The time period of website assessment included November 2010 through to January 2011, thus including the problematic Christmas Shopping season, the adverse weather conditions and resulting Royal Mail strike action. Retailers can take a lot from this report which indicates there is clear room for improvement in terms of their customer service options and support they provide.

Overall Performance – 51 Retail Websites Benchmarked

The study generally reported relatively high scores across the board for the 51 sites reviewed. The percentage scores allocated refer to the weighted average, so a maximum score from surveyors for the end to end customer journey would have resulted in a 100% score.  From the nine categories assessed in the study, Figleaves was revealed as the top performer with 89%, closely followed by Amazon with 87.9% and Dorothy Perkins with 86.2%.  The Figleaves site was praised for its engaging website which offers stylish and informative product pages along with an intuitive shopping basket.

In contrast H&M’s newly launched e-commerce site finished at the bottom of the table with a score of just 64.8% which is understandable considering the vast amount of user experience issues which have been reported with the site. WH Smith and Play.com completed the bottom three scoring sites with, 65.4% and 70.3%.  

Customer Journey – Findings & Results

The chart below indicates the retailers who performed best and worst across the customer journey.    Exceptional customer Service clearly proved to be the main differentiator for websites assessed during the festive period.

Christmas Delivery Issues

The study clearly shows the impact of the weather during the assessment period with just four from the 51 sites benchmarked managed to score 100% for on time deliveries, compared to the 38 retailers who managed to achieve this in the same study completed in the Autumn of 2010.  Of the top performing sites in this section, many had their own delivery network including Tesco, Littlewoods and Interflora. Although 26% of orders placed on Figleaves by eDigital’s mystery shoppers were late during the festive period, the retailer excelled in contrast to other retailers in the categories for email and telephone customer service contact, an area where online retailers tend to struggle to rate highly.

Customer Service Options

Overall scores in this category were the lowest of the entire survey, although many of the problems encountered were a direct result of the adverse weather conditions and some retailers coped much better than others.  Managing customer expectations was crucial during this period and many retailers were able to do this through clear homepage message and some chose to bring forward their deadline for guaranteed Christmas orders as they didn’t want to make customer promises they couldn’t keep.

Common problems included sites making email addresses difficult to find resulting in customers having to fill in contact forms with slow response rates. From the 51 sites reviewed only 14 of the retailers scored more than 50% for the speed of email response and worse still 10 actually scored less that 10%.   The speed of response is vital maintaining levels of customer satisfaction and Figleaves were able to excel here and scored 81.6% for customer service contact and 76.9% for the speed of response.  At the other end of the scale Asda direct, New Look and Play all scored 0% as no email contact facility is provided.

For telephone customer service Figleaves came a close second to Interflora who provided a clear contact number, polite and knowledgeable staff and a callback option for when their agents were busy.  In contrast WH Smith was rated at the worst performer and ASOS did not offer this as an optional service. Nevertheless ASOS did score well for email and also has a dedicated Twitter customer service account, an area which was not covered in the survey.

Retail Website Usability Survey ranks Figleaves No.1 is a post from WMpS, your one stop digital agency.

Most modern analytics tools today provide internal site search analysis functions. In today’s post, I will discuss how to use Coremetrics to track the on site search performance.

Understand Site Search Usage: Monitoring Site Search Effectiveness

The first step is to set a baseline for search usage and impact for ongoing comparison to future time periods. To do so, you might want to create some key performance indicators specific to your business which allows for consistent internal benchmarking of site search performance.

In Coremetrics, you can get internal search related metrics in the on-site search report. Also, you can apply an internal search segment to the top line metric report. The following table shows the output of a typical on-site search effectiveness analysis.

Search Terms Analysis: Reduce Unsuccessful Queries

Knowing what people search for on your site is very important. This information can not only let you understand visitors’ intent, since internal or external key phrases convey intent, it also could help you identify popular search terms that return no search results to users. By identifying theses terms and tuning your site search engine to return results, you can drive incremental revenue and customer satisfaction.

In Coremetrics, you can get the internal search terms in an on site search report. By sorting ascending, you can get the following output of a typical zero result return analysis.

