Wednesday, October 03, 2012

5 Tips to Improve Marketing Campaigns

Marketers spend millions of dollars on digital marketing campaigns every day. Analytics help marketers get the most of out of every dollar spent and drive great benefits for them and their organization. Data collected at each step of the way to conversion can help marketers and their agencies in optimizing each campaign's performance. Below I've outlined five tips on how to use the data to optimize marketing campaigns.

1. Target the Right Customers

For a campaign to have any chance of succeeding it has to reach the right customers. Clearly defining customer segments is a critical component of any campaign. You can use historical data from previous campaigns to determine which customers are more likely to respond to your campaign.
For an in-house email list, you can use attributes that you have available in the database and create a segment of customers with those attributes that have responded in the past. For display advertising, you can use email attributes or on-site behavioral data and use a technology like BlueKai to target and reach segments that look like those who responded in the past. For search advertising, determine the key phrases (words) that clicked with those customers and then use them as your starting point to figure out which keywords/phrases to use.

2. Target the Right Channels

The question marketers often struggle with is where to spend their budget. Which channel (e.g. direct mail, email, display, search, social, affiliate, etc.) or combination of channels is likely to be most effective for that particular campaign? Use historical data to figure the channels that your target segment is more likely to respond to.
Customers use various channels in their journey to becoming a customer. They use those channels differently. Use data (current and historical) to figure what a typical customer's (your desired segment) journey is and then determine where you should focus your efforts.

3. Develop Creative and Messages that Resonate with Your Customers

If your creative and messages do not work you will notice it immediately in the form of clicks. Use historical data and industry benchmarks to determine the expected outcome in terms of Click Through Rate. If your CTR is way lower, change the content, if CTR is higher, continue doing what you are doing.

4. Developing Engaging Landing Pages

Getting people to click on your ads or emails is a good start but is of no value unless those users take actions on your landing pages. Use the data to determine if users are engaging with the landing page or are they bouncing off without going any further. If the bounce rate is more than expected, take appropriate corrective actions. You should always conduct testing (A/B or Multivariate) to figure out what resonates with your customer and make them go to the next step.

5. Optimize the Conversion Path

The conversion path is the last step in converting a visitor into a customer. The job of the conversion path is to lead the visitor to final conversion. Every step of the path is there to convince the customer and drive her to take the end action, the action that defines the success of the campaign. Use the data collected on the conversion path to determine which steps are losing visitors. Conduct A/B testing and take appropriate actions to improve the steps of the conversion path.
Note: This article was originally published on CMSWire on Sept 20th,  See 5 Tips to Improve Marketing Campaigns Using Data on
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Thursday, July 05, 2012

Standard Definitions of Metrics: Creating a Culture of Analytics

Lack of standard definitions for the metrics causes people to report different numbers for supposedly same metrics, leading to confusion and total lack of trust in data.  No trust in data means that nobody is going to use the data to make strategic decisions and there goes all your efforts to create a culture of Analytics.
Having standard definitions is not as easy as it sounds.  It starts from you and your team having a clear understanding on how to calculate various metrics.   Some seemingly simple metrics can be calculated in various different ways and all of those ways might be right but getting one standard way of calculating those removes any confusion and gets everybody on the same page.
Let’s take an example and see how many ways you can calculate “COST”.  How do you calculate cost?
In case of Search Marketing, I am sure you are taking actual amount paid to Google or Bing. Right?  So that is actual media spend. But what about the cost you pay to your agency for running and optimizing those campaigns?  Where do they factor in? If all you are doing is Media cost then what about Display Advertising?  Is your Agency commission part of your cost? This agency is running and optimizing the campaigns so I am sure you are using that all up cost.  What about your internal email lists? What is the cost of that?   What is the cost of Social Media campaigns?  How do you calculate those? To have one definition of Cost you should calculate it in the same way across all media but most likely you have different way of calculating cost for different media/tactic.
Some more examples:
  1.  Conversion Rate? Is it measured in terms of visits, visitors, new visitors, non-customers or customers?
  2. How do you calculate a bounce? Is it page views based? Is it action based? Is it time based?
If your team is not clear on how to do this then how can you expect others in your organization to understand these metrics and trust the data. Creating a culture of Analytics requires trust in data and that trust requires standard definitions.

