How different is advertising on a portal homepage across the world…

In today's world, there are multiple versions of Yahoo! homepage (USA, India, Australia, UK…) and in various languages. All of these pages have ads and as one would expect the performance of placing ads on these pages in different countries varies dramatically.

It is very fascinating to measure the impact of the same creative with similar message placed on the homepage of the same portal like Yahoo! or MSN. The performance varies dramatically – why? Few major reasons that would cause this are:

1. The difference in the audience on the homepage in different countries. One particular country might be reaching business users vs. the other countries might be reaching to the average 25-45 year old women.
   
2. The differences in the reach and frequency. The average number of times an average user is exposed to the ad affects the likely hood of a consumer interacting with the ad. Also, the number of consumers reached also affects the performance – the less the number of consumers reached, the less the awareness of the product.
   
3. The type of creative could also make a lot of difference. Expandable banners could increase the performance in one country vs. the expandable ads in the other country might not attract the consumers as much.
   
4. A particular message could attract more consumers in a country vs. the other.
   

It should not be assumed that if a Yahoo! homepage ad performs very well in USA, then it will perform as well in India or Australia or Japan. The performance can and will vary. Historical results must be used to make the decision of placing the ad.

Increasing customer lifetime value

Every marketer wants to increase the lifetime value of their customers. One of the best ways is to make sure that the consumer only thinks about your brand/product/service/store/e-commerce store before making a purchase.

Amazon prime is a perfect example of a service which can be used to increase the lifetime value of consumers. This service is a one-time annual fee and provides free 2-day shipping and next day shipping for only $3.99 on most of the products sold on Amazon.com.

By providing such a service for a minimal fee, Amazon has secured all the consumers who have signed-up for this service. Amazon is one of the largest collections of products across all categories – household products, baby products, electronics, books, tools, clothes, grocery, and apparel. Therefore, the chances that you will not find a product on Amazon are very few.

They have also made sure that a consumer, when is ready to buy a product, uses only Amazon.com to find the product and checkout.

A consumer previously would have done a lot of research on various comparison shopping engines like Shopping.com or Pricegrabber.com and put in a lot of time to find the best deal. Now, the same consumer who has signed up for Amazon prime would just go to Amazon, find the product and check out. This would save a ton of consumer's time and the consumer would also get the product delivered to their doorstep in 2 days or even next day if they pay $3.99.

I have been using this service over the last few months and have ended up make more and more purchases from Amazon. It all started with monthly diapers, then I ordered a multi-function printer, then I got a car seat for my son, then comforters, duvet cover and the list goes on. Living in the city of Chicago, I save on 10.5% sales tax and get the product gets delivered to my doorstep in a day. I used to order only a few products annually from Amazon but now, I order a product almost at a weekly level. Guess what! I also have the Amazon rewards credit card now, which has hooked me to Amazon. I have even ordered products from Amazon using my phone, as I was at a physical store which didn't have the actual product I was looking for.

From a marketing analytics ROI point of view, if I was running the Amazon prime program, I would love to do the analysis of shipping cost vs. the increase in the consumer lifetime value. As the service has been available for a few years, I am assuming they have a positive return and is helping increase profits.

This indeed is a classic case of increasing consumer lifetime value.

Page Depth, Bounce Rate: Are they actionable?

Page Depth and Bounce rate are two very basic web analytics metrics. Page Depth is the average number of pages visited during a site visit. Bounce rate is defined as the percentage of users who land on a webpage and exit the website.

How actionable are these?

Not a whole lot.

Page depth would provide the avg. number of pages viewed in a visit – it could be the same page refreshed 10 times and show a page depth of 10. However, this page could not be providing any business value. A consumer who views 2 pages with very high business value would have only a page depth of 2. Thus, from a business point of view, the consumer who had a page depth of 2 is more valuable. Page Depth – Didn't provide the correct picture.

Now, let's talk about the bounce rate. A segment of consumers land on a web page, consumes the content, and exit the website, yielding a bounce rate of 100%. However, this webpage is able to provide all the information the consumers are looking for and leaving the website. Thus, if you look at the bounce rate metric, it doesn't provide the correct picture.

The better way to measure the effectiveness of your website, is assigning business value to various pages/activities of the website. Calculate an average value per consumer visit and improvise the website to increase the avg. business value per visit. Analyze the click-stream (pathing analysis) to help increase the business value per visit.

Is Click-through the correct metric to measure a creative?

The answer to the question is "IT DEPENDS". The metric to measure a creative depends on what a creative is intended to do. The best way to find out is to establish a goal of the creative. Typically, the creatives can be bucketed to mirror the stages of the consumer funnel – Awareness, Research, Buy and Loyalty.

Based on each of these buckets, the correct metrics to measure the effectiveness of a creative can be defined. The creatives in the "Awareness" bucket should be measured on CTR, as the creative is indented to increase the awareness or catch the consumer's attention. The next level of creatives in the "Research" bucket should be measured on the engagement or interaction of the consumer with the banner or landing page or the website. Thus, the correct metric would be interaction rate, page depth, time spent etc. The next level of creative "Buy" section would be aimed at driving consumers to make a purchase. As we all would agree the correct metric to measure these set of creatives should be conversion – revenue, number of units sold, number of sign-ups etc. The final set of creatives – "Loyalty" set would be driving existing consumers to either continue the services they had enrolled in or cross-sell/up-sell for different products and services. Thus, measuring how the existing consumers are interaction with the website would be the best way to gauge the performance of these creatives.

It gets more and more complicated but the better thought through the measurement process is, the better results one would be able to measure.

In-text advertising vs. Display Media

With In-text advertising gaining popularity among various advertisers, it is very interesting to see how they perform. Vibrant and Kontera are the two major players in that segment and have been known to show very high Click-through rate up to 5% at times.

When these publishers are on the media plan, they tend to skew the performance of the overall campaign and also make all the other publishers look not so good.

My thoughts are that display banner ads and in-text advertising comparison is not a APPLES to APPLES comparison. They should not be compared against one another.

My personal belief is that there could be a lot of accidental clicks in the in-text advertising. Site analytics should be used to gauge the business value of these consumers. Specific metrics like page depth, time spent and if there are any specific "call to actions" should be tracked.

I also believe that some consumers, who have get exposed to in-text advertising for the first few times, find it very interesting and a brand new concept. This allures them to click on the ad; however they did not intend to be on the advertiser's website. These kinds of situations cause a lot of accidental clicks.

This is a new medium and with time we all will get better understanding and provide accurate POVs to our business partners to better gauge the value addition from the in-text advertising campaigns.

Very high interactions on the banner, however very low clicks – Why?

Sometimes we notice that there are very high interactions on the banner, however the number of clicks on the banner is very low – how do we explain this? My personal point of view is that the banner could be providing a lot of information to the consumers that they need and thus the consumers are not allured to click on the banner.

A banner having a few videos embedded or a banner with a few tabs, with a lot of information about a specific product or a service would not attract consumers to click on the banner.

Does this mean the consumer is not interested in knowing more about the product – NO; the consumer already got so much information about the product that he or she doesn't need any more information at that time. I would rate this as a perfect consumer experience. As an advertiser, you have been successful in engaging the consumer and providing enough information to the consumer about the product or service.

THEY ALREADY GOT WHAT THEY WERE LOOKING FOR!

