certain paper, author, book?”

Web analytics comes from bibliometrics. Bibliometrics is the study of how an author or work is cited in scholarly works.

In other words, WOS finds the “links” between scholarly articles in the same way that Google finds hyperlinks between Web pages.

Just trying to search an author, I had a difficult time.

Errata (pl.) – an error in printing or writing.

Check out Journal Citation Reports in ISI Web of Science. – look at relevance measurements, i.e. Eigenfactor, Impact Factor

——————————

Issues with Scholarly Publishing, both monographs and journals

All of the forces in scholarly publishing are interlocked, so focusing on one aspect will not fix the problems with the system.

Increasingly, Scholars are working together collaboratively and cross-disciplinarily.

Scholarship and research are discipline dependent, i.e. depends on if your a scholar in physics, lit studies, communication or engineering

Forms of publishing evolving, i.e. principally to electronic format, but also in other ways.

Issues of assessment in the new scholarly publishing environment.

Tenure and promotion are integral components to the scholarly publishing regime.

Tenure is a guard for academic freedom. Tenure is a lifetime job.

You’re tracking engagements on your website, but are your analytics giving you the insights you need?

Avinash Kaushik has some interesting thoughts on measuring Engagement in his latest book, Web Analytics 2.0.  Here are the highlights:

Do ‘time on site’ and ‘repeat visits’ really measure engagement?
Fact is, measuring engagement with only quantitative data (web analytics) is a challenge.  Your web analytics tell you the “what”, but not the “why”. 

For instance, your site visitor spent 15 minutes on your site, but your analytics won’t tell you whether or not your visitor spent happy or frustrating minutes with your site.   Also, your analytics won’t tell you whether or not your visitor ran off to fix a sandwich for 10 minutes, meaning, he really just looked at your site for 5 minutes.  Further, your visitor came back 2 mores times (which your tool will count as a returning visitor) , but was he returning for updates or trying to find your contact information for the third time?  Unless he closed a sale (which assumes a happy experience), you really don’t know if your customer had a positive or negative engagement with your site.  You get the point…

Avinash notes that web analytics is “limited in that it can measure the degree of engagement, but not the kind of engagement.”

Based on this definition, recency and frequency of site visits really reveal just the degree of engagement.  So, five visits in a one-week period (the degree of engagement) doesn’t tell you if the visits were negative or positive, which Avinash refers to as the “kind of engagement”. 

In summary -

Degree of Engagement
- Web analytics measure the degree of engagement.
- It’s important to measure the degree of engagement along with outcomes.  Outcomes will tell you how successful the engagement was.  For instance, did the customer end up buying from you (outcome), as a result of coming back to your site a few times?

Kind of Engagement
- Surveys and  primary research measure the kind of engagement someone had on your site.
- Most likely someone who is making repeat purchases from you is happy with your products and service.  They keep coming back for more.

In order to measure engagement on your site, you should look at both - degree and kind of engagement – web analytics and qualitative data.

What are your thoughts on this and how are you measuring online engagement?

This morning we launched the ‘Marketing Cloud’ – (#mktgcloud) (#datarati) Database Marketing in the New Frontier at Sydney’s Hilton Hotel.

When we started the company 6 months ago, we wanted to find an organisation that we could support and one that we could give back to. We wanted to find an organisation that we had history with and one that was in line with our own company values.

Above all, we wanted to ‘Inspire’ our fellow digital and data-driven marketers!

Inspire them to challenge the status quo of the past and educate them on the new frontier of database marketing, the ‘Smarter’ way of using digital behavioural data to acquire new customers and retain existing customers.

As a result, we thought there was no better organisation to support than that of the Inspire Foundation www.inspire.org.au and their website www.reachout.com

This morning we raised $400 for them at our breakfast briefing. For every $50 raised, 5 Young Australians are provided access to Inspire’s programs  to help find the support they need.

The event this morning was sponsored by the world’s fastest growing marketing automation database provider (Marketo).

For those who were unable to attend this morning, the event video was captured by our friends at Viocorp and will be made available shortly.

Stay tuned this week for more!

What are your basic questions when you open your Web Analytics tool at work?

1. How many visitors came to our Website?

2. How long did they stay?

3. How many pages did they visit?

Simple questions that anyone can answer. How you interpretate the data is another story.

I am following Avinash Kaushik on his blog to keep up with his great articles and work. I read his book “Web Analytics an hour a day” and he came out recently with a new book. Web Analytics 2.0.

Claude

Dennis Mortensen, director of data insights at Yahoo!, has published a white paper on his blog on web analytics as it pertains to Microsites. It is important to remember that microsites and their parents must be treated differently. Download the white paper here

In another guest post on the Visual Revenue blog, I discuss the results of my second cookie survey.

*****

I recently surveyed 100 “internet-savvy” people to understand their attitude towards cookies and whether or not they considered cookies to be a privacy or security concern. As a follow-up, I surveyed another group. This time, my focus was the general internet user, who would not beexpected to understand the use of cookies. Getting to my target of 100 took slightly more time in this case as the respondents were not as engaged with the subject!