Other posts in the series

Wednesday, March 07, 2012

Finding (Not Provided) Keywords in Google Analytics

I rarely write tool specific posts on this blog but since I have recently been asked by a few people about this issue and it affects every web analytics tool, I decided to post it here.

A few months ago, Google, the search engine, started encrypting searches for user who are logged into their Google account while conducting the search. As a result of this encryption, the keyword that the visitors search to arrive to your site is not passed in the referring URL. Web Analytics tools rely on the keywords passed in the referring URL to build the search engine traffic report and in the absence of the keywords there is nothing to report, though they still see that the visits came from Google search. So Google Analytics now marks those visits that do not have a keyword but come from Google with “(not provided)” keyword instead of the actual keyword.

Finding those keywords

Google still tracks all the keywords search by logged in users but just does not pass it in the referrer to the site that the user clicks through to. These keywords are available in the Google Webmaster Tools. To see the report you will have to register your sites in Google Webmaster Tool. Google Webmaster tools will allows you to see all the keywords that were searched, the number of clicks your site got, the average position of your site for those keywords and the landing pages.

If you are not using Google Analytics on your site then you will have to login to Webmaster Tools anytime you want to see those reports.

If you are using Google Analytics then you can connect Google Analytics reports and Google Webmaster tools to get Webmaster reporting within the Google Analytics interface.
However there are three issues with this report when used with Google Analytics (or any another web analytics tool)
  1. You don’t get other metric (e.g. goal conversion) about the visits that arrived from the keywords.
  2. This list of keywords includes not only the keywords marked with “(not provided)” but also the other keywords that you see in Organic traffic report. So you will have to do extra analysis to see which keywords are hidden under “(not provided)”.
  3. If you look at Google Webmaster tool report then you will notice that there are a lot more impressions and clicks than those displayed in the Webmaster report and the Google Analytics report (see below). I was not able to find a reason why Google is only displaying the partial number of keywords, if you know the reason then please let me know.
To leave a comment please visit my new blog at

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Monday, March 05, 2012

Digital Analytics Association

This morning, Web Analytics Association announced that it is changing its name from Web Analytics Association to Digital Analytics Association.

Why the change?

All of those who have been working in this industry for few years know that the term “Web Analytics” does not reflect the actual work we do. As I wrote in my last post “Move Web Analytics Data Out Of Silo”, “Web Analytics” purpose was to report on one channel “your website”. Nowadays the story is different, customers interact with your brand in so many way, your website is just one small part of the whole “web” ecosystem. The work most of the web analysts do today involve more than web. Web Analysts today analyze many “digital” channels such as social, mobile and email along with web. Many are starting to deal with other digital channels such as “Set top boxes”. So definitely “Digital” is a better term to describe the work that web analytics groups undertake.

I like the evolution of “Web Analytics Association” to “Digital Analytics Association”.

What’s next?

Many forward thinking organizations have been doing “Digital Analytics” for quite some time. I am not just taking about providing an integrated view of Digital Data on one spreadsheet/dashboard, I am talking about connecting the dots of customers interaction in various channels and using the integrated data to optimize customer journey and campaigns.

These organizations are diving into the next step in this direction i.e. “multichannel” analytics. Many “web analytics” companies are already developing products to move the organization from “Web” to “digital” to “multichannel” analytics and optimization. (Full disclaimer: I work for iJento which is a multichannel analytics company. Many of our clients are already beyond “Digital Analytics” and have moved into the next phase of evolution e.g. “Multichannel Analytics”)

So will Web Analytics Association (WAA) Digital Analytics Association (DAA) be called Multichannel Analytics Association (MCAA or MAA) in future? I think so but for now I like “Digital Analytics Association”.

What do you think?

