Implementing web analytics successfully throughout an organisation requires more than just a “fire-and-forget” approach to the platform.  There are many elements that go into making it worthwhile for your business, including:

• a measurement strategy
• resources
• domain expertise
• data integration
• data visualisation
• site performance analysis
• online marketing analysis
• social media and competitive analysis
• measurement adoption throughout the organisation
• measurement governance
• and ongoing optimisation

Last year I posted some thoughts around “how to create a good measurement strategy” and we’ve now taken it one step further by having a questionnaire on Digital Balance to help you visualise your measurement footprint in relation to what we think is achievable for most organisations, and in relation to other respondents.

See how you shape up against everyone else.

Many companies struggle putting together an effective digital measurement strategy, often due to the lack of resources and/or the lack of understanding how it can provide an effective return on the investment.

And it is an investment.  Generally you’ll incur people and training costs, licensing costs for the various platforms and other ongoing costs – some of which are internalised.

But you can demonstrate an ROI, that will far outweigh the costs incurred, if you spend the time and effort in putting a solid strategy together.

Digital media will continue to grow.
Our audiences are growing online.
It’s pervasive in our everyday lives.

A good digital measurement strategy should be the desire within any business to enable more precisely targeted and measured results of marketing investments.

But effective digital measurement is not plug-and-play, nor should it be plug-and-pray, even with the most sophisticated or automated tools.

We now have the capability to capture millions of customer interactions, across multiple platforms (mobile, web, video, audio etc) and use that data to garner insights into customer behaviours online, so that we can optimise how we communicate with them.

But to do so, significant planning and organisational coordination is necessary to manage the sheer volume of data produced, and then, to put this data to work throughout an organisation. In addition, different types of vertical industries use digital marketing in different ways to achieve different business goals. In other words, digital measurement strategies look different for different types of organisations and is rarely achieved overnight.

But the fundamentals remain the same, irrespective of the industry vertical.

The key elements

A good digital measurement strategy includes the following 6 key elements:

1. Overall measurement strategy
2. Resources and skills
3. Data integration and visualisation
4. Data analysis and insights
5. Ongoing optimisation
6. Adoption and governance

Overall Measurement Strategy

An overall measurement strategy should align to your overall business objectives.  This helps ensure all digital marketing activities contribute to your bottom line – and demonstrates the value of digital measurement to executive management.

There’s really only three things that form the foundation to the overall measurement strategy:

1. What’re your business objectives?
2. Who is your audience?
3. What does success look like?

Most marketers answer those questions when they put a marketing brief together, so answering them at a higher level (the business reason for being) and converting those to a measurement strategy shouldn’t be a challenge.  There’s a lot of measures available for different verticals, but if you start here, you’ll be well on your way to defining a strategy.

Resources and skills

People determine the success of a digital measurement strategy: the number of employees dedicated to measurement and analysis, the resources at their disposal and their ability to troubleshoot issues and respond to requests throughout your company.

Data integration and visualisation

Effective digital measurement (not just of digital marketing) often requires companies to integrate multiple sources of data to create a more complete picture of their customers and business. Data integration – the passing of key values between systems – enables companies to create a more complete picture of their digital efforts.

Then there’s visualisation, which is the presentation and delivery of digital marketing data to meet the needs of different groups throughout the business.  These come in many forms, from dashboards to campaign summary reports to alerts.  The important thing here is to make sure that your recipients understand what it is they’re looking at, and, if they’re making decisions based on the data, they are interpreting it correctly.

This is commonly one of the areas where the biggest mistakes are made – misinterpreting the data.  Ensure that you don’t just report on reports…customise them to ensure that your executives have the information that they are looking for.  They probably aren’t looking for in-depth reports…they won’t have time to analyse them (that’s your job).  They’ll likely want trends and summaries.

Data analysis and insights

Data analysis skills are essential to turn web-based data into the understanding companies need to optimise and drive smarter marketing and business decisions.

Data analysis tends to be an ad-hoc activity, usually includes segmentation and quite often requires specialised software, such as Omniture Discover.

Ongoing Optimisation

Ongoing optimisation ensures the data measurement processes that you put in place are consistently applied over time. This includes using digital measurement data to identify optimisation opportunities and test different iterations of content. Mature optimisation should span all digital marketing channels, from web sites to paid search, and include automated A/B testing of multiple marketing updates prior to implementation.

