1. KISS – Keep it simple…

If you’re running surveys on the site itself then aim for maximum simplicity. This is really help with your response rates and also help with the quality of the data your collect. Try and be as efficient as possible in your data collection and always ask yourself when your designing the questionnaire what your will do with the data that you’re collecting. Do you really need to know?

Think about the effort involved in answering your surveys. Asking people to write in answers to open-ended questions can give you great data but also it can put people off. This questions should be optional and try not to ask too many.

2. Make it engaging

Try and make the survey as engaging as possible. You don’t have to have flashing lights and bells but think about how it looks visually, think about how it reads and think about whether it’s interesting. If it isn’t interesting and relevant then people won’t fill it in. So don’t focus just on the data you want to collect but also think about the experience of filling in the service itself. Is it something you would want to do?

3. Don’t ask everyone

Typically don’t invite everyone who visits the site to take part in your survey. Most survey technologies allow you to select a sampling rate which determines how often the survey invitation will appear. With surveys you need a certain amount of data for the responses to be reliable. I usually aim for a minimum of 500 respondents for the amount of data that I want to analyse. In you want 500 respondents you will need to figure out how many people you will need to ask to get that many responses and then compare that against the number of people that visit the websiste to get your sampling g rate. It will take some trial and error to start off with.

4. Test, test, test

Always test your survey before going live. There is nothing worse than finding out afterwards that something didn’t work properly or you weren’t getting the kind of data that you hoped. By then it’s too late. You can test it by doing yourself, you can test it by getting colleagues to fill it in and you can test it by putting it live with a very low sampling rate. Look at the data you get back. Does it make sense? If you’ve put some routing in where only certain types of people answer particular questions, make sure that’s working in way you expected. Once you’re happy, then put it live or increase the sampling rate.

5. Be prepared for feedback!

You’re asking for feedback and you will get it but maybe not in the form that you expected. Customers can see surveys as an opportunity to communicate rather than just to give feedback. Often if you have questions in your survey where respondents can write something down (such as “Can you tell us how we can improve the experience you had today?”), then they will tell you. That might involve a customer service issue, such as they tried to make a booking and failed and they want you to call them back.

Many a time I’ve seen those sorts of comments from surveys where the respondent leaves their name, address, telephone details etc expecting the company to get back in touch with them. If nothing happens, that’s not good so you should have a system that means you are regularly looking out for that kind of customer service issue.

6. Analyse wisely

Survey data is great but it needs to be handled with care. All data has bias and error in it and survey data is no exception. Certain types of people are more likely to fill in a survey than others and so you need to take that into account when you’re looking at the results. Wherever possible compare and contract your results either over time or amongst different segments such as by gender, age, frequency of use and so on.

Surveys are a great source of insight, but make sure that you make the most of them by getting good quality inputs.

Last time I looked at some of the characteristics of data collected from surveys, particularly data collected from surveys run on websites where you have no control on who is answering the survey. Generally this lack of control can cause some bias in the data which can cause some issues if you are looking at the aggregated reports. For example the data on the profile of visitors (i.e. gender, age etc) that you collected from survey data may not actually reflect the true profile of visitors to your site because of the different propensities of different groups to respond to surveys. So, does that mean that survey data is useless? Not really but it does means that it needs to be handled with a bit of caution.

One way to reduce the impact potential biases in the data is to trend the results over time. I’m always think that survey data is most useful when you have it running continuously anyway as it means that you have a constant monitor of the health of the site and you can refer to it to assess the effect of all sorts of marketing and product development activity. Having a continuous dataset also helps to reduce some of the bias. Say for example, that your survey shows that the age profile of visitors to your website is 40% under 35 and 60% over 35. We know that generally younger people are less responsive to surveys than older people and so we might suspect that there is a bias in the data towards older people. If however, 6 months later you look at the data and it shows that the profile has changed and that it is now 60% under 35 and 40% over 35 then, all other things being equal, whilst we still can’t be sure that the profile is absolutely correct, we can be reasonably confident that there has been a change in the profile over time and that the profile has got older. If we wanted to we could also check whether the change had been statistically significant or not.

