Surveys

There are obvious attractions to conducting international surveys as conduits of quantitative research.

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. Read more…

Even during recessionary times, the value of online transactions continues to increase. However, in a recent study carried out by Foviance, 44% of people surveyed said they didn’t buy online because they wanted to physically see the product. A further 18% cited the cost of delivery as a barrier to online shopping.

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

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

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.

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

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

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

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?

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

Last time in this series I looked at a number of different ways you might think about and measure customer loyalty. My view was that it’s not realistic to think about and measure customer loyalty as if it is a single entity but to create a loyalty measurement dashboard consisting of a number of appropriate and relevant indicators. These indicators might be behavioural, attitudinal or financial. To do this you will need to look at number of different data sources such as your web analytics data, surveys and other customer feedback data and any market or context data that may be available.

Following on from the tricky issue of looking to measure customer loyalty comes the issue of what to do about it. If you can look at the different aspects of customer loyalty through different metrics, then the question is: was do you do with this information? How do you act on it in a way that positively impacts on customers’ loyalty? How can you accelerate the building of loyalty when it’s in its ascendancy and how can you manage it when it’s beginning to decline?

On my customer loyalty dashboard I’m going to have a mixture of metrics. Some of them are going to be more strategic in nature, potentially even Key Performance Indicators (for example, a customer satisfaction index) and some of them are going to be more operational or tactical (such as recency or frequency measures). The strategic measures are going to be telling me how I am doing over the longer haul and the tactical measures are telling me what I need to do in the shorter term. The tactical measures are more likely to be behavioural metrics as, generally speaking, it’s easier to observe, react to and influence customer behaviour than customer attitudes.

RFM (Recency, Frequency, Monetary Value) analysis is often classically used to manage retention programmes. Customers are segmented according to how recently they have transacted, how frequently they have transacted and their value to the business. These segments can form the basis of differentiated retention and communication programmes depending on which segment the customer sites in. Customers who are in the top segment for recency, frequency and monetary value display loyal behaviour and are the ones that you don’t want to loose, and will probably deserve some special treatment.

A particular case of the RFM approach I think is the new customer, ie the customer who has just transacted for the first time. They’re a special case. It’s possible or even probable that you may not have made any money on them, you need to get them to transact again before you start to recoup your marketing costs. They are also at the steepest point on the “friction curve” which is the amount of effort required to get them to transact again. Retention is like momentum, once you get them started it’s easier to keep them going. In the case of the new customer, if you can get them to transact again, then they are more likely to transact a third time, and then a fourth and so on. So, customer retention, like conversion, is not one process but it’s a series of mini-events designed to move a customer from one state to the next.

The key advantage of RFM is its simplicity. It’s easy to do the analysis, create the segments and put together some specific customer communication. However, there are a couple of issues with it in my opinion. First of all, it’s assumes that people that behave the same on these dimensions will respond the same to particular communications. On it’s own it doesn’t help with the crafting of the retention marketing message. If you think of a multi-category retailer for example, different types of people will be buying different types of products. They may have similar shopping profiles but interested in completely different things. So, as well as knowing when to intervene, it’s also important to know how to intervene – what’s the trigger going to be?

The other issue is around recency. If you have a regular interaction in some way with your customers then by the time that you notice they’ve not been around for a while it may be too late. By the time they cancel the service, or stop visiting the site or whatever it is that means that they have stopped doing business with you, they could already be a lost cause. They might have stopped being attitudinally loyal some time earlier but it has taken a time to get to the point of being behaviourally disloyal.

So, we need to be able to anticipate changes in customer loyalty rather than just react to them. In many cases ,customers can give off signals or clues that their loyalty is shifting for the worse. They may change their patterns of behaviour, they may start calling customer services more often, and they may stop returning your calls. These are all indicators that changes are happening.

The role of predictive analytics in customer retention marketing is to give the marketer a heads up warning that something might be up with a customer. Predictive models look to identify customers who may be at risk based on the changes in other data. With all predictive models they will never be 100% accurate but if they are good enough they can at least reduce the risk of customers taking their business elsewhere. The inputs that go into these models will of course be specific to the individual business and the data that is available.

So, as markets become more competitive and retention becomes a more important facet of the digital marketer’s job description, it’s time to start thinking about customer loyalty seriously. What does loyalty mean in your business? Does it mean anything at all? If it does, how are you going to know if you’ve got it? What are the relevant measures? How can you impact those measures positively?

