Tracking Tourism: The Tourism Research Blog Archive for the ‘Data’ Category

Or far less temptingly: “why conversion funnels are your friends”

Your time is limited.  Business stakes couldn’t be higher.   Marketing channels keep multiplying. And there is so much data “to help you” that sometimes it seems easier to do nothing at all. 

But what if I told you that with some shrewd use of web measurement and site optimisation, you could achieve the following:

• Plug revenue leaks
• Stop a haemorrhage of online customers
• Delight your bookers
• Make more money
• Achieve analytics nirvana

I kid you not – using analytics to optimise your online booking process can deliver you all this!  (Well, maybe not the last one).

Deep focus on the booking process (or other major conversion points) on your site, combined with systematic testing is amongst the most powerful analytics you can do.  It is a place where you are almost certain to see return on investment from an analysts time invested here – provided you take action based on what you find.

So if this is really true why isn’t everyone doing it?  Well, many companies are – and they’re doing it very well.  Expedia, Hertz, Travelocity to name just a few.  And if your competitors are obsessing about this and you’re not, they’re at a huge advantage.    But many companies are not – and if you’re one of them all is not lost.  This post tackles some basic principles – and if you’re looking for a way of embedding web analytics and optimisation into your business, then I hope this is especially relevant for you.

And fear not – if you are a one man marketing, management, customer-service,  analytics,  social media,  web-guru machine that has to do everything yourself – fear not, this is still for you.  Perhaps even more so, because this is one place where if you carefully focus even a small amount of effort, you will see impact on the bottom line.

Let’s get over the barriers first

This will only work if you focus your efforts on the stuff that matters.  Conversion, revenue, customer satisfaction and specific online visitor behaviours.

So page views, visits and God-forbid (may I never hear this word ever again) HITS?  No!   Or to channel my inner Amy Winehouse – No! No! NO!!

Don’t fixate on these – they are just units of counting.   They are metrics, ingredients if you like – they are not the answer in itself. There’s not necessarily even a positive linear relationship with these basic metrics.  More page views may simply mean more people are lost and having to reluctantly look at more content before they can resolve their issue.

Using your time effectively and impacting the bottom line means measuring what matters – the things that make you money, save you money – or improve customer satisfaction and thereby achieve both.  In my presentation at the Eye For Travel Technology Conference at World Travel Market I put it in these terms:

It’s clear, I think, that counting page views, time on site or unique visitors alone – in isolation from real business goals – cannot possibly deliver you gains, savings or love.  But conversion/booking process optimisation can.  So time to introduce the conversion funnel…

How to look at your conversion process

The conversion funnel looks at the flow of visitors through the various steps of your booking process. 

At its simplest, as shown in the graph, you are using the basic shape of the funnel to identify potential problem areas, so you can dive in to the data and explore further.

For the data to be meaningful, you need to focus on narrow, linear processes like payment processes and form submissions – the good news is there probably only a small handful of these on your site.

You can do this manually in Excel, but with varing degrees of set-up all the major web analytics packages automatically present your conversion funnel information in a clear visual way. In Google Analytics, for example, you have to first specify your conversion goals and set up the associated steps in your funnel process.

But once you’ve done that set up, you have the raw ingredients you need to start booking process optimisation.

In my view, here is where you find the quickest wins in booking and conversion process analysis:

I work through this process, in conjunction with the conversion funnel data, in order to first find the complete disasters (like failing transactions due to technical errors).  I then aim to find out where for some reason or another people are simply not behaving like we would want them to.  And finally I am identifying specific pages or parts of pages that have to be improved and that must be the focus of any testing and optimising efforts.  Remember that web analytics can only tell what is going on – it can’t tell you why.  You need voice of the customer data for that.

So, note number two – “help!”    I would be happy to wager you a fiver that the people in your organisation that speak to customers on the phone, be they call centre staff or receptionists, probably have a better grasp of what is wrong with your website than your IT staff do.  They hear it every day.  Optimisation starts when the dots between end users of the site, customer facing staff and IT are joined up – and communication flows between them.

It is not always so simple, of course, and there comes a time where if you don’t have your own analytics resources, you probably need to buy them in.  But there is so much you can achieve to start with by yourself, using analytics data to inform testing and optimisation.

When to stop looking and start acting

Of course, analysis without action is simply idle indulgence.  Once your data has given you a theory you have to test it by making changes – and then see if the data shows an improvement.  The more systematic your testing, the more confident you can be in your results.  There are plenty of tools to help you – we use Google Website Optimzer with our clients.

Measurement and testing have to go hand in hand – otherwise you may find your “improvements” make things worse.  Assumptions alone aren’t enough.  Both Expedia and Hertz, my co-presenters at the Eye For Travel conference, made similar – really vital – points about booking process optimisation.  They both found that it isn’t always about making the process shorter, or in less pages – sometimes, quite counter-intuitively, more steps or longer forms work better for the customer.

Ultimately, using the data web and customer data available to watch, listen, learn then improve your site for the visitor is in my opinion one of the surest ways to improve the bottom line results from your web channel.

I will be presenting on this subject at Canada eConnect in January, so I’d love to hear your triumphs, quick wins or the barriers you face in booking process optimisation so I can tackle them head on at the event!

Getting better insight into your online marketing campaigns and why this matters

OK, I’m guessing that many of you already know which websites send you what kind of traffic.  I don’t just mean whether search engines send 60% of your traffic but also what other sites are sending you that other 40% of visits.   Such as press mentions, local directories, online articles, blogs that mention you etc.

But if this is all you know, then you could still work your data a lot harder – with the ultimate goal of less spend, more results.  With a little bit of web analytics customisation to your campaign activity, you could be able to answer questions like:

• Which paid button on XYZ page gets me more traffic – the one in the section about golf or the one in the section about fishing?
• Do either of these buttons lead to more people booking than the free text link also on that site or the direct email I sent to my newsletter subscribers?
• Is the banner ad I ran on the front page of a directory three months ago more successful than the one I am running there at the moment?
• I’ve been pushing a special offer to my email list and online – what’s the value of each approach?

What we are doing here is moving from just tracking generic sites and marketing efforts as a whole, to tracking specific Campaigns.  To do this you need Goals.  And for a travel and tourism company wanting to maximise their return on investment in today’s climate, this is a vital step forward.

