Matt Hopkins: Web Analytics Consulting & Online Marketing

Behavioural Targetting and Visitor Segmentation with BTBuckets3
Matt Hopkins posted in Visitor Segmentation, Web Analytics on November 15th, 2008

In October a new website was launched called BtBuckets, however it was only recently that the site was brought to my attention. BtBuckets offers a free service for you segement your website visitors based on their online actions and place them into buckets. You can set the buckets to only begin filling oncew the user has performed a set of actions a number of times or only once and they can stay in that bucket for as you as you want. Each visitor can be placed in as many buckets as you have predefined with an unlimited number of visitors per bucket.

The service works by using Javascript to tag every page on your site within the page HEAD HTML element so that each user action is recorded to the BtBuckets site. Within the BtBuckets user interface you can define sets of rules by which the buckets will be filled with visitors. For example if a user comes from google.com and fills out the last page of your sales process they can be placed into a “converted” bucket, or if they reach the second stage of your conversion process but have yet to reach the final stage then they can be placed into the “semi-converted” bucket. Perhaps even you have various services for different types of people on your site, like a section for consumers and a section for business users, you could define a bucket for each type of user depending on which pages they view.

BtBuckets is somewhat limited, and that is to be excpeted given that it is a brand new service and free. The limitation comes in the fact that you can only define your bucket rules based on 2 types of criteria, where the user came from (referrer) and which page the user is currently on. You cannot at the moment track details such as Javascript events which would be useful for tracking form level events, no temporal based metrics cannot be used so you can’t segment the users by nocturnal or daylight users, or any other useful metric such as geography.

Given its limitations, I still think BtBuckets could be extremely useful for small website owners, however it is in no way a replacement for services such as TouchClarity which has been aquired by Omniture.

So How can BtBuckets be Helpful Given it’s Limits?

The fun and very exciting part of BtBuckets for me is not so much the visitor segmentation which is available in most good web analytics packages but its the behavioural targetting feature. This is because it is a well known fact that lots of web analtyics installations get setup and left to collect loads of data and most often the data collected does not get used to make meaningful decisions or used to put changes into action. This is where behavioural targetting is an amazing and simple concept. All you have to do is define some rules to place your visitors into buckets based on where they came from and what pages they visit on your site, then you can define which website content each of those buckets can see on your website.

To give you 2 examples of how this would work we’ll look at an e-commerce site and an informational site. The e-commerce site might sell electronics and like all e-commerce stores will have a shopping cart process. If the end of the shopping cart process presented the user with a page that contained all of the products purchased as variables in the page query sting or page name then you could set a bucket rule to be triggered if someone purchased a games console like a Wii or PS3, we’ll call that bucket the “gamers”. Within the pages on the website we could them embed a piece of Javascript that would check to see if the visitor was in the “gamers” bucket and if so would show them other products that may be of interest to gamers such as large flat screen TVs or a link to their video games section. This way you would be presenting the visitor with more relevant content based on their previous actions and as Amazon has proven countless times, this works and increases conversions and enlarges the average shopping basket size.

The second example is an informational site, lets say its a sporting news site with a number of sections for each type of sport, so a section for football, fishing, horse racing, tennis and golf. Let’s assume that the conversion point would be to either signup to the newsletter and/or click on an advert. With modern context based advertising such as Google Adsense we can sometimes leave the advertising targetting to the advert providers (but BtBuckets blog shows how you can mix BtBuckets with Google Ad Manager). However in the case of the newsletter signups, we could set a rule that if a user views more than 2 pages about golf within a 2 week period we can present them with an option to signup for a newsletter all about golf, otherwise they get the general newsletter feature shown. We could do that for each type of sport presented. Or maybe we could put the visitor into many buckets, one for each type of content they viewed then present them with a newsletter signup that highlighted the sections they had previously viewed. I.e.

Signup to our newsletter on fishing and golf to get the latest up to date news and information.

These are only 2 very simple examples, I can think of at least 2 advanced uses whereby the bucket was called using invisible iframe HTML elements so that you can pass any information to the BtBuckets system, or maybe break down the Javascript code within the page tag to call the Javascript event directly based on other Javascript events such as form fields, however I would only suggest that for the very technically abled people.

All in all, I think this is a great addition to any web analytics implementation where you are not at a stage to purchase advanced behavioural targetting software but would like many of the advantages for free. If you have integrated BtBuckets into your site yet and have some nice examples to share please comment below.

First Party Cookie Confusion5
Matt Hopkins posted in Visitor Segmentation, Web Analytics on August 1st, 2007

Cookies in their own right are really simple little things, the idea being that every time you view a web page on a website, you will be given a code to store on your local PC. The next time you come back to that site it can read your stored cookie and so count you as the same visitor who has returned.

So where is the confusion?
Well, the confusion comes when you start talking about first party and third party cookies and how they are treated differently by web browsers.

A first party cookie is a cookie that is given to the website visitor by the same domain (www.domain.com) that the web page resides on. Whereas, a third party cookie is one that is issued to the website visitor by a web server that is not on the same domain as the website. I’ve made a diagram below which shows how the first party cookie and third party cookie differ.

So when might a third party cookie be used?
Generally third party cookies are issued when are third party is interested in tracking your website visitor traffic, this could either be by a banner advertiser who places a number of banners on your site and wants to know how many times it has been requested, or it could be a third party hosted analytics vendor that issues a page tag for each of your pages that forces a cookie on your site.

In the last situation, where an analytics vendor issues a cookie through a page tag the cookie is seen as a third party cookie because it is being generated by the analytics server which is having the tracking 1×1 invisible gif image requested from it by the page tag. It is however possible to have an analytics cookie issued by the third party vendor but still look like a first party cookie.

