Getting an unsubscribe is easy, however getting more people to subscribe is very difficult and expensive. Email marketers need to have a long term view and protect the subscribers they have. They need to treat reducing the unsubscribes as critical, if not more, as driving people to subscribe.

I have come across some cases where the list situation is so bad that the unsubscribes will completely wipe out the marketers ability to do emailing marketing in next few year.   In order to ensure that you have a viable list, you need to closely watch and analyze the unsubscribe and subscribe metrics, few metrics that you should look at are:

1. Total Unsubscribes/Total Email List in your database– This will help you understand what % of emails you can actually email to.
2. Percent Unsubscribes/Email Sent – This will help you figure out if you need to adjust your email frequency.
3. New Unsubscribes/New Subscribes – This ratio if greater than 1 than you are heading for trouble. Lower than 1 indicates that the direction is good though you need other numbers to understand the full impact.

In addition you should look at you ability to grow the list and see how soon you will hit the growth plateau. Once you hit the growth plateau, reducing the unsubscribes becomes even more critical.

Related Post: 7 Ways to Create Relevancy in Emails

Let’s face it, numbers are interesting for a while but a lot of numbers without a story leaves audience either unsatisfied or bored.  However a well-crafted story with data weaved in can keep your audience involved. It is still the data presentation but with a solid story that ties the data back to the business objective.

Data Analysts need to become better story tellers if they want to build a culture of analytics at their organization. Here are some of the elements of a good data story

1. What are we observing
2. Why does it matter
3. How does it look compared to past performance, competitor, baseline, goals etc.
4. Are we going to be able to achieve the goals
5. What is effective and what is not
6. What can we do today
7. What did we learn that we can apply in future

Anything that is not relevant to the goals/objective of the business/stakeholder should be removed from the story (move detailed data to the appendix).  The KPIs should form the foundation of the data story.

Focus on the story rather than the tables of endless data and you will find that the people are willing to listen.

Other posts in the series

• Standard Definitions of Metrics: Creating a Culture of Analytics
• Context is Critical: Creating a Culture of Web Analytics
• The Curse of Knowledge: Creating a Culture of Web Analytics
• Creating a Culture of Web Analytics

However, this is a big mistake because what they don’t realize is that ultimately they will lose the privilege to this “Free” marketing.  Here is how you can see this unfolding and ways you can measure the impact

1. In the beginning, engagement with the “irrelevant” email starts to slow down. People still open emails with a hope (if brand is one that they trust) that they might find something relevant to them.  This issue will be evident in your declining (or low) click-through rate.
2. Then when people get tired of opening irrelevant emails, they start to rely on subject lines to see if there is anything relevant for them. This issue will become evident in declining (or really low) open rates of emails.
3. Over time, all the irrelevance ultimately annoys people. Nobody wants a clutter of irrelevant emails.  Next step they take is to remove themselves from them madness.  You will see this manifest itself in higher or constantly going up unsubscribe rate.
4. All this madness will ultimately lead to SPAM complaints.  As these grow the domain will be blacklisted and with it will go the privilage of “Free” marketing.

So if you are one of those who think “Email” marketing is free, it is time for you to stop and reevaluate how you are doing your email marketing. Here are few tips to ensure that you continue to do you “Free” marketing:

1. Segment your customer base and only send relevant message to them , e.g. if a customer has never bought Women’s products from you then don’t send them an offer for 50% off on Women’s Clothing (See my post about Nordstrom’s email marketing Relevancy Matters in Email Marketing and 7 Ways to Create Relevancy in Emails)
2. Continuously test your segments to improve your Open Rate, CTR and Conversion Rate and Conversions
3. Based on your learning, further segment and develop micro segment to further drive relevance and effectiveness
4. Keep in Mind: Always be Relevant and Always be Testing

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 CMSWire.com

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Digital Marketing Jobs

• Social Media Data Analyst Bellevue, WA – Optizent
• Manager, Digital Analytics – E! and G4 Networks Los Angeles, CA – Nbcuniversal
• Web Analytics Analyst New York, NY – Eileen Fisher
• Director – Web Analytics New York, NY – MetLife
• Sr. Web Analytics / Research Engineer New York, NY – Nbcuniversal
• Senior Web Analyst Seattle, WA – Pop

Well the truth is that people have short term memory. They do forget and then start to interpret the KPIs, defined based on those goals, in their own way.  As the Analytics head/analyst/manager, it is your job to constantly remind stakeholders of the goals and KPIs.  All it takes in one or two extra slides in your weekly/monthly reports/presentation to remind people about what the goals are and how you are measuring them.