So,  if you cannot yet answer questions like those above about your site, then you need to look at some form of campaign returns analysis.   This involves campaign link tracking, setting specific goals within your web analytics tool and pulling results together in a way that factors in cost.  This is something you can do easily through most web analytics packages and a simple Excel spreadsheet.

Tracking Campaigns – an example.

Imagine that you run a hotel in Scotland and you decided to place an advert with a link on the front page of a  site like http://www.extramilescotland.co.uk/ to link to a great deal you have for golfers. In addition you also want an advert on the same page linking to a great deal for anglers. Just looking at your traffic sources in your Google Analytics data will not let you tell these adverts apart.   One of them may have worked, one may be a complete waste of money.

And, at the same time, you decide to email your past fishing customers telling them about a deal with a link to your site and you do the same for the golf customers.   It is starting to get really difficult to isolate precisely which of your activities are moving the needle.

BUT – there is a way round this.  Just a little tweaking of the names you give those links, you can tell all your ads apart without needing to do anything to your website.

Not only that, once you tweaked that URL, you would start to get really detailed marketing effectiveness information that would tell you a lot more than just where the visitor came from.  This is the wonderful world of campaign tagging (OK, not really that exciting – but so very useful!)  The “how to do this” is spelled out further down the post.

By identifying how people responded to different promotions, you can start to take control of what’s working for you.

But you need to take just a few more steps to start to make this really really powerful stuff.  You need to define what success is for you. You need to define what you want you visitors to do.  You need to define your Goals.

Campaigns + Goals = now analytics gets actionable

As Vicky argued in a previous post,

“online success is not about how many people come to your site in total, its about those people that come to your site and then do what you want them to do (or not!).”

In other words, you need goals.

Let’s revisit that example above and, had we tracked each different campaign correctly, we might get some figures like those shown in the table below:

The table above shows us

• The number of visitors to the site each type of campaign attracted,
• How many completed goals can be attributed to those visitors attracted by the particular campaign,
• What percentage of visitors per campaign achieved the goal.

If you did not have a goal defined, then you would simply know that more people came to your site but you would have little understanding of how they behaved.  It would be a bit like advertising a shop opening but not bothering to record what your customers bought – or if indeed they even bought anything at all.

Put simply, Goals allow you to assess how successful you are at getting your customers to do something you want them to do.  And some campaigns will be more successful at getting them to do that special something than others.  In the example above, we can see that the ‘golf email’ link was the campaign that was the most successful in getting customers to do what you wanted them to do.

A goal can be anything from a sale through to anything other tangible action you want a visitor to do on your site – for example, a brochure download or visiting the directions page.

But if you do sell (or make reservations) through your site, then we can take the final steps and start to measure very exactly what these different campaigns did for your bottom line.  If we assume that your site is ecommerce enabled, then the table above could start to look something like this:

And what could we conclude from these (fictitious) figures?

• A lower percentage of ‘fishing banner’ visitors’ complete their goal (’make a sale’ in this example) than ‘golf banner’ visitors – but the ‘fishing banner’ visitors spend more when they do get to the site.  The activity cost more than the email activity, but it paid for itself.
• The emails in both cases got more people to convert than the banner ads for the same interest area – but the revenue from them was much lower (perhaps the emails drove more last minute cheap deals than the high margin banner ads).
• Despite the lower revenue generated by the fishing email, it represents a superior return on marketing investment to the fishing banner ad because of its low cost.  It was a quick win and by no means a worthless activity!
• But look at the golf banner – in this instance our marketer spent £500 yet only acquired revenues of £300.  The activity had a negative return and doesn’t justify being continued.

Note that not all analytics packages will automatically calculate a Return on Investment or a Cost of Activity figure for you (Google Analytics does for adWords but not for customized links). Even if your package  doesn’t, it’s pretty easy to work this out from your data.  You simply need to paste it into a an Excel spreadsheet, and if you’re interested, the ROI formula we’re using here is:

(Revenue from marketing activity – Cost of marketing activity) / Cost of marketing activity.

So what?

When you only have a finite online marketing budget, you need to know whether you are spending it wisely.  Thinking in terms of campaigns,  goals and campaign returns allows you to work out exactly what is and what isn’t working for you.  It identifies whether marketing in Directory A is better than Directory B.  It enables you to work out whether emailed customers (for example) are more likely to buy or complete a goal with you than visitors coming via other sources.

This is giving you near-real time information about how successful your marketing is.

The technical bit – how it’s done

Although I am aware that there are a wealth of analytics products out there, Google Analytics is the most commonly used at the moment and so this section uses this tool as the building block.  The process would be broadly similar in other packages.

Campaign tracking: Campaign tracking looks daunting to begin with but essentially it means adding a bit of code to the URL you to direct people to your site from your banner ad, text link or whatever. For Google Analytic users, there’s a useful tool here to help you out.

Setting up Goals: I can do no better than to echo Vicky’s earlier post by recommending Justin Cutroni’s article here and  his video here.

Integrating adWords and ecommerce: try Google’s intro here.

Still confused?  Well…you can always hire us to sort out the issue!

Filed by Stephen (17/06/09)

I’ve already got web analytics on my site, thanks

Given that the team behind Tracking Tourism have recently become Scotland’s first Google Analytics Authorised Consultants (and are one of only six firms in the UK with this accolade to our name), it seemed natural enough that this week’s post would have something of a web measurement feel to it.  Because we’re jolly happy about our achievements.  Because we (quite literally) have the T Shirt – as modelled here so fetchingly by me.

And because while we’re finding that more and more businesses have the tools to allow them to undertake analysis, we have the sneaking suspicion that having the tools and using them for meaningful actions are two different things.

Imagine a tourism business networking event, not very far from you.  Two strangers strike up conversation:

Tourism Business: “So, what do you do?”

Stephen (or Vicky): “Well, we deliver customer insight and web analytics services”

Tourism Business: “We already have Google Analytics/Urchin/Nedstat thank you very much”

Stephen (or Vicky): “And what has your business done differently on account of the information that has given you?”