Measure internal site search quality: improve site search design

Bounce rate is most analytics’ favourite because it is the most useful metric to measure your external search and landing page performance. For measuring the internal search quality, we use a similar metrics called “exit rate”. In Coremetrics, you can use a Clickstream report to monitor where people went after typing the search terms.

If you classify traffic by site departure, second search, viewed additional results page, and product details page, you will get some useful metrics including search exits rate.

Once you get this report and discover the problem, you can take the following actions:

• High Rates of second searches: This means too many results are returned and search ranking system doesn’t show what the visitor wants. You can consider adding filtering query refinement technology to allow users to refine their searches without needing to enter a new query.
• High rates of site departure (Site Exits): This indicates that unsuccessful search results were confusing, causing visitors to depart. You need to investigate the search results page design and ensure that this page provides clear instructions of refining queries in the case that a visitor did not receive results.
• High rate of abandonment to the other page (Path Exits): This indicates that visitors did not find the information they were seeking via search. You should analyze results’ relevance to understand and improve search engine effectiveness.

Summary

The discussion above only shows how to measure internal search by using Coremetrics’ default reports, you could get more in depth if you segment the traffic by keyword terms or many other interesting things. Also, you could easily use the concepts above to do internal search analytics by using Google Analytics.

On-site search is very useful to enhance the revenue performance for many websites, especially for the ecommerce site. If internal search is important to your site, make sure you are tracking it adequately so you can improve it and increase your overall website conversion.

Internal Site Search Analytics Tips is a post from WMpS, your one stop digital agency.

Why Integrate Web analytics and CRM?

Those who are using web analytics as the main decision making tool can easily know a great deal about what happens on the website, like when your website sells something, but that means we are losing the most important part: off-line data. And the true success event often takes place off the website. More importantly, for all the great information we have from web analytics, it’s all anonymous. We don’t really know who the visitors are, so we can’t easily connect their website behaviour to other interactions.

In contrast, CRM is the system that stores all the information you have about your prospects or customers. It normally includes all contacts with customers while they were prospects, all customer service touches, what products they use and how much they pay for each. The main thing to understand is that CRM systems contain pretty much all data about customers that takes place after you know who they are.

What if we could take all of that anonymous website behaviour and somehow connect it with the known prospects or customers’ behaviour stored in the CRM system? Imagine if every time a customer filled out a form on your website, the sales person could see what that person had viewed on the website, what products they had looked at. That would get you more meaningful insight.

How to connect Web Analytics to CRM?

How to connect the session ID and Cookie Id in your web analytics platform with the customer ID in the CRM? Many enterprise web analytics vendors have provided an interface to connect your web analytics platform to a CRM system. The Omniture SiteCatalyst provides a transaction ID that allows you to connect online and offline data by establishing a “key”. By passing same ID in CRM to SiteCatalyst, you could upload offline data related to this ID, and it will be associated with all web metrics.  For instance by passing the return information to your web analytics platform by the transaction ID, you would know the website behaviours for the visitors who returned their items, which is information you normally can’t get from a web analytics platform.

Also, you can pass your web analytics data to your CRM.  The major web analytics suppliers all provide data export functions, and some enterprise vendors such as Coremetrics provide tags to track registers’ information. Therefore, you could pass the web metrics to your CRM systems by using the same registration ID.  For example, a visitor came to your website and filled in the lead form.  Coremetrics and your CRM system would both generate a record using the same registration ID. If you have exported the registration ID and other useful metrics to your CRM system, when this customer come to you or call your sales team, you will already have discovered what products he is interested in.

Summary

The history of web analytics tracks that of the web itself. We went from logs to tags, from IT-centricity to marketing–centricity. Today’s web analytics could be viewed as a smaller, narrower and more agile little brother of business intelligence. Eventually, as web analytics grows and integrates with business data, it may become part of business intelligence, and maybe an important part.

Deeper Insight: Integrating Web Analytics and CRM is a post from WMpS, your one stop digital agency.

What does it do exactly?

