However, in recent years there has been a dramatic shift away from this model. The traditional set-up of websites, micro sites and banner ads is increasingly being replaced by a new breed of apps, networks, engagement platforms and digital tools. Interrupting people with overt sales messages is out of style. Most of the time these things have almost nothing to do with the actual product the company is selling.

But how then do we measure these new experiences? The old model of reach, click-through and conversion isn’t really any different to direct-mail and was easy, but our brave new world often has very little to do with that old model. In order to understand how to measure this we first need to look at what these tools actually are and why they came about in the first place.

[N.B - I have never directly worked on any of the case studies I use in this post and use them only as examples or for hypothetical ideas]

The need for branded utility and customer engagement

Branded utility, and indeed the whole ‘customer engagement’ movement is born out of the increasing commoditisation of products. In the old days the only sources of information available to customers were advertising (and sales) and the advice of a very small peer-group. This meant that marketers had full control over who knew about their products and, more importantly, what they thought about them. This one-way communication to a captive audience allowed the ad-men to conjure up a complex meta-narrative of value and meaning around essentially rather dull products. People weren’t loyal to a product because they loved it, it was because they were brainwashed into believing that their lives would be so much the worse without it.

Skipping forward to the present day: in a world where information is available before we even know we need it, and where brands live and die based on the the ebb and flow of social media sentiment, we no longer need adverts to tell us how to think and feel about products. If I want to know which mobile phone provider is right for me a focused 20 minutes on the Internet reading reviews and asking my friends will form an infinitely more genuine and useful opinion than an ambiguous TV advert of people rolling around in fields of corn.

So marketers no longer control the message, and therefore a product is merely a product. Other than the actual efficacy and quality of the product itself our ability to influence people’s purchase decisions is very quickly vanishing. This is the central business issue behind this new move towards customer engagement online: as someone who works for an agency I get to see this first hand in the challenges presented to us by our clients. At bottom they all say the same thing:

Help!! We need to be more than just a product!

How to be more than just a product

“Stop interrupting what people are interested in and be what people are interested in” – David Ogilvy

If brands want to mean more to their customers than just the products they sell, then it follows that they need to develop a relationship with the customer over and above the experience of that product. This means doing and being something else in such a way that the brand itself becomes a meaningful part of our customers’ everyday lives.

The most famous and most often cited example of how this can work is Nike Plus. Nike sell running shoes, which people probably buy once or twice a year and which they probably never really think about when they’re wearing them to run in. Nike Plus transcends the product by providing a unique service which enhances the entire experience of running. It’s completely relevant to shoes but it isn’t shoes, and it allows Nike as a brand to own running and not just running shoes. This therefore becomes a platform whereby Nike are continually engaged with the running community and through which they have an immensely valid stage on which to communicate to this community about products when it’s appropriate.

Another excellent example is Charmin’s Sit or Squat mobile phone app, which allows a user to locate nearby public toilets when ‘caught short’. It even allows you to upload photographs and rate/review different locations. Obviously this has nothing to do with selling the product directly in the traditional sense (features and benefits), but at the same time it is completely relevant to toilets and therefore toilet paper – it’s a genius piece of advertising which the ‘audience’ keeps with them and uses again and again.

Lastly, there is one more example of this which I am sure will be familiar to readers of this blog, and which may come as a surprise – Google Analytics. At first this might seem odd, but if you think about it GA is actually one of the cleverest engagement platforms in existence: it’s free and ultimately exists to benefit Adwords revenue, but what more effective way could there be to engage, captivate and learn about businesses regarding their use of the web?

How does branded utility differ?

If we want to develop a framework for this stuff which differs to the usual model, we first need to understand how it differs in concept. Here are the key pillars:

Product disassociation – traditional advertising almost always points directly to a product. Even if we can’t measure the sale directly as we can with on-line conversion, it is generally possible to show some correlation between the communication and it’s impact on sales. However with branded utility the picture isn’t always so easy. What actual impact on Charmin’s sales does the Sit or Squat app really have?

Quality not quantity – branded utility is all about relationships, engagement, loyalty and advocacy. Unlike traditional advertising, it doesn’t necessarily matter how many people see the app or service, it only really matters how many people find it useful; feel an affinity to the brand because of it; and talk about it to their friends. Whereas hateful and irritating TV ads can still create positive brand equity through recall, it simply doesn’t work like that here.