Tourism Business: “……”

OK, we do sometimes make better conversation than that – but the point remains.  Using a web analytics tool and simply owning a web analytics tool are not the same thing.

The answer to your unique business question doesn’t come just because you got Google Analytics, Omniture or any other measurement tool.  A basic report or a dashboard is not analysis – on its own it cannot give you the answers you need to take action to improve your business.  Sadly, (or happily if, like us, you really really love this kind of thing) – web analytics isn’t ’something you’ve got, thanks’, its something you do.

Getting buy-in to real web analytics

OK, it’s something that we’ve banged on about in the past.  The theme of measuring your website is something that we’re spent a lot of time writing about  on Tracking Tourism (click here for all previous stories).

But despite our humble efforts, you probably still know colleagues, companies and possible even bosses who don’t see the “what’s in it for me” of really using online data to drive the business.

So here are five reasons you can use to convince the unenlightened that job security, profits and heck, near-nirvana, are likely to flow when you take your data seriously – and then do something with it.

1. Show them the money (and the glory)

People that run organisations spend a lot of time caring about where money is being made, saved and lost.  They are typically less interested in page tags, page views and referrer strings.  Buy-in to real analytics comes when it is framed in terms that relate to revenue.

And this doesn’t just apply to big business – every website with a commercial objective makes a contribution to money earned, money saved and yes, it also involves money being spent, either literally or in terms of time.  Real web-analytics is used to drive improvements in the efficiency of those costs.

So at its very simplest, don’t stop at reporting that there were 500 brochure downloads from the site this month – follow it through to its revenue implications.  We posted 500 fewer mail packs, saving £5,000 and can anticipate an additional 50 calls to the booking line in the next 2 weeks.And glory?  Well that relates to performance against competitors.  Unsurprisingly, revealing insight about this will also generate more excitement and action than reports about page views.

2. Show them the customer

The online customer can be perceived as more mysterious – even sinister – compared to its offline counterpart, despite the fact that they are often one and the same.  All the little things you observe about real world visitors seem to vanish online.  You do not even know if the “right people” are even finding your website.

But smart web analytics can help build a picture of the customer online.  For example, it can inform you about the vocabulary and intent of visitors to your site.  You can see the language and words customers use when thinking about you – something that is significant in an intelligent marketing campaign and to search engine optimisation.

Building pictures of real people, real customers – who just happen to be in the online phase of what will often become a real world relationship – can be very useful in breaking down fear and resistance in businesses wary of further web investment.  It can also reveal the shocking implications of poor customer experience online.  Which leads us too….

3. Show them real people walking away

If 99% of your visitors fell out of the back of the bus en-route to your business, week after week, wouldn’t you be as mad as hell?  There may be choice words to be had with the bus operator.  Someone would probably declare that “something must be done.”

And if the same is happening online?  If 99% of visitors are “falling out of the site” without making an enquiry, day after day.  Shouldn’t something be done about that too?  Smart web analytics demonstrates where people are leaving on mass, which pages are under-performing – but it also informs the actions and tests to improve those pages.  And, of course, it informs the financial cost of inaction.

4. Show cause and effect

Basic analysis tells you how people are finding your website site.  Good analytics tells you whether the money you are spending on marketing, promotions and SEO campaigns is actually making you money.  It tells you whether your actions are creating the desired effects.On the flip side, it can also reveal how your actions (or inactions) are costing you business, impacting your search engine visibility or causing your marketing expenditure to be wasted.

5. Show them the future

The very best analytics doesn’t just look backwards, it looks forwards. It attempts to use visitor behaviour, customer satisfaction and search trends to inform advance decision about promotional expenditure, staffing and priorities.

For example, with one of our clients, we have found a direct correlation between visits to specific pages of their website and physical visits to their attraction 5 days later.  A big peak in visits to those website pages means they can expect more people than usual on Saturday – which means opening the overflow carpark and bringing in more staff.

At a more simplistic level, if you knew that the peak time of the year for Google searches for weddings in Gretna Green was July, would you wait until September to advertise these packages on your website?  By staying ahead of the customer activity cycle, you predict the future to your marketing advantage.

Getting to the big-wins

If your reluctant friend is now convinced of the value of data, how do they get started on the path to true enlightenment?  Well, first get the data set-up right – by ensuring every page is tagged correctly, that filters are in place etc.  All things we’ve written about before.  Not one of the sites I have checked in the last two weeks has had every page of their website correctly tagged – and what you get in that scenario is garbage in, garbage out as they say.

Then focus on measuring the right questions for your business – what really matters and what do you need to measure in order to track that. Who needs convincing and what is the best way to report to achieve that.  And don’t lose sight of people in the numbers.  Tourism and hospitality are people focussed industries – don’t lose the customer in a sea of reporting.  Use the data to get closer to the customer and how the business is delivering on their needs.

Buy-in help or training if you need it – you don’t delay fixing the hot water because no one on your team is a plumber.  You get one in, or someone gets packed off to night school to learn.  Fast.  The same has to apply to web analytics – it is simply too important to the business bottom line to languish for a few years until someone magically figures out how to do it.  There is expertise out there (hint, hint) – it probably makes financial sense to use it.

And that near-nirvana I mentioned?  That occurs when you create a business culture where analysis is in the DNA.   And for the unconvinced, these businesses do exist.  More importantly they exist in the travel, hospitality and tourism sector.  Travelocity is one, but they can be the very smallest of businesses as well as the very large. They’re probably those same guys eating into everyone else’s market share right now.

If analytics is something you do, not something you get, then how do you do it?

Funny you should ask….  Next week’s eMetrics Summit in San Jose, California, kicks off with an analysis symposium to tackle that very question.  I will be one of the presenters charged with distilling all my best thoughts and tips on “how to analyse” into just 10 minutes each!  For me, it really promises to be the analytics highlight of recent years as I believe we have focussed for far too long on smart tools, not smart thinking.

To quote my friend and eMetrics Summit guru Jim Sterne as he re-mixes the Wizard of Oz:

“Why, anybody can have data. That’s a very mediocre commodity. Every pusillanimous creature that crawls on the earth, or slinks through slimy seas has data!  Back where I come from we have Summits – gatherings of great learning – where people go to learn how to analyze that data.

