In this article, I would like to focus on tactics to increase KPIs relating to return visitors or loyalty customers. The common metrics in web analytics tools to measure loyal visitors are return visitors, loyalty metrics like frequency and recency, or number of advocates.

It is very common that conversions for eCommerce are likely to be higher for returning visitors to the site. That is because consumers are researching online and may visit the site again later to finalize their shopping. According to Foresee's research, "Compared to shoppers who are dissatisfied with an online experience, shoppers who are highly satisfied with a retail website are 65% more likely to purchase online and 44% more likely to return to the website than those who are dissatisfied."

That is a very convincing statement to support why you should focus on converting your customers from new to return and make them loyal to your service and brand. Obviously, your customer has to be satisfied to be loyal, but let's focus on the web analytics and the tactics and strategies to improve the KPIs.

Let's layout some KPIs, and discuss some example tactics and strategies to improve it. Return visitors, frequency, recency, conversion rate, revenue distribution, new and return visitors distribution.

These are common measures you can acquire from the web analytics tool out there in 2010. It is typically based on site visitors who were cookied or not. Caveat is that if users delete their cookie or switch their browser, they won't be recognized as return visitors; instead they'll be tracked as new visitors.

What to look for in returning visitors are the number and growth in returning site visitors, conversion rate, and revenue contribution and distribution. You don't want to take action before knowing where you stand. The number of returning visitors doesn't mean anything, but if you add some context, then it can tell you a lot. Here is an example of what you can start off with:

I just randomly made this up, so don't worry so much about the actual values. The idea is understand your current numbers, and where you stand. You might want to start from here and understand the growth and distribution of new and return visitors, and their revenue attribution. Ask following questions on how well your site is doing on:

1. New and return visitors. Are they both growing or not? In above example, your overall traffic is growing, but return visitors are not growing. Sad...
2. Is your returning visitor's distribution relative to overall visitors low or high? 20% of the site traffic is return visitors, and about 80% new visitors.
3. How much is the revenue distribution between new and returning visitors? 20% of your site visitors are returning traffic and they are contributing 55% of the revenue!!
4. Conversion rate for return and new visitors. What is the difference? Typically, return visitors are much higher than new visitors.

So from this scenario some the actions you can plan and take are:

• Grow that number of return visitors. You need to look at recency metrics and continue to improve that number so return visitors are coming to your site in shorter time span. Market to them, and continue to offer fresh ideas, content, products, price, features, and services.


• Improve conversion rate for new visitors as much as you can. If you set up your site so users can opt-in to receive future message from you, you can remarket to the converted customers.


• Improve conversion rate for both new and returning visitors, and do it through testing. Always test, test, and test. Testing in this case is web site optimization through A/B, multivariate testing, etc.


• Don't worry so much about new and returning visitor's revenue distribution. Think about the growth of retuning visitors, because that means your strategy to bring in more loyal customers is working.

Few tactics I've mentioned here to move that KPI's are:

• Fresh content, services, products, ideas, right price, features, etc. You have to have something to spark the interests of your consumers so they come back.
• Re-marketing. Re-market to current customers because you know they are likely to buy from you again. Re-marketing could be done through newsletters, emails, calling, sending out coupons to current customers, DM, etc.

After quickly searching on Google, Yahoo! and Bing, I could not find relevant content relating to Web Analytics for Customer Support and Care. Some may blame me that I didn't do a great job searching for such studies and writings, but I am definitely not happy with the results I found on search as of the date of this writing. So I am going to brainstorm an initial roadmap on web analytics for customer support and care sites.

Outcome

Obviously, companies are trying to increase customer loyalty by providing great services, support, and care. From a corporate web site's perspective, there are 3 main goals to achieve in increasing customer loyalty.

1. Reduce support calls & cost.
2. Customers finding the solutions they need help for.
3. Serve relevant solution and care through the site.

KPI - Key Performance Indicators

Support Calls & Cost

• Number of support calls for specific reporting period
• Total support cost for specific reporting period

Customers finding the solutions they need help for

• Number of entry visits to support section by "Support Related Redirect" and "Search Engines"; Increase direct entry to support pages from sources and channels where customers with problems would likely to start from.
• Bounce or Exit Rate on On-Site Search and other initial point of interaction that takes users to list of potential solutions (like FAQ page); Customers will leave (bounce) if the site serve suboptimal results, so it is imperative to measure if your consumers are findings the relevant solutions.

Serve relevant solution and care through the site

• Customer satisfaction score (thru surveys)
• Number of Task Completion and Rate per key interaction pages/points (micro conversions); Identify pages and customer interaction points that tells us the customer experience -- Example: Feeback on Helpful Yes/No, Visits to Email Support Confirmation Page, Reduction in click-thru to contact us (page with off line number), etc.

Strategies and Tactics

I feel like most of the companies are just throwing FAQ pages up and not optimizing the results. However, there are companies out there doing great job in executing the customer support site, and by looking at the analytics tracking code, you can get some sense of understanding on how they're positioning their web analytics tool.

Also I feel like strategies and tactics are executed without having a plan or roadmap. Maybe many major firms do have a clear roadmap, but I don't find many discussion about it. I hear more about measuring conversions, engagements, SEO, PPC, site optimizations, etc.