 

CIMA Breakfast Bytes: September 18, 2008

CIMA BreakfastByte: Does Research hold any weight within the dynamic digital space?

Time: 7am - 9am
Location: The Westin (909 N. Michigan Ave, 3rd Floor, Governor's Suite)
Member Price: 25
Non Member Price: 35

Join CIMA for an insightful conversation with our industry leaders as we pull back the curtains on this informative topic.

How do marketers, agencies, and publishers justify digital ad spending when planning, executing, and analyzing campaigns?

Listen to how our panel experts recommend how you can partner with your clients to effectively plan and measure campaigns to deliver strong results. The discussion will cover whether panel data and syndicated research should or should not continue to be used in the media planning process. Our experts will debate the validity of 3rd party research analytics and discuss how effective syndicated research and panel data can be in the planning process.

We will cover what marketers can do to answer the challenging questions . . .
Is this data really accurate?
How does this compare to offline research metrics?
Why doesn't all syndicated research report the same numbers?

Agenda:
7:00am - 7:30am Check-In
7:30am - 8:00am Breakfast
8:00am - 9:00am Discussion
9:00am - 9:15am Q & A

Early Bird Registration (July 16 - August 29)
Member Ticket(s) - $25
Nonmember Ticket(s) - $35

Registration (August 30 - September 12)
Member Ticket(s) - $30
Nonmember Ticket(s) - $40

Location:
The Westin Michigan Ave (909 N. Michigan Avenue, 3rd Floor, Governor's Suite)

Moderator:
Blagica Bottigliero, President, Bsolutions LLC

Speakers:
Stu Rodnick, Sr. Director of ADlytics, Platform-A
Kara Manatt, Research Director, Cross Media Research, Dynamic Logic
Amit Prakash, Associate Director, Digital Marketing Analytics, OMD
Karen K. Scott, Interactive Associate Media Director, DRAFTFCB

 

Ad server reports clicks but Web Analytics tool does not show traffic

Often times the ad server would report that the online advertising (Display and Search) is generating clicks but the site analytics tool like Webtrends, Omniture etc does not report traffic. One of the first things to check is if the consumers who are clicking on the ads are actually loading the landing page.

One of the ways to check that is by putting an ad server tag on the landing page and thus be able to track the click to landing page load rate showing the abandonment rate from click to page load. The number of landing page load should match very close to the web analytics tool numbers.

There could be still some discrepancy of about 10-15% due to:

- Webanalytics tool click-throughs are de-duped

- The adserver code could be at a slightly different place than the web analytics tag and will have differing execution times

- Javascript disabled users would count the ad server tag but most of the web analytics tags need Javascript enabled browsers

Why digital ad servers should not be used to track DRTV?

A few weeks ago, I had a post which talked about using Doubleclick or any other ad server tags to track visits to the DRTV page. However, there are a few drawbacks to that:

• The DRTV landing page might get picked up with search engines like Google, Yahoo! etc and consumer would start to get to that page showing more visits to that page thus inflating your data
  
• The DRTV landing page would have links from the main website and consumers which get to that page showing more visits and this inflating your data
  

It is hard to completely isolate a page on your website, thus it would be really hard to measure the affect of DRTV using Digital ad servers.

One of the best ways to track DRTV is the traditional call volume and then tracking the number of orders or completed actions from the call centers.

CUIL is live

A new search engine was launched today, CUIL.
I personally have not used it a lot but I thought it would make sense to post a few links that provide a lot of insights about the new Search Engine.

http://searchengineland.com/080728-000100.php
http://searchengineland.com/080728-024035.php

Let's see if it really hits it big like Google.

Challenges in Online Advertising Measurement

Online Advertising is the only form of advertising that is measurable but there are a few challenges or drawbacks of online advertising that we need to understand. Some of the most common ones are listed below:

· Cookie Window: The cookie window needs to be set based on each business objective. Some businesses might have a longer purchase or research cycle which would mean consumers take more time to convert after they see the ad, example laptops computers. Whereas, there would be some business where the consumers would convert quicker. Thus, we cannot have a common cookie window for all the businesses.
· Cookie, Work vs. Home: Many consumers would see and click on the ad at home but might complete the transaction at home or vice versa. So, the cookie would not be able to close the loop and the advertiser would miss out on counting the revenue or the conversion based on the online advertising.
· Last Click: Typically, the ad servers reporting systems attribute the conversion to the last click and miss out on the multi-touch aspect. For example if a consumer clicks on a banner and then clicks on a Paid Search and then converts, the Paid Search gets 100% credit but the display ad also had some contribution in driving the consumer to the website and increase the consumer’s interest in the product.
· Cookie Deletion: It is knows that consumers tend to delete cookies from their computers. So, if the cookie is deleted, the ad server will not be able to connect the conversion to the ad unit that the consumer was exposed to or had clicked on. There is also an issue of 3rd party cookie deletion vs. 1st party cookie deletion. The 3rd party cookie deletion is more prevalent than the 1st party cookie deletion.
· Tags not firing: Several times the tags stop firing when expected example at the conversion event confirmation page – this could be due to many unexplained reasons and the sometimes all it needs is a tag refresh. It has also been seen that sometimes the IT personnel by mistake change the tag format and it does not work. One way to QC this problem is by using tools like HTTPlook.

Media Mix Modeling: Static vs. Dynamic

Media Mix modeling involving a lot of data collection and then a lot more number crunching and then comes the actionable insights based on the data.
Let's step back for a minute and think through this in some more detail. The data used to build these models goes as far back as 5 years (if you are using quarterly sales data) or at the very least 1 year (if you are lucky enough to get weekly sales data). So, let's assume on average 2-3 years of data is needed to build a media mix model.

Now, if you were to make decisions based on data which is 2-3 years old, are those assumptions still valid? The data that you are using is frozen in time 2 years ago. Just think through how much has the marketplace evolved since then? There are probably 2 new competitors in the arena and 1 of the big competitors has completely changed their marketing strategy. So, how actionable are the insights which would be derived based on this data? Probably not a whole not.

Thus, the amount of time and effort spend in create media mix models in probably not worth it.

There are some other options of using studies with Dynamic Logic or ComScore which might be able to provide similar insights and represent close to real time marketplace arena.

What attracts consumers: Free Samples, Coupons or Expensive, cool, sexy products

Like a few weeks ago, I was again walking up and down on Michigan Ave (in Chicago) and as a co-incidence M&M was again handing out their new ice cream bars. There were people all over with their ice cream. It was 90 degrees outside and I am sure consumers were enjoying it to the fullest. They did have the truck full of bars and people were consuming them fast.

While, walking back I passed the Apple store and I saw this long line – longer than the one for the M&M ice cream bars, I thought, "Wow! is Apple giving away something for free?" I asked someone in the line – "What is this line for" and she said "For the iPhone". I thought, on one end there are people in line to get free ice cream and on the other end people are standing in line to get the iPhone which is $199 and then a monthly service of $79. What a contrast!

Then a few more blocks near the Tribune building I see these two guys with the golden arches -"McDonalds" giving away coupons (Buy one get one free) for the Iced Coffee and there was no even a single person taking coupons from them.

It is just so amazing to see three such strong brands and the differences in how consumers perceive about them.