(Note: I am an ex-Board Member of WAA DAA)

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Wednesday, February 29, 2012

Move Web Analytics Data Out Of Silo

Web Analytics tools are great for providing a good view of one channel i.e. your website (ok, maybe slightly more than one channel e.g. some email, some social media, some offline). They worked great in silo for first few years of the internet because the only way for customers to interact with your brand online was on your site and websites were not an integral part of the business. Nowadays the story is different, customers interact with your brand in so many way, your website is just one small part of the whole "web" ecosystem and "web" is just one part of the whole "customer" experience and buying cycle ecosystem. Customer’s don’t think and operate in one channel i.e. your website. However, many "web analytics" tools do not even provide you full view of a customer journey and interactions online let alone the offline journey.

To understand today's customer and performance of your marketing efforts, web analytics data has to move out of it's silo and needs to be integrated with other data sources.

Many of you might be already be using 3rd party solutions to pull data from few sources into a dash boarding tool. That is a great start but it still does not provide you a complete view of customer journeys. For example, just because you have social media mentions on the same dashboard as your on-site analytics data does not tell you if those mentions are from your customers or somebody, who is neither a customer nor is your target customer, just blabbering in social media. But I will give you credit for thinking outside the Web Analytics tool.

To understand complete customer journey (i.e. 360 degree view of customer) and to conduct analysis that take you from marginal improvements in conversions to something that has a huge impact on the business you need much more detailed data than a web analytics report or a dash boarding tool can provide. First, you need to collect individual data for each customer in various channels then warehouse the data in one place where you join various sources via common key such as customer id, email address, phone number etc. Only then you can create and run complex cross channel queries to understand try customer behavior and campaign performance.

Many mature organization are already doing it or are working on it. If you are not then it is about time to start thinking about if you want to stay competitive.

Don’t think that just because you are using Google Analytics you can’t have this level of data because you can. You just have to push yourself and start thinking outside what your web analytics tool can provide.

How Can You Do it

Web Analytics tools already anticipated this needs so they have built a way for you to get the data out easily. You can use either of the two methods listed below to get the required data
  1. APIs – Many tools like Google Analytics provide data via APIs. Use those APIs to pull appropriate data into your datamart/datawarehouse.
  2. Data Feeds – Many tools provide data in a flat file that you can use to populate your datamart.
Here are few things to keep in mind before you start putting this data in your datamart
  1. Make sure your tools are configured properly to collect the data in the right format and
  2. Your data transformation process should be able to understand the difference between various custom variables that you have used in the data collection
  3. Various data sources also need proper identifiers (keys) to match them together.
This is not going to be an easy project but this is a critical step in using your web analytics data to stay competitive.

There are few 3rd companies who are already providing tools and service to help you with it. I recommend looking at iJento Datamart solution (Note: I work for iJento). You should also check out Gary Angel’s Blog. Gary has worked and written extensively on this topic.
If you have any question, I will be happy to chat. Email me.


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Wednesday, February 22, 2012

7 Analysis Tips for Improving CTR on Display Advertising

Not all display advertising is created equal, though when you look at your web analytics reports you are most likely not going to find the reasons that makes each campaign and each ad so unique. Web Analytics tools generally start tracking the performance of a display advertising campaign only after the visitors have clicked on an ad and landed on your site. What happens before a visitor clicks resides in an Ad Server or in a spreadsheet on someone’s desktop.

In my last post I wrote 5 tips for Analyzing and Optimizing Display Advertisingand one of those tips was to Improve Click-Through-Rate (CTR). In this post I am focusing on elements you should analyze and optimize to increase the CTR of your display advertising. (Note: I am not saying that you should solely focus on CTR, but assuming conversion rate remains the same, increase CTR on your ads will result in more visitors on top of the funnel causing higher number of conversions. )