The critical point for customer care and support from web site perspective other than lowering cost and increasing user satisfaction is; "find-ability" of the solution. Theoretically speaking, customers should be able to find the answer and solution to their questions with ease. Perhaps finding the solutions directly from search, and from both Search Engines and On-Site Search. I think Apple has done a great job at least from the outside (don't know anything about their data). Check out the forums and FAQ pages' title and how they are indexed on search.

Other major firms' site are great source to check and see how you your site's usability compare. In order to avoid subjective views, "Search Engines" are great tool to measure it. Example -- looking at what contents are index using "site:forums.xyz.com" on Google, and checking out their robots.txt files, using google search insights, competitors' twitter account using twitter measurement tools, etc.

Customer Support and Social Media

I feel like I read more and actually find interesting in this Web 2.0 era is Customer Support + Social Media rather than using web analytics to measure and optimize the web site's property.

Here are some articles on Customer Care/Support & Social Media (mostly Twitter)
Comcast: Twitter Has Changed The Culture Of Our Company
HOW TO: Use Twitter for Customer Service
Customer Support via Twitter?

From web analytics point of view, there would be additional KPI assigned to the road map if customer care and support becomes more integrated and practiced via Social Media, but I will hold that conversation for another time.

A conversion could be anything, it could be an instant that defines the moment of success.
Here are some examples:

• Orders
• Subscribed for blog feed
• Email subscription
• Clicked on twitter, linkedin, about us link
• Generated product review/testimonial
• Shared content (advocated)
• Clicked on on-site ads
• Acquired a lead

When you segment your data based on a specific conversion, you will find various segmented data sets yielding different results in KPIs. Assessing landing pages based on conversions are great because it allows you to focus on the first page that convinced your site visitor to complete a desired action/outcome.

Looking at this example chart, you can definitely see the head and the tail, which is based on the cut off you define. Let's say 50% of the revenue was contributed by top 5 pages. Given that there are total of 35 landing pages that contributed to conversions, it is insightful to know that these 5 pages helped you generating 50% of your revenue!!

Biggest question is, what strategy are you going to introduce to optimize the other 30 pages? Even if you improve (theoretically) all of these 30 pages to convert better, the likely hood for you to see this curve from head to tail will always exist. However, it is very important to continue and set goals > measure > report > analyze > optimize > set goals (repeat).

What you need to look at when you optimize the long tail pages are:

1. How much did your optimization plan and action supported improving the number of orders in the tail segment, and revenue growth from these long tail landing pages.


2. Engagement and visitor type difference in head vs. tail. How did your optimization impacted these visitors.


3. Bounce rate improvements based on your optimization to these critical and key pages.

You may ask, what about the pages that aren't showing up here in the landing page's long tail chart? If those pages are so important that you think it should contribute to your site's success, then it is a clear indication that the page is very ineffective, and it is a call-to-action to do something about it.

It is imperative to take these number of entry pages (segmented by orders) and divide it by overall number of entry pages served. For example, if you had 100 pages on your site and 90 of them were your entry pages, then we can say 39% (35/90) of the entry pages are contributing to conversion or your desirable outcome. This is a good metrics to see the allocation of effective entry pages that are supporting your site's goal. A great bench mark to gauge after executing your optimization strategy.

The most important thing is to see how your campaign and optimization strategy impacted the landing pages, and I am sure you'll see some difference in this data matrix.

Good luck analyzing your landing pages, and I'd love to hear your thoughts. If not on my blog, maybe through Twitter.

Recently, I have been working with web developer for one of our new site. It is always nice to be working with a developer who is sharp and with full of interest for web analytics. These developers work so hard to help us web analysts get proper tracking into the site. This article is a bit of a dedication to those who help us analyst get the data that puts bread and butter on our table.

Here are some scenarios I would like to highlight where web analytics will work very closely with IT/programmers and integrate to drive better web site experience for your site visitors. (I only highlighted the things that came up in my mind at the time of this writing, and skipped the obvious part, implementing analytics tag.)

On Site Search

It is common to find more major enterprise to invest in on-site search (or internal search) technology. An example is Google Search Appliance. Obviously, the IT team in your company would be installing this to the server.

Web Analytics analyst should get involved in this, because even if the appliance has some nice data around what people are searching on the site, web analytics application should have standard metrics relating to the search results, and eventually assess behavior data and impact to the outcome (bounces, orders, revenue, and many other interactions generated from on site search).

Depending on the search results and the outcome, web analytics analyst should work closely with the IT in optimizing the appliance, so relevant search results are given to your site visitors. Potentially what you do in this optimization can impact the outcome as well.

Error rate

Launching a new site, replaced shopping cart, replaced old products with new, ending a promo, etc. For whatever reason, there are chances that your site visitors are landing on error page or page showing that the content is not available. Typically the server will send back a 404 error message. That is why it is usually error pages are referred to as 404 error messages.

The majority of Web Analytics applications have the capabilities to track such visits to error pages. Web Analytics analyst should know what is the % of error page served. Hopefully you are aiming for 0%. Understand where visitors are coming from to arrive at the error page, and with what URL.