If I were to measure these three I think Apple is the clear winner as they were the only one making revenue and are locking down consumers with a 2 yr contract with AT&T, so not only is there a onetime cost but also a monthly cost and now with the new applications they are even charging for them as well.

Oh! and then there was this guy who had the M&M ice cream and the Coffee coupon, not sure if he bought the $199 iPhone.

Multivariate Analysis - Part II

Offermatica seems to be the industry leader in landing page multivariate analysis but I just found out about another tool called "Memetrics". This is a tool owned by Accenture and uses their Choice Modeling methodology.

They claim that Taguchi, which is a linear alogorithm is not the best way to predict human behaviour. Choice Modeling takes into account trade offs and preferences. Taguchi in a linear design and is a fractional factorial science which does not understand content interactions. While using Taguchi, enough sample for 16 versions of the page are required. Memetrics and choice modeling requires only enough sample for the number of attributes that will be tested and can then run a full factorial test in less time with less sample at a higher degree of confidence than Taguchi or Optimal Design (by Optimost) can.

Memetrics is the only tool which can easily optimize multiple outcomes, weighted outcomes and even offline outcomes. Memetrics is the only solution that is not a black box but rather a fully exposed, open analytical framework.

I personaly have not used Memetrics but it seems like a new technology that can be used for Multivariate testing.

Mobile Analytics: iPhone

I just installed the latest iPhone update yesterday and I am so excited with the new application. There is a lot to measure about this new application, a few metrics that I can think of are:

1. How many iPhone users started the install and how many actually completed it (the update takes about 1.5 to 2 hours, so it would be great to measure the completion rate)
2. Drilling down the consumer flow, the next thing to track would be how many users installed the applications
a. Frequency (1-3, 2-6, 7-10, 10+ applications)
b. Paid applications
c. Free applications
d. Applications by category
3. There are some “Westin” ads specially within the NY Times application, so measuring the Click-through rate on these ads would be a good metric to track

This new application has just changed the use of this product and obviously the sales of this product should go up dramatically as there were a lot of applications missing like Games, Mobile Banking (Bank of America), Newspapers (NYT), Social Media (Twitter, Facebook, Myspace)

The engagement with each of the above application would also be a good measure of how consumers are interacting with these applications.

Calculating View-Through attribution: Challenges

After analyzing the Google Analytics "Content Report" for my blog, it became evident that the "Post-Impression" attribution post is the most popular post so far. So, I thought I would expand a bit more on that same topic, some of the common challenges that could be faced during the test set up and also interpreting the results.

If this test is being done for a very large advertiser, delivering in the range of billions of impressions in a month, then it is virtually impossible to create a test and control group as the reach of the campaign would be so high. It is highly likely that all consumers will be exposed to the advertising.

1. If all the ads are not third party served, it is impossible to create a test and control, as if there is a x% of Ads which are site served, creating a control group will be hard across the entire advertising network
   
2. Even with all the ads being served via third party ad servers, consumers could get exposed to the Ads on different computers, example work vs. home
   
3. After the results if the lift is negative i.e. it shows that showing the ads did not increase the conversion rate vs. control group – this indicates that the "Brand" has very high brand awareness
   
4. Interpreting the results could be another tough nut to crack; DoubleClick in one of their studies has shown that for Continental Airlines that there is a 67% post-impression attribution. Lately, it has been seen that the post-impression attribution has been pretty low due to increase in online advertising.
   
   1. One more variable could be seasonality, the post-impression attribution has been seen to change based on the different times of the year. The consumer mindset changes based on the seasonality for example it could be very high during the back to school season for school products like laptops, ipods etc

Heavy Clickers account for 50% of the clicks

A recent study conducted by Starcom, Tacoda and ComScore suggests that heavy clickers account for 50% of the clicks. Please follow the link below for details.

http://www.smvgroup.com/news_popup_flash.asp?pr=1643

Sample Size Estimation

Continuing about the testing section (A/B, Multivariate) “Sample Size” is very crucial in the analysis. From a statistics point of view, the aim is to demonstrate with 95% certainty that the true value of a parameter is within a distance an error range of the estimate, typically the value of error range is referred to as the 95% confidence interval. OR in simple English, the aim is to demonstrate with 95% certainty that the result (example: Conversion rate, Click rate etc) would hold true 95% of the time.
It would be good to explain with a simple example. If there is a test for a click-through rate on a banner, there should have a big enough sample size to estimate if x% CTR would be true 95% of the time with 95% certainty. One more example is if there is an A/B test for a homepage test measuring clicks on a call to action button it is important to have enough samples or views of the page to be able to estimate their conversion or click rate with 95% certainty.
As a rule of thumb, the lower the response rate, the higher the sample size needed for accurate estimation.
For someone who is interested in know the math behind the test, please follow the links below:
http://en.wikipedia.org/wiki/Sample_size
http://www.surveysystem.com/sscalc.htm
http://www.isixsigma.com/library/content/c000709a.asp

Collecting Consumer Data – Email, Zipcode, Area Code: Geo Targeting

Collecting data through various sources like Website, Call center or even Online Banners can provide a lot of information about your consumers. Some of the examples are:

Email Address: If a marketing group is able to get a list of clean email addresses, then a relationship marketing program can be initiated which would help create a long term relation with the consumers. Sending out a series of emails based on consumers needs, can help build a loyal group of consumers

Zipcode: Knowing the Zipcodes of a loyal group of consumers can help understand which Geographical areas have higher quality of leads, and then the marketing can be Geo targeted. With the current ability of Ad servers like DoubleClick and Atlas it is possible to geo target online display advertising.

Similar to Zipcode, Area code can provide a density map of loyal consumers and target the consumers to help increase the loyalty and sales of products.

Having more and more information or data about your consumers helps better understand them and thus get your marketing dollars to work.

In the online world, it is much easier to collect this data through various ways – Online banners, Website, Microsite.

Multivariate Analysis

Multivariate Analysis is used to test multiple options at once. These multiple things could be 5 different images on a webpage, 3 different background colors of the homepage of your site or 3 different body copies for a particular offer.

Offermatica is a vendor which was acquired by Omniture which is known to help most of the clients and agencies to do multivariate analysis. One of the examples of their website does a very good job of explaining how and why this kind of Analysis is very crucial in taking the correct decision while making any changes on a website.

http://www.omniture.com/en/product_tours/offermatica/mvt

Offermatica also has a demo which explains the A/B testing for various elements on the webpage

http://www.omniture.com/en/product_tours/offermatica/ab

How does the cookie window affect measuring creative performance?

It is hard to measure creative performance or compare the creative performance if all the creatives were not live at the same time (start to end). The problem which comes is the cookie window. The creatives which are live for a longer period of time have the advantage of a longer cookie window and the creatives which are live for a shorter period of time have the big disadvantage. This is a bigger issue if one of the creative is live only during towards the end of the campaign.

Looking at the example below, the creative C, was live during the last 4 days of the campaign and thus does not have the cookie window advantage which creative A and B do.

Thus, it is recommended to think through the methodology based on the cookie window before starting the analysis. One solution is that measure the results 30 days beyond the campaign end date, then all the creatives will have similar cookie windows. However, this solution is not applicable in all situations specially if there is a big seasonality issue of the product example: Christmas candy canes, holiday decorations, customized products for a special occasion like Mother 's Day etc.

