Surveys

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…

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

One of my predictions for this year is that retention marketing will increase it’s importance in the online marketing mix. My thinking is that acquisition marketing activities through channels such as affiliates and search is becoming increasingly sophistacted. The tools are out there, the skills are out there and the improvements in efficiencies are beginning to tail off. That’s not to say that there aren’t improvements to be made. It’s just not the low hanging fruit for most organisations these days.

At the same time, conversion optimisation is gaining momentum. There has been a lot more focus on what actually happens when someone gets to the site over the past couple of years, spawning the growth of web analytics and multi-variate testing services. There is still probably a lot of mileage to be gained for most organisations through site improvements but generally the process is underway.

However, from the work we have done with a number of organisations it seems to me that companies generally heave a sigh of relief when they make the sale as if the job is done. Having been through the effort of creating awareness, acquiring the traffic and converting it on the site, the important asset that has been created, ie the customer, is then neglected and the organisation sets off in pursuit of new ones. The result is most customer databases are littered with customers who have only transacted once. For more, the definition of retention marketing is converting someone twice without having to acquire them twice. You don’t want to have to go through all that heavy lifting again!

So as part of this increased focus on retention marketing there’s going to be an increased emphasis on customer loyalty. Over the next few weeks I’m going to be taking a look at issues around customer loyalty such as “what is customer loyalty?”, how do you measure loyalty and how can you use data and insights to manage the retention marketing process more effectively.

What do we mean by loyalty?

What do we mean by customer loyalty? For example is it a state of mind or it is a set of behaviours? How can we really establish whether a customer is loyal or not? In our multi-channel world is the notion of loyalty even valid or useful?

These are tough questions that have been debated for many years and, of course, there are arguments on all sides. If you look up the definition of loyalty is usually describes loyalty as being a “quality” which suggests it’s more attitudinal than behavioural. However in our marketing world we’re generally interested in outcomes, like someone buying something. But are behaviours actually always the best indicators of loyalty.

As part of my MBA I did a lot of research into store loyalty amongst supermarkets in the UK. I measured how much people spent in each of the grocery stores that they shopped in over a 12 week period and created a metric to measure how loyal they were to various retail brands. What I found was that most people spent most of their weekly shop in one supermarket. So you could argue that most people seemed to be “loyal”. But what is the context to this behaviour? Are they only loyal to that store because it happens to be the closest or the most convenient? Would they in fact rather shop somewhere else if it was more convenient?

Another example can be found in financial services. People can appear to be behaviourally very loyal to their bank. If I look at my own behaviour I have had my main account with my bank since I was a student. So I must be very loyal, right? Well if I was asked whether I considered myself to be a loyal supporter of my bank I would say that I wasn’t. I’m behaviourally loyal but I don’t consider myself to be a loyal customer. The problem is that the pain of switching accounts to another bank is just too high at the moment. There are inertia effects at work and there is a great deal of inertia in financial services.

Of course, the internet is a technology that can break down inertia. There is the famous saying that “your competitor is only one click away”. I can now choose to get my groceries delivered by whichever supermarket will deliver to my area. I am actively encouraged to use shopping comparison sites to get the best deal from a selection of stores.

However the technology can also create barriers to switching which means for example, that our household generally get’s all it’s online groceries from one supermarket chain because all our orders are stored there. Similarly Amazon get’s a significant chunk of my expenditure in certain categories mainly because it’s so easy to buy, not because they are necessarily the cheapest.

So how do we measure customer loyalty? Do we look at behavioural indicators or should we be concerned about what our customers think of us? Or both? This is something I’ll be looking at next time. Till then…

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

I can’t believe I’m writing about this already but Christmas is coming… fast. Probably like a lot you, I’m going to be doing most of my shopping online. Spending a couple of evenings online is far preferable for me than battling down a busy high street. So online retailers in the UK can count on me making a modest contribution to the estimated £7 billion in online sales in the run up to Christmas this year.