The survey was posted on a group unrelated to web analytics on LinkedIn.com, on Facebook.com, as comments on some blog posts on the subject of internet privacy, and in 15 random categories on answers.yahoo.com. It was also circulated by email (thanks Andy, Mom, and friends!). To my knowledge, the respondents came from at least 8 countries.

In my previous survey, I asked 5 questions which assumed at least a basic understanding of cookies. In this survey, because I could not make the same assumptions, I asked 6 slightly differently-phrased questions, most of which had a yes/no answer and an optional comment field.

The Results

1) I know that cookies are put on my computer by websites that I visit.

The vast majority of those surveyed knew that cookies were placed on their machines, although several of those who responded “Yes” were under the impression that cookies were only placed by certain types of sites, like forums or social networks.

2) I know what cookies are and understand what they are used for.

Although more than half of the respondents claimed to understand cookies and their usage, quite a few of those who answered “Yes” felt that cookies were solely used for website preferences or helped to load a page faster. The overall understanding seemed rather vague (some considered them to be rather benign viruses) though many grasped the basic concept. It is interesting that although 84% know that cookies are placed on their computers when they visit a website, not all of those have tried to find out what cookies actually are.

3) I know the difference between 1st-party and 3rd-party cookies

Given the fact that 57% of respondents claimed to understand cookie usage, it is interesting to note that only 35% knew the difference between 1st-party and 3rd-party cookies. However, this segment did appear to genuinely understand the difference between the two types of cookie.

4) I have customised my browser settings with regard to the treatment of cookies.

The responses to this question were interesting. Of the 35% who responded that they had customised their browser settings with regard to the treatment of cookies, several admitted that they had customised their settings, but that they had no idea what the settings now were. On the other hand, there were several whose set-up meant that they were prompted for permission every time there was an attempt to set a cookie. Some blocked all cookies while some accepted them all, so there was no one setting that stood out as the most adopted.

The majority, those who responded that they had not customised their browser settings, commented that they had not changed their browser settings for a variety of reasons – because they did not know how to, because they would not know what they should change them to given their understanding of cookies, because they knew the default settings of their browser or because they regularly used an application to clear out cookies. We can infer from this then that most of the respondents use the default browser settings, which are often to allow 1st-party cookies and reject 3rd-party cookies (though not always: Chrome, by default, accepts all – you can then choose to reject all or “restrict how 3rd-party cookies are used” – I haven’t been able to find out exactly what this means (though I can imagine) but would be curious to know).

I asked this same question in my first survey, the response to which was YES: 42% and NO: 58%.

5) When I visit a website, I look for and read the privacy policy.

Though this question is not directly related to cookies, my interest included attitudes towards privacy on the internet and overall security concerns. The overwhelming feeling that I got was that “life is too short” for reading privacy policies. In addition, all privacy policies were considered to be the same and over-complicated with legal jargon which could hide any dubious statements. In general, it seems that privacy policies are considered a nice-to-have element of a website, but not a crucial part of a visitor’s navigation behaviour.

6) I have privacy concerns specifically regarding cookies being set on my computer by websites I visit.

A similar question in my previous survey (I have concerns about cookies being misused to compromise my privacy) generated exactly the same results. In this case, however, the reasons were slightly different – the distrust of cookies centred more around data being sold on, unwanted viral material etc than profiling and targeting. There was also distrust stemming from a lack of understanding of what cookies are and what they can be used for.

In a fascinating twist, quite a few of those who did not have these concerns said that it was because they did not understand what cookies are and what they can be used for!

Generally, knowing what cookies are does not seem to be a barrier to acceptance. In addition, ignorance of what they are does not seem to be a barrier to acceptance. Most of those surveyed did not know of any problems caused on their computers by cookies and for that reason did not fear them. This did not mean that they knew what cookies were or even had much interest in finding out. Again, as in the first survey, to a large degree, convenience is an important consideration – knowledge that, for example “Bebo forgets me if I delete cookies” outweighs any other possible concerns.

So, all in all, good news for the 1st-party cookie (depending on the default browser settings of your website visitor). For the 3rd-party cookie, however, apathy may mean that it is often blocked and largely misunderstood – like me, as a child

By STEVE LOHR

A TOUR of its carefully tended, 300-acre corporate campus here leaves little doubt why surveys, year after year, rate the SAS Institute, the world’s largest private software company, among the best places to work.

There is the subsidized day care and preschool. There are the four company doctors and the dozen nurses who provide free primary care. The recreational amenities include basketball and racquetball courts, a swimming pool, exercise rooms and 40 miles of running and biking trails. There is a meditation garden, as well as on-site haircuts, manicures, and jewelry repair. Employees are encouraged to work 35-hour weeks.