It allows you to view website clicks on your website through visual heat maps. This is important for a number of reasons such as monitoring landing page performance… and ultimately improving it. Is my call to action as effective as it should be? Are visitors overwhelmed with the options available? It gives webmasters the important information they need in an easy to understand format. The data can be broken down to a ‘Confetti’ mode which breaks down visitor behavior by search engine search term, browser or referring source as examples.

Visitor Click Heat Maps

Impressively the data can be viewed live which is a big plus point compared to something like Google Analytics which normally has a couple of hours delay.

It requires a JavaScript section of code to be added to the desired tracked page on your website in order to work.

Prices start from around £5 per month which we feel is very cost effective for the data which it provides.

Crazy Egg – Visualize Visitor Behaviour is a post from WMpS, your one stop digital agency.

Data Collection Challenges

The current web analytics data collection is based on JavaScript. The JavaScript initiates a 1px image request to an analytics service provider and the relevant information desired for an understanding of the visitor behaviour is sent along with the image request. Among the information gathered is both a persistent and a session cookie. However, although some smart phones like iPhone and Android do support JavaScript, there still are hundreds of different kinds of devices in the markets and most of them don’t support JavaScript yet.

Another issue is cookie support. If you use different analytics tools to measure your mobile site at the same time, you will be surprised by the results.  Two different tools can return totally different unique visitor numbers. These differences are because many mobile devices don’t support cookies and analytics vendors have different ways to identify mobile unique visitors.

Bypassing the issues

In order to combat the issue that some low-end mobile devices don’t currently support JavaScript execution, we have to send the image request manually instead of calling preset JavaScript methods. Fortunately, Google Analytics provides a smart solution by calling the server side snippet solution instead of sending ugly image request query string parameters. Google has pre-defined the image request server script in the most popular languages such as PHP, JSP and .Net. Users just need to download the preset script file and put a tracking snippet on the mobile site to call the pre-defined file. You can download the pre-defined file from Google Code, and then put the following snippet (PHP Version) at the end of your mobile site:

<?php
  $googleAnalyticsImageUrl = googleAnalyticsGetImageUrl();
  echo '<img src="' . $googleAnalyticsImageUrl . '" />';
?>

Unfortunately, many analytics vendors, including Google Analytics, haven’t provided any solution to bypass the cookie support issue. As a result, Google Analytics always counts a mobile visit as a unique visitor and displays inflated figures. In order to get correct visitor information, we need the persistent visitor ID. Some enterprise analytics vendors, such as Coremetrics, allow the users to send extra query string values with each tag to associate it with both a session and a cookie ID.  The cookie ID source value will be persistent and based on a value defined within the device and accessible via API or OS layer to the code generating the image request. And the session ID could be randomly generated with each device session or generated using a device session value. As a result, Coremetrics could be able to identify new visitors or repeat visitors.

Pitfalls of Current Mobile Analytics Tracking

Again, like standard web analytics, the data collected from analytics vendors is not 100% accurate. The cookie IDs generated by the device API are not always stable, and manually sending image requests do not support some high level analytics functionality such as event tracking in Google Analytics and Marketing tracking in Coremetrics.   With increasing popularity of mobile usage and high demands of accurate data, I think more improvements will be made in the near future.

Mobile Analytics Tracking: Challenges and Solutions is a post from WMpS, your one stop digital agency.

It’s important not to forget that ecommerce is still a relatively new concept, and it’s taken many months to persuade consumers that shopping online can be as easy (if not easier!) and as engaging as shopping in store. By making visually appealing, flash focused websites some brands are forgetting the usability aspect of online shopping, and making the online customer experience a lot harder and frustrating than shopping in store.

What makes a good ecommerce website?

There needs to be a compromise between attractive, eye catching design and simple, easy to use functionality. It’s important that while retailers are implementing new technologies and merchandising innovations they don’t lose sight of the overall goal of an ecommerce website; to convert customers.

In addition, the advancement of mobile browsing and m-commerce has put an even greater emphasis on usability. Smaller display areas and touch screen technology is forcing retailers to think more about how to enable consumers to purchase in the least amount of clicks possible. Mobile websites will be covered in another post at a later date but for now the focus is on fashion vs. usability.