I imagine that e-tailers all over the world are now refining their campaigns, optimising their store fronts and adjusting their merchandising strategies to maximise their returns during the peak period. But there was a timely reminder earlier this month from the IMRG (Interactive Media in Retail Group) here in the UK (an equivalent to Shop.org) that e-commerce is still a relatively immature channel for most retailers and that some aspects of their operations still require significant refinement.

The IMRG highlighted in its report that e-tailers need to improve their delivery services or face a backlash from consumers. They asserted that too many basic mistakes are being made, for example putting small items in large packages which then don’t fit through letterboxes. This view form the IMRG highlighted to me the need to consider the whole end-to-end process when we think about optimising the customer experience rather than just what happens on the website.

A huge amount of time and effort goes into optimising the visitors experience on the website and that’s great. Quite often on websites there is still quote a lot to be put right once decent measurement systems have been put in place. However, all that effort comes to little if the value created by a positive website experience is destroyed by a poor post sales experience.

When online, sites often attempt to survey me to find out about who I am and whether I was satisfied with my experience. Whilst I don’t answer every single one I applaud the attempt to understand more about me and to find out how I feel about their website, assuming that they do anything with the data. However, I struggle to remember if anyone has ever contacted me after I have bought something online to find out what I thought about the overall experience and importantly what I would do in the future as a result. Just because someone is satisfied with the website experience, it doesn’t necessarily mean that they end up wanting to do business with you again.

I would therefore suggest that if you don’t have a programme in place that gives you input to your visitors overall opinion of your performance then you’re missing a vital component of your overall measurement strategy. As well as understanding satisfaction with the on-site elements of the transaction, look to understand satisfaction with the off-site components of the transaction. For example:

• Were there any problems with the payment processes?
• What was their opinion on the delivery times and prices?
• Did the order tracking capability work well?
• Did the delivery arrive on time?
• Did they use customer services/My Account etc?
• Was that a good experience?

I saw a good example of such a programme being put in place at Emetrics in Washington. StubHub.com presented on what they were doing to track the overall customer experience. In addition to phone surveys, website pop-up surveys and exit surveys, they also ran “post event surveys” two weeks after the transaction had occurred on the site to find out if everything went smoothly for both the buyer and the seller and if not, why not.

As conversion optimisation processes become increasingly tuned, other aspects of the customer experience are likely to be where retention battles might be won or lost. So, find out what impact the experience had on the likelihood of them doing business with you again. Propensity scores such as “likelihood to return” and “likelihood to recommend” strong indicators of satisfaction and customer lifetime value.

So when it comes to evaluating performance in January let’s not just focus on sales, orders, conversion rates and the like. Consider also whether the experience worked for the customer as well – all of it. Remember, a customer is for life, not just for Christmas!

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

I got a bit of a wake-up call this week. An event which I thought was a long off is getting closer. The Emetrics summit in Washington DC in October is now only a matter of a few weeks away and the event organiser, Jim Sterne, has been marshalling his speakers.

Jim’s developed the approach for this Emetrics summit into a multi-track format. I’ve been given the job of moderating a track on “The Voice of the Customer” and there’s a great line up of speakers including my fellow ClickZ columnist Jason Burby from Zaaz.

I’m really pleased that there’s this track at the conference. If you have looked at any of my other contributions to this column, you will know that I believe that the “web analytics” world for too long has been too “site centric” and not “customer centric” enough. In other words people tend to focus on too much on the analysis of the site, rather than on the people who are trying to use the site.

The inclusion of this track in the Emetrics summit means that we are going to get exposure on some of the issues, challenges and opportunities of working with surveys and other customer data sources alongside the data collected from web analytics systems. I’m looking forward to it.

Jim’s email giving the speakers their instructions for the event made me start thinking about the whole area again and just what some of those challenges and opportunities are. The opportunities are plenty and pretty obvious. Augmenting your understanding of behaviour on the site by adding additional insights into who your visitors or customers are and what they think gives you both sides of the story. I often say to clients that web analytics data tells you what is going on on your website and survey data often tells you why.