7 Things to Analyze for Improving CTR
  1. Publisher - Publisher or the site where your ads are served has a lot to do with how your ads are going to perform. An ad served on MSN is not the same as the ad served on Most of the time such information is absent from the Web Analytics tools and hence never crosses an Analysts mind. Get hold of that data and bring it together with the other data to analyze and optimize your campaigns.
  2. Placement – By placement I mean the area of the site where you ad is shown. Ads shown at certain location on a page are likely to get higher CTR than the other ads. For example, an ad served above the fold is almost guaranteed to be seen by a visitor, while the one served below the fold is up to anyone’s guess. In both cases an ad impression will be counted but both will not generate the same CTR.
  3. Size – Size of the display ads makes a huge difference in its performance. Certain ad sizes tend to get more clicks than the others. When analyzing and optimizing your campaign keep in mind that size does matter.
  4. Day/Time – An ad served at midnight on Friday will have different CTR than the same ad served during lunch time on Wednesday. Analyze you campaigns in light of the day/time when the ads were served, find the best time to launch a campaign.
  5. Creative - Different creative invoke different reactions. Images, colors, fonts etc. are all part of the creative mix. Different combinations will have different CTR. Find out the best mix that drives not only higher CTR but also conversions.
  6. Unique Value Proposition (UVP) – Why should a person click on your banner ad and not another one on the same page? A boring ad just telling about your services will likely have a lower CTR than an ad that provides a unique benefit to the visitor. Factor the messages and UVP when analyzing your campaigns and making recommendations.
  7. Audience – Targeted ads will likely have higher CTR than a general broadcast ad. If you have a remarketing campaign then that is expected to have a higher CTR than just a general broadcast campaign. If your target audience is families with 2 kids under the age of 10 and household income is more than 100K then putting your ad in front of a single guy make making 50K is not going to generate a lot of clicks and conversions. Understanding the goals and objectives is critical before you start analyzing campaigns and making recommendations.
Even though I have listed 7 things to analyze keep in mind that it is not just one thing that will have an impact, you have to analyze all these things together. A blue creative that works better on Yahoo homepage on Wed might not be the best combination for at the same time. Yes, it is not going to be easy to get all this data but that’s why you are getting paid big bucks.
(Note: iJento has a solution that helps you bring such off-site data together with Web Analytics data, email me if you are interested to know more about it).

Always, Go Beyond What Web Analytics Provides

No Comments Here- I am in the process of moving my blog to so I am not allowing any comments here but you can leave comments on my new blog. Also make sure to update your bookmarks.

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Wednesday, February 15, 2012

5 Tips for Analyzing and Optimizing Campaigns – Part III

This is part III of my post on Analyzing and Optimizing Campaigns. In part I I talked about why your campaign analysis is probably is wrong. In part II I showed an example of how obsessing over reducing bounce rate might not get you anywhere.

In this post I am going to provide you 5 tips for analyzing and optimizing your campaign. Here those 5 tips:
  1. Optimize Cost of Advertising
    Cost is dependent on how much you pay per click or pay per 1000 impressions (CPC and CPM). You have control over these cost factors. Those who are running Paid Search campaigns should already be familiar with and should be working hard to reduce the cost (CPC). Those dealing with CPM display ads should know that those rates are highly negotiable. Do you research about pricing etc., play with these numbers and see what will yield the optimal result,. Take your analysis and recommendation to your media buying team.
  2. Improve Click-Through-Rate (CTR )
    CTR depends on several factors such placement, creative, unique value proposition, time of the ad, targeting criteria. Analyzes those factors and see where you are falling short and where are the opportunities for improvement. You can pretty much test all of these and improve them.
  3. Reduce Landing Page Bounce Rate
    We looked at improving the Bounce Rate in the last post. You can reduce the bounce rate by optimizing the messages on your ads, better targeting techniques and optimizing your landing page. If your value proposition and messages are aligned on the ads and the landing pages, you will see a reduction in bounce rate. Conduct A/B and MVT on your landing pages to see what works.
  4. Optimize Conversion Funnel
    Make sure the conversion path steps are optimized and any obstacles are removed. Remove any fields that are not needed e.g. if you don’t have a use for phone number then don’t ask fo it. Streamline the process. Conduct A/B and MVT to improve the conversion funnel. Use personalization, if possible.
  5. Improve Average Order Value (AOV)
    Yes, you can influence the amount a customer pays per transaction. Use on-site recommendations to up-sell and cross-sell to drive up the average basket size and the value. Use customer data to figure out what might interest a particular customer and put those in your recommendations. I have also found that some segment of visitors just won’t convert online, they are not comfortable. If you are able to spot those customers on time and engage them via sales call center then not only the chances of conversion will go up but the AOV will go up as well. In my experience, those customers, who deal with a live person, tend to buy more versus those who complete orders online.

