Consumer Insight

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

There is one thing about the digital marketing world – we are not short of numbers!

Typically we may have numbers coming in from our web analytic systems, our PPC consoles, our affiliate marketing systems, our ad-serving data and so on. On average a marketer may have at least five or six different sources of data, if not more. So, the challenge is to make sense of it all and last time I looked at what I call “macro data integration”. Macro integration is about pulling together data at the summarised level in order to be able to relate different data sets together and spot trends and exceptions.

However, as you want to be able to act far more tactically on your data, you may need to think about “micro data integration”. Micro data integration is where different data sources are integrated at a much more atomic or granular level. Often this is done at the customer level.

Why would you want to think about micro data integration and what are the benefits?

You might want to think about data integration at the micro-level for a number of reasons:

1. To enhance the value of the data that you hold on a customer or a product
2. To enable better diagnostic analysis of marketing activity
3. To be able to execute personalised or event driven marketing programmes

For example, you may want to be able to combine data from a web analytics system on browsing behaviour with online and offline shopping data so that you can be more specific and targeted in your direct marketing activity. Or you may want to look at the long term value of customers brought in by different types of acquisition channel.

Often the question is asked about the best place to integrate the data. Should data be imported into your web analytics tool or should data be exported from the web analytics tool into a CRM system or something similar? The answer is driven by what the objectives are and may involve both activities.

If the objectives are to improve the customer marketing processes then it is likely that the best route will be to export certain data from the web analytics system into the CRM system, as it is usually the CRM system that drives the operation of the outbound marketing activity. The customer database or CRM system provides the total customer view and the data from the web analytics system will be just one component that total customer view along side other data gathered from other systems.

Another reason why you might want to export the data from a web analytics system into another database is because you might want to analyse the data using other tools. Web analytic systems can report data in a variety of ways but there may be occasions when you want to do some more sophisticated statistical analysis using tools such as SAS, Clememtine, SPSS and the like. In some of the work that we do, we process data using a web analytics system to generate visitor level records which we then look at using data mining tools to look for interesting patterns of behaviour.

At other times, it may be useful to enhance the data in a web analytics tool by importing data in from other sources such as the marketing database, customer database or the product database. This is likely to be more useful when you maybe need a site centric view rather than a customer centric view, eg:

• Which type of people look at what type of content?
• Which acquisition channels given the greatest return on investment?
• Which campaigns tend to acquire the least loyal customers?

Last time I discussed the data management challenges around data integration and this is true of micro data integration as well. Thinking about what data to move will require some careful planning. Different data sources have different data structures and they won’t necessarily fit easily together. Often this may mean that the data may need to be manipulated or transformed in some way in order to be able to lay it along side the other data.

The volume of data being exported or imported is an issue as well. This will also impact on how often you do the data integration. Monthly, weekly, daily? We all know that web sites generate huge volumes of data and it is often impractical and unwieldy to extract the data in its rawest format. Think about what you want to do with the data and create summarised variables if possible. For example, if you want to have visit based recency and frequency data in the customer database, then it’s preferable to create a couple of summary variables such as date of last visit and number of total visits, rather than import the whole customer’s visit history.

The good news is that web analytics systems are becoming increasingly open and able to interoperate with other systems. The launch of WebTrends 8 and WebTrends Marketing Warehouse last month is another example of steps in the right direction for making it easier for users to “micro-integrate” their data.

Till next time.

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.

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

During the heady days of the dot-com boom in the late 1990s and early 2000s there was a common perception that this “new media” meant an end to many traditional marketing practices and I remember being told that as far as market research was concerned, that things were different in the “Internet world”.

Certainly, a more interactive experience for many businesses with their customers means that they had much more access to information on what their customers were doing (or not doing) than ever before. The web was perceived to be totally measurable through analysing behaviour on the site and that the use of traditional practices like market research was no longer appropriate to doing business in the digital age.

As we all know enormous amounts of data can be generated from online businesses about what visitors and customers are doing. It is possible to track their every movement through the website, you can tell how many people put something in their shopping basket and then either take it out again or fail to complete the check out process. It’s also possible to track exactly which creative of which ad was clicked on and which site it was clicked on to attract them to the site in the first place. We also all know of the challenges involved is generating meaningful insight from the vast quantities of web analytics data.

However, whilst site-centric data is very good at telling you what is happening it is generally very poor at telling you why it is happening. In addition, web analytics data can tell you all about what has happened in the past but doesn’t necessarily help you understand what might happen in the future.

Relying totally on analysis of web analytics data can be likened to diving down a motorway at full speed but only looking in your rear-view mirror. Whilst you can tell where you’ve been, you can’t tell what’s about to happen. Getting beyond the “what” and more into the “why” enables you to get beyond taking a purely historical perspective on the business and to form a view on where the business is going. To do this you need to get under the skin of your users and customers and understand why they do what they do and how they feel about it.

Surveys are one of the most common methods of understanding what customers think and how they feel and developments in the usability and affordability of online research tools means that conducting research amongst the customer base is now easier than ever before. However, there is a huge difference between deploying surveys that generate useful insight and pulling together a few questions or doing a poll on a website.