However, despite the many benefits many organisations still think of their data in silos. So what are the challenges to getting a more holistic approach to thinking about how the effectiveness of your online marketing programmes?

I think they probably fall into three main areas:

* Technical challenges
* Competency challenges
* Organisational challenges

The technical challenges are around getting the different data sources to sit next to each other in a way that makes it easier to analyse. This is the data integration challenge and I’ve written about macro data integration and micro data integration in previous articles in this column. The web analytics systems vendors are making it easier for us to be able to integrate survey data in with the site data and this is a good trend. Most of the major vendors now say that they can integrate survey data into their systems, but do look closely at exactly what they mean by “integration”. One Scandinavian web analytics company, Instadia, has gone as far as making customer surveys an integral part of their product with the ability to write, launch and analyse surveys from within the system. The survey data that is collected is stored in the same database as the visitor’s behavioural data. That’s what I call integration.

Competency and organisational challenges are probably two sides of the same coin. Analysis and reporting of continuous marketing data and the development and analysis of customer surveys are different skill sets. The web analytics industry is probably not mature enough yet for individuals to have had the opportunity to be exposed to survey work before getting involved in web analytics and vice-versa. Typically these may also be separate functions within an organisation. The web analytics data may be owned by the online marketing function and surveys may be owned by the marketing research or consumer insight function. Each function may not be familiar with the other sort of data and so it’s rare that they are brought together.

So lots of challenges and opportunities but things are definitely moving in the right direction. I’m looking forward to hearing the speakers at the Emetrics summit in Washington talking about how they have met those challenges, I’m sure it will be fascinating. I’d also be interested in hearing from you if you have some good examples of how you have successfully integrated web analytics and customer data.

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

Over the past few weeks I have been taking a look at the variety of data sources available for evaluating e-business performance in addition to the data that comes from your site. These additional sources include audience panels, surveys and focus groups. I’ve also been making the point that purely focussing on web analytics data rarely gives you the full picture.

To talk about this on a practical level let’s take a look at how multiple data sources can be used together to look at a specific business issue such as optimising conversion rates on the site. The simple premise is that if you know who is coming to your site, why they are there and what they are trying to do then you can develop the site to optimise these key customer journeys.

To help digital property owners understand how visitors are interacting with their site we use something called a Customer Journey Framework. This framework is an approach to understanding which visitors are trying to use the site, how they are using it and whether they are being successful in their goals or not. There isn’t a single source of data that will give you the answer to these questions. You need to draw the answers from a suite of different places.

The Customer Journey Framework comprises of three key components:

• Understanding the different types of visitors (Audience segments)
• Understanding why people visit the site (Intentions)
• Understanding usage of the site and the consumption of different content (Content)

Different people come to your site for different reasons and there are bound to be different segments of visitors. The challenge is to work out what the most meaningful segments are for your business that you can use for your marketing and site development activities. This is something we’ll take a look at in the future.

Working out who is coming to your site is where you might use audience panel data, surveys or internal data from customer of registration databases. The reality is that you might need to use all three to build up a true profile of the different types of users that you might have. Audience panels can give you a demographic profile (if your site is large enough) but they may not help you to segment your audience in a meaningful way.

Surveys can help you understand if different types of visitors are coming to your site for different reasons. We call these “intention modes”. What is the visitor’s intention when they arrive on the site? What is their goal? To use an e-commerce example, a visitor may come to a site in one of these modes:

• To browse for something and buy it they find something they like
• To do research for price comparison purposes
• To buy a specific product that they have already researched
• To browse around with no intention of buying anything

Visitors in each of these modes will have different goals and will also exhibit different behaviours on the site.

By linking intentions to visitor segments you may find that some modes are more pronounced in certain groups of visitors. For example, in some work for a high street retailer in the UK we found distinct differences in these modes were evident when we looked at it along age and gender lines. In this particular case, older females were tending to arrive at the site with higher levels of purchase intent than younger females. The younger females were looking to be inspired by the site to make a purchase whereas the older females were more likely to already have in their mind what they wanted to buy.

