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.