Needs, requirements etc. do change too and when that happens it your job to reassess the goals and KPIs to make sure they are still valid.  If not then you need to bring up the issue in front of all the stakeholders. You need to drive redefining the KPIs.

Other posts in the series

• Standard Definitions of Metrics: Creating a Culture of Analytics
• Context is Critical: Creating a Culture of Web Analytics
• The Curse of Knowledge: Creating a Culture of Web Analytics
• Creating a Culture of Web Analytics

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• Senior Web Analyst Seattle, WA – Pop
• Research Manager New York, NY – NBC
• Senior Analytics and Optimization Manager Seattle, WA – Zaaz
• Analytics and Insights Director New York, NY – Healthline Networks
• Digital Research Director New York, NY – NBC
• Vice President, Research New York, NY – NBC
• Project Manager, Research Atlanta, GA – Turner Broadcasting Systems
• Web Marketing Analyst-FLU000617 Everett, WA – Fluke
• Research Project Manager, PBS KIDS Sprout Philadelphia, PA – NBC
• Web Analytics Manager Newton Upper Falls, MA – Tripadvisor

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

• Context is Critical: Creating a Culture of Web Analytics
• The Curse of Knowledge: Creating a Culture of Web Analytics
• Creating a Culture of Web Analytics

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.

(All images provided by GAPremium.com – Google Analytics Tips and Tricks)

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.

Digital Analytics & Marketing Jobs

• Project Manager, Research at Turner Broadcasting Systems (Atlanta, GA)
• Web Marketing Analyst-FLU000617 at Fluke (Everett, WA)
• Research Project Manager, PBS KIDS Sprout at NBC (Philadelphia, PA)
• Web Analytics Manager at Tripadvisor (Newton Upper Falls, MA)
• Digital Customer Analytics Sr Consultant at American Cancer Society (Atlanta, GA) 
• Web/Digital Analytics Auditing at Audit Bureau of Circulations (Arlington Heights, IL)
• Senior Web Analyst at Pop (Seattle, WA)
• Online Analytics Specialist, Communications, Brookings at Brookings (Washington, DC)
• Director, Brand & Multiplatform Research at Turner Broadcasting System (Atlanta, GA)
• Web Analytics Analyst – Business Intelligence Group at Cmn.com (Houston, TX)
• Senior Manager, Digital Research at NBC (New York, NY)
• Senior Analyst, Modeling, Segmentation, and Forecasting at ARC Worldwide, An Affiliate of Leo Burnett (Chicago, IL)
• Senior Analyst, Digital Measurement | Optimization at ARC Worldwide, An Affiliate of Leo Burnett (Chicago, IL)
• Senior database developer (Sr. SDE) at Optizent (Redmond, WA)
• Associate Games Analyst at Arkadium (New York, NY)
• Director of Analytics at Arkadium (New York, NY)
• Web Analytics Engineer – Contract (POP) at Pop (Seattle, WA)
• Website Analyst at Simplexity (Reston, VA)

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.

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)

