RSS
 

Posts Tagged ‘super web analyst tips’

What’s your analytical framework? (part two)

28 Jul

Last time, I talked about “Analyze” and what you need be careful about.

In this post, I am going to talk about the first “M”, Monetization. In web marketing, when people talk about monetization, they usually refer to one of the two activities: the first is about how to utilize the real estate on a website and sell the space to other companies who might be interested in showing ads there. For example, if you look at www.dell.com/home you’ll find there is Nokia and Vizio sitelets at the bottom of the page.

The second is actually what I want to address here: “monetization” in web analytics’ sense, refers to the process and methodology to quantify the possible financial impact of an optimization effort based on certain facts and assumptions.

Monetization is a great tool to sell an optimization project. Obviously agencies use it a lot in their fancy presentations. Shane Atchison and Jason Burby from Zaaz wrote a whole chapter “Monetizing Site Behaviors” in their book “Actionable Web Analytics” (You can preview most pages at Google Books They built a simple yet nice template. You can view the sample here.

Of course, the same concept and similar template can be used by practitioners to prioritize the optimization roadmap.

At Dell, we have a site optimization meeting on the weekly basis. One critical item always on the agenda is to review the testing & targeting roadmap. When we first started with testing, we feared that we didn’t have enough testing ideas. But once the ball is rolling, we often find ourselves faces the problem of too many ideas. Almost everybody from HIPPO’s to sales agents has something in their minds that they are curious about and want us to test. Of course, only less than 20% of those ideas are truly valuable. Spending any time on 80% of the rubbish, your testing program is at risk to fail. How do we get to identify the 20%? We do monetization-we estimate potential impact from each test and prioritize based on dollars amount as well as ease of implementation. Using data instead of emotion to argue with those who are passionate about their testing ideas is such a life saver.

In addition to help prioritize test roadmap, it can also help us prioritize the analytics roadmap. Does analytics need a roadmap? Yes, of course! As a web analyst, I always find myself swim in the sea of tons of different requests from executives I support. The only way for me not to drown in the sea is to prioritize by monetizing all requests. The question I always ask myself is “how much more revenue or margin I can bring to the company if I spend this many hours on this request?” Astonishingly, many times I found the answer is “none, not really”. So I move on. I am lucky to have an alignment with my direct management on this process. He and I often sit down, during our weekly meeting, to review the projects I am working on and prioritize it based on monetized values. Of course, we both realize that we still have to do some monkey works nobody wants to do, such as clean up data etc. That is just part of our lives and we all have to live with it. But if you are spending most of your time in the monkey works, I am pretty sure soon you will either burn out, or find yourself replaceable.

Our time is VERY precious. In fact, I believe the most valuable asset a company has to drive site optimization is not any of the tools, either free ones or the ones they pay millions of dollars, but the time of its web analysts. We can either protect this valuable asset or let it be ruined. We shouldn’t do any analytics just for the sake of analytics. Nor we should do any analytics only to satisfy somebody’s curiosities. The end result of analytics should be either concrete recommendations to the business that some parts of the website needs to be changed, or some test ideas that the testing team can take over to execute.

Monetization is a great friend of web analysts and we all should wholeheartedly embrace it. Sadly I find often we rush from “Analyze” to “Optimize”, without really “monetizing” the efforts. Do it now, and it will help you live longer and happier.

  • Twitter
  • Facebook
  • Delicious
  • Yahoo Bookmarks
  • Plaxo Pulse
  • Google Bookmarks
  • Digg
  • Windows Live Favorites
  • FriendFeed
  • Hotmail
  • Yahoo Mail
  • Share/Bookmark
 

What’s your analytical framework?

23 Jul

There is no lack of talking about analytical framework. Each of them has its pros and cons. The one we created is called “A.M.M.O”. Let me explain this to you and see if you like it or not.

First, let me explain what the acronym really means:

“A” stands for “Analyze”; First “M”, stands for “Monetize”; Second “M”, stands for “Mobilize”; “O”, stands for “Optimize” .

See, I managed to find four words all end with “ze”.