There are 2 ways of achieving this:-

1. Create a DNS alias for third party analytics server so that it looks like it is actually part of your domain and so anything issued by this server because 1st party (including cookies)
2. Have the Javascript page tag create a cookie at run-time and then pass the cookie value back to the analytics server so the cookie is created within the page and so becomes a 1st party cookie.

The obvious advantage of the DNS alias option is that you can have a smaller page tag which is quicker to load, however the cookie making page tag has an advantage over the DNS alias because no structural changes need to be made to the site’s infrastructure and the implementation of the tag should be more straight forward.

So, in the end you want to aim for a first party cookie as these are typically blocked by fewer browsers than third party cookies. To give you an example, I did a test a few weeks ago using on a site using a third party cookie and measured that over 70% of the cookies were being blocked. After a similar test using first party cookies only 30% were being blocked. This shows that although cookies aren’t as accurate as we might all like, all cookies are not created equal.

My First Engagement4
Matt Hopkins posted in Visitor Segmentation, Web Analytics on July 24th, 2007

No, i’m not getting married just yet.

But I have just released my first white paper which focusses on website visitor engagement and it follows on nicely from my orginial post on engagement scoring.

Let me know what you think.

What is Visitor Segmentation and How Will it Help Me?0
Matt Hopkins posted in Visitor Segmentation, Web Analytics on July 8th, 2007

When looking at the topic of web analytics you will no doubt run into something called visitor segmentation. This is really just looking at different behaviours of the visitors that come to your website.

I’ll explain it a bit further by using the analogy of a TV programme we have here in the UK called Big Brother (BB). Personally I’m not a great fan of the show but hopefully it’s something that people can relate to.

In BB there are a number of people that live in a house for a period of 8 weeks or so, this house is covered with cameras so that everything that happens in the house can be monitored. In the same way a website can record everything that happens when someone is on the site using page tags or web server log files.

Within the BB house people move from room to room, they chat to each other and group together, they might swim in the pool or eat some food. All of this behaviour is tracked by the house cameras and then analysed later by psychologists and the public. On your website you can identify which pages people go to, whether they fill out a form or even if they play a Flash game and get the highest score. This site activity is then later analysed to identify behaviour patterns, the most popular types of content among other things.

So what’s the point of this analysis?
The result of BB is that Endemol, the programme creators earn money from a variety of sources including public phone voting, advertising and television appearances. Also the main purpose of commercial websites is to generate revenue by interacting with website visitors and getting them to part with their cash in a variety of ways including purchasing products, services or clicking on advertising.

And where does visitor segmentation come into it?
Well, for BB they need to identify people within the house that do similar activities be that talking on the sofa or eating all the time. Then after grouping these people they can identify which behaviours are the most popular with the public and cause Endemol to generate the most amount of money. Once this has been achieved they can plan what type of people they need to recruit for next year’s show. We are now on BB 8 and we can see that the housemates have become more and more irritating with each iteration of the show, but maybe that’s what generates interest in the show and so creates more revenue.

And on your website you can group visitors by their online behaviour like which content types they consume, whether they comment on articles or come back to the site on a regular basis. Once you have identified these groups of people you can then see which generate the most revenue for you and attract more visitors like this via your marketing campaigns, or maybe create more content that satisfies your revenue generating groups.

So in essence visitor segmentation is about group people who act in similar ways and then if you can make one person in each group happier then you can hopefully please most of the people in each group and ultimately generate more revenue/interest/brand awareness. Of course it is never that simple as people are notoriously unpredictable but habits do form over time and so it is possible to capitalise on these.

Happy segmenting!

Web Analytics as a Marketing Tool: Scorecards0
Matt Hopkins posted in Visitor Segmentation, Web Analytics on June 16th, 2007

We should all know that to do web analytics for web analytics’ sake is not a good thing, all those numbers, a few words, but without meaning they are pretty useless.

As a web analytics consultant I like to find ways in which web analytics data can be used in new and interesting ways to assist in making better business decisions.

This is where I introduce the concept of scorecards, banks and lending institutions use a method called credit scoring to identify people who they consider to be eligible for receiving credit. The way this scoring works is to take a number of factors about each person, such as their gender, age, employment status and where they live. They then apply a value to each factor, so if the person is below 25 years old then they get a score of 1 for age. If they are employed and have a salary of over 30K pounds then they may get a score of 15 for employment status. Add all these scores together and you get a scorecard per person.

Each person’s scorecard then has an overall score which determines how eligible they are for credit and how much credit. Now, currently in web analytics we have the concept of visitor segmentation which takes different visitor behaviours as differentiating factors but I’m not aware of a product that allows scoring of these factors in a similar way to scorecards.

So, what is the benefit of scoring website visitors and what should we score them on?
In my opinion the main thing website visitors do is view pages, or on web 2.0 sites they may trigger events. So let’s say that we apply a score to certain pages or events. If we use the example of an e-commerce site where books are sold, a search result page could have a score of 1, a book description page may have a score of 2 and the sign up page for the latest books email newsletter can have a score of 5.

Now when we take a site visit that includes 2 searches, a view of a book description page and a sign up for the newsletter the total score for this visit is 9. If a visit consists of only 4 searches then the total score is 4.

In this example the same number of page views were present but because we score each of the page views, we know that the initial visit was worth more than the latter visit. This also means that we can group visits together by their score and find out commonalities between high scoring visits and also low scoring visits.

Also there is no reason why we can’t use this technique to filter out website spiders when using log file analysis, because spiders will want to visit every page so they will have outrageously high scores.

Whatever you decide to use this technique for, you will need to decide which pages to score, what the scores will be for each page and the thresholds for visit/visitor scores you are interested in.