Just optimizing for one of the above variables might not yield the desired improvement; optimize all of them to achieve the maximum ROI.

Comments? Questions?

Follow Me on Twitter: @anilbatra
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Wednesday, February 01, 2012

Bounce Rate Optimization Is Not Always The Cure: Analyzing and Optimizing Campaigns

This is part II of the series on Analyzing and Optimizing Campaigns. I wrote in my previous post that when analyzing campaigns many web analysts just focus on the web analytics data. Some venture to include the cost and impression data of the campaign but they still don’t have a complete view.

In this post I will show you how their lack of complete view results in wrong analysis and wrong conclusions.

Below is the data I used in my last post. This is the type of data most Web Analytics tools provide and hence “Web Analysts” tend to use.

What is missing?

Where is the cost of products and profit margin data? Without that information, you don’t know if this campaign is successful or not. Right?

The sad reality is that many web analysts don’t have access to profit margin data and hence they look at what is available to them and start recommending A/B testing (see my post One Awesome Web Analytics Tip: Think Beyond Web Analytics). And their first target generally is Bounce Rate. Oh… look bounce rate is 50% it is too high, we need to reduce it. Right?

Wait...There is More...

Let’s assume that you are able to get hold of additional data. Now let’s see how the campaign looks if we add that data. Below I have added cost of Goods Sold data (keep in mind there are additional costs in real life).

It is evident now that the campaign is bleeding money. If your business goal is to increase conversions at any cost then you might be ok but if you goal is to increase conversions without losing money then this campaign sucks.

Ok, so what should we do now? If your answer is still bounce rate then you are wrong. Look at the data below, even with a bounce rate of 0% you will never make this a profitable campaign.

So next time get all the data before you jump to the conclusion that all you need to do is reduce “Bounce Rate”. Bounce Rate Optimization looks tempting to tackle but it is not always the cure.

Stay tuned, more coming soon on this subject.

Follow Me on Twitter: @anilbatra
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Tuesday, January 24, 2012

Analyzing and Optimizing Ad Campaigns – Part 1

I am going to start this series of post with few questions for you.  Here is some data pulled from a Web Analytics tool. This data is for a “Display Ad” campaign:

Most of the web analysts today get the following view of display advertising from their Web Analytics tool.  Looking at this data and some publicly available information they will get started on the analysis and recommendation.

Though some other analysts will say, Wait… I need more information.  Google Adwords has done such a great job in providing cost data and almost all of the analysts have dealt with some kind of paid search campaign, so they know that cost of campaign plays a role in the analysis of campaign.  So they demand it.  Well this is where most of the web analytics tools fell short, cost data generally resides in some other tool and it is not easy to get that data. But how said that Analytics was easy.   However, I am providing full data with cost so that we can continue with this post. Keep in mind that many analysts will continue without cost data. If you are one of them then stop and look for the campaign cost data.

Now the above view sort of mirrors what you are used to seeing in Google Adwords. 
So what do you think? Can we analyze this data and take some actions? This is what many web analysts end up doing.  Some will be brave enough to venture into segmenting by repeat v/s new visitors, mobile v/s non-mobile etc. If you are doing some kind of segmentation then you are already moving in the right direction.  However there is more….  I will write about that in my next part.  Meanwhile, let me hear from you.  What do you think?  Where should we focus? Is everything looking good? If not then, what is wrong with this campaign? What is your recommendation?

Part II coming soon.

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Thursday, January 12, 2012

Social Media Sentiment: Don’t Get Caught Up In Raw Counts

Are you obsessing over the total number of mentions, number of positive mentions and negative mentions? If you are then you are not alone.