And when they come out, they think deep thoughts and leverage their marketing investment, and with no more data than you have.  But – they have one thing you haven’t got – a ticket to the eMetrics Analysis Symposium!”

Its not to late to get your analysis symposium ticket here.

And if sunny California (or eMetrics London in a few weeks time) or the thinking great thoughts can’t tempt you, those of you in Scotland are welcome to attend a free Web Analytics Wednesday networking event in Glasgow for some drinking and chatting instead.  It take places this Wednesday 29th April and you can register to attend here.  Stephen and I hope to see some of you there!

Posted by Vicky

We work a lot with tourism and travel providers operating customer satisfaction feedback systems to help improve their services and offerings.   But I’ve met a few people recently who have expressed scepticism about customer rating systems generally and it strikes me that this scepticism could be the result of not looking at the data in a more rounded context.  Or due to receiving data derived from flawed methodologies.

The scepticism was expressed along the lines that these kind of things always show that 90% of customers are satisfied. The implication of this is that rating systems aren’t really telling you the full story. So, while we’ve previously written here and here about using comment cards, these recent comments show that there is still a little more ground to cover in this area.

I can understand the view that customer rating systems are inadequate – but this typically occurs only if you are looking at the data derived from the customer in isolation.  As we wrote in one of the previous posts, “comment cards are just one of a suite of businesses information sources”. In other words, you shouldn’t rely on comment cards alone for customer feedback in its broadest sense. (And with such rich data all around you, why would you want to ignore the other sources?). But let’s start by looking at this “90% satisfaction guaranteed” issue a little closer as I feel that a rating like this is not as pointless as critics suggest.

To my mind it’s all about context. A 90% satisfaction rating expressed as a snapshot of customer sentiment can be fairly meaningless. However, a 90% satisfaction rating for an activity compared to (for example) the rating for a different activity, a different period or even a different location does start to have some meaning.

It’s about trends, not absolute scores. It’s about comparisons, not absolute ratings. It’s about context.

But let’s have a look at this using some real data.

Context One – data over time

The following charts are drawn from ‘real life’ but have been anonymised.

Starting with the one on the left (click on it to open a larger version in a new window), the orange line represents a lower control limit (one standard deviation from the average downwards meaning that 68% of all monthly results ever fall within this range – and if you are interested in why I’ve used only one standard deviation, see the  second comment at the end of this post). The average is the grey line in the middle. The blue straight line represents an upper control limit (again one standard deviation but upwards). I’ll explain the purposes of the control limits in a moment. There is also a dark grey line which is the trend of the scores. 

In this first graph we see a green line charting the percentage of people who completed a comment card for a particular aspect of their experience and who said that they were satisfied. Looking at this line, we can see that indeed it hovers around the 90% mark but that there is some variation. So what can start to take from this information?

Firstly, you will expect some degree of variation when analysing data one month to the next – it’s just natural. But there are times when a change is ‘unnatural’ and this is when control limits come into play as they alert you to when something has fallen outside of the normal corridor of performance. And these control limits can only be derived from looking at this data in a historical context as this gives you the most realistic guide to what is normal and what isn’t.

Secondly, looking at the trend line you will notice that, if anything, it has dipped a little. It’s probably nothing to be worried about.  But if, for example, the line represented a customer service rating and, despite months of internal training, around one in ten of your customers were still leaving feeling that they had got substandard service.  Wouldn’t that be a concern to you?

Context Two – data compared

In our second example on the right (click to enlarge), there is now a second line of data about a different service.  This was rated at the same time as the first service and by the same respondents.

Firstly, it should be noted that these lines are not moving in lockstep (they actually have a correlation coefficient of around 0.25 indicating a practically non-existent relationship).  The trend lines further indicate that the levels of satisfaction are moving in opposite directions and so we have a clear indication that, despite the high ratings for both lines, the responses are nevertheless suggesting that there are differing levels of satisfaction with them.

Now we’re starting to get towards something useful.  We can start to ask what is going on to make people less satisfied with service A than service B over time.  It is even possible to start to test operational changes to look for a positive uplift.

You MUST be happy!

The context in which the customer feedback was taken can also affect satisfaction levels (although the data I’ve worked with suggests that aggregated satisfaction levels tend to be quite similar).  For example, I analysed the results of feedback from one destination where the respondents were required to hand the completed score cards  straight to the accommodation provider collecting the forms. Unsurprisingly, 85% of people claimed to be elated by their recent accommodation and 5% dared to only be satisfied.  In a context where the data was collected more anonymously, this split would probably be something more like 55% and 35%.  In both cases, we could argue that 90% were satisfied although the second example is probably closer into the truth.

In a situation where you do have frequency data for all the scores (ie counts of how many excellents compared to how many satisfieds), it is worth looking at it in some more detail to get sense of how sentiment is shifting.

For example, are there more ‘good’ than ‘excellent’ scores?  for most items but for a few that situation is reversed?  This might indicate that while 90% are satisfied (’satisfied’ being, as noted earlier, the ‘goods’ and ‘excellents’ summed) the balance of satisfaction lies at the lower end than the upper for most items. And that those that buck this trend are worthy of note.

But what is satisfaction anyway?

But there are still important questions floating around in the background here and they probably all flow out of the main one of, “what does ’satisfaction’ mean?”

What I mean by this is satisfaction indicate something good, all right or possibly inadequate.  For example, the data behind the charts above is coded in such a way that the rating delivered by a respondent is give a numeric value (eg bad = 1, adequate = 2 etc).  From this it is possible to calculate that your visitors were 4.2 out of 5  happy this month.   Unfortunately, such an approach can also demonstrate that your visitors were 1.4 out of 3 female, something that is just plain silly.

So, the approach we have taken for the purposes of top line reporting is simply to allocate results to discrete bands – if, for an example, the score is a 1 or 2, then it shows that the customer was dissatisfied, and anything above that suggests satisfaction.  This means that you get an easy overview of the level of satisfaction.

But, you might say, I’m not interested in people being satisfied, I want them to be elated!  A noble goal to be sure but I’m not sure just how elated one can be at the process of buying a coffee or noting that the toilets were clean. There are some things that just don’t excite people that much to cause them to rate them highly in feedback forms!  They’re only notable when they go wrong!