Samples, how can we have a similar concept online?

I am sure everyone has had a free sample ice cream or candy bar. I just had two M&M ice cream bars while walking up and down on Michigan Ave. Anyone who has walked on Michigan Ave today between 12-2 has to have had it. They had trucks sitting on the road and they were giving them away. Even the trash cans on Michigan Ave were showing that – see picture below (I wanted to take a picture of the trucks but missed it). How measureable are these samples? Yes, we can model out how many ice cream bars were given out and use an average conversion rate to guesstimate the impact of the samples.

While walking back, I thought how we can have a similar concept online. Yes, we cannot give away free ice cream online but there are a lot of different products which can be sampled online. For example: When Sony or Microsoft release new video game for Playstation or Xbox, demo versions of the games should be available to download online for the gaming enthusiasts to play. After the demo, there should be a promotional offer to buy the full version of the game. This would be fully measureable – from number of demo games played to how many gamers bought the full version of the game. One more thing could be free versions of browser or software like picture organizer.

Even looking at upward in the funnel, there can be online banners and the click-through rate would help understand the initial engagement with the game. Nowadays even in-banner gaming is a possibility with the fancy rich-media banners.

Awareness, Interaction, Engagement – What are these and How to measure?

These terms are used so often in online advertising but what exactly do they mean to an advertiser and how to measure them?

Awareness as the word suggests is when the consumers get aware of the brand. This is the first touch point of the brand with the consumers. This could be done via various channels – Online, TV, out of home (billboards), Search etc. Increase in brand awareness overtime can be measured with pre-post analysis, reaching out to consumers and finding the % awareness in the marketplace and after the campaign doing a similar survey again. The Ad-recall is also a good metric to measure awareness.

Interaction is when a consumer interacts with the brand – this could be mouse-over a rich media banner or calling in for a sweep stake or even clicking on a banner. Some of the common metrics to track this is click-through rate, interaction rate, # of calls received etc.

Engagement should be when the consumer really engages with the brand and goes "Beyond" the basic steps to know more about the brand and tries to build a relationship. Some examples could be signing up for a newsletter, browsing through the site in detail, downloading a widget etc. The deeper the level of engagement the more engaged audience or consumers the brand has.

Calculating Weekly Goals

It is important to calculate weekly goals to accurately keep track of the campaign performance and be able to make changes to the campaign if it is performing below expectations. One of the most common ways to calculate the weekly goals:

Using Historical Data – If a similar campaign has been live in the earlier, then that previous campaign's results can be used to directionally predict the metrics of the upcoming campaign. Sometimes, even using an x% increase year over year or quarter over quarter can be used. One more factor to be considered in the seasonality of the product/service. If the upcoming campaign falls in a higher or lower seasonality curve, then it should be taken into consideration.

After the actual campaign goes live, the forecasted numbers should be validated with the actual and learnings should be applied during the next campaign to reduce the discrepancies.

All consumers are not equal

How do you define the value of a consumer is who visits a website? If someone visited a single page more valuable or someone who visited multiple pages much more valuable.

I guess it depends on page content. If the single page that was visited was one of the most valuable pages on your website, then that one page visitor should be the most valuable consumer on your website. Such pages could be the core product information or landing page. One more example could be the "Submit" more information page i.e. if the consumer takes some action at your website. If the consumer shares some personal information like email, phone, address then this is a perfect time to start a relationship marketing programs. Ask them what they need and provide them…

A consumer could also visit multiple pages which might not be so valuable and as they would not show stronger engagement with the brand or the product.

In the direct response model, especially with an e-commerce engine in place, a funnel based measurement system works very well to evaluate the value of the consumers. The further down the funnel a consumer is the more valuable he/she is. However, in the case of branding websites, there is no linear path that a consumer is supposed to follow. Thus, in the branding websites each pages needs to be assigned a value and then evaluate the value of your consumers.

Click Sequence Tracking – Search, Newsletter, Display, Search

It is very important to keep track of the click sequence before conversion while using more than one channel for a particular campaign. This is very important to apply correct attribution to each media and also to understand how all the different media channels are helping or cannibalizing each other.

The overlap in Search and Display is very high; I have seen numbers as high as 33%. This number could be even higher for products if there is very long research phase like buying a higher priced item example laptops.

There are some advanced reports in DoubleClick's DART (Exposure to Conversion) report which provides the last 10 clicks (by site, placement, time) which helps understand which publisher engaged the most consumers for the first time and then which publisher led to most conversions with least number of other publishers coming in between the first click and conversion.

Some other cookie based systems can provide overlap across all channels – email, search, display, affiliates (DART cannot provide this as DART tracks only Search and Display).

 

Tracking DRTV using Site Analytics

With the advances of web analytics technology, it is also possible to track the impact of DRTV using the web analytics tools like Google Analytics, WebTrends, Hitbox etc.

Usually, the DRTV ads have a unique URL associated with the TV ad, where the advertiser intends the consumer to go to. So, tracking the number of visits to this page would help understand how many consumers the campaign is driving. Using web analytics tools, funnels can be created to track the complete path of the consumers if the campaign involves making a purchase or registration.

One more interesting thing can be done at the call center is giving the customer service representatives a unique URL to open in their browser whenever they receive a phone call, and then finish the process online – tracking this funnel would help understand the complete drop-offs and help make process optimizations.

Measuring the effect of Marketing Efforts – Part II

One more way of measuring the effect of marketing efforts is to do a pre marketing and post marketing efforts analysis. Trending one of the key metrics like conversion rate, ROAS, click-through rate etc. If there is an upward trend, it shows that the marketing efforts have paid off however if the metric is trending downward this is not a good indicator.

Upward or downward movement is a very good indicator but other factors should be considered in making the right decision. The trending could be due to other factors like competitive effect, seasonality, etc.

How to measure lift from any marketing effort?

It is very important to measure the lift from the marketing campaign. This is essential to understand how much more products or consumers are converting due to the marketing/advertising efforts.

A basic test/control methodology should be used to find out the lift in conversion rate due to advertising. This is easy to understand with an example of the grocery store - how likely is someone to buy a product if he/she has not seen any advertising and then how likely is someone to buy a product if he/she has seen advertising. The % difference between the two is the lift from the advertising.

In simple words you found out the purchase intent without the advertising (Control group) and then with the advertising (Test Group). The % difference between the two provides the lift.

How easy is it to implement or execute this?

For Online marketing, it is not hard at all. Comscore has a panel of over 1MM consumers whom they monitor their online behavior. Their solution creates a test/control group based on Ad exposure and conversion.

DoubleClick also has a cookie based solution to segment consumers who saw the ad and those who did not and then calculating the lift due to advertising.

For offline marketing, there are mathematical models which have been used in the past to calculate the lift.

Cost of having the Website Down: Amazon.com

Today I wanted to purchase a product from Amazon.com which I had added to my wishlist yesterday.
After going through the complete process, when I was about to check out, I get this error message that the website is down. I have been using Amazon for years, and I was shocked.

My mind just started thinking, if this site stays down for even 20-25 mins, how many orders will be missed and how much revenue Amazon will lose. Even if we assume they get 100 orders/min with an average order value of $50.00, they would loose $5,000/minute which would translate to about $100,000 - $125,000 in 20-25 mins.