The final link is then to layer these visitor segments and their modes onto the actual content of the site. This is where web analytics data is important and the linking the behaviours that you see on the site back to what you know about visitors and what they are trying to do. So, in our example above are the younger females looking at different types of products than the older females and so do those products need to be merchandised differently on the site to maximise conversion?

Linking behavioural data and profiling data can be tricky. It’s certainly easier if you can identify at least some of the site’s visitors through say a registration process or a transaction. You can match the profiling data captured in the process with the actual behaviour on the site and use that information to generalise for all traffic. It is also possible to link survey response and site behaviour data as well, though certainly here in Europe you need to be mindful of privacy concerns about identifying individuals.

The framework we’ve looked at here is one example of bringing together data from different sources to get a holistic view of what is happening on the site. It’s also just that – a framework, which can be adapted to suit the circumstances of your own site and information sources.

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

In my previous article we looked at the need to combine detailed data from site analysis systems with additional consumer insight gleaned from surveys. This week, some thoughts on how to ensure your surveys are as effective as possible.

First of all, there are many different forms of surveys that an online business might run and they can vary on a number of dimensions. For example you might be surveying visitors on your site as opposed to customers, you might be collecting some general background information or you might be asking about a specific issue. The survey might be a once off survey or it may be run on a continuous basis. Indeed some of the different dimensions that might be involved and considered in the development of an online survey include:

• The purpose of the survey
• The target audience for the survey
• The type of survey and how the respondents are recruited
• The number of responses needed
• The expected response rate
• The purpose of the survey

Purpose of the survey

Any survey should have clear objectives; there must be a reason why you want to do it. There could be more than one research objective in a survey, but it is important that they are clearly stated, easily understood and are not contradictory. From the objectives everything else flows, ie the type of survey needed, the target audience and so on.

The target audience for the survey

It should be apparent from the objectives who you want to talk to in the survey. You may not want to invest time and effort understanding everything about everybody who visits your site. Your primary interest will be about finding out the right information about the types of visitors who are of most interest to you, like customers, subscribers and so on.

The type of survey

Having determined what the survey’s objectives are and who you want to survey, you are in a better position to decide on the type of survey that is most likely to meet your needs

On the whole, there are two main types of online surveys:

• Pop-up surveys
• Site based surveys

Pop up surveys (as the name implies) pop up in a window on your site. They must generally be short and easy to answer. Site based surveys are potentially more extensive surveys that people are directed to on a separate part of the site or on another site altogether. Some of the key differences between pop-up surveys and site-based surveys are highlighted below.

The number of responses needed

Another key consideration for your survey is the number of completed responses you need. This can vary enormously with the type of work you are carrying out and the target audience for the survey itself. In general terms for consumer analysis, you would ideally be looking for about 400 respondents to allow you to be able to do any meaningful analysis.

Response rates

Having determined how many respondents you think you need, you then need to think about how you are going to get them. For a pop-up survey, visitors are typically randomly selected on the site and presented with the pop-up survey invitation. For a site-based survey, people will either be invited by e-mail or via an invitation on the site.

In either case, only a proportion of those who are invited to participate in the survey will actually do so and complete it. This proportion is known as the response rate. This response rate can vary from survey to survey and it has been found that response rates to surveys are influenced by:

• The style and quality of the survey’s first page
• Relationship with the web site and/or the brand
• The level of interest and relevance of the survey to the potential respondent

If you are inviting people to participate using an e-mail, then the style of the e-mail and the subject line will also be an important factor affecting the response rate. You should try and make the call to action as interesting and as engaging as possible so that it cuts through the noise in their Inbox. You should use language be appropriate to the type of business that you are and also the relationship that you have with the potential respondent. Many e-mail and survey systems allow you to personalise the invite and this can be used to good effect to improve the chances that someone opens the e-mail and then acts on it.

Survey frequency

The final consideration will be on how often you are going to run the survey. A great many surveys are only run once to get some insight into a particular issue, eg the effects of a new site design, but some surveys such as a customer satisfaction monitor might be run more than once or on a continuous basis.