Academics have studied the company’s benefit-enhanced corporate culture as a model for nurturing creativity and loyalty among engineers and other workers. Six years ago, in a report on “60 Minutes,” Morley Safer called working at SAS “the good life.”

But that good life is under threat today as never before. SAS’s specialty, a lucrative niche called business intelligence software, is becoming mainstream. Free, open-source alternatives to some of the company’s products are increasingly popular. On the other end of the spectrum, the heavyweights of the software industry — Oracle, SAP, Microsoft and, especially, I.B.M. — are plunging in and investing billions of dollars.

“It will be a dogfight,” says Bill Hostmann, an analyst at Gartner. “SAS has never faced a competitor like I.B.M. And I do think I.B.M. sees SAS as a big, fatted cow.”

The term “business intelligence software” applies to a wide range of products and services, but all the technology is aimed at helping businesses mine nuggets of insight from mountains of data. SAS has traditionally specialized in advanced software to analyze huge data sets and to generate predictive statistical models for large corporations and government agencies.

Credit card companies, for example, use SAS to detect unusual buying patterns in real time, and to spot potentially fraudulent charges. Giant retail chains use SAS to tailor pricing and product offerings down to the store level. Telecommunications companies use SAS to identify the few thousand customers, among millions, most likely to switch to another cellphone carrier, and to aim marketing at them. SAS software is also used to parse sensor signals from North Sea oil rigs, combined with weather and structural data, to predict failure of parts before it happens. Of the 100 largest companies worldwide, 92 use SAS software.

But as the stream of companies’ collected data turns into a torrent, SAS and other software companies are trying to find new ways to harness it. The information is generated not only by computerized systems for tracking operations, customers and sales. It also comes from new data sources like Web site visits, social network chatter and public records accessible over the Internet, as well as genome sequences, sensor signals and surveillance tapes, all in digital form.

This data explosion, experts say, is an untapped asset at most companies, which lack the tools and skills to exploit it. Yet the long-range potential, they say, is to use this data for far more fine-grained analysis of markets, customer behavior and operations, making business more of a science and less a seat-of-the-pants art.

“Now, the data is available so business can move toward evidence-based decision-making,” says Erik Brynjolfsson, an economist and director of the Center for Digital Business at the Massachusetts Institute of Technology. “This market is a huge opportunity.”

That opportunity is not lost on SAS. “Our advantage is the incredible depth of our technology, developed over years and applied to specific industries,” says James H. Goodnight, the chief executive and a co-founder of SAS. “No one can match our toolbox.”

Indeed, no one underestimates SAS’s technical prowess. The big question is whether the company’s seemingly pampered culture can embrace the higher-octane institutional metabolism that it will need to succeed.

“We know we have to change — no question about it,” says Jim Davis, 51, a senior vice president at SAS. “Our market space has changed dramatically in the last 18 months or so, more than at any time over the 33-year history of the company. We can’t sit back. Things are only going to get faster.”

THE company traces its roots to a time when computing was costly and for the few. Originally called Statistical Analysis System, it was founded in 1976 by Mr. Goodnight and three colleagues from the agricultural statistics department at North Carolina State University. Its techniques were initially used to calculate the intricacies of soil, weather, seed varieties and other factors to improve crop yields.

To build an audience, Mr. Goodnight spent nights packing up boxes of computer tapes and manuals, which he sent to university and corporate researchers. Soon, companies wanted him and his academic colleagues to develop software tools tailored for industry. In 1976 at a users’ conference, 300 or so people showed up, many from business.

“That was pretty much an ‘aha’ moment for us, that it was time to expand beyond the university,” Mr. Goodnight recalls. “It was a little scary, cutting the academic umbilical cord. But I was convinced we could do it.”

He and his colleagues at SAS developed their own programming language and software tools, and designed them for eggheads like themselves. Users were analysts with Ph.D.’s, working with programmers and employed by the largest companies at the forefront of using computing in their businesses, including banks, national retailers, insurers and drug companies.

SAS invested heavily in research and development, and even today allocates 22 percent of the company’s revenue to research. The formula has paid off in steady growth, year after year. Revenue reached $2.26 billion in 2008, up from $1.34 billion five years earlier.

Yet the company also faces the classic challenge of being the innovative pioneer — enjoying rich profit margins but facing new competition from rivals seeking to gain market share with lower prices and substitute technology.

In the last two years, the major software companies have scooped up companies in the business intelligence market. Among the larger moves, SAP bought Business Objects for $6.8 billion, I.B.M. bought Cognos for $4.9 billion and Oracle picked up Hyperion for $3.3 billion.

Still, those companies compete in the broad swath of the business intelligence market for reporting and analysis products. Such data on sales, shipments, customers and operations amount to a numbers-laden portrait of the recent past. The SAS stronghold is a more sophisticated kind of software typically called “advanced analytics and predictive modeling,” which uses historical and current data to try to peer into the future and model likely outcomes.