All this information will help your IT folks to set up a proper redirect, or even get communication started with product/content managers to serve or activate relevant content to replace that error.

This is a great article on 7 Ways Of Handling 404 Error Messages. Checkout what some companies have done to address 404 error messages.

Redirects

Major corporations investing heavily on online/offline campaigns are likely to be setting up redirects to direct site visitors to relevant landing pages. Redirects would most likely be set up by IT, and web analytics would be working with the marketing and IT folks to set up appropriate campaign tags to assess marketing efforts.

It is not only important to assess the campaigns/redirects, but to understand if there were any impacts when redirects were taken down or if redirects' usage has changed (re-using old redirects for new campaign, etc.). Having a transparent communication process in place for managing redirects would be crucial to a success in web analytics practices.

Other Area of Technical Web Anlaytics

There are many other web analytics data that will help programmers and IT folks to understand what system environment the site visitors are in. Some of the metrics are Browser Versions, Operating Systems, Connection Speeds, Flash Versions, JavaScript Versions, etc.

It might be a good idea to sit down with the IT and Programmers before any major releases take place, and review if any upcoming programming or changes in infrastructure could impact site visitors or customers' site experience.

Sharing Results with the Programmers and IT

Programmers and IT folks deserve to know the results, too. When web analytics analyst creates report for managers and key business groups, it would be great to share the results with the technical people as well. I had the privilege to work with smart IT and programmers in the past, and sharp technical folks ask brilliant questions which in many occasions should have come out of marketing managers.

Having a conversation about optimizations and exchanging ideas with technical folks can benefit web analytics analyst in many ways. For example, having their resource to tweak on-site search and increase revenue can definitely make our contribution to business shine.

Considering that there are so many metrics and dimensions to deal with, just by tagging Google Analytics tags on every page on the site. Analysis and reporting could get extremely complicated.

What I recommend is to relax and look at the basic metrics, but applying smart segmentations. If you're going to puke before looking at the data, might as well puke after looking at bad data.

I've asked my family's friend to see if I can check out e-commerce data and use it anonymously. So here you go.

Very high level data, including overall site visits (which something we may not care at all). The important thing is to leverage available segments and apply few basic custom segments to better position yourself in a pool of data.

What I did here is applied "Visits with Transactions", "Non-bounce Visits", and "Users who visited the shopping cart, but did not purchase". It is amazing that even a site with average bounce rate of 32%, can have a significantly bad data that is puke-able.

First, visitors adding item to cart was only 5% of all site visitors. I think that's pretty bad, but let's assume that is good. Now, what about only 2% of the shopping cart visitors completing the order (0.1% of all site visitors)?

This pretty much paints the big picture of different level of opportunity pie, and how much qualified visitors you're brining into the site. Is this still good or bad? I don't know, maybe the average order value is super high ($1,000 or more..) and this site is making huge money... but that's something you and your business need to decide and set goals.

Looking at average pageviews with those segmentations (same as pages per visit), it is amazing that visits with transactions are going through roughly 31 pages just to convert. Notice that visitors who hit the cart page, but didn't ordered was 17 page views. That means people who hit the cart and ordered are going through 14 additional pages on average. Definitely some opportunity to optimize the conversion process. What does your data show?

Average time on site shows a similar story, and it is quite obvious that customers are going through some rough experience just to convert.

Just applying simple segmentation to high level data like visits, page views, and average time on site, can give you some very high level, but very insightful data to initiate of some more data diving.

One of the ultimate outcomes for a website is customer satisfaction. First site I think of e-commerce site with great customer satisfaction is Zappos.com. It is one of the ultimate models to service and satisfy customers. Zappos definitely made me think about customer satisfaction more than before. Before we think things in complicated manner, I think we need start somewhere and understand the measurement points for web analytics, because web analytics is the bread and butter of this blog.

Check out this diagram I made. Apologies if it is not pretty...

When a business owns an e-commerce site and deals with web analytics, we (web analysts) need be mindful of the key points that support and complete the e-commerce service. Allow me to share you my idea around three web analytics action and measurement points:

Point 1 (Website and Customers)

This point shows the interaction between customers and the website. In other words, it is the "experience" part for the customers going through your store. From web analytics point of view, we would measure and assess:

• Pages that the customers viewed that contributed to completing a key event.
• Entry pages with high bounce rate to understand what to optimize.
• Conversions.
• Visitor types and their behavior differences, and outcome analysis.
• Advanced segmentation analysis on various dimensions of data against outcome.
• And many other fantastic data we get out of web analytics applications.

These are typical metrics and KPIs that are available straight out of the web analytics applications, which requires customized tracking and set up for key events on goals. The business should know what KPIs should be gauged to understand how their site is doing against their goals.

Optimizations and testing will impact this area (point 1), so customers' interactions with the site will give us actionable data that supports decisions to improve customer experience. Measured and assessed data should tie with customer satisfaction because your website has to serve your customer with products/services/content they are looking for, and have your site optimal so that customers can convert with ease.