Another way of reducing bias in your data is to segment your data. In fact I would say that you absolutely have to segment your data to make it useful and to understand it properly. So whilst I might not be confident that the profile data is properly representative of the reality, I can still use the profile data to look for differences in some of my key metrics such as customer satisfaction or the Net Promoter Score (NPS). I can compare satisfactions scores amongst the younger age groups and the older age groups to see if there are any significant differences and because there often are, I should always be looking at these key metrics amongst key segments of the site’s visitors. This is because changes in the visitor profile of the site can have a significant impact on the changes in these key metrics. Let me give you an example.

As I mentioned last time you can see differences in metrics like satisfaction score or NPS amongst different segments depending on their familiarity with the site or the brand. Often people who are visiting your website for the first time will have lower scores for satisfaction and NPS than those who have visited before. Let’s assume that you have been running some campaigns either online or offline and have driven a significant amount of new traffic to the site. The survey you’re running on the site will probably reflect the increase in new visitors and as a result it’s possible that the overall satisfaction score will go down. This not because people are overall less satisfied with the site experience but because you have a greater proportion of people answering the survey (i.e. first time visitors) who generally tend to give lowers scores. Nothing may have actually changed in the site experience itself, the only change has been in the mix of visitors to the site. In fact, the satisfaction amongst first time visitors can have stayed the same and the satisfaction amongst repeat visitors also can also have stayed the same but apparently overall satisfaction can appear to have gone down.

So, on the face of it online survey based data looks to have some serious issues with it. However, by understanding the source of these issues and interpreting the data wisely can ensure that you can get some real value from this rich source of customer insight. And remember…segment, segment, segment!

Having looked at some of the fundamentals around web analytics metrics over the past few weeks, this time I turn my attention to survey based data and metrics. Over the past couple of years there has been a growth in the number of organisations that are running on-site survey based voice of the customer programmes. These might range from simple “do it yourself” approaches using free or low cost survey software, to a packaged tool like 4Q or more sophisticated programmes like ForSee Results or iPercpetions. As with all things, you pay your money and you make your choices.

This growth in the use of survey data and other customer insight tools is great to see. I always say that web analytics can tell you what happened and when, but rarely tells you who or why. That’s where tools like surveys come in. They give you a different perspective to what’s happening or not happening. But like with your web analytics data, it’s important to understand the fundamentals of where the data comes from and what that means in terms of how to use it and interpret it.

Most online customer insight is captured on the site; either through a site intercept survey or using a page level feedback mechanism. Site intercept surveys usually offer an invitation to a sample of the site’s visitors to take part in the survey and then the survey is completed at the end of the visit. So, you generally have no control over who or which type of people are offered the invitation to take part in the survey and you have no control of which people actually decide to complete the survey. This means that the survey data will generally have a bias in it but you also don’t necessarily know what that bias is unless you have another source of data to compare it with. If you find from your survey that 40% of the respondents are male and 60% of the respondents are female, that doesn’t mean that 60% of your site’s visitors are female. What is literally means is that 60% of the people who answered your survey claimed to be female. It’s possible that in this instance, the true proportion women visiting your site is actually nearer 50% but that women had a higher propensity to answer the survey than men did, making it look like there were more women than men. Unless you have another source of data, like from an audience measurement panel, then it’s going to be difficult to know whether the profile of people in your survey is representative of your website’s visitors or not.

Typically there are some general biases that you might see in site-intercept surveys. One that we often see is that people who generally know you are more likely to respond to your survey than people who don’t. This can manifest itself in many ways. They are more likely to have transacted or interacted with you, they are more likely to be customers and they are more likely to be the more frequent users of your website. Quite often you might ask a question about how often the respondent has visited the website in the past. If you look at the survey data and compare to your web analytics data (and there are issues over both sets of data) then typically you will find that you have a greater proportion of people who are repeat visitors to the website in your survey data than is recorded in your web analytics data. Typically there are also usually demographic biases as well. Men are generally less likely to answer surveys (though there are expectations depending on the subject matter) and generally the younger generations are harder to get feedback from than the older generations. So, your respondent sample is often underrepresented by young male. If this is a core audience for you to understand, then this is something you need to be aware of.