This same issue came up recently while I was speaking on the subject of Social Media at a local event. One person got very concerned when I said that a lot of social media conversations are marked “Neutral” in most of the social media monitoring tools. The reason is that tools are not yet advanced enough to classify everything and so when in doubt the conversation is marked “Neutral” rather than “Positive” or “Negative” .

So what do you do in this situation, when you know that the sentiment numbers are not right?
Short answer is: Don’t obsess over the raw counts.

Let’s face it; you will never get an exact count of mentions about your brand, products etc. let alone the sentiment counts. Here are few reasons why the number won’t be accurate
  1. Tools - The number of mentions will change with the tool you are using. Different tools have different sources of data and different way of classifying spam, and hence the numbers won’t match between various tools. In other words you will never know exactly how many mentioned about your brand, products etc. are happening in Social Media.
  2. Tool Setup – The way you setup your tool will result in different count of conversations.
  3. Keywords - A generic keyword like “Windows” will bring many more results than “Microsoft Windows” however “MS Windows” will bring a different count and so on. The mention count will change depending on your keywords.
  4. Tool Updates – Tools are changing every day. The count of mentions and sentiment change as tools roll out updates to their algorithm. As for the sentiment, tools are changing the way they assign sentiment to the posts, so if last month something was classified as “Neutral”, similar post this month might be classified as Positive, due to changing algorithm as tools become better each day.
So you can’t really count on the raw numbers, so don’t obsess over them. A better measure of sentiment is the directional movement in sentiment which I calculate using what I call: Sentiment Indicator. I wrote about Sentiment Indicator in my post Sentiment Indicator: Social Media KPI

Sentiment Indicator = (Positive Conversations – Negative Conversations)/(Positive Conversations + Negative Conversations)

Sentiment indicator allows you to see if you making an improvement in positive direction or not. Though it is still dependent on actual count, the impact is minimized or neutralized as tools become better in classifying both positive and negative mentions.

Also, keep in mind that there is a lot more value in the actual conversations than just the counts. Finding value in actual text of the conversation requires manual scanning of the social media conversations. In these conversations is where you will find valuable information to help you optimize your marketing, products, PR etc.

Comments? Questions?

Other Social Media Analytics posts that you might have missed:
Follow Me on Twitter: @anilbatra
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Thursday, January 05, 2012

One Awesome Web Analytics Tip: Think Beyond Web Analytics

I am sure you have heard of a story about a guy lost his ring in a dark alley. It was really dark and he could not see anything, so he went to a nearby lamppost and started searching for his ring underneath it. When asked why he was looking for ring under the lamppost, he said “because it is bright here”.

That’s what most of the web analyst do. Even when the problem might exist somewhere other than what their web analytics data can show majority of the web analytics folks just look at “Web Data” for the answers. Why? Because that’s all the data they have easy access too. It is brighter there.

Here are some other things which are in the “dark” areas. It is time for you to shine light on them:

  1. Ad Server – There are several factors that impact a performance of a campaign, many of them don’t show up in your web analytics tools, they reside within ad servers or with 3rd parties. Example: Which pages the ads was shown, what time was the ad shown etc. I will write more on this in a future post.
  2. Conversions – Conversions can happen offline in-stores or via phone. Most of the time these won’t show up in your web analytics tool. I wrote about this in my post Are you Optimizing the Wrong Steps of the Conversion Process?
  3. Social Media Conversations – Conversations about your brand, products, offers happen outside your domain and impact how people react to your campaigns, engage with your site and ultimately impact the conversions and bottom line. Many companies have started to collect the conversation data but they might sit within a different system owned by a different department.
  4. Mobile – Mobile usage is growing every day. More users spend their time on Mobile. If you don’t have an integrated view of the mobile data with other data sources then you will end up barking the wrong tree.
  5. Third Parties– Some companies do not sell any products on their site. Their sites are mainly there to provide information. They sell their products via 3rd parties. However these 3rd party sites also provide information on products, provide reviews, have user communities etc. You don’t need to visit the official company site to make a decision to purchase something. For example, you might never visit Samsung’s site to buy a Samsung TV. All the research you need is already available on Amazon or Best Buy. Similarly, many insurance providers sell their insurance through 3rd party agents. Game companies sell their games via 3rd parties. What does web analytics data show in this case?
What you need is integrated data sources that provide you data other than just web analytics. I am not saying that it is going to be an easy task to get all this data but at least start thinking about those and see how you can bring them all together.