Marketing as a service – increasingly people expect something in return for receiving messages, and not only where branded utility is concerned. SAAS tools like Spotify, which allows a free version with ads and a paid version without, has created a popular belief that advertising is a choice – if I have to see it then I want something in return. This is another reason why branded utility is increasingly in demand for brands. Marketing must offer a genuine service in order for any message to be accepted by the consumer.

Advocacy is more than a passing comment – in the digital age it isn’t enough for people simply to tell their friends about your product in the pub. True digital advocates are somewhere slightly closer to a mobilized sales-force; they must be proactive in sharing their experiences and bringing others in to the fold. This can only happen if doing it is easy, meaningful and if they have the right tools to go about it.

Measuring and optimising branded utility

Ultimately, branded utility differs from traditional advertising because it is about creating and driving owned and earned media, which behave incredibly differently to bought media and all the models of traditional advertising. But how then do we measure these strategies?

Aligning corporate goals

Very simply, why the hell are you even doing this really? What exactly do you think it will achieve in terms of profit, retention, sales blah blah blah. If you can’t align the initiative to what it is ultimately supposed to do then your measurements won’t offer any true value in terms of how effective it is for the business. Start from the most obvious and top-line corporate goals and trace these down to your metrics for the initiative. This will become more apparent in the following sections.

Differentiate impact and effectiveness

The natural tendency with branded utility is to measure it like a website. Let’s take the Charmin app as an example. Depending on how it was tagged we could tell how many people download the app; how many times they use it and for how long; to what extent they share, upload and rate content; and also perhaps what they think of it with the aid of some qual research. We can also identify key behavioural goals which in some way mirror a ‘conversion point’, thus giving us a kind of funnel analysis.

However, all this tells us is what the impact of the app is. We can track lots of data on how well it works and how much people love it as a toilet app, but this doesn’t tell us whether it is effective a as a brand communication. It could be number one in the iTunes app store and raved about on national TV, but does this really mean that people will buy more toilet paper? This is the effectiveness of the entire initiative and relies much more heavily on controlled qualitative studies and business modeling.

Build a measurement framework

Create a framework for the metrics you will report on, and ensure it reflects the distinction between impact and effectiveness described above. I have written more extensively on how to build a measurement framework on another blog post, but I have also sketched out a brief hypothetical example based on the Charmin example here. This is, in part, based on Forrester’s general framework for measuring engagement, which fits very nicely with this type of digital activity

Beware correlation and causation

It is amazing how many mistakes are made regarding correlation and causation in web analytics, but it is especially easy with branded utility. For example, take the example of Mazda’s online community forum for MX5 owners, which is certainly a form of branded utlity. They would be interested in to what extent people who use the forum are likely to renew vs. people who don’t. This is easy to do: take a sample of user-drivers vs a sample of non-user drivers and compare the renewal rate. However, this is incorrect. People who use the forum are already more engaged with the brand than people who don’t; this is the very reason that they find it and sign up to it. So the fact that they are more likely to renew could have nothing to do with the forum at all. The real question is: what is the incremental value of the forum or, more clearly: how many people renew because of their experience on the forum. This is not as easy to answer.

Don’t be afraid of intangibles

The answer to the above question cannot necessarily be answered with straight data, and will seem like an intangible business question, but unfortunately it is exactly these kind of questions which we need to answer to address the effectiveness part of the measurement. Rather than go into detail here, I will point you in the direction of a fantastic book: How to Measure Anything by Douglas Hubbard – if you haven’t read this I strongly recommend it. It will enlighten you about just how much of the supposedly unmeasurable can indeed be measured.

In brief summary

The digital relationships we have with our customers are changing. The old advertising and sales modelsdon’t apply in this new world. Embrace the future!

Further reading:

Contagious Magazine Special Report – Branded Utility

Brand Utility – a neat presentation from brandutility.net

Branded Utility Day at Ogilvy – presentation on Youtube

Another branded utility slideshare presentation

Brand Utility, the blog

Why are the numbers different?