But I guess that this is all really confirming something we’re said in the past – if you are measurng customer satisfaction but only skimming the data then you are potentially wasting your time.  Only through a more indepth and intelligent use of it can start to yield up the nuggets useful for your business.

Filed by Stephen (07/04/09)

Optimizing business, not just websites – music to my ears

The eMetrics Summit is always a chance to learn directly from the best web analysts and emarketers in the world. But at this week’s Toronto eMetrics I really felt Travelocity took it to another level. Shankar Mishra, Travelocity’s Director of Enterprise Business Intelligence presented on developing an enterprise web analytics strategy.  Not reporting.  Not doing cool stuff because it is interesting.  But building a framework that relates all web metrics to business outcomes.

A framework prompted, he reckons, by a question from Travelocity’s Chief Financial Officer John Mills, of: “Where is all the money you claim to be generating?”

Travelocity and the brands they own – such as Lastminute.com and World Choice Holidays – are naturally sitting on vast quantities of data.  The web is their business, so there is a critical imperative that they are continually optimizing not just websites, but web businesses.

Shankar expressed a refreshingly strategic view of the importance of true analysis, not simply data and measurement.  Too often organizations struggle to simply measure what they have, reporting on what their tool has to offer them, not on the business essentials.  He explains:

“You need to come up with independent metrics based on the business objectives and outcomes, not the tool’s data….and a framework which relates all web metrics to business outcomes”

“Socialization” of business critical insight

Travelocity’s focus is not on what can be done with the data they have, but what they want out of that data.  The framing of the right questions, focus and clarity of goals, strategic optimization of the business – not simply the website.

I have always felt that web analytics is a subset of a wider intelligence strategy – which is perhaps why I found Shankar’s session so valuable.  My own presentation in Toronto was about moving from clues in web analytics data, through to surveying and conducting user testing with real customers, in order to understand motive and real context.

But context, insight and causality are not sitting within your analytics tool – they’re with the people inside the organisation and the cutomers you engage with.  Why Shankar blew my mind so thoroughly is he presented an enterpise level analytics strategy that factors in human nature and politics, as well as actionable measurement.

They go through a circular analytics process whereby they:

• monitor
• analyze
• prescribe
• act

It is the prescribe stage that leaps out for me.  It includes the usual testing and prototyping – but it is the “socialization” aspect that in my mind is the critical one.  It is the step almost invariably missing – the people bit.

Through “socialization” – or “the people bit” if socialization sounds a little too George Orwell for you -  they know what is and isn’t compelling to the internal folks that are affected.  They create checkpoints of who needs to be convinced and what people will really find useful.

“don’t even think about data – just figure out the question. Then start talking to people who are stakeholders, the people down the line who’ll be impacted”

Before they think about data, they examine needs, usefulness and what compromises may be required to achieve traction.  This isn’t proclaimed from above (or as is even more common, unsuccessfully attempted from the bottom up) – socialization appears to be an essential consensus building process for the success of their strategic analytics.

This is a lesson that I believe businesses of all sizes can take on board.  People typically take actions based on what other people tell them about data – not because you have the best tool set on the planet.  People take actions because other analysts and marketers tell them stories about data – in language they understand.  And because those stories have a meaning that resonates to their specific role, they will be invested in making decisions that ultimately improve the business.

Too often we look at what is interesting, or we report about web data in vocabulary that means something to us – not to finance or operations.  Shankar makes the point at the heart of their socialization process, which is that “it has to be compelling to someone else as well – and it will be more compelling if the person impacted has been involved.”

I thank Jim Sterne and the eMetrics team for an excellent conference – and I also thank Shankar at Travelocity.  It’s a great return on your time and energy to have your brain so thoroughly stimulated!  Roll on eMetrics San Jose when we get to wrestle with “how to analyse” – because, you know what, I just happen to have a few theories on that

Post by Vicky

What I took from ITB Berlin Day 2 is that systematic testing and analytic pays.  Haven’t we mentioned that before on the odd occasion?

Testing pay as you go mobile ticketing

It was fascinating to learn about dBahn’s – Germany’s national rail provider – development and testing of  mobile ticketing.  They already have electronic ticketing for book ‘before you travel’ journeys.  Travellers can receive their ticket in the Form of a 2-D-Barcode via MMS (Multi Media Message). The code is scanned like an online-ticket from the telephone screen by the conductor (read more).

But dBahn are also running a highly sophisticated test of pay as you go mobile ticketing, utilising the phone as a wallet.  The ticket and payment is entirely integrated into NFC enabled mobile phones (NFC is a new, short-range wireless connectivity technology – more here).  Users get a monthly bill for travel as with their phone bill.

dBahn have integrated all public transportation between Hannover and Berlin and are using real paying customers to test the service.  If it works then in 2011/2012 it will be rolled out across Germany.  If it doesn’t – or if NFC phone adoption does not reach critical mass – they will “review strategy”, potentially walking away.  Now that’s an efficient test.

Site optimization…. more than middle or side, green or blue

One of the business case examples that most impressed me was Mr & Mrs Smith, the boutique hotel specialists focussing on romantic getaways for couples. (Check out their blog and see if you can resist spending!)

Utterly focussed on their specific target market, meticulous in understanding that market and their needs, testing and analysis seems like the oxygen their business breathes.

They are using multi-variant testing and high end web analytics (Omniture) to test and retest the critical elements of their site.  This is to ensure that every part of the site – from forms, to descriptors – are converting into business at the highest possible rate.  This is a serious approach to online optimisation – something that I fear the industry generally can often lack the knowledge, or perhaps confidence/skills to really attempt.

Every aspect of their marketing campaign activity is measured and its performance judged carefully according to tangible conversion factors such as new membership and revenue per member.  The information gleaned informs their subsequent actions and means that as a relatively small business, they can be as lean and profitable as possible.

Tamara and James, the driving force behind Mr & Mrs Smith, modestly reflected that there has been a huge amount of learning on the job, particularly in terms of developing and bringing in-house the skills they required.  But I strongly believe (well I would, wouldn’t I??) that their efforts in this area demonstrate conclusively how using data intelligently can establish a business as a real market ‘player’ and have a distinct advantage in these difficult economic times. 