Comments? Questions?

Follow Me on Twitter: @anilbatra
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Tuesday, January 03, 2012

Cost of Advertising: CPM, CPC and eCPM Demystified

The purpose of this post is to clarify the terms CPM and CPC and also show how to convert from one model to the other.


CPM stands for Cost per 1000 Impressions (number of times the ad is shown) (M is Roman numeral for 1000). Generally display advertising (e.g. banners) is sold in CPM. If the ad is shown 1000 times the cost will be equal to 1 CPM price. For example, if a publisher charges $10 CPM, that means your ad will be shown 1000 times for $10. If your budget is say $10,000 then mean your ad will be shown 1,000,000 times ($10,000 *(1000/$10) ).

Total Impressions = (Total Cost or Budget) * (1000/CPM)

If you are trying to find out how much you will pay for a given number of impressions then you can use the following formula

Total Cost = (Total Impressions * CPM)/1000

If you notice in the above calculations, there are no mentions of how many people the ad will be shown to or how many clicks will be generated. CPM advertising is solely based on impressions. In theory if you don’t set a frequency cap (i.e. the maximum number of times one person will see your ad) then you could end up serving all the impression to one person only. (If you would like to know more about frequency cap then drop me a line and we can talk further).


CPC stands for Cost Per Click. Google Adwords made this model popular. Generally search and text advertising is sold by CPC model. In this kind of advertising model you just pay for number of clicks you get on your ads irrespective of number of impressions it takes to generate those clicks. For example, if the CPC is $1.00 and your ad is shown 12,000 times but gets no clicks then you pay nothing. If you get 10 clicks on your ad then you pay $1.00X10 = $10.00.

CPC = Total Cost/Total Clicks

Total Cost = CPC * Total Clicks

Comparing CPM to CPC and vice versa

The goal of advertising using one model versus the other is really dependent on what you are trying to achieve. If your objective is to generate Brand awareness then you might engage in display advertising which will most likely be sold in CPM model. While search ads on Google or text or display advertising on Google Ad Network are sold in CPC model.

Often you will end up comparing two models to figure out where and how to spend your money effectively. To do direct cost comparison you will need to convert CPM to CPC or CPC to CPM pricing.

CPM to CPC conversion

Below is a formula that you can use to calculate a CPC equivalent of a CPM model

CPC = ((Total Impression *CPM)/(1000 *Clicks)

Below is a spreadsheet to show you the same calculation. Let’s take an example of a campaign that costs you $10 CPM and generates 50 clicks in 50,000 impressions.

Know value
Know value
Expected or Known
Total Cost
Impressions * (CPM/1000)
Cost Per Click
Total Cost/Clicks

The above $10 CPM campaign is equivalent to a $5 CPC campaign.

CPC to CPM conversion

Below is a formula that you can use to calculate a CPM equivalent of a CPC model

CPM = (CPC*clicks*1000)/Total Impressions

Let’s take an example of a campaign that costs $4 per click and generates 100 clicks, resulting in a total spend of $400. Let’s say it took 50,000 impressions to generate those 100 clicks.

Known value
Know values
Total Cost
Impressions * CPM/1000
Cost per 1000 Impressions
Total Cost/(Total Impressions/1000)
Cost per 1000 Impressions


The CPM value you get when you convert CPC into CPM is also known as eCPM (effective CPM).

Note: eCPM is also shown in Adsense reports, in that case it is

Total Adsense Revenue /(Impressions/1000)

I have developed few calculators to calculate CPM and CPC, feel free to use them.

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