Point2 (Business and Website)

You are wondering what does business or website owner got to do with the actual site that relates to customer satisfaction. To be honest, after going through many websites, I've seen many situations where I doubt site owner's care and ability to maintain the site to serve its customers. (Maybe I am shooting my own foot here... lol)

Some of the actions taken by business owners could be adding/updating content, executing promotions, supply inventory info in e-commerce database, etc. All these actions could lead to customer satisfaction. Taking the supplying inventories as an example, I've came across sites where when I put an item into cart, it'll show "out of stock". Leaving me with no choices, but to leave the site.

From web analytics stand point, we would love to measure everything that impacts the bottom line, but here are examples of metrics and actions pertaining to customer satisfactions:
Inventory availability vs. demand orders, Promo response and application/redemption, site optimization A/B or multivariate test, fresh content uploads rate vs. avg. visitor frequency, applying SEO best practices, etc.

Point3 (Customers and Business)

I believe this is an important point as well as other common web analytics involvement affiliating with customer satisfaction. After customer purchase items from the website, orders have to be fulfilled, issues have to be solved; direct marketing may take place online or offline, etc. The speed and timing of fulfillments, customer supports, and customer communications are pretty critical to customer satisfaction.

From web analytics stand point there quite a few measurement points. Here are some examples.

• Direct Marketing (online/offline): Delivery, Response Rate, Conversions, etc.
• Fulfillment: Ship to order ratio, average order to delivery time
• Support: Customer response, incident reduction, support forum usage
• Direct testimonials to business, survey request to customer and analysis

Hopefully, this article gave you different take or angle in viewing customer satisfaction relating to e-commerce. Some of this concept may apply to non-commerce site, too. I guess having a creative mindset in web analytics field does not hurt. That's what I believe in.

In this writing, I would like to focus on remarketing with email. Keep in mind that in order to remarket via email, people have to have registered on your site or who have opted into your email program.

The best starting point is to review the type of site you are running the remarketing program. Your site could be an e-commerce, content, lead generation, etc.

Second point, what interactions by these identifiable visitors would you consider as abandonment? Abandonment in this case could be anything that you consider people who didn't execute the desired action, after subscribing or registering for communication.

Examples based on an assumption that the site visitors are logged into the site and registered for email communication:

• People who added items into shopping cart, but did not completed checkout.
• Visited a specific page (like promotional page), but didn't complete a desired action like subscribing/registering/applying for promo.
• Didn't sign up for magazine subscription.
• Didn't bounce, saw old product, but missed viewing the new product pages.
• Registered users with twitter account (based on profile info), but didn't click or attempted to show interest in following the site owner or company's twitter account.

You get the idea. Remarketing could yield positive results. According to a research found on a study for ExactTarget and TicketsNow, researchers found that transactional emails sent after a shopper had abandoned items in a shopping cart actually worked to improve overall company revenue by 30%. Therefore, remarketing could be an effective marketing tactic based on people's interaction with the site. So far, re-marketing sounds great and very promising.

A lot of the findings based on research talks about X% improvements in revenue or increase in conversion by Z% after doing XYZ. That makes me wonder about the kinds of web analytics data, which should be reviewed prior to investing your time in executing remarketing program.

Here are some ideas that I think web analytics metrics can help you decide rather to tackle remarketing with email or not. Idea is to understand where your site stands in terms of data, and gain a better sense of expectations from remarketing.

Number of registered users who can be emailed:
You site may be selling your products pretty well, but if a lot of them shoppers didn't opt-in to your newsletter (for whatever reason) then the overall volume of re-marketable people may not be significant.

Imagine an e-commerce site where the owner decided to give customer an option to opt-in for email communication at the thank you page, which caused only 1% of the overall customers to register. Let's assume there were 10,000 absolute customers to date. That means there are only 100 remarketable customers. Is that a good number for you to invest in re-marketing? You decide.

Email click-through rate (unique):
If your average click-through rate (unique clicks per email delivery) was around 1%, then only 1 response would be generated from 100 emails that you sent out (based on the above example).

If your site is awesome, and have 1,000,000 absolute customers, with 1% of them registered for email, there will be 10,000 remarketable customers. With 1% click-through rate, you have about 100 responders to your remarketed email.

Conversion rate:
Self explanatory, but let's assume your re-marketing tactics are so great (the best in the world), and 10% of the email responders actual complete the desired action. With the case with 1,000,000 absolute customers, you'll get 10 conversions.

Average order value (AOV) or Average Conversion Value (ACV):
Regardless of your site being e-commerce or not, it would be important to understand how much your conversion are valued. Say your site sells an item that worth $100 on average, then that will be your ACV or AOV. With the 1,000,000 customers example, that will mean your remarketing efforts may generate about $1,000 in value. Not sure if that is good yet.

Return on Advertising Spending (ROAS):
So you may be happy that $1,000 is a great outcome, and now you can go buy your team some drinks... but wait. Ask you self, about how much money are you planning or expected to go out of your pocket for this remarketing program?

Let's say you paid this one freelancer that use to work at a top marketing agency for about $1,500 (I'm sure it is more in reality...). Using above scenario, your ROAS is 67% (1,000/1,500). You basically lost some money just running the re-marketing program. Nice... maybe that's why you hear a lot of fancy agencies having trouble making their clients happy because clients might be seeing this ROAS below 100%.