Digital Analytics & Marketing Jobs

• Project Manager, Research at Turner Broadcasting Systems (Atlanta, GA)
• Web Marketing Analyst-FLU000617 at Fluke (Everett, WA)
• Research Project Manager, PBS KIDS Sprout at NBC (Philadelphia, PA)
• Web Analytics Manager at Tripadvisor (Newton Upper Falls, MA)
• Digital Customer Analytics Sr Consultant at American Cancer Society (Atlanta, GA) 
• Web/Digital Analytics Auditing at Audit Bureau of Circulations (Arlington Heights, IL)
• Senior Web Analyst at Pop (Seattle, WA)
• Online Analytics Specialist, Communications, Brookings at Brookings (Washington, DC)
• Director, Brand & Multiplatform Research at Turner Broadcasting System (Atlanta, GA)
• Web Analytics Analyst – Business Intelligence Group at Cmn.com (Houston, TX)
• Senior Manager, Digital Research at NBC (New York, NY)
• Senior Analyst, Modeling, Segmentation, and Forecasting at ARC Worldwide, An Affiliate of Leo Burnett (Chicago, IL)
• Senior Analyst, Digital Measurement | Optimization at ARC Worldwide, An Affiliate of Leo Burnett (Chicago, IL)
• Senior database developer (Sr. SDE) at Optizent (Redmond, WA)
• Associate Games Analyst at Arkadium (New York, NY)
• Director of Analytics at Arkadium (New York, NY)
• Web Analytics Engineer – Contract (POP) at Pop (Seattle, WA)
• Website Analyst at Simplexity (Reston, VA)

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. 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.
Questions/Comments

Web Analytics Jobs

• Project Manager, Research at Turner Broadcasting Systems (Atlanta, GA)
• Web Marketing Analyst-FLU000617 at Fluke (Everett, WA)
• Research Project Manager, PBS KIDS Sprout at NBC (Philadelphia, PA)
• Web Analytics Manager at Tripadvisor (Newton Upper Falls, MA)
• Digital Customer Analytics Sr Consultant at American Cancer Society (Atlanta, GA) 
• Web/Digital Analytics Auditing at Audit Bureau of Circulations (Arlington Heights, IL)
• Senior Web Analyst at Pop (Seattle, WA)
• Online Analytics Specialist, Communications, Brookings at Brookings (Washington, DC)
• Director, Brand & Multiplatform Research at Turner Broadcasting System (Atlanta, GA)
• Web Analytics Analyst – Business Intelligence Group at Cmn.com (Houston, TX)
• Senior Manager, Digital Research at NBC (New York, NY)
• Senior Analyst, Modeling, Segmentation, and Forecasting at ARC Worldwide, An Affiliate of Leo Burnett (Chicago, IL)
• Senior Analyst, Digital Measurement | Optimization at ARC Worldwide, An Affiliate of Leo Burnett (Chicago, IL)
• Senior database developer (Sr. SDE) at Optizent (Redmond, WA)
• Associate Games Analyst at Arkadium (New York, NY)
• Director of Analytics at Arkadium (New York, NY)
• Web Analytics Engineer – Contract (POP) at Pop (Seattle, WA)
• Website Analyst at Simplexity (Reston, VA)

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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 Anilbatra.com. 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 anilbatra.com 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.

Always, Go Beyond What Web Analytics Provides

Comments? Questions?

Web Analytics Jobs

• Web Marketing Analyst-FLU000617 at Fluke (Everett, WA)
• Research Project Manager, PBS KIDS Sprout at NBC (Philadelphia, PA)
• Web Analytics Manager at Tripadvisor (Newton Upper Falls, MA)
• Digital Customer Analytics Sr Consultant at American Cancer Society (Atlanta, GA) 
• Web/Digital Analytics Auditing at Audit Bureau of Circulations (Arlington Heights, IL)
• Senior Web Analyst at Pop (Seattle, WA)
• Online Analytics Specialist, Communications, Brookings at Brookings (Washington, DC)
• Director, Brand & Multiplatform Research at Turner Broadcasting System (Atlanta, GA)
• Web Analytics Analyst – Business Intelligence Group at Cmn.com (Houston, TX)
• Senior Manager, Digital Research at NBC (New York, NY)
• Senior Analyst, Modeling, Segmentation, and Forecasting at ARC Worldwide, An Affiliate of Leo Burnett (Chicago, IL)
• Senior Analyst, Digital Measurement | Optimization at ARC Worldwide, An Affiliate of Leo Burnett (Chicago, IL)
• Senior database developer (Sr. SDE) at Optizent (Redmond, WA)
• Associate Games Analyst at Arkadium (New York, NY)
• Director of Analytics at Arkadium (New York, NY)
• Web Analytics Engineer – Contract (POP) at Pop (Seattle, WA)