Eye-Catching Design

Whistles

This very unique new website from Whistles was launched earlier this year, and sparked controversy among critics. Yes it is different, yes it is innovative and eye catching, but have the designers got a bit too carried away? The style of the site means that users are engaged right though the browsing process, with model imagery used from the homepage, through the category listings right to product details. They also integrate well with social media, offering links to share via email, facebook and twitter. The imagery on the site is good, and there are plenty of alternate images and zoom utilised on everything.

However, it’s the small things that Whistles have forgotten, such as a search box, or refine feature, making it very difficult to find something specific, or within a certain budget for example. Also, there are no sub-categories in the top navigation bar; rather they are displayed on the main page, mixed in with the model imagery. This makes it very easy to get lost in the middle of the screen and not be able to find the category that you want.

Fashionable but Usable

River Island

I thought it was fitting to use River Island as my example in this category as this site used to be another culprit of ‘Flash overkill’. However River Island has recently revamped their site to make it more usable. And in my opinion they’ve done a pretty good job! They’ve managed to retain some individuality by allowing refinements according to swatches of colour, pattern descriptions and even ‘occasion’. They’ve also spruced up their product listings by inserting a top product into a larger box at the top left of each page, similar to a profile picture layout. Not only does this act as a merchandising tool but serves to differentiate River Island product listings from those on their competitor’s websites.

They’ve also added a quick look or closer look feature, which allows you to either view the product in a pop up box while still on the category listing page, or to go right into the product details page for a closer look. Although this is a nice feature, there isn’t much difference between the information given in the two options, aside from a few ‘get the look’ items on the product details page. By adding more in depth product information to the details page River Island could have added value to the feature and given consumers a more compelling reason to visit the product details page if they were interested in the product, thus increasing the chance of up and cross-selling more items.

Finding a Compromise

For many companies, both new to ecommerce and old hats that are looking to revamp their offering, it is important that the basics of usability and good customer experience don’t get lost in a web of flash and unnecessary gadgets. There are some new technologies that will genuinely enhance your customers experience online, but there are some that will detract from it. In all elements of ecommerce, especially web design, usability should be a key consideration, because if your customers can’t use it they won’t buy from it.

Fashion vs. Usability is a post from WMpS, your one stop digital agency.

Now we have established that Hypothesis testing is a way of systematically quantifying how certain you are of the result of a statistical experiment. It starts by forming a null hypothesis such as “Design A performs better”, and then converts it to a mathematical statement. Finally, we need to put in a probability distribution to test it using a specific confidence level. In today’s article, we will apply this to our real world analytics application: A/B Testing.

What is A/B Testing?

A/B testing is one of primary tools in any data-driven environment.  It’s a way of conducting experiments where you compare a baseline control sample to one or more test samples by assigning each sample a specific single variable change.

For example, you might have a landing page that shows the latest products list. You’ll want to test various layouts to try and maximize the sales made by this page. Normally, we use “conversion rate” to measure the page’s performance. By assigning the control sample and each test sampling similar traffic, we can make a decision by observing the conversion rate.

The Fake Data

You might ask: “Why do we need hypothesis testing if we already have conversion rate to measure the performance?”

Assume you are running an email campaign to show the latest offers. You have 3 versions with different layouts: control sample, test sample A, and test sample B.  You run an A/B test before formally running the campaign. Here are the results you might get:

In terms of the results, could we make a judgement that A is best now? When the sample size is large, the results might turn into this:

Don’t be surprised if you get these results, because the sample size matters. The problem is when you run an email campaign you wouldn’t be able to get a large test sample size. If you make a judgement only based on the comparison of the conversion rate, you might just make a wrong decision.

Hypothesis Testing

In order to avoid the wrong decision, you might need to use hypothesis testing to justify the results you get, especially for the small sample size or similar results.

Remember the statistics principle we mentioned in the last post: if a small probability event happens in your test sample, you could reject the null hypothesis. In this case, the null hypothesis could be that the conversion rate of the control treatment is no less than the conversion rate of our experimental treatment. So mathematically

H₀ = P – P_c ≤ 0

Where P_c is the conversion rate of the control and P is the conversion rate of one of our experiments.