Additional/potential value generating from re-marketing outcome:
This is where your segmentation in web analytics tool come into play. If your remarketing efforts are to drive certain outcome or interactions, what does your web analytics segmentation tells you about those visitors after they converted or show potential additional value?

Say you segmented those 10 converted customers (visitors coming from re-marketing email), and found out that they advocate the site's product through tell-a-friend feature. You happen to know that tell-a-friend or advocacy by customers generate about $50 in value (this is just an assumption for example).

Therefore, 10 customers telling 10 of their friends generated additional $500, making your ROAS 100%. The idea is to look at the segment of different interaction and outcomes so your remarketing efforts are based on interactions that drive the biggest return.

These interactions could be existing even before you run the re-marketing program, so understand the values from different "micro conversions" using segmentations. Here is a nice article that talks about "Macro and Micro conversions".

I guess the challenging part is to assign some kind of $ value on to those micro conversions, or those segmented interactions that leads to additional returns for your site/business.

A visual to support my writing...

When a dashboard is shared across different teams, then a specific group with special interests will start to ask more than what is in the dashboard, because their job duty is to support specific tasks within the business life cycle of the online business. In some occasions (probably most of the time), for example, when you create a web analytics dashboard for CEO, you'll always hear from some other team manager saying that that dashboard doesn't help "me" in anyway, and start to go off about what would be a great dashboard for him/her.

In a typical scenario, honest and hard working web analyst will try to make everyone happy, so he/she ends up making either an obese dashboard containing bunch of colorful charts, or bunch of redundant reports catered to every single managers in the company. You think a web site with one goal/objective would have a clear and simple dashboard for all to accept easily created, but it could get tricky. I believe it is important to think of dashboard as a starting point to dive into a pool of data.

The best way to create an effective dashboard, following points should be taken into consideration:

1. Understand the bottom line and the desired outcome of the web site.


2. Ask, why do each key business units exist in your firm and what is important for them to execute their job?


3. Know the kind of decisions your end users or business partners can make based on your dashboard.

Effective dashboards usually include:

• An intuitive graphical display that is thoughtfully laid-out, and easy to navigate.


• Logical structure so information is easy to consume.


• Regular and frequent updates of dashboard for relevance to current conditions.


• Answer fundamental questions about the web site's goal or overall business unit.


• It alerts issues or problems in such areas (error pages, ROAS, revenue, conversion rate, etc.)


• Supports decisions that impact the business or online strategies.

Killing multiple data needs with one dashboard

I am just using this phrase to push the minds to think in an effective way, so that KPIs are reported effectively to reduce redundancy. Check out this image of a scenario where you have multiple business units involved in a web site, and how they could have more data needs than that one Executive Dashboard which was created to make CEO smile and move.

The images may imply that web analyst will need to make 5 dashboards at the end of day. However, if you craft the dashboards effectively, you could potentially consolidate them to fewer reports. How you do that will depend on web analyst's skill to work with those key players and identify them KPIs for an actionable insights.

Here is a basic framework that I crafted to support approaching in creating that awesome web analytics dashboard (Tier 3 is not necessarily a dashboard, but most likely a report or an analysis.). It is intended to demonstrate that your dashboards or reports should be positioned and focused on catering the KPIs to appropriate audience/groups, and show different tiers of audiences which could reflect organizational structure with different focus on reporting needs.

Side note: If you give a web analytics access to each business units and allow one smart and motivated person to do their team's web analytics support, you can build allies of web analytics specialist. This could be a long term strategy to build web analytics culture within your company.

I hope you don't underestimate the impact of screen resolutions to the conversion rate. I have seen many sites where the call to actions or the important messages are under the fold.

Notice that height with 768 ranks second for this one site. (I have seen 1024x768 rank first for some sites)

Here is an example of a CPG site. Perhaps selling is not their main goal, but they have a call to action "Buy Online", so let's assume that is their main site objective for this case.

Notice that the red box (call to action) goes away when I changed my resolution to 1024x768. They are increasing the chance for visitors to bounce, because it makes the gap between the customer intent and that landing page's objective to widen.

1280x800

1024x768

I have seen cart add ratio decrease tremendously when call to action to buy is under the fold for specific screen resolution. I guess it's time to review your screen resolution and make sure that the drop off is minimize by a simple redesign.

I would also have to mention that not only people adjust their screen resolution differently, but browsers can also allow users to set different zoom percentage. That also affects your site's performance, but at this point, it is probably wise to test and find out. Good luck!!

I am not sure if there are a lot of companies out there that relies on the web analytics applications to integrate external data and report it out of the web analytics application other than the Business Intelligence tools.

The idea of integrating and assessing offline metrics to web analytics data is nothing new, but I feel like I don't hear much about offline metrics that impacts online business and strategies.

In this post, I would like to talk about some of the key operational metrics that web analyst or eCommerce managers should look at when assessing the success of your eCommerce business.

In web analytics, we focus a lot on marketing, traffic source attributions, conversion rates, etc. It is vital to also think about your customers' experience beyond the website or after they completed the transaction. In eCommerce, there are a lot of things happening even after customers completed purchasing. Orders have to be processed, transactions will need to be settled, products need to be wrapped, shipped, confirm delivery, subtract inventory, process returns, etc.