It's not only just revenue but consumers getting frustrated with the situation. Many people just order pretty much everything (clothes to diapers to electronics) from Amazon.com. I think pretty much they have the complete market share but however if there was a new consumer on the site during this time, it would be a bad consumer experience and Amazon might lose a consumer. Calculating the consumer life time value would help understand the value of a new consumer.

Moral of the case - Keep a close eye on your e-commerce engine and fix it ASAP.

Bought Display and Search Media, Online sales should go up: Really?

A lot of advertisers tend to think in these terms. People think I bought media, people are looking at my ads, they are clicking on them as well, my sales should go up. Really? Why? In most of the cases it will or should go up, but sometimes it will not. Why? Have you checked your website analytics, i.e. how is the consumer flow? Are there are places where people are dropping off a lot? If yes, that thing needs to be fixed. For example: if consumers are not flowing through after step 3 in the process, then there is something wrong for example is the next button not working or is the next step that you intend the consumers to take not clear enough. The funnel below shows it all…

In an ideal world, every advertiser likes to have a straight funnel i.e. all the consumers flowing straight through the checkout process as shown in the image below.

Net net the most important thing is to track as much as you can, to find out where the consumers are bailing in the checkout process and try to fix that. It might not be an easy fix and it can take a few tests to figure out the exact reason and then you can have your Advertising "$" working for you i.e. generating "$$$".

This is a very common problem seen with e-tailers who have a pretty complex checkout process which could involve more than a simple 5 step process. I have personally seen processes which are over 40+ steps and if there is no web analytics is place, then the advertising team is blind to where consumers are dropping off.

The solution is available fairly easily - use tools like Google Analytics, Webtrends, Omniture - you can define the complete consumer flow, look at visually where people are clicking or missing out on clicking, and then act. It's a circle which goes on - track, test, implement, track again :)

Post-Impression Attribution for Display Media

Often times while measuring an online advertising (Display/Online Banners) campaign only post-click activities or revenue are attributed to the campaign. One more factor which gets missed out is the post-impression activities and revenue.

Just to clarify the differences in post-click and post-impression, post-click activities are those which are the actions taken by consumers after they clicked on the Ad. Often times, consumers just view the ad and do not click on the ad but later on go to the website and take the action – this has been termed as Post-Impression activities.

I believe that the post-impression attribution should be higher for campaigns which are more branding as opposed to direct response. Post-impression action happens when the ad leaves an impression in consumers mind and the consumer comes back later to the site and takes the action. Thus, shows increase in brand awareness of the product. When the consumer clicks on the ad, that indicates that the consumer engaged with the brand and then took action.

There are many ways of calculating the post-impression attribution i.e. what percentage of the post-impression activities should be attributed to the campaign. One of the ways is to create a test and control group. For the control group, it needs to be made sure that they are not exposed to the Ad. The next step should be to see how many post-impression actions are taken by the control group. The lift in the conversion rate (post-impression actions/impressions) in the test (group exposed to the Ad) and control group is the post-impressions attribution. Essentially, in this methodology the control group conversion rate is the rate at which the consumers would convert with no exposure to the test Ad.

Similar methodologies have also been used in the past to establish the effect of marketing in retail stores.

Google Analytics vs. Active Meter

I currently use both these site tracking tools on my blog. One of the biggest differences I see is that in Active Meter I am able to get real time data. Whenever there is a visit on the site, the active meter shows the visit – also it includes if the visitor was unique or returning.

How the visitor was referred to my site, the location of the visitor and also if they used a search engine to get to my site – what was the phrase they put in the search engine.

Getting all this information real-time is awesome; I guess this basic analysis helps a lot.

On the other hand with Google Analytics there is more than a 24 hour lag in getting the report. I looked up this morning and still June 2, 2008 report was not available. In this day and age, I think we should be able to get a lot more frequent reports. At least some basic information should be available real-time.

I also use Pagealizer which provides the scroll length, click time, page view length etc. and all this information is real-time as well.

Aarav is 5 weeks old and has a GOAL..

Site served vs. Third Party Served Ads: What is the difference?

In the online advertising world, it is very important to know how are the Ads served – site served or third party served (DART, Atlas etc). What is the difference really and why is it so important?

The difference is that when the ads are site served, it is not possible to the ad metrics like – impressions, clicks, post-click and post-impression activities. The metrics need to be provided by the publisher or the site serving the ad. There are some workarounds to the problem like using a click-command to get click and post-click activities via the third party ad server but being able to get impressions and post-impressions activities is not possible. Also, if the media buy is a CPM (Cost per 1,000 impressions) it is hard to get media spend, as it would be calculated based on the impressions. So, one of the drawbacks is NOT being able to easily get metrics.

The advantage of site serving is that the page load times are usually faster. Since, one layer of communication – site with the Ad server is removed.

The advantage of using third party ad servers like DART and Atlas is that the daily reporting is seamless. The reports for the previous day can be pulled the next day using the easy to use interface.

Most of the advertisers are now moving to third party ad serving solutions, so that the reporting is seamless and also being able to get post-impressions activities is a big advantage.

PageAlizer...

"PageAlizer" is a great tool useful for providing site analytics. I am sure there are a lot more out there (Webtrends, Omiture etc) providing the same information.

This tool provides how much time each visitor takes to click and where do they click. It also provides a visual map of the pages, how much did the visitors scroll down and where did they click. It helps understand the length of the page upto which consumers are scrolling down to. This is very important as if the content that you want the consumers to see is below where they are scrolling up to then they are missing what you want them to see and take action. Also, if the "Call to Action" button is below that scroll then they are missing that as well.

The report also provides the time till click.

I installed it for my blog and it is giving a lot of information.....

Bounce Rate, Leakage Rate, Abandon Rate

I was in a meeting with the Google Analytics team yesterday and heard a lot of these metrics. I had heard about the abandon rate and also leakage rate but Bounce Rate was something new to me. I had seen this in Google Analytics and had always interpreted it in a different way.

The Google gurus explained me that "Bounce Rate" has been termed as the "SEXIST" metric of website analytics. Bounce rate means indicates the % of visitors to your site who exit the site by viewing only one page i.e. they did not get to any other page of the site and left the site.

From a business application point of view, one would want to think that if a lot of consumers are exiting your site from a particular page, then probably there is something that needs to be fixed on that page. And if this page was the homepage or the landing page to which the online media is driving traffic then there is definitely a problem.

Some other applications:

For a retailer if this is the start shopping page and then people are leaving the site – big problem!

For a financial services or an insurance company – consumers leaving the homepage or the account login page – big problem!

For a branding website – consumers leaving the landing page to which media is driving to – your branding is not working!

How does Digital Ad Serving Work?

How do all these banner ads get placed and how does this work? First let's talk about what is an Ad Server, as per ZDNet's dictionary, an Ad server is defined as a "Web-based server that delivers banner ads and popup ads to the requesting Web pages. For companies that sell their own ads, the adserver may be an inhouse or co-located machine at an ISP, or it may be owned by an Internet advertising company".

The Ad publisher or the site where the ad will be placed contacts the Ad server to get the creative every time the page loads. Based on how the inventory for that particular placement has been sold, the ad server provides the particular creative to be published. This communication happens real time and sometimes slows down the page load. This is the main reason by sites like MSN, AOL do not want to use third party ad serving on their key homepage placements as they have over 100MM impressions in a day and if the page load is slow, they might lose consumers coming to their site.