Concerns that have existed in the general market research world about possible biases by conducting research using online methods are not relevant to the online business. If a customer is doing business online then they are generally likely to be able to be researched using online methods.

Conducting research online can have many advantages over the more traditional approaches such as using face to face interviews or telephone research:

First of all, costs can be dramatically reduced as there is a much lower cost associated with actually collecting the data in the first place. Typically the actual costs of just collecting the data can be 50% of the overall costs of a market research study using face-to-face or telephone data collection.

Secondly, project times can be reduced. With some of the tools available for conducting online research it’s possible to write your questionnaire into a system and have it “in the field” within a day or so.

However, just because online research can be cheaper and faster, it doesn’t mean that it doesn’t deserve the same kind of rigour in its design. A badly designed survey is still a badly designed survey and it doesn’t matter whether the data is being collected face to face, over the telephone or on the web.

A badly designed survey can not only have an impact on response rates but can also have an impact on people’s perceptions of you as a brand. So whilst the increased accessibility of online research means that potentially many more businesses can use surveys as part of the marketing intelligence tool kit, they still need to ensure that those surveys have some degree of expertise applied to them as well.

Next time, some thoughts on how to improve the effectiveness of your online surveys.

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

One of the things that you hear being talked about a lot more about these days in the wacky world of web analytics is “segmentation”. But I sometimes wonder what people mean when they talk about segmentation. I think it’s one of those words that is used more often than it is necessarily understood. Understood in the marketing sense of the word anyway.

I’ll take one example. One of the largest and most successful web analytics systems vendors has a section in their report menu called “Segmentation”. What we actually find there are reports on the most popular pages and sections of the site. I’m not too sure what that has to do with segmentation. Other vendors talk about segmentation as well but mean different things. Sometimes they talk about the ability to filter along different dimensions or the ability to analyse the data by combining different variables. So, segmentation could mean reporting particular data, filtering data or analysing data. All of these things are good things, and potentially even useful things, but are they segmentation?

I dug out some of my marketing text books to see if there was a consensus view in them about what segmentation actually is. I found that what they tend to talk about is that segmentation is a means of identifying different groups of people in order to develop different strategies for each group. So, segmentation is a purpose rather than an outcome and I think that’s the difference between classification (which is what a lot of analysis tools do) and segmentation which is what marketers or marketing analysts do.

The point of segmentation is that you do something as a result of having it. For example:

• You target different groups of people with different messages in your acquisition campaigns
• You present a different site experience dependent on your understanding of who that person is
• You interact with different people differently dependent on where they are in a customer lifecycle

In one of the books that I looked at that was actually written 20 years ago, the authors described three conditions of a good segmentation*. They are:

Homogeneity – the degree to which people in the segment are similar in ways that is interesting to you

Parsimony – the degree to which the segmentation would make every person a unique target

Accessibility – the degree to which you can describe the segments in ways that help you deploy differentiated marketing strategies

That all sounds pretty theoretical (well, it was a text book), so what does this mean in practice?

My interpretation of this is that a good segmentation has to be robust, useful and actionable. There are many ways that you might segment say a site’s visitors or your customer base from simple classification approaches through to complex statistical techniques but they have to pass the sense check of being robust, useful and actionable.

You might simply classify according to some demographic or geographic variables. For example classifying the customer base between male vs female is a form of segmentation but it is only robust and useful if men and women exhibits differences that are potentially useful to you and only actionable if you can realistically target them in different ways.

Alternatively, you might develop a segmentation based on some attitudinal variables. Many years ago I was involved in a project where we segmented the visitors across the number of different sites we had in Europe according to their attitudes to online shopping and their motivations for visiting the site. Whilst the results were certainly interesting and highlighted some interesting differences in the visitor profile of the different sites, we had to question how useful it was to us. How were we going to action the insight? We couldn’t identify and classify people arriving on the site by their attitudes nor could we easily use it in our retention marketing activities as we didn’t have people’s attitudes stored on our customer database.

So, I think that there is always a balancing act in satisfying those three conditions of homogeneity, parsimony and accessibility in a good segmentation. In our own work, we tend to use behavioural segmentation approaches as it makes it easier to act on the outcomes. This may often involve using statistical methods such as cluster analysis to segment customers into groups that are distinct from each other in a meaningful way like their browsing behaviour or their purchasing behaviour.

However, we are also mindful of the ability to the client to be able to act on the results. There is no point in developing a sophisticated methodology that identifies some really meaningful segments if there is neither the skills nor the tools available to realise the opportunity. For example if your email tool is not easily integrated into your customer database then it’s going to be difficult to execute improved target marketing initiatives. It is best to start with something simple and develop the capabilities to act in line with the development of the insight itself.

As it’s getting to that time of the year, in my next article I will be taking a personal look back at 2005 and reflecting of the some of the key events from my perspective and trying to get a sense of where we may be heading in 2006.

* “Marketing Decision Making – A model-building approach” by Gary Lilien and Philip Kotler