The competitive thrust that really grabbed SAS’s attention came in late July, when I.B.M. announced that it planned to pay $1.2 billion for SPSS, a maker of predictive modeling software. I.B.M. has placed SPSS and Cognos into a new business analytics and optimization group. That business will be supported by 200 scientists, and the company has said it will retrain or hire 4,000 consultants and analysts to work in the group.

“This is the big growth strategy for I.B.M., the company’s next big play for this decade,” says Ambuj Goyal, a computer scientist who is general manager of I.B.M’s business analytics software unit. “SAS comes from the legacy world of statisticians and programmers. The real opportunity is in deploying this technology broadly in corporations.”

To counter I.B.M. and others, SAS is looking to forge a tighter relationship with a big technology services company. It is also shortening product development cycles to 12 to 18 months, down from 24 to 36. “That’s what the market expects,” Mr. Davis says.

The most sweeping change is the company’s move toward the Internet model of software delivery — as a service that customers tap into over the Web, much as Google and other Internet companies do. SAS has dipped its toe in, with some initial products. But a major expansion is planned, supported by a sprawling $70 million data center scheduled to begin operating next year.

The remotely delivered software is part of a drive to broaden the market for SAS technology beyond an elite corps of quantitative analysts and into the rank-and-file of corporate professionals.

Analysts say the company’s strategy looks sound, even if the outcome is uncertain. “SAS has to do a lot of things right to succeed,” says Peter Sondergaard, senior vice president of research for Gartner. “But if it executes correctly, it could be a winner.”

ACROSS its campus here, there are signs that the SAS culture is evolving with the times. Rick Langston, 54, a senior software manager who joined the company 29 years ago, smiles and shrugs when asked about the 35-hour workweek. After leaving the office, Mr. Langston routinely checks on work e-mail at home.

These days, he explains, SAS is a global company with far-flung project teams, and overnight e-mails can resolve problems and speed things along. Deadline work to meet product development schedules, he adds, can mean long hours at times. “But this is certainly not a place where you are working 60-hour weeks, week in and week out,” he said.

To be sure, the corporate cocoon in Cary can breed insularity. SAS, for example, was slow to recognize the brewing challenge from free, open-source alternatives to some of its products. A free programming language and set of software tools for statistical computing, called R, has become increasingly popular at universities and labs.

The company shifted course earlier this year and modified its software so programs written with R work seamlessly with SAS technology. “Shame on us for not engaging more with the open-source community,” says Keith Collins, senior vice president and chief technology officer. “But we’re committed to doing that now.”

THE architect of the SAS culture is Mr. Goodnight, a lanky, laconic billionaire. The benefits have built up gradually over the years as a series of pragmatic steps, he says. The day-care program began after a valued employee was about to leave to take care of her young child. The on-site medical checkups grow out of the belief that “good health is good business,” he says.

Today, SAS estimates that its health care center saves the company $5 million a year, by providing care more cheaply than an outside insurer and by not having employees leave the campus for doctor’s visits. Employee turnover at SAS averages 4 percent a year, versus about 20 percent for the overall software industry.

The office atmosphere is sedate. There are no dogs roaming the halls, no Nerf-ball fights, no one jumping on trampolines — no whiff of Silicon Valley. The SAS culture is engineered for its own logic: to reduce distractions and stress, and thus foster creativity.

“The SAS model is sensible and durable; there’s nothing faddish or ephemeral,” says Richard Florida, a professor at the Rotman School of Management at the University of Toronto, who has studied SAS and is the author of “The Rise of the Creative Class.”

During the technology boom at the start of this decade, SAS considered a drastic change in its model: going public. Goldman Sachs bankers were brought in as advisers, and in 2000 SAS recruited a former Oracle executive, Andre Boisvert, as its president.

Under Mr. Boisvert, SAS installed a new financial reporting system and paid the sales force incentive commissions rather than salary only. But when technology stocks plummeted, the appeal of selling shares to the public also receded. Mr. Boisvert resigned from SAS in 2001 and is now an independent investor and consultant.

Mr. Goodnight recalls those days as a brief period of New Economy surrealism, and going public as a path wisely avoided. SAS, he says, is a culture averse to the short-term pressures of Wall Street, which he characterizes as “a bunch of 28-year-olds, hunched over spreadsheets, trying to tell you how to run your business.”

Unlike many other tech companies, SAS has had no recession-related layoffs this year. “I’ve got a two-year pipeline of projects in R & D,” Mr. Goodnight says. “Why would I lay anyone off?”

Mr. Goodnight, though 66, has no plans to retire himself. His fingerprints, colleagues say, remain all over the business, especially in meeting with customers and in overseeing research.

He is not only a statistician, but also a bit of gambler who enjoys calculating his chances. For example, he is co-author of a paper that simulated millions of possible outcomes in blackjack.

Mr. Goodnight regards his new rivals the way a confident card player might. He likes the odds, and he likes his hand.

“We’re pushing as fast as we can to stay ahead — on the cutting edge of everything,” he says. “We’ll do fine.”