All of these required actions or processes correlate to customer satisfaction. Sure, you can improve your traffic by 200%, and increase conversion rate by 300%, but if your customers aren't receiving the products within promised timeframe, then they will not become a repeat buyer or even advocate your service. Here are some of the metrics you will need to take into consideration.

Shipped to Order
For a given time range of data, it is the percentage of shipped orders per total orders. If one of the weeks in previous months show less than 100%, then maybe something is wrong with the fulfillments or the orders aren't getting processed.

Duration between Orders to Delivery
If your slowest shipment option in your eCommerce service is 5 to 7days, you definitely don't want to see seven plus days for this metric. Your customers do expect to have their product delivered on time.

Number of return orders and percentage of returns
So you have increased your conversion rate and successfully optimized the campaigns to drive higher revenue, but you didn't realized that more products are getting returned due to whatever reason. When you're working with a great eCommerce solution, you should be getting back the number of returns. Make sure to gauge this key data.

Percentage of in-stock per order
Maybe your site is selling hot out items quickly, but the orders surpassed the amount of units available in inventory. You want to know what percentage of orders are actually available for delivery. If its below 100%, you might want to double check which products are short on inventory, and for what reason is it short. (sky rocketing number of orders for particular item?)

Remember, that one of the key desired outcome of websites is to "increase customer satisfaction and increase loyalty". Make sure to close the loop in your supply chain/logistics/fulfillments by taking action upon these key metrics.

Inside the short, troubled life of a music start-up

Here is the background of SpiralFrog's business.
SprialFrog is a free digital music download service, which became the first company to convince a major music label to offer downloads on an ad-supported basis. The company's goal was to give away music and supported itself by selling advertising. SpiralFrog's management believed that the record companies would rush to do business with anyone who competed directly with illegal peer-to-peer sites, and they have focused heavily on simple plan to grow their business via aggressive marketing to turn visitors to loyal users.

Here are some key highlights from the article. Mostly a reflection of bad decisions. Let's see how you see it...

• Low registration rate, pages per visit, time on site, high bounce rate


• Company burned $26.3 million while generating sales of just $1.2 million


• The dramatic falloff of site traffic coincides with the board's decision to cut spending on affiliate marketing


• Management says "it needed to build volume and then swing over to quality. If you didn't build the volume, you could never get ads on the site from tier-1 advertisers".


• At one point, traffic grew from 2 million visitors to 3 million in the following month


• Affiliate marketing programs were introduced to spread the company's brand across its own site and many other affiliated sites.


• In January 2009, SpiralFrog's 2008 sales and marketing expenses came to $11 million (twice the $5.6 million the company paid in music licensing that year).


• One of their promo's ROAS was $60 per registered users. In worst case, they paid $490 for registered users.


• When the marketing programs were halted, traffic numbers crashed. SpiralFrog saw just 775,547 unique visitors in October, a fraction of the site's monthly peak of 7 million.

These are clear indications of two different HiPPOs (SpiralFrog management & investors) with one objective to make money, and outcomes driven by poor management and allocation of efforts. In other words, objective of making money off free music, with one outcome to drive enormous site visitors without putting emphasis to convert new visitors to loyal customers.

HiPPOs: Borrowing Avinash's term "HiPPO's", which means Highest Paid Person's Opinion.

From web analytics analyst stand point, it is important to diversify your analysis and translate the data into information for business. In SpiralFrog's case, web analytics analyst should emphasize their analysis and insights on:

• Measure sticky traffic driven by marketing efforts. Look beyond increase in site visitors. 1,000 visitors with 1 person converting, and putting more money just to increase that 99.9% of non engaged traffic makes no sense...


• Translate metrics to further optimize landing pages to increase conversion and ROAS.


• Gauge web metrics data and support CRM efforts to tackle efforts that work in converting new visitors to return visitors, and optimize marketing messages to drive loyal visitors.


• Test various campaigns that drive return visitors/ad clickers.


• Stop investing in marketing campaigns that don't work, and support business with metrics that eat lower hanging fruits instead of trying to consume the entire tree.

After all, at some point, management should have noticed that increase in traffic doesn't translate to great services and user satisfaction. Few ways web analytics analyst could do to assess learnings and support increase in satisfied users are (in SpiralFrog's case):

1. Segment your loyal visitors data, and see who they are and how are they engage with your service.
2. Web Analytics can only tell you the "what", but not "why". Take surveys, and assess the qualitative insights.
3. Look at visitors who bounce vs. visitors who don't bounce when arrived to the site. "High bounces equal throw more money to bring visitors that don't bounce" is a mistake. Is it a specific ad, keywords, ad group, banner color, or CTA that caused high bounces?
4. Measure and optimize rigorously on CRM efforts. You are dealing with loyal visitors, what can you do to increase and turn them into your "advocates"?
5. Track affiliate networks that drive loyal visitors. Affiliates emphasize on numbers rather than quality. They get paid for what they refer, not for referring satisfied visitors.

It sounds like I'm talking trash about SpiralFrog's web analytics analyst, but I can really understand the challenges in small companies that is backed up by huge amount of cash. HiPPOs are probably the bottle neck, and is the primary cause of not being able to take best actions based on web analytics insights.

