Why Mobile Analytics is different and exciting?

• Mobile/Tablets are replacing the way we interact on internet. Tablets are taking the experience of browsing at home from desk to a couch or dining table.
  
• Screen sizes and ways to intereact with smart phones and tables are very much different than traditional desktop.
  
• Websites can be upgraded when needed but there is no guarantee that the user will upgrade the app even after you push an update to the store.
  
• Monetizing the ads in websites was easy as the publisher chose where to place the ads. In mobile apps the ad placement have little or no control on where to place ads. Gaming the users in such a way that they think that there is enough incentive to watch an ad is the best way to make sure the ad was delivered right.
  
• With mobile apps now the publishers get complete access to the location profile of the user and thus making ads more valuable if done correctly.
  
• Functionally the apps have much more control on the device when compared to a browser running on the machine. When was the last time Apple checked that you programmed a website correctly for Safari? but they like to check each app and update for an app in the app store.
  

Also there is no business without a customer. So to establish the opportunity I would like to point you to a wonderful experiment that ft.com is doing at Grand Central Station: FT.com Mobile Story

Also check out this conversation. Really good example of how mobile analytics is different:Expedia Story of Mobile App Analytic

comScore Monetization Analytix (MAx)

One of the popular news site in India is, The Times of India. Their site is:
http://timesofindia.indiatimes.com/

They intent fully put cheesy content to the bottom so people scroll down and click on that content so they make extra cash on these page views (by selling display ads).

Example of the content (annoys the heck out of me when an old newspaper puts nonsense content like this. Where is the editorial integrity gone?)


Especially the life and style.

So another newspaper in India made a mockery out of their content using this Ad.
Link-Which newspaper do you read?

Kareena Kapoor is one of the top actresses, but the point here is no one knows answer to simple stuff. The beep is for calling out Times Of India’s name.

Most managing editors have guilt for such cheesy content but then to stay alive in digital business those page views are needed. This makes it very important to serve limited content and make more money out of the ads. This is where MAx offering from comScore can help.

Geek Hangout

I always hear about the various events that people participate in. I managed to get a good list of such events. I probably call it as the Geek Hangout.

Here is a list of some of these conferences where the fun happens:
Game Developers Conference
CeBIT
LAUNCH Festival
SXSW Interactive
London Web Summit
Signal
Ignition West
Structure Data
Microsoft Dev Connection
Combinator Demo Day
VentureBeat Mobile Summit
Where
Data 2.0 Summit
ad:tech
Ad Age Digital
DEMO
The Next Web
Future Insights Live
Wired Business Conference
BlackBerry World
D10
E3
Tech Policy Summit
Microsoft TechEd North America 2012
LeWeb London
Structure Data
Velocity
Microsoft TechEd Europe
Google I/O
Microsoft Worldwide Partner Conference

Techmeme does a complete list
Techmeme events list

Linkedin Adverstising

As I am writing this blogpost I am on a train journey back to Washington DC. The Acela express provides free internet connectivity but at the same time probably optimizes the use of internet by blocking the ads.

One example of this was the double click/Dart iframe actually shows the call made to doubleclick but has no display ad being served.

What is interesting is the data about me that LinkedIn passes to Double click and which I think is pretty specific segment information as it is accurate and provided by me :)

lang=>Language
u=>Encoded user id?
func=>my job function first word in the title
func=>my job function
coid=>Company ID
csize=>Company Size
zip=>My Zip code
cntry=>Country
edu=>My grad school
edu=>My undergrad school
gy=>Graduating Year
gdr=>Gender
age=>Age
seg=278http://www.blogger.com/img/blank.gif
seg=>Target Segment
grp=>Target Group

Just thought this is exciting. For a free service I do not mind LinkedIn using my data. Also for what it matters I will probably never click on a display ad and thus the ad spend is a waste for the advertiser as my propensity to act on the message provided by the ad is ZERO :)

Also if you would like to opt out here is a video:

Set comScore debugger

I got a comment from a user few days back. I want to mention that this works only for comScore's scorecardresearch.com tracked traffic.

To setup the Digital Aanaltix/comScore beacon Debugger
1) Open any browser and then go to any website.
2) Browse to any page on the internet and set a bookmark.



3) Edit the bookmark and change the URL. The following shows the steps in Mozilla.
Step 1: Find your bookmarks and right click to see the properties option.


Step 2: When you click on properties the following window opens and the location/URL needs to be updated.