Lot’s of questions but they’re not necessarily difficult ones. The key thing I believe is to think them through carefully and build your customer loyalty dashboard accordingly. As the saying goes “Be careful what you measure, because what you measure is what you will get”.

This article, written by Neil Mason, was originally published on Clickz.com and is republished here with permission.

In my last article I started to explore the notion of loyalty. What do we mean by loyalty? Is loyalty about the way we behave or is it about the way that we think? And if even we can get a definition of what it is, how easily can we track it, measure it and manage it?

A lot of the answers to these types of questions will depend on what industry or vertical you happen to in. The notion of loyalty is different if you are selling biscuits (or cookies) than if you are selling cars. The frequency of the decision and the purchase decision process itself are very different. Some commentators believe that loyalty is essentially a behavioural phenomenon. Certainly in terms of managing retention, it’s primarily the behavioural drivers that are used to trigger marketing events such as promotions or emails. But that’s not necessarily because the altitudinal components to loyalty are not important, it’s just that they are harder to work with.

My own view is that the notion of customer loyalty is often nebulous, difficult to define and hard to measure. But we shouldn’t let that put us off! Often in the work that we have done for clients we see the disproportionate value of repeat customers to the overall business. So, how do we define and measure loyalty?

In my ideal world I wouldn’t have a loyalty measure, I would have a loyalty dashboard. I don’t think it’s really possible to measure and manage customer loyalty using a single metric, I think you need a number of different indicators giving you different perspectives on how visitors and customers are thinking about their relationship with your brand. It’s not just about how they behave but it’s also about what they think and the emphasis between the two will be dependent on the kind of business that you are in.

In our online world we’re pretty good at tracking behaviours and so it doesn’t come as a big surprise that behavioural data is often used to describe customer loyalty. In a quick survey of various web analytics tools, most of those that have a “visitor loyalty” metric base it on the frequency of visits or perhaps the number of “conversion” events. What they generally don’t do, however, is take into account what the visitor does when they get to the site. So, someone who visits 3 times and spends 5 minutes on the site each time is considered to be more loyal than someone who visits one and spends half an hour on the site. So, a frequency metric may be interesting but may not necessarily be very useful when it comes to thinking about loyalty.

Then there is the issue of recency. Does recency have anything to do with loyalty? Does the fact that someone visited my website yesterday make them more “loyal” than someone who last visited last month? Probably not. But if they have visited more frequently in the past and have visited more recently, then they are displaying characteristics of “loyal” behaviour. Recency and frequency analysis in conjunction are better than looking at them individually but we’re probably still not getting the full picture.

I think that customer loyalty also needs context. We all generally live in a competitive world. We are fighting for our share of the wallet, the budget or just someone’s attention. We want our visitors and customers to spend more time or money with us than with the other guys. To be able to measure this context I need some other data, I’m not going to get that from a web analytics system.

Other data sources that I can add to my loyalty dashboard to give me this context include 3rd party sources such as audience panels or my own surveys. Not everyone is going to have access to panel data such as Nielsen NetRatings or Comscore but if you do have that data, you can use it to add to context to your web analytics data. As a simple level you can measure the duplication or overlap between your audience and that of your closest competitors or you can drill into more depth and look at the amount of time visitors spend on your site compared your competitors.

If you don’t have access to these types of services, you can get at some competitive context by asking your own visitors through the use of surveys. You can ask your own visitors which other sites they visit and if relevant how much time or money they tend to spend on these others sites. The data can then be analysed to produce some loyalty metrics that can be tracked over time or across different visitor segments.

Surveys can also be the vehicle to give you a wealth of powerful attitudinal information for your loyalty dashboard. Measures such as “propensity to return” and “propensity to recommend” have been shown in the past to be strong predictors of loyalty and customer lifetime value. Satisfaction can also be used as a leading indictor for changes in loyalty and the benefit of these types of measures is that they can give you an opportunity to act before it’s too late. Often customers can become attitudinally disloyal before they actually change their behaviour.

There isn’t a “one size fits all” approach to measuring customer loyalty and I would encourage you to think about measuring customer loyalty using a composite approach of different metrics drawn from different data sources. Create your own customer loyalty dashboard.

From insight needs to come action and next time I will have a look at how we can utilise data-driven insights in our retention marketing activities. Till then…