Search engine optimization is the process of tweaking a website to appear in the top listing of any search engine. Search engines were introduced to enable people to find organized information from the net using specific keywords. SEO is carried out by almost all websites who appear at the top of search engine listings. SEO can be carried out using software, online applications, and custom professional services.

Search engines such as Google typically have recommendations for sites to be configured to increase their search-ability. Essentially, SEO is developing a relationship between the informational site and the search engine it wishes to appear on. There is a great deal of free information on the web regarding SEO, but understanding and using it is often a difficult task. Coupled with the fast-pace at which relevant SEO information changes – it is very difficult for individuals or hobbyists to be successful with SEO.

The terms used among SEO professionals are usually enough to push most people away from managing their own SEO. This creates a problem where sites pay a great deal of money to have ‘SEO Professionals’ come in to optimize their site – but there is no one way to hold these ‘professionals’ accountable for their work. This problem created the need for online applications that automatically do the work of  SEO professionals, and display statistics related to the sites progress. This way users can see the effectiveness of the work being done.

When SEO first became popularized, the only tools available were downloaded applications and SEO professionals. There were many problems with these two methods – software often contained viruses or were too general, and professionals were expensive. This led to the development of rich internet applications that were inexpensive, customized to the user, virus free, and have the ability to track results easily. These rich internet applications allow the user to use the same tools as SEO professionals without having to fully understand the vast technicality of SEO.

The whole process is fairly easy to understand when the details are removed. Search engines have little robots that go to every website. These robots are given rules by the search engines, and told what to like. The robots then report back to the search engine and say “I like this site, I give it a 4 out of 10”. Then the search engine displays that site in the 4^th slot when people search a topic that is covered by the site. While very general, this is 90% of what is going on with SEO. The problem comes into play with convincing the robots to like you.

Many SEO professionals will try to trick the robots. Methods used by SEO professionals are done to give a short-term increase in traffic so that websites have to continue using the expensive SEO professional to maintain their traffic. Google is constantly finding ways SEO professionals take advantage of the system, and block their methods. The SEO professionals just keep coming up with new tricks. This behavior not only hurts the relationship between the site and search engine but can ultimately lead to a website being banned from the search engine entirely. Be careful who you trust with your SEO…we recommend that people stay away from downloads in general – but to also find a good online SEO suite instead of using expensive SEO professionals.

Lytico makes complex SEO features easy for everyone to understand.  Increase website traffic with Advanced Segmentation, Custom Reporting, Motion Charts, and more. For more information please visit us at: http://www.lytico.com

]]> http://spacefillerpling.wordpress.com/2009/11/21/google-analytics/ Sat, 21 Nov 2009 18:52:34 +0000 spacefiller http://spacefillerpling.wordpress.com/2009/11/21/google-analytics/

Google Analytics is very useful and free tool to help you measure your Online Marketing success. Here are a couple of pointers what you can see and how to interpret the analytiocs to optimise your results:

• Dashboard
  visual and statistical overview of your account. Compare current and past traffic, (scroll over graph to see) number of visitors, evaluate the current & previous month’ performance
  Key metrics:
  - Number of page views?
  - Number of average pages per visit
  - Average time spent on site
  - Bounce rate (% of visitors that do not progress beyond the landing page – expresses that users diodn’t find content relevant)
• Visitors
  insight into the traffic of youre site
  new visitors, returning visitors –> check loyalty
  Map Overlay: check which global areas drive most traffic
  length of visit, browser capabilities, languages
  Define your target audiences for geo- or language targeting for PPC, incl. more area-specific keywords for SEO
• Traffic sources
  Direct- (True TypeIn, Bookmarks), referring- (Listings, directories, reference sites), search engine traffic (paid & organic combined)
  If you use AdWords, you can use auto-tagging to differentiate paid from organic search. Manually tag campaigns on other ad platforms such as Facebook, Twitter etc. Improve post-click activity
• Content
  which landing pages attract traffic from which keywords, keyword research, bounce rates, click path analysis
• Goals
  Urls tagging allows Goals to be set-up for conversion measurement (e.g. “Thank You” page after filling in a form or purchasing a product), Set them under “settings”, compare how different traffic sources, keywords & types of visitors achieve goals, work out CPA, set up a funnel (expected path from landing page to goal completed page) –> improve conversion rates

Web analytics: Tell how your users are interacting with you website

1. Analyse behaviour data that infers the intent of a website’s visitor
   Check the referURLs, search terms & find our which problems visitors are expecting your site to solve.
   Click stream: Click density analysis, segmentation, key metrics that define the visit and content
   “intent interferences”
2. Analyse  outcome metrics that show how many visitors have performed the goal actions on a web site
   Analysis of KPIs to find room for improvement
   Orders/ Leads: Revenue (How, Why), conversion rates, problem resolution
   “Nuances of outcome”
3. Analyse the user experience
   Test why users acted the way they did & how it can be influenced
   Research customer satisfaction
   A/B split testing, multivariate testing, listening labs, single page heat maps
4. Segment your visitors & target your audience
   Segmentation based on Referral URL analysis:  Search Engine, True Type-In, PPC, referral site etc. Check conversion rates, click path, landing and exits pages
   Optimis your landing pages to improve conversion
   Check connection speed, operating system, browser and optimise for targeted audience
   Segmentation based on geographical location (country, provinces, towns)
   First timw vs, repeat visitors

