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:
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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?

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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

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

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.

Formula
CPM
$10
Know value
Impressions
50,000
Know value
Click
100
Expected or Known
Total Cost
$500
Impressions * (CPM/1000)
Cost Per Click
$5
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.

Formula
CPC
$4
Known value
Clicks
100
Know values
Total Cost
$400
CPC*Clicks
Impressions
50,000
Impressions * CPM/1000
Cost per 1000 Impressions
8
Total Cost/(Total Impressions/1000)
CPM
$8
Cost per 1000 Impressions


eCPM

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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Wednesday, December 28, 2011

10 Conversion Optimization Posts You Must Read

You will find many examples of how various headlines, call to actions etc. can be tested to drive more conversions and how many companies have successfully done so. Maybe you have done it too. However deep inside you know that moving the needle from 3% to 4% is huge but still there are 96% of your visitors/visits that did not convert. I have compiled a list of some of my blog posts that show you how can move the needle further up by taking action on things that are generally not found in tips and tricks books and articles.

  1. 5 Things That Could Be Hindering Your Conversions
    In this post I have listed 5 fundamental things that are part of most of the online forms but could be preventing the visitors from converting.
  2. Underline the Clickable Text and Link the Pictures
    Sometimes you just have to do it without doing testing. Underlining the clickable text and linking the picture to a page are few of those things that really do not require testing.
  3. Are Form Validations Invalidating Your Conversions?
    Are the data validations on your sites form hindering your conversions? This posts gives you something to think about.
  4. Is CAPTCHA Eating Up Your Conversions?
    Though CAPTCHA is a great tool for blocking spam it could be coming in the way of user experience and resulting in a lower conversion than you would have had without it.
  5. 7 Ways Of Handling 404 Error Messages  - 404s are hard to avoid. Even if you have done everything correctly users might mistype the URLs and get a 404 on your site.  This post shows you how various companies are handling them effectively to drive engagement and conversions.
  6. Conversion Optimization: Go Beyond A/B Testing and MVT
    A/B testing and MVT are a great way to help you drive more conversion on your website. A/B testing and MVT help you decide the best layout, headlines, images, message copy etc. that motivates the visitors to complete a transaction.
    However, A/B testing and MVT will only get you so far. If a visitor does not complete a transaction during later steps of the funnel then there are generally other reasons than those that can be simply fixed by changing the page layout, copy, images etc. .
  7. Is Your Conversion Rate Wrong? – This post explains how your conversion rate calculations are wrong.
  8. Conversion Tip: Making the Most of the Email Confirmation Thank you Page  -
    Thank you and confirmation pages are the most ignored pages. This post shows how to effectively use those pages to drive further engagement and conversions.
  9. Are you Optimizing the Wrong Steps of the Conversion Process?
    Due to organizational structure, many marketers/analysts get a partial view of the customers’ conversion process data.  This results in optimizing the wrong steps of the conversion funnel /channel. I describe my recent experience while purchasing a laptop to show how focusing on one channel only can lead to wrong
    results.
  10. Most likely your Conversion Rate is Wrong
    Most of the web analytics tools just allow you to see a view of single channel conversion rate i.e. web conversion rate. However, as I discussed in my post "Are you Optimizing the Wrong Steps of the Conversion Process?", customers don’t care how your channels are divided or who is responsible for what channel at your organization. They care about their money and will use whatever channel they feel most comfortable with.  Are you considering other channels when calculating your conversion rates?
And a bonus:
Significance of Statistically Significant Results in A/B Testing
Do not make the mistake of jumping the conclusions too quickly when running A/B tests, wait for statistically significant results.


I hope 2012 will bring you lot more conversions. Happy New Year!!!

Comments? Questions?

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Monday, December 19, 2011

One Prediction and Five Web Analytics Tips for 2012

For past few years I have made several predictions about Web Analytics. This year I am going to make only one prediction but will provide five tips for 2012.

Prediction

This year the push will be towards “Multichannel Analytics”. Integration of various data sources, e.g. email, CRM, social media, call center etc. , with Web Analytics will take center stage.

Five Tips for Web Analytics

  1. Expand your web analytics to consider other data sources
    We all know by now that no one channel exists in isolation. Web, email, mobile, social media, catalog, stores, call centers etc. all impact each other. Web is a just one part of the customer’s experience and journey towards purchase. To fully understand customer behavior and optimize your marketing you have to go beyond web analytics and look at data from other channels.
  2. Move from “How Many” to “Who”
    Majority of the web analyst today analyze “How Many” e.g. how many people landed, how many bounced, how many converted etc. “How many” is a great start but it is time for you move to “Who”, e.g. who bounced, who did not convert etc. and then think about how to engage with those “Who” did or did not do something. (if you need help with this then ping me)
  3. Understand the data structure behind your web analytics data
    I am surprised that many web analysts today don’t understand how the web data is structured, how it is collected, where all the variables that are passed in your JavaScript end up at and how various data elements are related to each other. If you are one of those analysts, take some time to understand the data structure. Open a raw web server log file and start from there. If you company is porting the web analytics data into a database then open up that database and look under the hood.
  4. Learn SQL
    This is going to be critical. You can only do limited segmentation and optimization with aggregated data that is provided in the web analytics tools interface. To really understand customer behavior and capitalize on that you should be able to extract the data from the backend. Even if you are not going extract the data yourself, having an understanding of SQL will give you tons of ideas on segmentation, optimization and targeting.
  5. Make friends with “HiPP” (Highest Paid Person) and say goodbye to “HiPPO”
    HiPP is your friend, not foe. If you really want to create a culture of analytics in your organization then make friends with HiPP, get them on your side. You need their support. Stop using the term “HiPPO”.

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Wednesday, December 07, 2011

Do you need Real-Time Web Analytics?


Real-time web analytics provides you a view into what is happening on your site at that very moment. It is really interesting to see where visitors are coming from, what keywords they are searching, what pages they are viewing etc. Though most of the time that’s where it ends i.e. it is interesting but not very valuable. As many web analysts have stated time and again, the value of the analytics comes from the action you take on that data. So, unless you are going to take actions in real time you really don’t need real-time analytics. However, I can understand the temptation to use Real Time Analytics for instant gratification.

Side Bar:
Recently Google Analytics joined the bandwagon of providing Real-Time analytics. Other notable real time web analytics vendors include Chartbeat, Woopra. As of now, Google analytics only provides a very limited view of real time stats at this point, though I am assuming that it is just the beginning and Google will roll out more stats in its real time reports. 

Few cases where you might want to (or be tempted to) use Real-Time Analytics
  • You launched a new campaign e.g. paid search, email newsletter, TV ad , and would like to see how people are reacting to those campaigns.
  • You added new promotions on your site and want to see how visitors are reacting to those promotions, so that you can tweak those promotions in real time.
  • You added new stories, links etc. and want to see if anybody is clicking on them so that you can make some changes based on instant feedback. I can see the usefulness of this feature for news and media sites.
  • You made some technical changes e.g. changed tracking code and want to see if those pages are being recorded in Google Analytics. Real time reports can serve as QA tools.
  • You launched a new feature on your site, launched a video, deployed a new game and would like to know if your visitors are using it or not.
Keep in mind that even if you are ready to make changes in real time, you might not have statistically significant results based on few data points that you get in real time reports. If you have nothing better to do then you can for sure kill your time with some real time view into your site traffic.

Views from Twitter

 
 
What do you think? Have you found Real time analytics to be useful? How are you using it? Please share your views.

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