Therefore if the probability of H₀ is low enough, we could reject it and go for the alternative hypothesis, that is “the experimental email campaign has a higher conversion rate”. That is what we want to see and quantify.

In order to measure the probability of H₀, let’s say P(H₀), we need to know its probability distribution. The sampled conversion rates are all normally distributed random variables just like the coin flipping. Instead of seeing whether it deviates too far from a fixed probability we want to measure whether it deviates too far from the control treatment. There is another statistic rule: the sum or difference of two normally distributed variables is itself normally distributed. With this rule, we could do Z-Test and calculate the 95% confidence interval like we did in the coin flipping example.

Z-Test

Mathematically, the calculation of Z-Score for the probability of H₀ is:

Where N is the size of the experiment sample and N_c is the size of the control sample.

Do you remember that we used the z-score of 1.96 to correspond to the 95% confidence interval? This time it’s a little different. We will use 1.65 instead of 1.96. Why? In the coin flip example, the null hypothesis is P = 0.5. Therefore we could reject it if the probability is too high or too low, but this time we only care one way.

If the z-score falls in the blue part, we assume it is a small probability and reject the null hypothesis. In the coin flip example, the blue part distributes on both sides of the normal distribution. It is called a two tailed test.

In this A/B Testing example, we’ll only reject the null hypothesis if the experimental conversion rate is significantly higher than the control conversion rate. The blue part is only on the right trail of the normal distribution. This is called a one tailed test.

Using the formula above, we could get the following results:

We could find that none of the z-scores is large than 1.65, which means the results would likely change if the sample size goes larger. In this case, we can’t decide which one is best, and we’d need a larger sample size to get a right decision.

Hypothesis Testing in A/B Testing is a post from WMpS, your one stop digital agency.

Hypothesis Testing

Hypothesis testing is something that you can use to make decisions based on experimental data. It can systematically quantify how certain you are of the result of a statistical experiment.  For example, you might want to test if coin flipping would give a fair result. So you make an experiment where you flip the coin 100 times and you get results of 52 times for head side and 48 times for tails side. Would that be fair enough from the statistical view? That’s why you want to do a hypothesis testing.

Most hypothesis testing uses null-hypothesis. The null-hypothesis, denoted H₀, typically proposes a default position, such as the coin flipping is fair. And it is typically paired with an alternative hypothesis, such as the coin is biased, denoted H₁. Normally, we put the hypothesis we care or we want to be true as the null-hypothesis. And the main goal of hypothesis testing is to tell us whether we have enough evidence to reject the null-hypothesis.

Turning to statistics

After stating the relevant null and alternative hypotheses, we’ll need to think in a statistical way. In the above coin example, if we say the coin is fair, that means the probability of getting a head side should be 50%. But the results of the experiment we got were 52%.  But is this normal because we just flipped it 100 times as the experiment?

Statistically, if the probability of something is very low, we consider it as impossible. In this case if probability of having the variance between the experiment results and hypothesis is small enough, we could reject the hypothesis. That’s because if something is considered to be impossible and it happens in the small set of observations, there must be something wrong with the hypothesis.

Mathematically

Now we have to do it completely mathematically.  The null hypothesis in the coin example could be expressed as

H₀:p₀ = 0.5

A 95% confidence level means we reject the null hypothesis if p falls outside 95% the area of the normal curve given above. We can see this corresponds to approximately 1.98 standard deviations.

Then we use “Z-Test” to get the z-score which tells us how many standard deviations away from the mean our sample is, and it’s calculated as

The p is the sample mean, and the P₀ is the expected mean, and N is the sample size.  For the coin example, P₀ is 0.5 and N = 100.

In our experiment we flipped the coin 100 times and got heads 52 times, so the sample mean is 0.52%. After calculating the z-score by the formula above, we get the z-score as 0.4 and make the conclusion that the coin is fair.  If we use coin 2 and get heads 60 times, we can reject the hypothesis and say that coin 2 is biased.

What’s next

Of course, this could be much more complex than coin flipping in real world applications. Fortunately, hypothesis testing for the A/B testing is usually quite similar to coin flipping. In my next post, I will show you how to apply our hypothesis testing knowledge to A/B testing to determine whether new features actually affect user behaviour.