And on the subject of measuring Twitter…

PhoCusWright at ITB was twittered with great aplomb (with the bloggers to thank for that I think). In fact, Twitter was used so heavily during the event to share comments and ask questions of the panels that the hashtag #ITB09 ranked as high as the 5th hottest Twitter topic of the day.  Lots of hype for the tool of the moment.

But – are your Twitterings generating results or wasting time?  Are you influencing or invisible? Well at last you can find out.

Eric Peterson has come up with a great tool  – Twitalyzer – specifically for Tracking Influence and Measuring Success in Twitter.  You can even combine your own exported data from Google Analytics with Twitalyzer.   Twitter addicts and sceptics alike should check out the Twitalyzer blog – you’ll be able to judge whether its worth your business’ attention based on hard evidence!

So what might PhoCusWright at ITB in 2010 bring?

Well I hope it will bring a lot more tangible examples like this.  Businesses using tools and technologies – not for technologies sake and not because of the hype – but to systematically improve customer experience and business profitability.  Those firms that will best emerge from these challenging conditions are those who know where to cut and where to spend – and that requires data and smart analysis.

Posted by Vicky

Analytics outlook report shows its not all doom and gloom

The Web Analytics Association released their Outlook 2009 Survey Report this week.

As a Board Director of the WAA,  I was one of the people who presented the research findings to members in a webcast.  And I was struck by just how much relevance the findings have to anyone marketing a business or organisation online, in particular to some of the travel and tourism marketing questions we have discussed here at Tracking Tourism.

So while the full research and podcast is available to WAA members only (one of many good reasons to join!) I thought I would share some of the report’s findings.

WAA members can sign-in to access the research and download podcast, or if you’re not a member you can find out more about the Web Analytics Association here.

My pick of the research findings

A total of 653 web analytics users (from online marketers and finance  to business intelligence analysts and business owners) from around the world participated in the WAA survey. Respondents were asked questions that included their use of web analytics today, their planned usage in 2009 and when they envisaged their 2009 investments to be made.

1. Measuring what matters to the business bottom line

The WAA found that making sure business decisions are driven by analytics will be the big motivator in 2009.  They explain “It was also a top focus in 2008, but there’s a significant increase in the number of organizations keeping this top of mind in the next year. Clearly, when budgets are tight, demonstrating ROI is going to be key.”

They also suggest that this significantly increased focus on supporting business decisions  is because organizations are more aware of the vital role web analytics plays in demonstrating the effectiveness of marketing campaigns.

Personally, I think there is also the factor that individuals, teams and entire business units are sharply aware of the need to demonstrate how they contribute to the business bottom line – and are not by implication in any way expendable.

2. Investments in people above technology

A symbol, perhaps, of the maturing online marketing environment – 2009 is the year when people say they will be investing more in people and their training than they will in the purchase of new tools and technology.  The report states that “Training will get the biggest share of budget for over 43% of organizations.”

Hooray!  I have never seen the point in investing in analytics technology, then expecting untrained, inexperienced staff to deliver earth shattering insight from it.  It doesn’t work that way.

Even for businesses that are forced into reducing their staff headcount, it is critical that those who remain are equipped with the skills they need to contribute positively to the bottom line.

3. Online marketing and analytics spend is not in total freefall

At least that’s what the online marketing professionals say.  The research seems to back it up – according to an Epsilon CMO survey, 63% of senior marketing executives intend to increase interactive and online marketing in 2009, 23% expect it to stay the same. Only 14% say it will decrease.

The WAA report a similarly optimistic picture: “Last year, nearly 69% of survey respondents said they would be increasing their investment in web analytics. This year, that number has gone down to 52.1%….Only 4% will be decreasing them in the next year.

4. Video, mobile and consumer generated content are where we’re focussing

As travel and tourism marketers know full well, video and user generated content is where its at – and mobile is finally looming large on the radar.  The WAA report that “Video is strong and will only get stronger, but the biggest growth will be in measuring KPIs for Mobile Media, which will more than double”

Think of us web analysts as canaries in the coal mine – we have to sniff out how to measure the stuff that marketers want to spend on.  So where there’s interest in measurement, there’s money to be spent close behind.  And it seems budget will be flowing to video and mobile.

This is just my pick of the WAA Outlook 2009 report findings that I think particularly relevant to travel and destination marketers.

The full research, podcast, slides and much much more is avalable to WAA members.  Visit www.webanalyticsassociation.org for more information.

Post by Vicky

Just when we were getting to grips with Web 2.0 and Travel 2.0 and Tourism 2.0 and Kitchen Sink 2.0, there has been looming on the horizon the possibility of Web/Travel/Tourism 3.0.

But what does this mean?

Have a bunch of under-employed bloggers just got a little over-excited and made up a term that has no relevance to the rest of us but makes them look clever?  Or does it actually signify something that will have an impact on the way we do business?

Well, I think it’s a little of both – certainly at this stage.  The ambitions stated for Web 3.0 projects will have an impact on day to day life once realised but I think we’re not close to mass deployment yet so there is no need to start panicking.  However, I thought I would dedicate this week’s post to looking at some of the basic questions surrounding this potential change, starting with the two most fundamental ones, “What is 3.0″ and “So what?”

What is 3.0?

As is often the case, it’s possible to start with a Wikipedia definition of 3.0 which states:

“Web 3.0 is one of the terms used to describe the evolutionary stage of the Web that follows Web 2.0. Given that technical and social possibilities identified in this latter term are yet to be fully realised the nature of defining Web 3.0 is highly speculative. In general it refers to aspects of the internet which, though potentially possible, are not technically or practically feasible at this time.”

Which isn’t really that helpful.

However, the rest of the article goes into some more detail and, overall, the impression is that the ambition of Web 3.0 is to create an internet that is simply with fewer boundaries than we (often unconsciously) experience at the moment.  And while these ideas are mainly being considered at a technical level that baffles the rest of us, there are indicators of what this might eventually mean for how we interact.

The semantic web

For example, commenting on a recent TrackingTourism post, Phil Caines of Tourism Tide said

“As far as where we can look for the next ‘wow’ change, I can only guess, but if you asked Joe Buhler, he would undoubtedly say “The semantic web of course!’, and I think he is right.”