Design your website with clearly defined goals in mind. A goal = action that results in revenue for a company
Define your objectives clearly (e.g. enquiry or contact from client, newsletter sign-up, downloads etc) and then start the 2 phases of the conversion process:

Phase 1: Analyse

Optimise your conversion rates by braking goals down into steps = events/ micro conversions.
- Funnel analysis/ click path analysis to influence that goals are achieved.
- Check conversion rates on each event page, identify problems, optimise pages & prepare for the consequences (i.e. prepare your sales team)
- Aim for a clean, uncluttered site with clear information achitecture and easy navigation
- Clearly indicate youer call to action to complete the desired action & be honest about your promise
- make the user feel comfortable with the conversion and its associated process.

• track and collect the right data
  Log file analysis: clicks taking place on server, accurate raw data, record visits from search engine spiders & failed requests 
  Page tagging: capture more information, third party service, report on events, Java script required

Once you have more clarity on the conversion funnel and you know where you have gone wrong, solve the problem by taking first a general macro site perspective (increase overall conversion) and then a micro page-level perspective and testing the impact of the solutions.

• Characterise your visitors
  Check entry page, landing page, exit page, visit duration (length of time in a session), referer (internal, external, search, visit, original), click through/ -rate, Page views per visit 
  Optimise the way your visitors reach your site & how they move = click path
  Reduce the barrier of entry (in terms of thought and time), push the user to do a particular activity (e.g. length of stay on a page, clicking through to a specific other page etc.)
• Characterise your content:
  Page exit ratio, single page visits, bounces (single page view visits), Bounce rate
• Track, analyse and optimise your conversion metrics
  Event (recorded action), conversion (completing a target action), Impressions (brand/ advert exposure), Opens (email)

Define your test variable for conversion optimisation. Only test one at a time to not get confused. E.g. test different calls to action, shapes of buttons, color schemes, wording etc. but always only one at a time. Settle for the best and move on to the next one. A/B split-tests can be a very useful practice here.

Tip: Try a question to open your call to action. The sub-conscious mind will respond “yes, I am actually interested”. Then offer your solution, explain briefly the benefits and finish with the promise of better things to introduce the opportunity to complete the desired action - e.g. fill in your email address for subscription.

Lastly, check what is happening after the user completed the desired action. Allow users to share additional information with you/ share feedback to further improve the user experience in future.

Đọc cuốn Actionable Web Analytics có 1 đoạn ví dụ thật thú vị, chép lại cho bà con đọc chơi:

“… Ví dụ, có 1 công ty sản xuất phần mềm tên X đã xác định 1 nhiệm vụ tiên quyết là phải thu hút được càng nhiều người càng tốt vào website điền vào mẫu hồ sơ thông tin của họ để đội sales có thể liên lạc. Làm thế nào đạt được điều đó? Phân tích web đã dạy cho chúng ta rằng luôn luôn có những cách hết sức ngu ngốc để kéo hàng đống người vào để đăng ký 1 cái gì đó. Đối với công ty X thì cách đó là cho người ta 1 cái gì đó, thí dụ như 1 cái iPod Nano chẳng hạn. Thật chẳng may, với cách này, công ty sẽ chi cả đống tiền để thu về một mớ ‘đầu mối’ (sale lead) chất lượng kém.  Sẽ có hàng đàn người đăng ký vào website, điền form chỉ để nhận phần thưởng. Đó có phải là 1 ý hay khi phải trả lương cho nhân viên sales phí thời gian liên lạc với những người này?

Thực tế cho thấy, nếu công ty X thật sự làm theo cách này, thì họ sẽ có thể nhận được rất nhiều đăng ký mới trong 1 thời gian ngắn. Điều đó cũng có nghĩa là công ty X sẽ gây phiền hà cho rất nhiều người không hứng thú với việc nhận cuộc gọi từ nhóm sales của công ty. Nhưng trên hết, chất lượng leads của công ty sẽ tụt xuống gần zero, nghĩa là nhóm sales sẽ mất thời gian gọi điện thoại mà chả thu được gì.