The reporting part of the Ad server then captures the number of impressions, clicks, post-click activities, post-impression activities and all the other standard metrics based on how and what things have been tagged.

Cookie Window, what is it, what is the optimal length?

A lot of times we come across the term – “Cookie Window” or media folks talking – “There is a 30 day cookie window on this campaign”. What does that mean? This means that consumers who clicked on a digital banner or a search result have 30 days to return to your site and take the action that is being tracked. If the consumer had clicked on the ad, it will be shown in post-click activity and if the consumer had not clicked on the ad, it will be tracked as post-impression activity.
The optimal length of the window should be chosen based on each individual campaign or business, it should be tested and then the correct decision should be made. Typically, a click to conversion or action lag report is available from the ad server like DART or Atlas which shows the time tag between the click and the conversion or action. This is very important for DR (direct response) businesses which are trying to either sell something on their website or have a clearly defined action as then they can adjust the cookie window based on that.


As shown from the chart above, this is a clear increase in the number of actions up to day 10 and then there is a decline through day 15 and after that there are minimal actions. Thus, looking at this chart a cookie window of 15 is the optimal length for this business.

Launch Campaign, Measure and then….

I have worked on a lot of online campaigns, majority of the times the campaign is launched, and then the analytics team typically starts reporting on the KPIs (Key performance indicators) on a weekly or bi-weekly or monthly level.

The key question is what does the strategy or the marketing team does after getting the results. The marketing team should look at the reports carefully at the most granular level (placement or creative) for display campaign and then start optimizing based on the results. Sometimes combination of metrics needs to be taken into account for example the cost, engagement rate, interaction rate, conversion rate etc.

It is very important that the optimization takes place sooner rather than later. The optimization process should be establish and then followed week over week. I have seen that this is very important for campaigns whose key success metrics are ROAS or ROI. As the campaign manager is trying to generate highest revenue for each dollar spent.
If the campaign’s purpose is to increase the branding/engagement then it is important that the placements driving higher Click-through or Interaction rate should be served higher impressions. The relevant publishing sites should be used. The capability of Ad networks like Ad.com, Specific Media, and Context Web should be fully utilized to get lower priced media and target the relevant consumer targets.

Some portals like Yahoo! are also able to use Behavioral Targeted placements to better target the consumers to help increase the brand awareness and also increase the conversion rates.

The key is to start optimizing sooner rather than later..

Importance of Setting up correct Tracking – Always test before going Live

In my previous posts I have stressed about setting up tracking correctly. I think I found an example to help explain that today. If tracking is not set up correctly, one may end up reporting metrics which are “Too good to be true”.
What if one fine day you pull a impression click report from your Ad server, example DART and get 1,000 impressions and 500 clicks. You will be like, 50% Click-through Rate. This ad must be a rock star! Every other person who was exposed to this ad, clicked on it. When I last checked the average click-through rates, I think they were in the 0.1%-0.2% range. Where did this 50% come from?
First thing I do is contact my Ad ops team to check if the tracking was set up correctly, did the publisher implement the tags correctly. To me, this seems to be a case where either:
1. Impressions were not being counted correctly OR
2. Clicks were being counted 50x
3. I have also seen instances where for every impressions a click was being counted – somehow the click tag was firing with every impression
Most of the times, it ends up being a tracking issue or the site did not implement the tags correctly.
These issues are usually an easy fix but what if there was a one day placement (the ad was supposed to be live for only 24 hrs) and there was an error. The data reported would be incorrect and there is no time to fix this issue. We all know that some of these one day placements at major sites like MSN, AOL, Yahoo!, CNET, CNN etc cost a lot of money – a few hundred thousand dollars per day. This is a lot of money and missing out tracking on so much money is never good.
So, again please always make sure the tracking is set up correctly and ask your Ad ops team to test before going live.

Measuring Multi-Channel Campaign

It is very common for an advertiser to have a campaign that targets consumers across various channels - DRTV, Email, Display, Search, Billboards etc.

One of the most important thing while measuring from all of these channels is setting up the correct data requirements, tagging etc so that accurate data is available at the desired level to be able to measure the success metrics.

Each channel would also have a different success metric for example in Display, it is very easy to attribute conversion up to the placement level but in case of DRTV, it is very hard. If the campaign involves 5 networks with 2 creatives and running at all day parts - then the number of unique 800 numbers would be very large. Sometimes, it is possible to get them but it can be a challenge even with huge advertisers.

The case explained about DRTV applies to all the offline channels - Billboards, Print media etc

Online is so easy to measure and is as accurate one can get in measuring marketing efforts.

While looking at a holistic picture, it is possible to identify the peaks and valleys based on what media went live at what time. It helps establish the cause and effect relationship across media channels. Sometimes, we see a 1:1 relationship but sometimes it is not worth using a particular channel.

In my opinion, using Email to acquire consumers is not a very good idea, they should be used to maintain a relationship with the consumer. One example could be maintaining a relation with a patient or a car manufacturer could keep sending service updates about the car.

Aarav Prakash

I have been not posting anything on the blog for the last few days, as I am busy taking care of my newborn boy - Aarav Prakash :)

He came on April 27, 2008 at 1334 hrs.

It is funny, I keep thinking do I need to start creating a dashboard trending his number of diapers or the amount of food he is eating :)

He is showing a day over day increase in both of those metrics...there seems to be a 1:1 co-relation.

The case of "WASTED" impressions

In my earlier posts, I have talked about optimizing the media in various forms like - Frequency, Creative Optimizer. There are more ways to optimize - Geographically, day parting, week of the day etc.

But the big question is WHY OPTIMIZE? A media buyer would say - I was given a budget of $10 MM and I bought the most effective media. Well, the buyer bought the media which he/she thought would be most effective but what happened in reality?

If the media is reaching out the consumers at a frequency of 15 and optimal is 10, then 5 impressions per unique user are wasted. Now, in terms of $ if the average cost per 5 impressions is $.01 (assuming $2 CPM) then we are wasting $.01 per consumer. If the campaign is reaching 50 MM unique individuals, then the loss is $500,000.

Yes, $500,000 in wasted impressions - money used to reach out to consumers who have already seen your ad 10 times.

So, as per my previous posts there are many advertisers who if not paying attention to frequency are wasting impressions and increasing their cost per acquisition. Personally, I believe this was/is the case with Vonage, Netflix, Zecco, Sarah Marshall (the movie which is about to be released).

In the online world, cookies should be used to identify if this is an existing customer or not and then try to up-sell or cross-sell their products.

Similar math can be done to estimate the wasted impressions based on day parting (what time of the day does your market segment most active), day of the week, Geographically etc.

Another example is engagement, move impressions out of placements which have a lower consumer engagement (click-through for online, phone call rate for DRTV etc). Move the impression to places where there is higher engagement. Make the most out of your marketing $, it is money which could be used for a new product launch or your next big campaign like holiday campaign in Q4.

It is very important to do a mix-media modeling if you are using various channels (Out of home - Cabs, Billboards, buses, trains, Online, Search, TV) to find out the overlap of consumers within each of these channels. This could be hard but hitting the same consumer again and again from all channels - NOT A VERY GOOD IDEA :). In simple terms - all the media buyers for various channels should talk before planning, estimate reach and frequency, if possible research the day parts and then launch the campaign.

