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.
Saturday, April 26, 2008
Thursday, April 24, 2008
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 :)
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 :)
Wednesday, April 23, 2008
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....
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....
Tuesday, April 22, 2008
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.
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.
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.
Monday, April 21, 2008
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.
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...
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.
Subscribe to:
Posts (Atom)