Kết luận: điều quan trọng là đừng bị kích động sử dụng phân tích web cho những mục tiêu ngắn hạn. Phân tích web mà không gắn kết với 1 chiến lược marketing toàn diện lâu dài (tập trung vào các mục tiêu trọng yếu của công ty) thì cũng giống như một chiếc tàu không có thuyền trưởng. Các thủy thủ, thuyền buôn và những người khác có thể chạy lòng vòng làm các cải tiến nhỏ cho hoạt động của tàu, nhưng cuối cùng có thể làm cho tàu mắc cạn. Tập trung quá kĩ vào các loại tỉ lệ chuyển đổi (conversion rates) mà bỏ quên hoạt động thương hiệu và các nhiệm vụ không thể định lượng được khác có thể đưa đến thảm họa. Luôn nhớ rằng mọi thứ đều đi theo các mục tiêu chính của toàn business thì sẽ tiến đến thành công.”

Bản thân tôi cũng đã được biết một trường hợp tương tự như ví dụ trên từ công ty của 1 người bạn. Công ty cũng tặng quà cho khách hàng bằng cách kéo hàng đống người vào website đăng ký. 1 lượng teen hùng hậu đã được “huy động” vào chiến dịch này trong khi khách hàng mà công ty hướng đến hoàn toàn không phải thuộc giới này. Sales nhận được 1 danh sách toàn là teen nên chẳng buồn gọi điện thoại –> thất bại.

Vẫn chưa nhìn ra chỗ trục trặc trong cái chiến thuật trên, cô bạn của tôi vẫn kiên trì với suy nghĩ sẽ sử dụng phân tích web để phân tích đám traffic tương tác trên website, từ đó lọc ra được visitor nào là thật sự quan tâm, visitor nào chỉ vào để cốt nhận quà thưởng. Vâng, để thõa mãn ý muốn này, phân tích web sẽ phải làm công việc tracking từng visitor một và gắn cho mỗi đứa 1 cái identity cụ thể, analyse từng đứa một chứ không phải là analyse theo nhóm như hiện tại. Mới nghĩ qua đã thấy cách này ‘đụng’ tùm lum:

1- luật riêng tư: có lẽ ít người biết chứ ở Mĩ có nguyên 1 cái luật chống ‘theo dõi’ (spy) hành vi cá nhân của từng người dùng với danh tính của họ trên Internet, dù là “vì họ đang vào website của mình”. Ở Việt Nam chắc chưa có luật này.

2- về mặt kỹ thuật track hành vi cá nhân có thể thực hiện được nhưng khá phức tạp, phải thay đổi nhiều thứ trên site, phải tốn nhiều năng lực xử lý dữ liệu hơn mà có lẽ giá cũng không rẻ chút nào.

3- về mặt phân tích, bạn đã chuẩn bị ngồi phân tích dữ liệu của hàng ngàn profile dữ liệu để lọc ra ai mới là người quan tâm thật sự?? Xem từng profile một? Không khả thi rồi. Dùng các chức năng khác của Web Analytics tools? Cái này chưa thử nên chưa chắc chắn độ mạnh của nó, nhưng chắc chắn là : hoặc là tốn kém vô vàn năng lực xử lý hoặc là sẽ rất hạn chế trong việc xử lý dữ liệu, rõ ràng là vậy, chỉ có thể chọn 1 trong 2!

Toàn là khó khăn và trắc trở trong khi đó nổi lên 1 câu hỏi rất lớn:

Tại sao phải tốn công ‘cào’ cả vàng lẫn cát cho vào 1 thúng, xóc lên, xong rồi lại phải ngồi tỉ mỉ lụm từng hột vàng ra khỏi cái đống đó???

I was inspired by KAIZEN Analytics recent post on web analytics tools market shares in the automotive industry, to do a similar study on online news outlets in the Nordic countries, specifically Norway, Sweden, Denmark and Finland.

Just as Kaizen, I will use WASP to inquire each web site what tools they use. Moreover, I only care about core web analytics, not ad trackers etc. I also took the liberty to add the LinkPulse numbers (as WASP does not recognize it, but I know who they are).

I would like to emphasize at this point that this is a research project in the making, and I’m publishing preliminary results to see if there’s interest in these numbers out there. For now, I have far more data for Norway than for the other countries because it’s much easier for me to decide which ones to count and which ones not to count. Basically I’ve tried to include web sites that correspond to daily news papers, as well as online only sites which center around dissemination of news, may be portals or niche sites such as sports or economics. Another criteria I’ve been considering is amount of traffic according to official metrics, but haven’t followed this strictly so far.

Further work on this research will include establishing more rigid selection criteria and gathering more data from the other Nordic countries. Other considerations to make are groups of sites that buy tools collectively because of common ownership, as well as using the fact that most sites have more than one tool.

Another interesting possibility is to factor in each sites’ official traffic numbers. Thereby we could see something about what tools account for the most traffic, or some such.

As of now, I have counted the tools on 66 sites, of which a little under half are Norwegian.

One difficulty is to account for at least two “disturbing” factors in the data. One being that one of the tools, Google Analytics, is free and therefore has a far lower threshold for use than the other tools. The other disturbance is that all the Nordic countries have one tool that is used across the board due to common agreement to provide official metrics.

I haven’t decided how to account for some of these issues and therefore present the data with a big juicy footnote to consider it largely incomplete. I think, nonetheless they give an interesting view of what is being used generally in news sites.