So, with all these potential issues, does that mean survey data is rubbish as people often claim, particularly when they don’t like the results? Well, no it doesn’t, but it does mean that you need to treat it carefully, to be aware of some of the potential biases that may exist and what the impact of those biases is on the metrics that you are reporting. In general to take aggregated survey results at a point in time at face value can be a bit dangerous. You need to find strategies to try and overcome these biases and you also need to understand the impact of these biases on some of the key metrics that we use survey data to report on such as customer satisfaction and Net Promoter Scores. This is what I’ll be looking at next time. Till then…

When the graphs are going up and to the right, people are generally happy. Life feels fine when there’s good growth in the right kind of metrics and despite the tough economic trading conditions over the past 18 months, the digital economy has generally been doing OK. Here in the UK spending on online advertising grew by just under 5% in 2009 compared to 2008 with spend on Search up by just under 10%. Whilst that isn’t the kind of stellar growth seen in previous years, the indicators are positive. Business confidence amongst advertisers is higher than it’s been previously and we’re continuing to see growth in the active online audience (up 10% vs. a year ago), some of that being driven by the growth in population of social networking sites such as Facebook.

But is “good” good enough? We all know that graphs that go down and to the right are generally bad news, when is good news really good news? Trends in looked in isolation are useful (after all “a trend is a friend”) but they’re much more useful when you have some context to compare them against. Most of us operate in a competitive environment and so it’s useful to know how we compare against our competitors, so we can put that trend into context. Whilst we generally have relatively easy access to our own data, getting data on competitors can be a bit tougher. The availability of that data is also very dependent on the type of industry you are in, the market that you are in, the size of your own digital property and the size of your competitors. So it can be difficult to generalise about how to get competitive benchmarking data but here potentially are a few avenues worth exploring.

Commercial services
There are a number of commercial services available in most markets around the world that provide data on the traffic and customer profile of different websites. Companies such as ComScore and Nielsen NetRatings create panels of users and monitor their internet behaviour. Their reports provide detailed information on a site’s traffic patterns and visitor profiles so it’s possible for example to compare say the reach and frequency of visitors to your site against those of your competitors. Some companies, such as Compete and Quantcast, use hybrid methods to collect their data and estimate a site’s traffic. Hitwise aggregates data from ISPs to build up a profile of the market and can use that data to provide raking reports on traffic to different websites in your category.

The commercial model varies from each provider of these services. However the good news is that in some cases you can get access to some limited data for free. However, there are also a number of other free services which may be useful.

Free services
Data on the internet is being aggregated all the time, all over the place and in some cases you can hands on it for free. Tools such as Google Trends for Websites, Google AdPlanner, Alexa and others all enable you access reports on website traffic and visitor profiles on your competitors and compare them to yours. The way that the data is collected varies from service to service and it’s worth taking the time out to understand (as much as you can) how the data is collected and how the reports are created. You need to be aware of the potential limitations of these types of services particularly if you are dealing with websites with relatively low traffic volumes. Caveat Emptor! – even if it’s free.

In some cases you may be also get some benchmarking data as part of the service offered by your web analytics vendor. In this cases you won’t be able to see your competitors data in detail but you will be able to compare your own performance against a group of similar websites to your own.

Do it yourself
In the examples above the data that you can get is mainly looking at traffic volumes, visitor profiles, search behaviour and the like. You can also go out and collect your own information on your competitors. You can use additional research techniques such as surveys, focus groups and usability testing to find out how you compare against your competitors in different ways. It won’t be the quantitative data that we’ve already looked at but it will be customers’ opinions about how you compare and can be enormously insightful in helping you to evaluate your competitive position. Again this type of research needn’t cost the earth. You can get benchmarking data from customer surveys like 4Q and low cost remote testing methodologies are being developed all the time.

As we come out of the recession and the market begins to accelerate, it’s always good to ask yourself whether good is good enough. You can do that by keeping an eye on what’s going on around you. How you do that will depend on your circumstance but hopefully there’s a few ideas here.

Not only will they extend your reach and influence geographically, but they also expand the size of your potential samples, while reducing costs considerably compared to securing a similar scope of respondents locally. Surveys do have obvious limitations against one-to-one qualitative studies, but with skilled questioning and efficient organisation, excellent results can still be gained for far lower overheads.

Of course there are also numerous challenges involved, not least the logistics of recruiting a panel provider that can operate successfully in the desired region. In most countries this isn’t a problem, but in some it does tend to take a little more time and effort – notably Indonesia, Kenya and strangely enough, Foviance’s latest locality, Singapore. Determining and recruiting the right type of respondent can be tricky. A particular type of user or segment will have differing criteria and often, screening questions must be country-specific. When recruiting segments defined by the client, screening questions should be adapted to suit the differences between countries.