“Analyze” is straight forward. That’s what we, as web analysts do every day, so no need to explain, right?

One thing I want to caution is though, we need make sure that the data we are looking at is correct. If the raw data is not even correct, then that’s “garbage in, garbage out”, regardless how sound your analytical framework is. The analysis results might be misleading and hurting the business, rather than helping the business.

In the web analytics world, we usually are dealing with two sets of data. The first set of data is either the overall metrics at the site level or the most granular data at page level. For this type of data, it will be hard to make mistake. And if you ever made a mistake, even your HIPPO will quickly point it out for you!

The second set of data is at “segment” level. At this point, all web analysts should know that “segmentation” is at the core of any web analytics project. If you don’t know how to segment your visitors or traffic and analyze visitor behavior accordingly, then you should really go back to the training camp.

But the risk is also with “segmentation”. Anybody has experience with Omniture Discover on Demand or Omniture Insights (previously Discover on Premise and prior to that Visual Sciences) or similar tools, probably understand what I mean. There are two major factors could screw the data. First, the exact way to build the segments: in Discover or Insights, you have many different ways to build a segment and not all of them give you the same data. Second, the sampling issue: due to huge amount of data, a lot of web analytics applications can only give you a sample rather than the complete data set, depends on how much data you are asking for.

With this understanding in mind, it is imperative for any web analyst to have a healthy amount of suspicion of the data accuracy. Not only the data pulled by somebody else, but also the data we pull. Yes, we make mistakes too!

There are ways to mitigate the issue of course. (we have hope-it’s not the end of world!). What I usually do is to make sure that I can get the data from two different data sources and compare them side by side. If you are using Omniture, you should always try to compare the data you get from Discover vs. the data from SiteCatalyst. For example, if you are building a segment for a group of pages, you should at least compare the total visits for that group of pages from Discover with the sum of all visits of those pages. These two numbers will never match but they shouldn’t be way off. If I see a huge difference between them, that’s a red flag to me. [Bonus tip: if I have to bet $10 on either one, I will definitely bet on SiteCatalyst and go back to review the data I got from Discover]

Ok, that’s all I want to say about “Analyze”, assuming you’ve got the rest. In my next few posts, I’ll touch upon the other letters one by one and round up with a final summary. Stay tuned!

  • Twitter
  • Facebook
  • Delicious
  • Yahoo Bookmarks
  • Plaxo Pulse
  • Google Bookmarks
  • Digg
  • Windows Live Favorites
  • FriendFeed
  • Hotmail
  • Yahoo Mail
  • Share/Bookmark
 

Got the million dollar slide?

22 Jul

Daniel Waisberg asked an interesting question in the web analytics yahoo group recently, “just got curious: when you send a Web Analytics doc, do you add a cover page? Suddenly it looks to me like a barrier to conversion (i.e. reading)…”

I replied back, “We don’t do cover page internally. Cover page is only for consultants. :)

This got me think more about the so-called “million dollar slide” . The idea is for whatever project / presentation, you need have one page of executive summary which basically tells the audience, especially executives what are the key takeaways.

The format I usually follow is like this and we call this “answer first” internally at Dell.

  • Situation: what’s the context for this project?
  • Challenge: what challenges/difficulties the business is running into?
  • Solution / recommendation: what specific solution / recommendation for the business, both in short term / long term?
  • Monetization: what’s the estimated annualized revenue / margin upside the business can expect from these recommendations? If the executives see millions $ upside, they will surely approve to move forward with the recommendations!

I think this is probably a very good format that we should do more as the web analyst community. As analyst, we love data, charts and tables but sometimes we forget our audience. What do they want to learn? How we can make it easier for them to understand what we want them to do? I don’t think we are doing that enough and we should certainly make this as part of the gene of web analytics.

Got your million dollars slide? What is the specific format you typical use for the executive summary?

  • Twitter
  • Facebook
  • Delicious
  • Yahoo Bookmarks
  • Plaxo Pulse
  • Google Bookmarks
  • Digg
  • Windows Live Favorites
  • FriendFeed
  • Hotmail
  • Yahoo Mail
  • Share/Bookmark