Reach and Frequency

I often report numbers showing 22+ frequency and always wonder if it really true? Why are we showing the same Ad to the same person 22 times. Something is wrong with the data... but, if it is for every campaign it must be true. Probably, that is how it is...OK.


I was driving in downtown Chicago (taking my wife to the Yoga class), and guess what - I start seeing Zecoo trading's ads. First ad - I said "Hey, the brokerage account that I use for trading, cool!", the next minute I see 3 cabs with the same ad for Zecco trading, I thought great! they are really try to acquire new customers. Believe it or not, in my round trip of less than 2 miles, I saw 8 Zecco trading ads. At the end of the drive, I was like OKAY! I get it, Zecco trading is offering 10 free trades and I use them every month. Forgot to mention... I see their online banner ads during the day at work.


The above is a classic example of over saturating the market with ads. Advertisers need to realize that there are only "n" number of consumers out there in the market. By spending more money, we cannot increase our market size. The more money the advertisers spend, their expectations for returns also go up. Is that justified? No! By showing more ads, you are reaching the same consumer audience and you cannot force them to start using your service or buy your product.


In my experience in the online world, 4-6 Ads bi weekly per user should be the ideal frequency. If a consumer does not convert due to exposure 4-5 times, chances that he will convert with 22 views are also very slim :)

Different systems used to measure same metrics

Many times there is more than one system used to track the same metrics. Many companies use their legacy internal systems to track revenue, margin, visits to their site, which they do not want to give up and also use the new systems like DART, Atlas to track the same metrics.

There will never be two systems which will track exactly the same. There will always be a discrepancy among the two, but the two big things are to identify what the discrepancy is and why does it exist.

Once identified the systems should track in the same direction - i.e. if one shows 20% increase in revenue the other should indicate the same.

In my experience, I think the best way to identify these issues is to map the data flows for both the systems. Once the data flow is mapped, then identity how much difference exists at each step. Also, it is possible that one system must have a longer cookie window than the other - this is a very common issue I have seen in the Online Marketing arena. So, these things could impact a lot.

One more thing I have noticed is that - sometimes systems are set to track a different level. For example: System A could be tracking at creative level and System B could be tracking at placement level. System A could be capturing only certain transactions (example - only certain product categories) but System B could be tracking everything. System A could be tracking only post-click data but System B could be tracking both post-click and post-impression.

One of the common example is trying to match Google Analytics Site data to DART Spotlight tag data. Yes, both of them are same company now but they track differently and never track the same. Some of the variables could be: session time, tags not firing correctly, tags not firing every time etc etc.

It can be a tedious process to identify the differences but once identified the reporting/measurement process becomes so much easy....

Sort Sort Sort....

Sorting is such a simple thing, but it helps so much in interpreting the data and making business sense and insights.


Below, I take an example of only 5 data points but with 15-20 data points all over the place, it is hard to understand the data and derive insights.


In the first chart, it is easy to make out that impressions and CTR is not a 1:1 co-relation but in the lower chart where the Impressions are sorted with highest to lowest, you can see a clear inverse relation - the higher the impression the lower the CTR. Thus, if a media planner were to look at this data, it would come to mind to move the impressions from the lower CTR sites to the higher ones where the CTR is much higher.

Creative Optimizer

If we think about large advertisers - advertising over a billion impressions per month, optimization would be very hard or next to impossible. Possibly, there would be over 1,000 live placements and if there is more than 1 creative...GOOD LUCK with that.

But thanks to DART's DoubleClick Creative Optimizer you can set the the metric you want to optimize on - Click-through rate, conversion rate, revenue etc. What the optimizer does is that it dynamically moves the impressions to the creative which is delivering higher click-through rate, conversion rate or revenue...and the great thing about it is that it works at placement level. So, if on one particular placement creative A is performing better - it will get more impressions and if on another placement creative B is performing better - it will get higher impressions. One more good thing about this is that you can set a minimal threshold, example serve a minimum of 10% of total impression for each creative.

However, there are some drawbacks with every good system, one of the biggest that I have noticed is that you cannot start with a set creative split. It always starts with an even split. So, if you have 2 creatives it starts with a 50/50 split.

But hey, optimizing at creative and placement level and that too AUTOMATED, I will take it any day.

A/B Testing - Display Ad/Site/Newsletter...

Most of the analytics peeps keep talking in their jargon, one of the favorite is A/B Testing. The marketing folks most times start wondering what are they talking about.

A/B testing is essentially testing creatives - for the campaign. This could be applied to almost any form of advertising - Display, Site, Newsletters. The basic principle is keep an even split among all the creatives and try to measure the impact on the action - click through rate or conversion event. Some people think it is for only 2 creatives but one can use it for multiple 2,3, 4,5....

One important thing is that the sample size - it should be big enough to provide statistically significant results (t-test).

The first time I did a A/B test was a HomePage design for one of my clients. We were trying to measure which one of the two HomePage designs drove higher consumers to better use the entire website. Typically, on a Homepage you want information for existing customers, knowledge center and an acquisition section (to attract new customers). The banking websites do a pretty good job of this.

Since, then I have tested multiple forms of creative (display), emails (different forms of emails newsletters and then measuring the call to action rate), websites.

Dashboards...

I am sure people in marketing have been hearing this term -"Dashboard" and a lot of time people wonder what is it?

As I see - A dashboard is a graphical representation of the key success and diagnostic metrics used to measure the performance of the campaigns.

I was browsing the internet and found this great site which talks about Dashboards - http://chiefmarketer.com/crm_loop/roi/marketing_dashboard/

Personally, I have built a lot of them for various types of campaings - Direct Marketing, Branding, Channel Performance (email vs. phone vs. display vs. DRTV), Site Performance, Product Performance (product A vs. product B)

As an example the table below shows all the key metrics week over week but it is like staring at 100s of numbers and it is hard to make business sense out of these. What exactly is going on...

Now, looking at the dashboard below (same data as the above table) helps understand all the key metrics in a clear and consise way thus helping make better business decisions and also come up with questions - why did a particular metric drop or rise.

Data Trending

I just realized a few weeks ago the importance of trending data. I had always set up dashboards to show trends, so it was always second nature to me - looking at trends.

Just imagine looking at a dashboard which does not show any week over week trends. Just a weekly refresh showing the previous week's results.

It would be obvious to me why the final recipients of the dashboard would not be happy, what is missing? I would not be able to get the answers to questions like - how was this metric 3 weeks ago, make me realized a ah! Week over Week trending is missing.

Just imagine this data shown in 5 separate charts, how will you derive any business insights out of this? But looking at the chart above you can clearly see that there was a clear upward trend until week 3. WHY? Probably seasonality or launch of campaign ...Why sudden drop in week 4 and then again an upward trend in week 5. Was week 4 just an anomaly or was there really a reason for this drop?

All these questions are sparked looking at week over week trends.

Data Representation/Visualization

Data can be seen in many forms but how easy it is to interpret to make business decisions is the key. A CEO or CMO cannot be presented to look at 100s or 1,000s of rows of data in excel or a SAS output which even analysts find it hard to read.

Not even CEO or CMO, presenting data (1,000s of rows) to someone who is not playing with the data on a day to day basis is not fair.