Step 3: Copy the following javaScript

javascript:void(window.open("","daxDebug","width=600,height=600,location=0,menubar=0,status=1,toolbar=0,resizable=1,scrollbars=1").document.write("script%20language=\"JavaScript\"%20id=dbg%20src=\"http://webmetrictools.com/dax/debugger.js\">"));

Step 4: Replace the URL with the javaScript. The final screen looks like:


Okay now that I have it, how do i use it?
1) Go to the website where you want to see the comScore beacon values
2) Click on the bookmark list while on the website and then click the bookmark for debugger.


3) Here is what you should see.

Are you a Black Belt in Web Analytics?

I came across this quiz on one of the LinkedIn groups I subscribe to. I think that the title of "Black Belt" doesn't mean much as different things work for different people. What is interesting though is that all of us as practitioners should be asking these questions.

So check out the quiz
Web Analytics Black Belt quiz

Questions:
1) Do you know why your website exists?
2) Do you know how you make money on your site?
3) Do you know whats people's experience of your site?
4) Do you know how many manage to accomplish the task?
5) Do you know what people like or dislike with your site?
6) Do you know the monthly return rate?
7) Do you know the volume of your returning visits?
8) Do you know how important your various traffic sources are?
9) Do you know your top 10 entry pages?
10) Do you know their conversion and bounce rate over time?
11) Do you know what type of content is consumed most?
12) Do you know the value of the visitors that consumed various content?
13) Do you know how many of your visits use internal search?
14) Do you know what type of searches your visitors do?
15) Do you know your head vs long tail?
16) Do you know the value of your long tail?
17) Do you know your best converting keywords?
18) Have you grouped the keywords in various buckets based on volume or value?
19) Do you know your top referring URLs?
20) Do you know how these URLs perform on conversion, bounce and revenue?
21) Do you know where people lick on your most people click on your most important pages?
22) If you have a desired path funnel do you know where people drop out?
23) Do you track your social media efforts?
24) Do you know how much ROI your social media engagements bring?
25) Do you know your most profitable online ad campaigns?
26) Do you know your most profitable offline ad campaign?
27) Do you use a bid management tool to maximize your online campaign ROI?
28) Do you know how your adwords cannibalize on your organic traffic?
29) Do you track your email marketing campaigns correctly?
30) Do you evaluate and optimize the content of your emails?
31) Do you have a KPI dashboard and how frequently do you monitor it?
32) Do various part of the organization use various dashboards?
33) Do you know the type of segments that are important for your business?
34) Do you break down your numbers per segment?
35) Do you know what actions directly affects your conversion?
36) Can you tell how the factors that affect conversion correlate with each other?
37) Have you done A/B test on your top entry pages?
38) Do you have a systematic methodology for A/B testing?
39) Do you have a long term strategy for your testing?
40) Do you document your findings?
41) Do you spread the knowledge of your findings?
42) Do you know what your competitors do?

I think if you know answers to most of these questions you have indepth knowledge of your web analytics program.

Building Analytics/Decision Management Capabilities in your organization

I just finished reading the book: The Deciding Factor: The Power of Analytics to Make Every Decision a Winner written by Larry Rosenberger, John Nash and Ann Graham. All three of them were associated with Fair Issac (FICO) at one point.

The authors seem to have great amount of experience in the the field of building analytics and decision management capabilities for their client. Most of the book has generalizations about how to do this. I found the last chapter of the book very useful, other than that it seemed like a listing the facts session.

The last chapter definitely is a good read for someone at a Manager Analytics or Director Analytics/decision management role.

The first thing I am going to list is the high level steps in building the decision management capabilities in your organization. I am only listing the high level steps, please read through the details in the book to get the complete perspective of the authors. All copyrights to the methodology belong to the authors/fico.

Decision Management Methodology
Stage 1: Set Strategy and identify the business opportunity.

• Identify and prioritize opportunities
  
• Assess the scope of the opportunity
  
• Create a high level plan to address opportunities and scope
  

Stage 2: Identify critical decisions and potential decision yield

• Create decision inventory and business process flow
  
• Identify and design pilot model to address the objectives
  
• Capture your baseline decision yield
  
• Quantify potential improvements to your decision yield
  

Stage 3: Design the business architecture for your decision environment

• Determine the best analytic approach
  
• Design your decision environment
  
• Design your decision platform
  
• Define the decision management roles, responsibilities and decision rights
  

Stage 4: Build the data environment required to inform decisions

• Design data flow: sources and sequences
  
• Assess and address gaps
  
• Map connectivity to third-party data providers
  

Stage 5: Build mathematical models to improve decisions

• Gather data required for modeling
  
• Build your models
  
• Test Model performance
  

Stage 6: Build and modify the operational environment to enable decision execution