Adoption and governance

The final part is to ensure that measurement is adopted across everything you do.

Digital marketing data has little value unless rules are are in place to ensure its consistency and quality over time – and that it gets to the right people at the right time throughout your business. Training is essential to ensure people who need to use the data know how to – and can interpret it accurately.

Governance provides defined processes for managing various aspects of digital marketing programs, including implementation and change management, security, data and measurement consistency.

Who’s responsible

Companies are.  Maybe you are, if you’re reading this.

You may need (or want to have) a central group responsible for it.  They’ll be responsible for the distribution of and insights to the data, to all levels of your organisation.  They’ll also be responsible for the platform, the implementation, the ongoing change management and so forth.

Don’t expect (or even request) your digital, or traditional, agency to be responsible for this.  That’s not what they are there for.  But, frankly, they should be asking you about measurement.  They should have a vested interest in ensuring everything they do (and especially recommend) is adding value to the bottom line.  If they’re not interested in how things are performing, you should consider another agency!

Two final thoughts

Defining your overall measurement strategy comes before selecting your measurement platform.  There’s no point in paying for a measurement platform when you don’t have the strategy in place.   You’ll find it very difficult to justify the costs if you approach it that way and you’ll likely get very little out of it.

And, finally, the data should be used pervasively across the business to make business decisions at every levels of the organisation.

Just a quick post…finished presenting at the Perth Think Tank (#perththinktank), talking about the value of measurement and measuring user engagement.  Around 220 people were there, including the Lord Mayor of Perth!  I’m honoured.

Many thanks for all the kind comments from everyone afterwards and the encouraging tweets during!

120 slides delivered in just over 7 minutes…

I’ve uploaded the presentation to SlideShare, and I’ve put it below too.

So, it looks like I’m on the road again with a couple of presentations.

On Friday 27th Aug, I have the pleasure of presenting at the ADMA Digital Day in Perth.

And then, on September 21st and 22nd, I’m honoured to be presenting again at the Marketing Higher Education Symposium in Sydney.

I guess I have a couple of presentations and a morning workshop to put together…

Looking forward to both of them; looking forward to seeing you there.

Albert Einstein once had a picture hanging on his wall at Princeton that read:

Not everything that can be counted counts, and not everything that counts can be counted.That picture should be up in every business optimization unit across the globe.

I’ve often said just because you can, doesn’t mean you should.  And in many cases, you just plain can’t.

Despite the capabilities of many web analytics applications, sometimes the things that count just can’t be counted by web analytics.  For example, User Experience (did they get really get what they wanted) can’t be counted through web measurement.  It may well be answered through a variety of other methods, such as focus groups or qualitative research, but sadly web analytics can’t provide the answer.  Yet.  Maybe one day, when we’re all chipped, chapped and chafed, and tracked from pillar to post, but in the meantime…well, you get my drift.

Unfortunately, in many cases, companies still measure absolutely everything, report on way too much, and get nothing out of it.  They end up with analysis paralysis, which devalues the optimization proposition.

In a survey conducted by eConsultancy in 2008, 78% of companies don’t tie their data collection strategy back to their business objectives.  Staggering.

Not everything that can be counted counts

It’s vitally important to stick to business KPI’s.

When you’re driving your car, you only want to know a few key things; how fast you’re going; how much fuel you’ve got left are typically your two KPI’s.

On a day to day basis, you don’t need to know how much oil you have in the bottom of the engine; or the temperature of the coolant; or your tire pressure.

So what makes you think that your stakeholders want all that information?

Think about who you are providing insight to.  If it’s your CEO or CMO, think of them as the driver and structure your reports accordingly; your web team might be the mechanics, so they want to know how much oil is in the engine and how hot it is.

What’s important is that you pick out the few key performance indicators that are tied to the business objectives (and campaign objectives) of the stakeholder – and they get narrower the further up the chain you report.

Your CEO probably won’t want to know CPC for an online campaign, but they probably will be interested in how many sales have been generated off the back of it.  Your CMO may be interested in CPC, but probably not (too much information).  Your campaign manager will be interested though, so they can continue to optimize the campaign.