Is it possible to recruit an English-speaking panel? In some countries such as USA and Australia, there shouldn’t be much of a problem, but what about countries like France? It’s certainly possible to define an English speaking panel in a non-English speaking country but this has a cost implication as these people are more difficult to recruit. This cost may be offset if there are a number of free-text entry fields as there is no necessity to have comments translated. In addition, there would be no cost for translation both for the survey script and the verbatim comments. In some regions such as India, many different languages and dialects are spoken and English is often the ‘lingua franca’ that allows all the people to have a voice. Of course, there are disadvantages of using English speakers as they would most likely have a high level of education and a higher social economic status. They may also work for a multi-national country and so be more ‘western’, or they may be urbanites (rural communities in many countries may not take part due to the unavailability of a computer).

When it is deemed necessary to use a local language for the survey, original questions must firstly be translated. This may sound easy but it can often prove problematic due to the quality and nuance of the translation. A translator must be trusted to deliver exactly what you need.

More often than not, an English person who is fluent in another language will not be able to conduct a translation in a meaningful way. They may not be aware of the everyday (as opposed to the academic) translations for certain sentences and terms. It can prove to be very off-putting providing opinions to a non-national who seemingly hasn’t bothered to ask you properly. Also, they may not be aware of the translations for technical terms such as ‘battery-life’, jacks, HDMI and so on. Also, from a more practical point of view, translations of an English sentence which fits very well into the survey design will often be too long when translated into a language such as Russian. A conversation with the translator is then required so that a better sentence can be constructed.

Screening by socioeconomic status might simply not be possible in some countries in which those people in the lower categories do not prove literate either in English or in their own language. It is always necessary to consider from the outset whether it is feasible to fulfil certain screening criteria in particular countries of interest. For instance, in areas of India, smartphone and laptop usage will not be prevalent in those aged 55+ but will be more representative (and consequently, more weighted) for those in a younger age bracket e.g. 25-34.

With experience and endeavour, excellent results can be gleaned from international surveys. I usually split survey results in four ways:  by overall results, by region, by age and by gender. I don’t generally drill down deeper than this, by region then by age for instance, unless something really interesting crops up in the data. To give you an example, in one survey I conducted there were some really strange results within the USA. On further inspection, over 70% of responses had come from those aged 65+, providing a very biased view on mobile phone usage. To restore balance we had to re-recruit those in the younger age ranges and weight the responses appropriately so that all ages were represented equally. It just goes to show the trust you have to place in your panel provider.

Organising, conducting and analysing the results from international surveys can be a time-consuming but very enlightening process. It genuinely brings to the fore the often stark differences in attitude, usually between Western and non-Western countries, towards modern devices like smartphones as well as emerging technologies.

This article was written as part of our February newsletter

Originally written by Amanda Roach

This white paper provides insight from over 100 respondents, into the buying habits and behaviours in the run up to the busiest time of year for retailers.  We probed respondents to understand their motivations, the barriers they are confronted with, and examples of best and worst websites that exist.

This paper highlights that customers are already using multiple channels as part of their purchase process dependent on what it is they are buying and the information provided at the various touch points. By enhancing the cross-channel customer experience, retailers can diminish the impact of the main barriers to conversion.

This month’s newsletter combines November and December. In this last newsletter of 2009 we reflect on a difficult but eventful business year.

We saw customer experience play a critical role for many organisations strengthening their digital presence and cross-channel offering in order to provide the best possible levels of service for existing and new customers alike.

Looking back at 2009, Paul assesses how predictable the current landscape was at the outset, and then looks positively towards a tricky but exciting 2010. Meanwhile Amanda shares her insights from a conference on mobile, for those more concerned about sweating the small-screen stuff, and Lis takes a look down both sides of the narrowing divide between mobility and accessibility. Also, I look at the non-rational consumer, a lucrative segment.

Finally, Clare has been analysing our research into online retail habits ahead of the festive shopping season. If you would like a copy of the whitepaper as soon as it’s released, please e-mail info@foviance.com

I hope you have enjoyed your regular newsletters throughout the year. I’d also be very happy to hear from you directly with any feedback.

Marty.