The Semantic Web is a key part of 3.0 ambitions.  Put simply, it is a development that would enable web sites to be able to understand the relationship between things.

Let me unpack that last paragraph a little.  At the moment, web sites can be seen a bit like an encyclopaedia.  For example, there might have entries on separate sites with the following information:

• Boston is in Massachusetts
• MIT is in Cambridge, over the river from Boston
• MIT undertakes work in Biotechnology

As a human, you understand that there is something linking these statements but a computer doesn’t.  So the aim of the semantic web is to enable computers ‘intuitively’ to understand that these three statements are linked. Simple, eh?

So what?

Ignoring the technical practicalities of this, you’re probably asking the question, ’so what?’ by now.  To my mind, this kind of advance has the potential to make the internet ‘blend together’ in a far more efficient way than it does at present.

So, it could be used, for example,  to develop sites that are able to offer best travel packages based on the question, “I live in Boston but I want to watch Manchester United at home some time in October, staying in a budget hotel with easy access to public transport.  What are my best options and when is the best time for me to go?”   This is not an impossible question to answer at the moment but you will probably need to go to 3+ sites to even start to work out an answer.

On the other hand, the semantic web should make that question a lot easier to answer.  All the separate elements of the question (Manchester United playing times, flight times, lodging info etc) would be understood seamlessly and then used to deliver a swift, comprehensive answer.

Another example of how the Semantic Web could be used in marketing is contained in the following article: What the Semantic Web — or Web 3.0 — Can Do for Marketers.

Mobiles and ubiquitous connectivity

As I mentioned earlier, Web 3.0 also seems to imply an internet that is simply more ubiquitous and less bound than at present.  This means, for example,

• The continued march of the internet onto mobiles as well as the simultaneous blurring of the boundaries between those mobiles and computers;
• The rise of ubiquitous computing where connectivity is as common as the air you breathe (see this recent MIT article on the possibility of receiving wireless as you drive for example).

But what does this all mean to travel and tourism?

That’s a difficult question to answer (but one that will seem frustratingly easy in hindsight).  In some ways, the answer could be something as simple as , ‘what we’re doing now – but a lot better’ but that ignores the possibility of developments as revolutionary as Tripadvisor and Facebook have been in the last five years.

So, dipping our toe in the quagmire of prediction, our guess is that the web as an experience will become more of a hive than a collection of isolated websites.  What I mean by this is that one site will have the the potential to blur with another and so the web will be more of a collective than previously. If you cast your mind back to an interview we conducted with travel futurologist Ian Yeoman, one of the main points made was that:

“The traveller will want more in less time or with less effort – this has implications for everything from the format of events through to booking processes and the nature of breaks.”

And, in this context, consumer demand will dictate that they want more efficient access to information than they currently get. In other words, if there are still pain-points involved in reaching your data, then customers will be less inclined to pursue your offering to the point of booking when there are easier alternatives.

Another implication is that sites will need to ‘tagged’ effectively in terms that other sites and, more importantly, customers understand. Perhaps the implication is that we are moving from ’search engine optimisation’ to simple ’search optimisation.’

But the future is still hazy so I throw the floor open to the hive mind of our readers and conclude by asking, “What do YOU think 3.0 will mean and what might it look like for travel?”

Filed by Stephen (11/12/08)

Using Google Insight for tourism and travel research

I recently wrote a post on using Google Trends for tourism and travel insight and this post expands on some of those themes by talking about the Google Insight product.

Before I do so, it might be as well to remind ourselves of the difference between Google Trends and Google Insight.  As I noted in the previous post, “…Google Trends shows data relating to traffic to websites while Google Insight shows data related to search terms.”

So, in a nutshell, Google Insight offers a great way of understanding how people search for particular terms and, more importantly,  the contexts in which they do it.  For a tourism destination, for example, this means that it is possible to judge where your destination lies in comparison to competitor destinations and whether there are opportunities to broaden your market offering.  For a specific tourism or travel business, you can capitalise on the fact that brand names are increasing dominating searches in order to see where you stack up against competitor businesses.

It’s probably best to illustrate this with a concrete example and for this I’m going to look at some tourism businesses in Aviemore, a destination that offers year round outdoor activities close to where I live.  I’m going to concentrate on two businesses – the Aviemore Highland Resort and the Hilton Aviemore. I have selected these two simply because they are both large hotels, they both cater for a similar clientèle and they both undertake marketing expenditure.

Inputting the brand search terms ‘aviemore highland resort’ and ‘hilton aviemore’ brings up results that look like this (or click on the image below for a larger version).  I’ve applied filters to the results so that I receive data based on the relative popularity of the two search terms from people within the UK in the period Jan 07 through to October 08.

You can see from this graph that the two hotels pretty much shadowed each other up until about July this year when the Aviemore Highland Resort started to drift away downwards from the Aviemore Hilton.  Now, there have been periods of divergence before but this recent period strikes me as being longer lasting and deeper than previous splits so, if I were Aviemore Highland Resort, I would now have concrete proof that for some reason, I was no longer making as big an impact when compared to my close rivals. As such, I would either know why (eg marketing budgets might have been changed) or I would be starting to ask serious questions to find out why.

Move from assumptions to proof

But now let’s introduce another search term into the mix to get an idea of whether it’s more a case that the Hilton is performing exceptionally rather than the Highland Resort performing poorly.

In this example, I’ve introduced the term ‘Aviemore Hotels’ as my benchmark term.  Whereas the previous terms are brand terms – and likely used for navigational search by people who are already aware the establishments exist,  ‘Aviemore Hotels’ is a more open search term that requires no knowledge of existing brands in the area.  Therefore it is more of a general benchmark indicator of the broader level of interest in hotels in the area. The result is shown in the graph below (click for a larger version or visit Google Insight here).

One thing that you should notice quickly is that the Hilton seems to trend more closely with the ‘Aviemore Hotels’ line than the Aviemore Highland resort does.  Indeed, the raw data enables us to determine that there is a stronger statistically provable correlation between ‘Aviemore Hilton’ and ‘Aviemore Hotels’ than between ‘Aviemore Highland Resort’ and ‘Aviemore Hotels’. In other words, the Hilton is performing in line with the market and the Highland resort less so.