Not everything that counts can be counted

Here’s a tricky one to measure…a TVC brand campaign and it’s effectiveness at driving sales.

Now, don’t get me wrong – there are things you can do that will help, such as vanity URL’s that redirect to another URL so that you can measure traffic through the original URL.  But still, it’s a tricky one.  Despite the advancements and sophistication in the technology, there is still no way to fully integrate offline and online reporting.

Einstein often referred to that picture.  He was a smart cookie; and it definitely rings true for web analytics.

Bit of surprising news, Adobe is buying Omniture for $1.8billion.

Still taking this in and many thoughts running through my mind at the moment.  Seen Adobe purchase a few companies in the past and the results have been varied.  They are, after all, a product-based company, offering little in the way of services.  Omniture on the other hand, is a service-based company.  Very different business models, very different thought processes.

I wonder if Omniture will keep their name too…?  They have a massive brand equity  ($1.8billion) so it would be a shame (I think) to see that dissapear to something like Adobe SiteCatalyst.

And I wonder too if they retain autonomy over the direction of their offering?  I see that they are to become a business unit of Adobe, with Josh James as the senior VP of the new business unit.  I hope that they will continue to develop their products and services at the speed that they have us all accustomed too.

And I really hope their services don’t get impacted.

Interesting times lay ahead for Omniture.  Interesting times, I’m sure, lay ahead for their customers too.

Please, keep us all informed on plans, impacts, directions, etc.

Just presented on how to get value from your analytics platform to around 150 people at the Marketing Higher Education Symposium 2009, at the Sydney Convention Center.

136 slides in 30 minutes (quick fire)…lots of nodding heads and a few chuckles too, which is always nice.  I think it went well though.

If you were at the conference and have questions, please feel free to ask using the comments.  If you weren’t at the conference, and have questions, don’t be shy.

You can get access to the presentation slides (although it’s a bit tough without the voiceover).

If you’re thinking of trying to compare web analytics results by vendor, or even by the original log file, you’re in a for a very tough time.  Just thinking about it is a mistake.

Genie + bottle + uncork = Long time, not good time

The problem is that while there are basically two different methods of data collection (via server logfiles or via JavaScript tags), the variables associated with both, in a real world environment, make it almost impossible to compare results.

And therein lies your genie out of bottle, and you scrabbling around trying to justify the results.  You’re best off not even trying.

Server Logfiles

Every web server stores requests made of it to a log file (providing logging is turned on).  Programs like AWStats and many others out there, read through all of the entries, sorting out all of the requests into general reports, such as page views, visitors, visits etc.  They can also generally weed out spiders from search engines, so you get a little closer to unique “human” counts.

But there still remains a big problem with this method and that is “caching”.  If a person re-visits a page, quite often the second and subsequent request will actually be served from the browser memory, or cache, and no request will go back to the web server for logging in the file.  Secondly, with the advent of network caching servers, the same thing occurs.  So, you end up with under-counts.

JavaScript tagging

Enter JS tagging.  Small bits of JavaScript are added to the page, typically within the body section.  After the page loads, the JS executes a request to the vendor logging server, passing information to it, such as page name, time and date, information about the visitor etc.  Nowadays, a cookie is also generally set, to spot repeat visitors.

The benefit of this method is that it typically defeats caching as each time the code is executed a new request is made to the vendor.  The other benefit is that other information can also be set and sent, such as custom events, campaign codes etc, which is very important to enhance the overall and customization capability.

The downside is that they generally require the user to have JavaScript enabled (which most do) and, the accuracy is determined by the location of the code and if the code executes in a timely manner, before the user has clicked to another page.

Genie, bottle, out

Imagine trying to compare fuel consumption on two similar vehicles over an identical distance.  While the vehicles might be identical, the way the vehicle is driven, the amount of traffic, the number of times you stop at lights, the temperature and humidity etc, all affect fuel efficiency.

You get a similar challenge when comparing vendor results, the variable in this case though are the way the user interacts with your site.  And unfortunately, the disparity in results often leads to dissatisfaction with the vendors solution, rather than an understanding of how the differences can occur.

Let’s say you use both Google Analytics and Omniture and Web Server Log Files.  If you browse to a page and let it fully load, you’ll no doubt have the same counts across all three.  1 visit, 1 page view.  The log file will also show all of the other associated requests, such as CSS, JS, images and the likes.  But this is not a real world comparison.