In this issue:

• Looking back on 2009 and forward to 2010
• Researching the mobile experience
• The fine line between mobility and accessibility
• Tapping a lucrative segment: the non-rational consumer

An interesting report was published recently by E-consultancy that gives a useful insight into the state of play of the web analytics industry here in the UK. They surveyed around 700 people from “practitioners”, agencies consultancies and vendors around a number of themes including the use of analytics within organisations, the amount of investment being made and the use that people are making of the data that has been invested in.

When asked how many web analytics tool they were using within their business over 50% of people said they were using two systems or more. Quite often the scenario was that a company was using a paid-for tool and then were using Google Analytics as well. Relatively few organisations were using Google Analytic exclusively. This is a trend that I have observed as well, an organisation has a system from one of the major vendors and then also deploys Google Analytics “to see what it is like” because it’s free. This result throws up some interesting questions like: “is this a good idea or not?”. One the one hand you can argue that since it’s not costing anything, then what’s the problem? Maybe Google Analytics does some things better than the system that you already have in place. On the other hand software like Google Analytics might be free to buy but it’s not free to implement (that takes time and effort) nor is it free to maintain (that takes time and effort too). Given that many organisations find it challenging to properly implement and configure one web analytics tool, does it make sense to try and manage two?

The other thing that struck me about this is that two systems will inevitably been giving different results. So which one do you believe? There’s a saying that a man with two watches can never tell the time. I can understand organisation wanting to try out different tools but at the end of the day I feel its best to stick with one and make sure that it’s giving you what you need.

There was some good news from this report about the adoption of other tools, particularly Voice of the Customer tools. Over 60% of organisations said that they looked at customer survey data. I think that if this survey had been done a couple of years ago the number would have been a lot lower. It’s good to see that businesses are beginning to realise that you can’t measure the effectiveness of the digital marketing strategy just by looking at data that comes out of a web analytics tools and that you need other data, particularly customer insight data to fully understand what is going on.

There are some worrying signs from the report. Organisations admit that they are still often not tying up their data collection strategy to their business objectives and relatively few said that they were definitely getting actionable insights from their web analytics. Quite a number thought that a lot of the data they had wasn’t particularly useful for decision making purposes and the clue to the reasons why came when you looked at the resourcing of their web analytics programme. 45% of respondents didn’t have a dedicated web analyst and when you look at where the money is being spent, the biggest chunk is usually on the technology rather than the resources to extract the value from the technology. So it’s hardly surprising that organisations are finding they are struggling to get insight from their web analytic programme that leads to better decision making.

The signs from the report suggest that there is progress being made in the UK but more vision is required at the right levels of organisations to tie their business strategy and the measurement strategy together. Reasons often cited as being a major barrier to having an effective online measurement strategy included lack of coordination, lack of senior level buy-in, budget and resources rather than problems with the technologies. And I think that if there is a difference between what is happening over here on this side of the Atlantic to what is happening in the US, it is probably more to do with those factors than anything else. For those of you in the UK and the rest of Europe, it’s worth taking a look at the report and seeing how you benchmark.

A few weeks ago I took stock of the web analytics market, particularly looking at some of the key trends in Europe. This week to get a sense check from the position of someone in the US, I turn to a fellow WAA Board Director and friend Avinash Kaushik. Avinash is also an Author and one of the leading thinkers about web analytics and where it’s heading, having actually “been there and done it” previously at Intuit software.

This is what Avinash had to say.

Avinash, you had a busy year in 2007. What were some of the highlights for you?

It has indeed been a hectic year, becoming an Independent Consultant and Analytics Evangelist role at Google and publishing Web Analytics: An Hour A Day in June. Along they way speaking at conferences and running around the country became normal! Oh then there is the blog, Occam’s Razor, my baby (!), that took more time than I could ever have imagined.

I think the biggest professional highlight has to be the book. In five months sales have vastly exceeded my expectations. Since all of my proceeds go to charity (The Smile Train, Doctors Without Borders) it has meant a nice amount raised for them.

The book is a great primer and reference document for all things “web analytics”. But in this fast moving industry, isn’t it a risk writing a book? Are there some parts of the book that you think you might have to rewrite soon?

The core of the book I think will stand the test of time (and by that I mean five years at most! ). But there are many sections I would update. The book has been out only five months but I would add new things to the SEO section. Ditto for blogging metrics, I have slightly changed two of the six in the book and added a brand new one. I touch on Social Media but when I write the next version of the book I think things will be more settled and I can add more interesting things.