The first pie shows the usage of web analytics tools in Norway, Sweden, Denmark and Finland, weighted so that it totals to 100% even though some sites use more than one tool. These numbers are schewed by the fact that almost half the data are from Norwegian sites. Another difficulty is that TNS metrix and Gemius are “forced” tools in some countries, but also used voluntarily in other countries. This is an issue that I will work to solve.

Norway only

The second pie focuses on the Norwegian data only. Also I have removed both Google Analytics and TNS Metrix. Google Analytics is free and therefore used by virtually everyone and TNS Metrix is the official tool in Norway, therefore used by literally everyone (at least all the sites that I gathered data about). The usage of these may be interesting in and of itself, but may overshadow interesting facts about usage of the other tools.

IAB FORUM – MERCOLEDÌ 4 NOVEMBRE 2009

Presentazione realizzata da  Marta Sudati Beatriz, Corchado Rubio e Alessio De Marino

So you developers, coders and Mac people don’t like Internet Explorer (IE) and you REALLY don’t like IE 6. I see it on Twitter, I see it in the media, I see it every where [http://www.techcrunch.com/2008/03/25/save-the-developers-stop-using-internet-explorer-6/]. I know some of you don’t like it because you prefer Safari or Firefox (I prefer Chrome). Some of you point out legitimate technical issues with IE6. Maybe it’s not as compliant as other browsers or maybe it doesn’t have as many cool plug-ins or the performance isn’t as impressive. Maybe you just don’t like The Man (Microsoft). Whatever the reason you hate IE or IE6, GET OVER IT.

Personally, I am not a big fan of IE6. Although I use IE8 on a daily basis, it is not my default browser. I also use Chrome, Firefox and Safari every day. I say all that to point out that I am a seasoned browser user and I don’t have any hidden agenda here.

The push to “ban” IE is humorous. For you all that are promoting the ban…I love the big banners on your websites (attempting to force users to upgrade) and the little icons attached to your profiles (with the line through the IE logo). That’s all real cute. You seem to think that the visitors to your websites (your customers) need to change (are wrong), “let’s force the upgrade”. <sarcasm>That is a great customer service attitude people.</sarcasm>

I know that other browsers are gaining market share. I have read articles that IE only has 50% of the browser market now. What market are you looking at? Most likely a tech site with a more tech savvy customer base. I maintain the analytics for well over 50 websites and while browsers like Firefox have gained ground Internet Explorer on average still has 75% of the traffic. Of IE’s 75% on average I would say IE6 is about 17%. That is a pretty large segment of your market. So do you want to ignore them because they have a crappy browser? I bet the clients you are developing the website for do not.

In a perfect world, everyone would have an updated browser that is fully compliant. We don’t live in a perfect world. Many of my clients still get traffic from IE5. Am I saying you have to support every browser forever…NO! But if you are getting significant traffic from the browser no matter what version, you need to support it.

What I am attempting to say here is…bite your lip, put in the IE exceptions and make the site work across all browsers. You may not like it, it may be a royal pain in the butt, but if you don’t you WILL risk losing a customer/client.

It can be quite challenging to get your management’s buy-in on measurement strategy, analytics resources, and the need for optimization.  Though it’s almost 2010, we still have Avinash Kaushik evangelizing web analytics and Bryan Eisenberg advocating for the need to test and optimize (and rightfully so!), not to mention the many analytics and optimization sessions offered at Marketing Conferences. 

Fact is, a lot of companies need desperate help, don’t get it yet, or don’t realize the missed opportunity.   And here you are, trying to sell them on everything you believe in…  So, how do you get your management on board?  There are obviously several ways to get them on board (e.g. “Stragegies for Embarrassing your Organization”, Avinash Kaushik, Web Analytics 2.0, p. 429), but another way to get their attention quickly may be to give them a reality check on where your organization stands. 

Use the [x+1] Digital Marketing Maturity Model to assess the maturity level of your organization.   The [x+1] Digital Marketing Maturity Model defines the capabilities required to convert prospects to customers through an integrated marketing experience.

 Pillars of Digital Performance-Based Marketing consist of
• Target Market
• Offer
• Creative
• Analytics
• Marketing Integration
• People / Skills

Are you a broadcast marketer or a value validator?   Do you know any organizations that are channel champions?  I can tell you that when I worked at AOL back in 2003, we were between value validators and channel champions.  Pretty impressive, given that it was 2003!  But, aside from smart marketers, we also had management support.

 
Another source to help you assess your digital marketing maturity would be Webtrends’ Digital Marketing Maturity Model (DM3).

The DM3 (beta) provides a framework for assessing and building digital marketing maturity over time in six core areas:
• Measurement strategy
• Analytics resources and domain expertise
• Data integration and visualization
• Data analysis and insight
• Adoption and governance
• Ongoing optimization

So, how mature is your digital marketing?  And, what are you going to do to take your organization to the next level?