The problems start to creep in when users start to browse your pages.

Some will click links before the page has completely loaded (JS will not record activity).  Some may have JS turned off (JS will not record activity).  Some may be with an ISP that utilizes caching servers (log files won’t record activity).  If Google JS is at the top of the page and Omniture is at the bottom of the page, Google may record a page view, but if the user doesn’t let the page fully load, Omniture possibly  won’t record the page view.  First party and third party cookies are based on user settings and may not be set, affecting visitor counts.  And the differences continue.

But over the years, vendors have strived to get as close to the truth as possible, using very sophisticated JavaScript – and they have to; it’s a multi-million, if not billion, dollar industry, which continues to grow as site owners demand more flexibility and insight into user behaviour.

If you are looking for a solution where you can customize your insight, then the JS tagging option is your best bet, as it provides more flexibility.  You can track clicks that wouldn’t be recorded by a server log file; you can track flash interactions; you can track campaign activity, you can track shopping cart volumes and revenue and products.  And, with certain providers such as Omniture, you can target content based on the user’s previous activity across your site, using products like Omniture Test and Target (saving that for another post).

So, the thing to do is to try not let that genie out of the bottle, otherwise you’ll spend the rest of your time trying to put it back in.

At best, you should expect (and explain to your stakeholders) that differences will occur and provide a rationale for the differences.  As long as you’ve implemented your tagging correctly, you should be pretty close to the truth.

And remember, web analytics is not about the absolute numbers…

I was chatting with one of our School Deans today about various results and he posed the question “Is it possible to see which courses people viewed after seeing one course?”.  His interest was based on the fact that the user doesn’t always purchase the “most frequently visited course”.  They often view one thing, but end up purchasing something else, and our reporting doesn’t highlight that behaviour.

Now, that got me thinking…that’s probably pretty common behaviour.  So how can we make that visible?

Pathing is common

Of course, it’s easy to show page pathing (which pages are viewed before and after a certain page), section pathing (similar but for a section), but pathing isn’t available across multiple visits (for the obvious reasons).  Traffic pathing is available on s.props, so as long as you report something into an s.prop, you can generate paths to/from it.  Paths are very valuable to see where a user goes after visiting a specific item such as a page, or how they got to a specific page.

However, the problem arises when you want to see something across multiple visits.

We’ve just had a similar problem with multi-visit campaign results, where the success event was being attributed to the latest campaign id, which wasn’t neccessarily what we expected.  In our case, due to the sales cycle being long (typically 1-3 months), many visits will occur and the user won’t always come in with the same campaign code.

For example, we might send them an email which drives them to the site.  The user engages, finds out what they need, but doesn’t convert.  They then come back a few days or weeks later by either typing in our web address directly, or come in through a search engine.  In either case, the success event (if they convert) would be attributed to the latest campaign, for example, Google or Direct/Typein (as we also have a VISTA rule).

Enter Campaign Stacking…

So, to provide some visibility to this activity, we worked with our consultant who recommended we implement Campaign Stacking, which, through the use of a cookie, appends a different campaign code (if they have one) to any previous one.

So, in the above example, we now have reports which show conversions by campaign combination.  We accomplished this by setting up a new eVar and writing a cookie (through an s_code plugin) appending the next campaign code to a previous campaign code.

Now we should be able which campaign combinations are driving conversions, over multiple visits.

Now stay with me…

I’ll bet we can do the same thing to understand product view combinations over multiple visits, leading to conversion.

In our case, a product is a course, but no reason this couldn’t work for any product category.  In our case, we don’t want to see which course “pages” they visited (we have that through course page pathing).  We want to see course pathing across multiple visits (or the same visit).

By setting an eVar with the name of the course, and using the same methodology as above, we should be able to get a view on this activity and user behaviour.

In theory, we should then be able to export the data and generate promo-type content that says “People who liked this course, also liked these courses…”

That will then help us to cross-promote “related” courses – not what we think are related, but what users are thinking are related.  Do that on an automated, daily basis and you really start to apply some value for the user.

That’s one of the great things about Omniture – flexibility to do this.

Guess what I’ll be trying over the next few days…I’ll update this one over time, if we get it working.