New tools will come with time, as will new sources of data and my book, or and those of others, will accommodate for that. But the biggest goal of Web Analytics: An Hour A Day is to teach you a new way of thinking, that I think will be relevant for quite some time to come.

All that said Willem from Wiley was over the other day asking me to start work on the next version!!

What do you consider to be some of the key industry developments to have been in 2007?

I get the distinct feeling that we will look back at 2007 and remember it as a turning point, a good one, for the industry.

Why is that?

Every site in the world seems to have Google Analytics – a leading indicator that even the most common person with tangential interest in data now has access to a great web analytics application. More interest translates into more mind share.

The industry has consolidated quite a bit. Omniture has built on top of its already impressive growth by acquiring Visual Science (/WebSideStory / HBX), in addition to Instadia (Analytics + Surveys), TouchClarity (Behaviour Targeting) and Offermatica (Multivariate Testing). This year all roads seemed to lead to Salt Lake City!

WebTrends is going through some temporary management turmoil, but with its excellent set of solutions I expect them to come back strong.

There were more web analytics consultancies launched, more than on you can count. Ditto for web analytics conferences. Actually a real interesting trend was how many non-analytics conferences had “web analytics day” or “web analytics pre-intensives” – a real sign of growing demands.

It was also a year of Web Analytics 2.0. An expansion of the core definition of what web analytics is, stretching is beyond just clickstream to include qualitative research, testing, competitive intelligence, multiple dimensions of outcomes etc.

So what are some of the key drivers?

Many, if not all, of the trends above were driven by a singular phenomenon: The web is becoming serious business.

It seems odd to say this in 2008 but in many companies web, and web analytics, have been a silo that someone else is taking care of. Websites are becoming the most important customer touch point and the most important revenue generator even for businesses that are not first of mind.

The consolidation in the industry, the increase in interest (tools or conferences) and expansion of the definition is a reaction to the demands now being placed driven by a desire to move beyond printing reports (to perhaps printing money!).

How would you assess where the web analytics industry is at the moment from the point of view of software vendors?

Full of opportunity.

Money and fame awaits all. Well at least those who are willing to work hard.

The vendors have done well thus far, mostly, but they are still scratching the surface of what is possible. Many big websites still don’t use web analytics. There are many growth opportunities in the Software market (aside from the current favourite child: hosted). We are not even scratching the surface of integration with data from other parts of the company and other tools should we decide that web analytics is not a silo but a part of “Business Analytics”? So there is a lot to do and appropriate financial rewards for companies that help accelerate the move beyond clickstream.

What about the people side, i.e. the end users and consultants?

There is a read dearth of skilled practitioners in our industry. And that has stunted the amount of progress that can be made (because the 10/90 rule still applies – spend $10 on software/services and $90 on people who can actually analyze data and produce insights). If you are a skilled person, you can name your own salary (but make sure you are on the web analytics 2.0 continuum and not 1.0), and if you are someone who wants a great Analytics career then now you know where to find it.

Consultants will thrive in any field where the rule is 10/90, because they can bring their expertise to bear on the $90 part of the equation. Additionally because of increase in the demand you are noticing many more consultancies (mom and pop and grandpa included), and an interest from the “big boys” for mature web analytics consultancies (example: our good friends Zaaz acquired by WPP). To make optimal amounts of money Consultancies, like other companies, are finding that they can’t be a one trick “let me parse your log files” pony. They are being forced to evolve into areas such as multivariate testing, competitive intelligence, usability etc.

What are some of the key trends that you see at moment? Where’s the market going?

The problem with Web Analytics 1.0 is that it is an exercise in data torture and reporting with long lags in taking action (if any). Data torture needs to get automated and expanded, decision making needs to get automated; people need to be left for smart hard things (vs. what happens today!). Smart companies will start to exploit more things like Multivariate Testing, Onsite Behaviour Targeting etc because in each case you are leaving humans to understand customers and create content and you are letting intelligent solutions create the right customer experience based on data. Won’t happen overnight, but are on this train for good.