Incidentally, even if we take the figures for Aviemore Highland Resort in isolation, using the raw data (available if you have a Google Account), we can see that the term ‘aviemore highland resort’ is now performing outside of control limits (defined as standard deviation x 3 – see more here about control limits) as shown in the graph below.

As virtually all web sites have cycles, we should expect to see some changes throughout the year but this suggests that the current change lies outside of what might be expected within these cycles:

So what does this mean?

For the Aviemore Highland Resort it means something may be wrong, beyond the level of a mere seasonal wobble.

My first actions would be to look at spend, bookings and occupancy data to see if there has been a corresponding drop in revenues.  (Afterall we are just talking about search activity here!)

I would look in depth at web traffic and conversions to identify which visitor segments and traffic sources I have lost search activity and potential business from.    I would also look closely at marketing activity and assess whether a drop in advertising spend has lead to this drop in search volume – and whether there is a cost effective way of rectifying that.  Afterall, it is common for people to respond to TV and other forms of offline activity by going online and searching on the brand name.  Is this what is occurring here – and does it even matter to the bottom line?  I’d want to know.

And if I were the Hilton Aviemore?  Well, I be heading off to Google Trends and comparing our overall website traffic for clues.  I’d be looking at my revenue and web analytics data to see if I was benefiting from this displaced search activity – and whether I was converting it into revenue.  And I would bullishly be looking at what I was doing right and be tempted to invest in doing more of the same.

So, Google Insight – used wisely – has the power to act as warning device for your business.  It’s free (and this article has only really touched on a small number of its features), so can you afford to ignore it?

Filed by Stephen (03/12/08)

Google’s business model is simple. It wants you to spend your money wisely on Google business products and, to help you achieve those ends, there are tools to make your spending decisions more informed.

Looked at from another angle, they offer a bunch of tools that you and I can use free of charge.

This post forms part of a series over the next few weeks that will show you how to make the most of tools like this – as well asking some more probing questions about how far they can really help you.

This post originated in a question I asked myself recently, “what exactly does the tool data in Google Trends and Google Insight show and what has this got to do with travel and tourism?”

At a top level, the answer is quite simple. Google Trends shows data relating to traffic to websites while Google Insight shows data related to search terms. However, what they have the potential to give you is considerable and so for this post, I’ll talk just about Google Trends, followed in the future by Google Insights and then finally a post dealing with some more ‘philosophical’ questions these tools have thrown up.

What is Google Trends showing and why is it useful?

OK, let’s start with Google Trends. If you click here, you’ll open up a new window with Google Trend data for visitscotland.com. At this point, you’ll see a graph showing daily unique visitors to the visitscotland.com site over a period of about 2 years. You’ll also see a bunch of data below it. Let’s look at those two elements in turn.

Before I get going though, I would like to stress that I’m using visitscotland.com here as an example only. The point of this is to look at data for your own site (assuming you have sufficient traffic) and to use the techniques contained in this post.

The graph shows a representation of the number of times visitscotland.com has been called up via Google. Note that this is not searches for visitscotland.com in a search box but rather the number of times someone has visited the site and Google has been in a position to capture that data (with some caveats).

Now, this graph can show a lot more but I want to mention the lower half of the screen before getting into that as it is where the data starts to get really interesting.

On the left, you get an indication of where the visitors to visitscotland.com and coming from. In other words, you can see by geography where the warmest prospects are.

In the middle, you can see which other sites were also visited alongside visitscotland.com. In our example, you can see sites ranked that you might expect to see – and depending on your perspective, this might be comforting or unsettling. For example, if you saw visitireland.com as the most visited other site, you would know that there was a real fight at this level to attract visitors who were torn between destinations.

And on the right hand side, you see the search terms that are most often associated with that site. Again, this might be revealing or comforting. For example, if you run a website for a DMO in a whisky distillery town and people find you only by the brandname of your whisky and not under something more generic like ‘whisky tourism scotland’, then this would be a sign that your site isn’t attracting as many visitors as it could.

But the fun really starts because you can start to compare sites.

Let’s demonstrate this by taking our example above and adding a few more sites – visitbritain.com and visitsweden.com. It should now look like this.

Let’s start with the graph. It shows that visitscotland.com attracts more visitors than visitbritain.com or visitsweden.com. It also shows that visitscotland has different peaks and troughs to the other sites at a global level (predominantly the effect of Hogmanay I would guess).

In the bottom half of the screen, you’ll see that you can segment this data by region and by website. You’ll notice that under the ‘ranked by’ tab, you’ll see how each geographic area performs for each of these sites. You’ll notice in our example how Scotland and Sweden are broadly similar in terms of interest in Germany. If, in the upper right of the screen, you use the drop-down box to change ‘all regions’ to ‘Germany’, you should see something like this.

So what’s this saying? It’s saying that, in this instance, people in Germany have show a greater propensity to visit the visitscotland.com site at a different time to the visitsweden site. That might be on account of a campaign by visitscotland in Germany…or it might just show a different ‘natural’ search pattern (and I’ll show you in a coming post how you can go about finding that out). If we assume on this occasion that German’s simply are more interested in visitscotland.com at the periods suggested, wouldn’t it make sense to have the website ready to react to this niche interest at the time? The data suggests that it might be wrong to assume that people think of destinations in a uniform way and that you need to be ready to respond to the customer when they actually come calling, not when you think they ought to be calling.

Conversely, if the spike was the result of an advertising campaign, this gives an indication of how long its effect lasted and how big it was in comparison to the spike caused by possible competitor marketing.

(I’ll hasten to add, I’m not passing judgment on visitscotland.com but just using them as an example – for all I know they might well be doing all this already!)

What I’ve described rather quickly in this post is one, powerful view that the travel and tourism industry can use to get a deeper understanding of how it sits in the online world. But, as is often the case, you need to look at other areas in order to build upper a more mature understanding and so this represents just one part of the picture. In the coming weeks, we’ll develop this theme further with more tips on these free tools.

See post 2 in this series – Warning bells you can’t afford to ignore: courtesy of Google Insights

Further reading:

Competitive Intelligence Analysis: Google Trends for Websites