Web analytics analysts are facing a steeper challenge in 2009. If these HiPPOs start to pretend they know all or everything about web analytics, investors and businesses will continue to burn cash for nothing. Plus they'll blame the analysts and eventually media will use web metrics to put blame at web analyst indirectly. Maybe that is a good stimulus plan for this economy... having these investors burn cash... j/k.

Good luck to all of the web analyst in the world !!

What I need to make clear is that this article is not intended to discuss how to make your web analytics tagging perfect. It could be nearly accurate to address your data needs in areas of high priority, but everything can not be tracked perfectly accurate in 100% manner. You have to accept that truth. Also web analytics helps you find actionable insights and trends, and the main objective is not to use it as a report for reporting accurate numbers.

Shopping cart is one complicated beast. Some eCommerce sites may have their shopping cart handled by third party applications or vendors, complicating web analytics tagging delivery. Also there are many if and else conditions so web analytics implementation may not handle every little single data capturing instances.

Let me list out some possible shopping cart tagging/tracking/data challenges, and see if your web analytics handle such instances.

• Item addition and removal. You may take cart add into consideration, but not car removal.
• Login session considerations. Some carts require login after an item is added to cart or some time before the confirmation. This dependencies may throw off your tagging exepectations.
• Page refresh on order confirmation page (thank you page). If your analytics software counts conversions based on pageviews or don't dedupe redundant sessions on confirmation page.
• Campaign variables not being pass along to cart, or conversions not attributing properly to campaign tagging.
• Search Engines indexing shopping carts with items in it, which could inflate unnecessary cart add. Not really a tagging issue, but felt like mentioning it here.
• Any optional activities and trackings required to address those usage. Example, promotional code, shipping calculations, billing info update, edit profile, etc. Anything in such nature may cause page refresh, and could be a potential cause of data inflaction.
• Different combos of quantities, products, special offers, etc. Depending on how these instances are handled, item, quantity, and revenue may not sync up right. Example, a user buys two pens with special promo of buy one get one free: You could tag it so it reflects two orders of pens applying average price on each, or two orders of pens with one full price attached to only one of the order, or handles it as one pen order and special offers tracking in separate bucket. The key is to have your data expectations aligned to what is really getting tracked.

The most important thing is to have your analytics planning well defined in advanced, and QA properly. Learn the trade offs, and be flexible about it. Do not lose focus on addressing the desired outcomes!!

Connecting the dots between site objective and desired outcomes could be done in multiple ways, and do not expect to answer every possible scenarios including stuff that is okay to know. Focus on NEED to know and ACTIONABLE KPIs.

Avinash, a well known web analytics evangelist says three specific desired Outcomes from a website are:

• Increased Revenue


• Reduced Costs


• Improved Customer Satisfaction/Loyaly

Now stepping a back a little bit, on a higher level, the three main Objectives for making a website are:

• Acquire


• Convert


• Retain

In a nutshell, the web analytics framework should support you (web analyst), to en light key business people with actionable insights on the desired outcomes to the site objectives. There should be nine main initial points of thinking to help you start analyzing and inspire actions through data.

1. How your acquisition initiatives impacted your desired outcomes in terms of revenue? So the three areas of points would be: Acquisition vs. Increase Revenue, Acquisition vs. Reduction in Cost, Acquisition vs. Customer Satisfaction/Loyalty. 3 points of interests here.
2. How your tactics in increasing conversions impacted revenue, reduction in costs, improving satisfaction/loyalty. Another 3 things here.
3. How did your customer retention efforts impacted your revenue, costs, and improved satisfaction/loyalty. Another 3 things here.

This sounds good, but it can be confusing... For example, with "Acquisition vs. Reduction in Cost", you may ask how can you reduce cost when you're investing your money to drive traffic? Well, maybe one of your acquisition efforts is SEO, so that you could spend "less" on PPC campaign and drive free traffic.

To make things less confusing, let's take a look at a visual of an idea and possible KPIs to look at for these 9 different points (actions points, web analytics methods, whatever you call it...).

These KPIs are just examples of what I thought really quickly, but hope you get the point.

The challenges for web analyst is to work with people, managers, executives, marketers, etc. Even if this methodology explicitly clarify the what needs to go into dashboards or actually do the changes, if those people don't help supporting the actions, then the data could become useless.

The main point of this visual table is for web analysts to vision and make decisions to connect the dots between Outcomes and Site Objectives. KPIs won't do all the work, analysts will need to dive into the analytics software and do more analysis to be confident in the findings and justifying the next action. Action to make more money!!

Depending on your reporting needs, you may come across making a weekly/monthly/adhoc reports just for an acquisition, conversion, or retention efforts. In that case, make sure to not forget the basic principle of 3 outcomes, and do your analytics magic to find actionable insights through the metrics you (including business people) think are important.

Remember, connecting the dots (analysis) is the fun part of doing web analytics.

The referring sites report under "Traffic Sources" describes how people referred from other sites, but if you really want to separate out various marketing channels (email, image search, partner sites, etc.), you may want to set up some filters. Basically segment your data to only look at this traffic source group.