Statistics and Web Analytics – Hypothesis Testing is a post from WMpS, your one stop digital agency.

About Predictive Analytics

Basically, the main idea of predictive analysis is to use past data to predict future events. The statistics models is used to capture relationships among many factors to allow assessment of risk, determine market patterns, or predict potential opportunities for growth.

Why Is It Difficult to Include in Web Analytics?

One of the most important factors responsible for reliable predictive analysis is data quality. Because all predictive events are based on the data we have, the information can only be as effective as the abundance and accuracy of data available. Due to the mechanism of data collection, unfortunately, web data for the most part is completely anonymous, usually incomplete and unstructured.  For example, cookies and JavaScript could be blocked by some proportion of users or it could be loaded and activated improperly due to slow network speed or other technical reasons.  And it is important to know that these are quite normal for data collection in web analytics today. So it is really hard to do predictive analysis when the core things you are relying to capture data are anonymous cookies and sensitive JavaScript tags.

Available Predictive Web Analytics

Many web analytics solutions providers have started to research to offer predictive analytics solutions along with web analytics. One example is The Intelligent Offer, which is provided by Coremetrics and generates personalized cross-sell and up-sell recommendations based on web data. It uses cookies to build individual visitor profiles based on historical and current session data. The profile is then fed into the personalisation algorithm, which determines if and how recommendations are tailored to the individual.

Traffic prediction also is a feasible possibility for implementing predictive analysis in web analytics. Because visitor traffic is time series data and relatively cleaner than the other metrics we get from the web analytics tool, many statistical models could be used to analyze it and forecast future traffic based on past data.  Here is a mobile traffic prediction example we did for a client. We employed a polynomial regression model to forecast future mobile traffic.

Web analytics Needed for Multichannel CRM

Many companies now have realized that basic click stream analytics don’t tell the whole story. They are looking for a more intelligent way to find hidden customer behaviors. In order to implement predictive data analysis for web data, they need to expand their web analytics strategies and integrate web analytics with CMS (customer relationship management) systems.

With multichannel CRM, aggregated web data could be integrated into offline channels and provide great insight, even from simple analysis. It would enable online analytical processing (OLAP) to produce historical perspectives on customer acquisition cost, cost per conversion, etc. And combining web analytics with predictive analytics, such as data mining, could provide both historical and predictive insights.

Predictive Analytics: Do They Work? is a post from WMpS, your one stop digital agency.

• The length of the form – is it too long?
• Is there help available for completing all of the form elements?
• Are validation messages clear and easy to understand?

Tracking a custom form is not a standard feature of most Analytics packages. This is how we would recommend doing it through Google Analytics.

1. Make sure your site has Google Analytics installed for all pages and it is tracking correctly.
2. Setup an over-arching goal within Google Analytics for the conversion page you want to track, the thank you page of a successful sale for example.
3. Specific events on your website can be tracked via ‘Event Tracking’ which is an advanced feature of Google Analytics.  Most forms within websites use JavaScript validation as a 1st port of call. We will integrate ‘Event Tracking’ within the existing JavaScript validation to track form events.

The ‘Event Tracking’ tag looks like this: _trackEvent(category, action, opt_label, opt_value). Let’s say for example that your form validates whether the 1st element of your form (first name for example) is required. If a user leaves this element empty and submits the form it will trigger the relevant piece of validation for this. The site may show an alert box which tells the user that this element has been left blank and is required.

Now let’s add an ‘Event Tracking’ tag within the piece of JavaScript code which has just been triggered, such as _trackEvent(My Website Form, Validation Error, First Name Blank). Now leave the 1st element of your form intentionally blank and submit the form. The same validation message will appear but this time the event will have been recorded within the Google Analytics profile for your website.

It gives us key information on how a website form is performing. What causes the most errors? How could I improve this? It allows us to actively make changes to a form and test whether it has improved the overall conversion rate. If monitored regularly & improvements are made it should lead to an optimally performing form!

Improving Form Completion Rates with Google Analytics is a post from WMpS, your one stop digital agency.