Most people first recognise a problem with web analytics data because they are trying to reconcile absolute numbers between two different systems, for example when comparing visits in Google Analytics with clicks as reported by Atlas (or some other ad tracking tool). The following are the key reasons why these numbers don’t match:

• The terminology used to calculate metrics usually differs slightly. For example, unique visitors must always be unique visitors within [a certain time frame]. Different vendors may use different time frames. Neither is right or wrong; they are just different. This same principle can also apply to lots of other metrics, and sometimes on a much more subtle level.
• Whilst advances are constantly being made, there are currently no agreed standards to these definitions. Analytics vendors often try to name-drop ABCe standards (at least in the UK), but these are generally considered to be outdated and were created for reporting on visits that derive from banner advertising and search; not for web analysis. Here is a good synopsis of the current state of standards.
• Tracking methodologies, such as cookies, packet sniffers and IP addresses all collect data in different ways and all have pros and cons to the way in which they do this. See example below for further info on this one.
• The Internet is composed of a huge array of different technologies, which are all constantly evolving and changing. These technologies play a big part in the accuracy of data collection.
• New browser versions invariably feature new types of technology that allow increasingly savvy web users to hide their on-line behaviour, or even block this behaviour by default.
• Robots and spiders crawl Internet pages in order to e.g. index what is in them for search engines. Data quality in web analytics is a race to keep up with these creatures!

Cookies and Unique Visitors – An Example

The issue of cookies is generally the biggest area of confusion. A client of mine was recently comparing Google Analytics to their incumbent provider, Sophus3. They noticed large differences in unique visitors and wanted to understand why. Whilst this issue is in some respect the product of all the points raised above, the main cause is the type of cookie used:

With Google Analytics, visitors are tracked using 1st party cookies. Estimates suggest that around 1% of users block these cookies and a further 4% block JavaScript. GA is therefore unable to track these users, so real visitors may be under-counted by about 5%.

Sophus3, on the other hand, uses 3rd party cookies. Many browsers block these by default, so estimates suggest that around 65% of traffic is lost due to the combination of this and JavaScript blocking.

Sophus3 then use IP address to track visitors who have blocked cookies. However, most broadband providers use dynamic IP addresses, which change periodically. In some cases, the IP address could change every time the person switches on their computer. Therefore, Sophus3 will register individual people as multiple visitors, and overall numbers will therefore be inflated.

The following chart illustrates this issue in a more visual way (numbers are rough estimates to illustrate a point, and are not meant to be accurate):

How cookies can affect data accuracy in web analytics

Whilst 1st party cookies are generally considered in the industry to be best practice, in truth neither is perfect. For more information, here is a more detailed overview of how cookies affect web analytics data.

Get over it!

The issue of data accuracy can cripple companies and cause vast amounts of wasted time. In truth there is no solution, it is much better to:

• Understand the limitations in as much detail as possible and ensure that all recipients of web reporting and analysis are familiar with what the numbers do and don’t tell them.
• Focus on trends and segments, and not on absolute numbers. This is easy to do when the focus is on analysis and not pure reporting; insight never comes from pure numbers.
• Where numbers such as unique visitors are required for decision making, confidence levels should be used to make reasonable judgements about those numbers.
• If we set a consistent base-line of data at the most accurate that we can get it, then we can use this data to make accurate trend assumptions and draw conclusions about time-series analyses.

This kind of language continues to dominate all kinds of discussions about web analytics, including blogs, forums, and articles – and even reaches into the field of the experts; just look at the way Google Analytics defines direct traffic. It’s even more worrying when I hear the way my clients talk about it!

The fact is, this definition of direct traffic in web analysis is extremely misleading. It’s true that the direct traffic bucket does include bookmark traffic and typed URLs, but these days (unless you are very strict about your campaign tracking parameters) it can and does include all kinds of other stuff. All it really means is that the session started without a referrer being passed by the user’s browser, and this can happen for lots of reasons as defined in this rather neat list. I have done some tests on some of my clients’ sites and estimate that in some cases up to 90% of ‘direct’ traffic is infact banner ad or PPC traffic!

Here’s an exercise you can perform that will demonstrate exactly how prolific this problem is: as you’re browsing the Internet and following links from one site to the next, you can check the referrer that is passed by typing the following snippet of code into the address bar of your browser:

javascript:alert(document.referrer)

For example, if you visit a site like AOL and click on one of the advertising banners, when you arrive at the destination page replace the URL with the code – a pop-up will appear showing you the referrer (or nothing if one wasn’t passed). Try this with different types of sites, banners and links. Also try it with different browsers. As you will see, quite a lot of the time the referrer is blank. This means that your visit would have been counted as direct traffic in the analytics reports of that site!

So, it’s time stop thinking of direct traffic as people typing in your URL, this isn’t necessarily the case. ‘Other’ or ‘unknown’ would be a more accurate description.

It’s also time to realise the importance of campaign tracking on your inbound links, as Avinash Kaushik points out in his definition of analytics direct traffic. If you always ensure that your links are passing source and campaign info, then you are forcing the referrer field to be populated even if the browser doesn’t pass it. Here’s an easy way to build campaign tracking URLs in Google Analytics.