The purpose of filtering out any referring domains relating to non-marketing channel is to really understand which web sites are contributing traffic to your site. And these referring sites are directing traffic to your site for either two reasons, which are either good or bad.

The good reason is your site is so good, that other sites couldn't resist putting up link(s) to your site. That results from this should be really great because it has the potential to boost your PageRank in Google, increase your sites or products awareness, contribute increase in revenue or orders, etc.

Although, additional links for other sites are good, there could be bad reasons, too. Extreme case is getting links from phishing/scam/spammed sites, which could bring down your quality score. Other reasons are, people are talking bad about your site/services/products/content, and sites are referencing to your site. Hopefully the anchor link doesn't say something irrelevant to your site...

Some of the key metrics to observe from these referring sites are similar to looking at your traffic source metrics. Visits, Pages per visit, avg time on site, bounce rate, desired outcomes (conversions), order, revenues, etc.

How to set up a filter or create a segmentation so that you can narrow your report to non-marketing channel - referring sites:

1) Go to "Advanced Segments" > "Create new custom segment"

2) Use "Source" dimension from the dimensions selection list, and use "Does not contain" expression. Enter values such as "mail", "google", or any domains associating to your partner sites, affilicate sites, etc.

3) Use "Medium" dimension and select "referral" to make sure you're working of data set coming from Referring Sites report.

Now that you have a custom segment that with cleaner data set representing non-marketing channel referring sites, you may find something interesting about those traffic sources.

Enjoy!!

• Which cities or countries have a great interest in the topics/services/products that you serve?


• How can you reach the potential site visitors in the empty part of the map?


• What is the difference in traffic behavior and performance for people viewing your site from different location?


• Biggest question is "What can you do to leverage this geographic data and increase your reach and increase site traffic?"

Before I look at these questions individually, I think geographic data is useful even if you're targeting users within your local area. Why? Because I think internet gives you the opportunity to grow your business/services/content beyond your local area, and learn from others outside of your local area.

Also just a piece of advice, if you're willing to serve your site to specific region you might want to consider the following strategies.

1. Use Google Webmaster Tools and set your "Geographic Target" in the settings.


2. Leverage country specific domain.


3. Obviously, use language specific to the region you want to target.

Now, back to my main questions and my insights surrounding it...

Which cities or countries have a great interest in the topics/services/products that you serve?

I came to learn that in past few days or weeks, my site had the most traffic from the city of London, and San Francisco came in second. I knew that UK is going wild with their interests on web analytics, and it definitely supported traffic to my site. However, people from San Francisco had lower bounce rate with higher pages per visit. This is where I had to drill down and try figuring out the "why" part.

So the point is, if your site is serving content that is readable to other countries, and traffic outside of your country is supporting your site's objective (buying, reading, registering, etc.), then geographic data becomes very important. If people in London is reading more content on my site, and supporting great outcomes, then maybe I need to act by writing in UK English style.

What does it look like for your site? Is the traffic from other countries significant enough to consider enhancing/adding sites targeting to those visitors?

How can you reach the potential site visitors in the empty part of the map?

Looking at the BLUE box that I circled within the map, I am thinking those countries should be considered in serving content to potentially acquire higher traffic/readership. Why those parts of the map in the entire world? Well, China (Mandarin), India (Hindi), and South America (Spanish) are the top languages that make up the top 4 languages (including English). These three languages (population of native speakers) combined is 2.7 times higher than the number of English speakers. Some may argue, there are more population in other countries where there are more households with connection to the internet. So it is not only the number of native speakers, but looking at their connectivity to the internet and interests to the subject served from your site is more important.

This is where you need to leverage "Language" metrics, where the data captures the preferred language that visitors have configured on their computers. Why do you need to look at this metrics? Well, it could be the English speakers with English language preference visiting your site from Asia, Europe, Africa, or South America.

Back to the original question, if you're trying to reach the people in other region it is important to target regions with high potential to generate the desirable outcome based on population, language preference, internet connectivity, interest to site's topic, etc. Let's leverage these geographic and language data from your web analytics tools!

What is the difference in traffic behavior and performance for people viewing your site from different location?

Earlier, I've mentioned that site traffic from London performed differently from visitors from San Francisco. The difference in metrics is the key in taking necessary action to optimize your site. If you're targeting a specific group of people from specific region, but you're not getting a great site performance as other regions, then it is time for you to dig and drill into your web analytics application to find out what is going on.

Maybe it is time to open a site or add content in other language catering to users outside of your local area.

What can you do to leverage this geographic data and increase your reach and increase site traffic?

Based on the precious geographic and language data provided by web analytics application, it is time to think through and take actions based on your actionable data/insights.

Here are some ideas and possible actions you can take (including the points stated earlier):

• Create/add sites or content catering to people other than your local region. Example) Start a site written in Chinese, Hindi, Spanish, etc.


• Take advantage of Google Webmaster Tools to geographically target your site in search.


• Take advantage of domains specific to certain countries. Example) .jp, .us, .nl, .eu, etc.


• Add or optimize your content to serve increasing interests from other regions.


• Participate in search campaign (PPC) and target based on geographic location and language.

Hopefully, I was able to make you re-think about geographic and language data, and stimulate your interest in looking into opportunities to expand your site's potential across the world.