• Build your decision management application
  
• Build decision rights: organization structure, skills and compensation
  
• Roll out decision process and rules
  

Stage 7: Continually improve the decision environment

• Operate your new decision environment
  
• Confirm realization of your decision yield
  
• Identify and implement changes to your decision environment
  
• Feed new knowledge back into your decision environment
  
• Identify new decisions to improve
  

These are the questions that everyone in a decision management team should be asking to their colleagues across the organization at one point or the other

1. What are the possibilities?
   
2. What are the important opportunities in alignment with company strategy or problem to be solved?
   
3. What are the most important decisions related to these opportunities?
   
4. How should opportunities be pursued based on priorities?
   
5. Making decisions: exactly how long does it take?
   
6. Making decisions: For which product or product line is this an issue?
   
7. Making decisions: Is the delay/issue with decisions across the product lines?
   
8. What are the important decision points?
   
9. Who is making the decisions?
   
10. How are the decisions made?
    
11. What is the current decision performance?
    
12. Can the financial, functional, and technical decision making be improved?
    
13. What are the improvements possible?
    
14. How well do we know our customers?
    
15. Is our strategic segmentation approach integrated into our operating environment?
    
16. Is our customer segmentation granular enough to enable customer specific treatment?
    
17. Are we using analytic capabilities to enhance effectiveness? are we making decisions using judgmental best practices, standardized rules, predictive models, or real-time optimization?
    
18. Who is making which decisions and who executes which functions? based on what criteria and authority?
    
19. How consistently does this play out across channels, product lines, geographies, and so on?
    
20. How coordinated are our marketing activities across different product lines?
    
21. Are our customers or prospects receiving conflicting messages from our organization?
    
22. What is the impact of introducing new strategies into our operating environment? how much retraining is required? What modifications are required in our underlying systems infrastructure?
    
23. Is our organization adept at assimilating change?
    
24. What is the quality of our organizational communication vehicles?
    
25. Are we making decisions through manual intervention or human review, or though scalable, automated rule systems?
    
26. What is our sense of cost versus quality for this area, and what is the optimal balance?
    
27. Are our customers being lost because of ineffectiveness and slow turnaround times in our organization?
    
28. How much time do we need to return a decision to a customer or prospect?
    
29. What is the potential gain of an incremental increase in system processing time for a decision?
    
30. How strong are the current analytic capabilities and what new analytic capabilities are needed?
    
31. What existing models are outdated and need updating or redevelopment?
    
32. Are we periodically updating business logic based on market learning?
    
33. What changes are required to improve the performance of our business logic?
    
34. Do we have the right skill sets and resources assigned to the most valuable priorities?
    
35. What changes to organizational structure, roles, responsibilities, and requisite skill sets have we identified?
    
36. What training and communication vehicles are required to ensure a successful and ongoing rollout of new initiatives and strategies?
    
37. What new information needs have we identified?
    
38. Do we have the right information available at the point of need?
    
39. What additional input data, internal or external, is required?
    
40. What core business process changes or improvements have we identified?
    
41. How will we manage these to ensure consistency, optimal performance and agility?
    
42. What are the best analytic methods to apply to our decision sets?
    
43. Which specific decision areas must we address?
    
44. What capabilities do we need to create and modify?
    
45. What organizational changes do we need to make in terms of roles, responsibilities and structures?
    
46. Could a forecast of future outcomes or customer behavior help us?
    
47. Is manual review of data part of this decision or business process?
    
48. Is the decision complex enough that modeling the results of these decisions could lead us to make better decisions?
    
49. How do we efficiently and effectively integrate the necessary data into our decision environment?
    
50. Is this data accurate?
    
51. What processes are in place to ensure that it is correct?
    
52. What are the methods for identifying and correcting inaccurate data?
    
53. Are we using the best sources of external data?
    
54. Are only partial customer records available for this decision?
    
55. Are we appropriately collecting, logging, and storing data acquired through our transactions and interactions, both to guide later decisions we make and for reporting purposes?
    
56. At what point in the process should we acquire and pay for the data?
    
57. What should the contractual terms be to support long-term use?
    
58. How do we integrate the necessary data into our decision environment, with sufficient service level agreements to meet our processing requirements?
    
59. How much can we improve our analytic performance?
    
60. Which characteristics have the greatest impact on model outcomes?
    
61. Is it operationally feasible?
    
62. Who owns the ongoing management and maintenance of our decision-making environment?
    
63. Who needs to be trained in using these capabilities?
    
64. Are we realizing the improvements we expected?
    
65. Can we identify areas for additional improvement?
    
66. Are these new decision areas we should address?