One would then argue oh! I will summarize the data and bring it down to a table. My thought would be great, but there is something called Data Visualization. Let's take an example, below is a table showing spend and revenue. The ROAS (Return on Advertising Spend) in the table is defined as Revenue/Spend.

This shows the data but it is not very easy to visualize in a table. Now, look at the charts below.

This chart shows the % spend by site and the % revenue generated due to advertising at each of those sites. It is very easy to visualize that MSN was the most efficient site in terms of generating revenue. MySpace had the lowest efficiency.

The chart below is another way of showing the spend efficiency by site.
Aren't the two charts a better way to look at the data?

Diagnostic and Success Metrics

It took me a while to figure out what is the difference in these two metrics and one day I was explained in a very nice way, which was embedded in my mind.

Success Metric - A metric which the CEO or CMO would care about. Example: Total new clients or revenue
Diagnostic Metrics - A metric which is used to explain or diagnose the success metric if it over achieved or under achieved. Example: Click-through rate, Interaction rate, Landing page visits

I feel it is very important to put them in the measurement plan and also realize if it is feasible to calculate them. The reason I talk about feasibility is that many time due to technological limitations, it is not possible to get all the data required to calculate the metrics.

For example our friends at Google do not accept third party vendors (example: DART) to serve display ads (banner) and thus it is hard to get the number of impressions and clicks or any other post-impression, post-click activity for the banners served through the Google ad network. Yes, I do know that Google just bought DART - and trust me until this day, they do not accept third party tags.

I have been in various situation where earlier it seems feasible to get the data but when the campaign really launches, it becomes hard or impossible to get the data. OR sometimes the correct tags are not put in place which enable to get the data in the correct form.

Consumer Flow

The image above shows the consumer flow or consumer experience to sign-up for a Newsletter. It is very important to capture each and every step of the process to be able to understand - "At what stage or step do we have the highest drop-off" But looking at the above flow, do you think one will be able to get a complete understanding? Some people might say - Yes, of course why not. What do you think?

Now lets lets take another look:

This flow is showing each and every step that a consumer will go through before being able to sign-up for the newsletter. One can measure a drop-off rate at each of the above steps and then be able to put a cork on the leak, helping increase the "sign-up rate."

The above example is a very simple flow - at times there are about 10-20 steps in the complete process. I have had the privilege of looking at a flow of over 100 steps as well, it was was a check out process for a leading e-commerce company.

As with every step of measurement, a many people do not believe in mapping a complete consumer flow but trust me it is one of the most important steps.

Quick Tip: Use Microsoft Visio to draw consumer flow in minutes

Measurement Plan

Writing up a Measurement Plan is very important especially for the skeptics. As I have learnt over the years it helps explain the "WHY MEASURE" and the "HOW TO MEASURE" of measurement process for a campaign.

This document is often skipped and this causes problem like the various parties involved in the campaign launch are not on the same page. One example is: The agency and the client being on the different page in terms of campaign objectives, KPIs etc

This document ensures that all the stakeholders of the campaign (agency, brand manager, product manager, marketing team, advertising team) understand that campaign objectives, consumer flow, KPIs, reporting needs, dashboard structure and the reporting frequencies.

What constitutes the plan:

1. Campaign Overview
2. Consumer Flow
3. Tracking mechanisms like DART tags, Webtrends tags, Google Analytics, Omniture Tags, Hitbox tags, email vendor tracking etc
4. Diagnostic Metrics - example: CTR, Interaction Rate, Email open rate, Email click rate, Phone answer rate
5. Success Metrics - example: ROAS, ROI, Conversion Rate
6. Dashboard - showing the key metrics help to understand the performance of the campaign. Data representation is an very important part the process. I will be providing some illustrations soon.
7. Reporting Frequencies

Happy Measuring....

Measurement Process

Yes, like for everything else there is a process which helps in measuring accurately. Just as a top line, the following steps are very important:

1. Define the campaign objectives
2. Define the key performance indicators i.e. success metrics
3. Anticipate or forecast the results, example acquire 100,000 new clients or generate $1,000,000 in revenue or generate a ROI of $5
4. For online marketing - make sure the tracking is in place to get the accurate data
5. Analyze the data and Report Results

And the circle of Measurement continues….

Why & How Measure?

The first thought to everyone's mind is why do we need to measure? Well, so I guess a very simple answer is that - you measure so that you know what you got out of your marketing campaign or marketing dollars. If you are a brand manager, you need to justify that spend. If one spends $1 MM what do you get out of it?

You cannot quantify everything in terms of ROI i.e. how much revenue did we generate by spending $X. There are many other ways to measure marketing:

1. Brand - How many more people know about my brand i.e. did the awareness of my brand increase? One can measure this by conducting surveys for example using Dynamic Logic.
2. Increasing visits to your website is an indicator that your marketing dollars are paying off.
3. Increased unique visitors, time spend on the website are some other metrics which can be used

Many a times as an advertiser, one needs to justify why does he/she needs that $10MM budget. OR what we also call as Business case.

Sometimes one needs justify in terms on "$" value that spending $10MM would help the business's bottom line by $20MM. Based on each business model, there are different ways how this can be done.

The simplest and easiest way is to use a ROI model i.e. Revenue/Spend to get the amount of revenue generated per $1 spent in marketing.

If ROI is not the measure, which is actually not possible in a lot of cases where there is no e-commerce platform, there need to be other ways to create business models. For example: If there is a action that an advertiser wants the consumers to take - it would be a good idea to attribute a "$" value to that action. This action could be "find a store" or "contact us". So, if you know that for every find a store, a consumer would make a $100 purchase, you can attribute revenue to each "find a store" and thus be able to work on your business case.

Who am I ?

Hi - I thought the first post should be about me, who am I - what do I do, where do I come from and why am I writing this blog.

I currently work at one of the Ad Agencies in Chicago, IL, USA.

Professionally, I have two Masters Degrees - one in Electrical and Computer Engineering and the other in Management Information Systems from the University of Illinois at Chicago (UIC). While, at UIC I worked for an online grocery store providing hard to find food items from all over the world (www.ethnicgrocer.com) where I managed the complete operations (ordering goods, forecasting, customer service, analytics, and logistics). I did understand the basics of running a business at that shop, and still remember every day I worked there. My gurus Parry Singh and Subash Bedi did do an awesome job of training me.

After graduating from UIC, I started working at TNS NFO Research where I was involved in forecasting the sales new products i.e. tell the clients like P&G, Kellogg Snacks how many units of their products would sell if they were to launch. It was fun working on products like Tide, Keebler cookies...

In July 2005, I did took the big step into the exiting world of Online Advertising - working for an full service agency in Chicago. Learnt a lot there while working in their measurement team - "measured" email campaigns, website performance, online banners, search, DRTV. It was fun!!!

In Feburary 2007, I moved over to another agency in Chicago and since then have measured Billions and Billions of ad impressions. I was tasked to set up the analytics platform for one of the largest Online Advertisers in the country. Started with creating weekly dashboards, led the client to know more about their seasonality curves - conversion rates, click rates. Worked with Datafeed, search, display, DRTV.

Net net - I do have about 5+ years of consumer analytics, across various industries, clients, channels. Measuring marketing efforts all over.....