I also believe that 2008 will see a more serious attempt to get Web Analytics to become a part of “Business Analytics”. We are still a silo in most companies (data and people!). We will see more collaboration and innovation in helping web data become a core part of the company data to truly give end to end visibility (and maybe the holy grail of multi channel analytics / impact). Won’t happen all in 2008, but we might get serious.

I am optimistic that we don’t have untouchable islands of data like we do today. Search Engine Optimization, RSS, Social Media, etc. They are all becoming mainstream yet the current generation of tools mostly stink at tracking them. You can track them, but if you are willing to row your leaky boat all by yourself to that island. I think this will change.

Oh and we are not done with consolidation in the industry.

It’s going to be fun!

I reckon so, thanks Avinash

I have just got back from presenting at a seminar on internet marketing which was targeted at small and medium sized businesses. Pulling together the material for the presentation made me think about how you put together an effectiveness digital marketing measurement programme when you don’t have much of a budget. I am a great believer in the quote from Arthur C Nielsen that “the price of light is less than the cost of darkness” but companies have to live within their means and for small businesses that means that often they don’t have huge amounts of money to spend on data collection and analysis.

So what is an effective web measurement strategy for a small company doing business online? Well, it doesn’t look that much different to the strategy that a large organisation might employ just the scale and some of the tools might be different. A small business still needs to have a holistic approach to measuring their online channel and to have the right tools in the toolbox. A small business still needs to have clearly defined online goals and objectives which can be translated into a set of key performance indicators. A small business still needs to have in place the right processes to ensure the integrity of its data. Some of these aspects may in fact be easier for smaller businesses than larger ones. It may be easier to define the business goals and KPIs as there are less people involved in the process. It may be easier for a smaller business to manage their processes to ensure that pages are correctly tagged for example and that campaigns are properly tracked. It may be easier because it might just be one person doing everything.

Where it might be harder for smaller business is to take a holistic view of measuring their online channel by having multiple tools in their toolbox. In my view this holistic approach comprises of four main components:

An effective strategy for measuring and optimising website performance has four key components:

• Good market intelligence
• Sophisticated visitor behaviour analysis
• Excellent user profiling
• Effective site performance tracking

Market intelligence provides the context for the businesses own performance. Whilst the majority of a digital marketer’s time can be spent focussed on the brand and the site, it is important to remember that the neither the brand nor the site operate in a vacuum and that external factors and forces are at play. Larger businesses might buy into 3^rd party data providers such as ComScore, Nielsen NetRatings and Hitwise. These services are often out of the reach of small businesses and may mot even be suitable for sites with lower traffic levels. However, a small business can still uses online resources such as government statistics and sites such as ClickZ to keep a breast of trends in the industry.

Visitor behaviour analysis comes from web analytics tools and you can now get sophisticated reporting packages for free or at low cost. Obviously Google Analytics is free to use and will suit many businesses’ needs for a long time to come. Microsoft are launching their own service soon. For those willing to invest a little bit, there are other tools that are suitable for small businesses. One that I particularly like is Clicktracks for it’s simplicity of use combined with some powerful analysis features.

User profiling is the process of getting to know who is using your site and why. The basic principals of marketing are about understanding your customers and meeting their needs. In our online environment the basics that a business needs to know are:

• Who is visiting my site?
• What are they trying to achieve? What are their goals?
• Were they able to do what they wanted to do? If not, why not?

This sort of data can be collected from surveys and there are plenty of cost-effective web survey services around (such as SurveyMonkey, Zoomerang and so on) that allow you to create online surveys at a reasonably low cost. Just because a survey can be cheap to run, it doesn’t mean that it can be low quality. Attention needs to be paid to the type of information that you are asking for and the way

Finally, site performance measurement looks at the effectiveness of the site from a technical perspective. It concerns aspects of the site such as the speed of page delivery, site availability and the responsiveness of transactional processes. A Forrester report on this subject showed that users think that slow web sites are less interesting, less believable and less trustworthy. If you are small business trying to cut through the noise of the internet, you don’t want to burden yourself with these kinds of perceptions. So measuring and tracking your site’s speed is an important component of the mix. If you can’t afford to buy into continuous services such as Keynote or Gomez, then you can find sites where you test your site speed for free on an ad-hoc basis.

For small businesses the “price of light” may not be the actual price you need to pay for data services but it’s the time you need to spend managing, interpreting and understanding the data that you can get. But in this competitive environment doesn’t it make sense to work smarter?