• Clearly defined key performance indicators (KPIs)
• Holistic approach to measurement
• Integrated data strategy
• Investment in “humanware”
• Ability to execute

Clearly Defined KPIs

At analytically empowered organisations, considerable effort goes into defining digital key performance indicators. To do this, the digital strategy must be clear and coherent. If the strategy isn’t clear, how can you possibly measure its success? In my experience, defining good, robust KPIs is not an easy task. As a result, KPIs are often not very good. Going through the process forces an organisation to think hard about its strategy, define what success looks like, and make a commitment to measurement. If you can’t measure it, then you can’t manage it.

Holistic Approach to Measurement

The old saying goes, “If the only tool you have is a hammer, then every problem looks like a nail.” Ever since the log file was developed, the digital marketing industry has been banging away with its web analytics hammer. The analytical empowered organisation understands that it needs a whole toolbox. Web analytics provides some but not all of the answers about digital performance measurement. It’s great for telling you what is going on but even a well-configured web analytics tool (itself a rarity) isn’t very diagnostic. Organisations need to invest in additional quantitative and qualitative data sources to truly understand what is going on and why. Additional investment requirements include voice-of-the-customer feedback on a number of levels (based on visit, page level, and post-experience), ongoing user experience measurement and analysis, site performance tracking, and contextual information about trends in the marketplace.

Integrated Data Strategy

A holistic approach to measurement also requires a unified approach to data integration. An organisation also needs to understand how all the pieces of the jigsaw fit together. This requires some effort around data definition (what the metrics actually mean) and where different types of data will be housed. In an ideal world, data is integrated around known users but this may not always be appropriate or possible. Different data types have different characteristics, so planning is needed to understand how the different components fit together. For example, some internal data may be on a customer level, but digital data is often based on cookie level data and one customer may use a number of different devices to interact with the organisation resulting is a number of different cookie records.

One powerful outcome of data integration is the ability to match behavioral data with data around attitudes and opinions. By integrating web analytics data with voice-of-the-customer data, it’s possible to look at the relationship between what people do in a digital channel and the experience they have. This type of integration gives the organisation the ability to measure outputs (things that happen in the channel) and to understand outcomes, which are often the most important things to know.

Investment in Humanware

All the hardware and software in the world will get you nowhere without “humanware” to extract insight and value from data. Too often in the past, investments have been made in technology without appropriate investments in people. The result is often disappointment, if not failure.

Today, analytics teams are taking a more multi-disciplinary approach. As data becomes more integrated, an integrated approach to analysis and insight is needed as well. Web analysts must start working alongside customer insight specialists and user experience experts, sharing their knowledge and expertise.

Ability to Execute

Organisations gain a competitive advantage from the application of insight, not by the generation of insight. Insight has no value unless something happens as a result. So the analytically empowered organisation has the ability to execute and make decisions. This has implications beyond the immediate concerns of analytical technologies; it also concerns a business’s entire technology landscape. Often, a product or site development process and technology constrain an organisation’s ability to affect change. So the analytically empowered organisation must develop strategies in technology and processes that enable it to act on its insights.

This article was originally published on ClickZ

It’s challenging to develop good Key Performance Indicators (KPIs). It’s easy enough to come up with the right metrics, but clarifying robust objectives is more difficult. It’s also easier for e-commerce sites to develop KPIs with strong objectives.

But most sites don’t sell stuff, so why do they exist? They exist for good reasons and those good reasons need to be expressed in clear and definitive terms. That’s the hard part. So, make the objectives clear and measurable, describe what “good looks like,” and select appropriate metrics that measure outputs and outcomes. So once that is done, is that they end of the story? Not really, it’s just the beginning.

Once overall site goals are in place and appropriate KPIs have been defined, the next question is: which sections of the site are working well and which ones aren’t? If we don’t know the answer to this, then how can we focus our efforts properly on site optimisation? This is why we need not just site goals but page goals as well. A page goal defines what that page is trying to achieve. It answers the question, “Why does this page exist?”

Stating clearly why a page exists and its objectives are is a very useful exercise, particularly when you are designing a page. Page goals help you focus what the page is trying to achieve and feed directly into the development of the information architecture of the page – for example, at wireframe stage. Page goals are particularly useful in circumstances when there are multiple stakeholders involved all trying to get a piece of the action on a page; without page goals you can end up with pages that don’t work particularly well for the business or website visitor.

In the same way as for defining overall site goals, page goals should be as clear and precise as possible. Something like “To help users achieve their goals” doesn’t really cut it. Which users? Which goals? In what way? It has to be clearly defined. Once it’s been clearly defined then a measurement framework can be developed for each page that describes how the page’s success can be assessed. Once the measurement framework has been developed, then measurement systems can be configured to include the right kind of metrics in the right kind of way. With page level measurement frameworks,  it also important to consider the context of the page itself. How do people get to the page? How far is it into a customer journey?

Consider the classic product page in an e-commerce site. A product page has a tough job. The primary purpose of a product page is generally to persuade a website visitor to add a product to the basket. To do this it needs to provide all the information required in a clear and concise way. To measure the success of the product, you could look at metrics such as the add-to-basket rate. You could also measure the effectiveness of different components or tools on the page in terms of how they influence the add-to-basket rate. But often a product page on an e-commerce site is also the landing page. Often it’s the first page that a visitor sees on a site. For them it’s also the home page. So, the product page also has the goal of building trust and consideration for new visitors. It doesn’t just have to persuade the potential customer to buy the product; it has to persuade the customer to buy it from you. By identifying these additional page goals, you also identify the need to measure success using additional metrics, such as the bounce rate for new visitors.

Not every single page on a site necessarily has to have page goals, but certainly they should be in place for each different type of page or section on a site. Page goals are useful to help define what good looks like at the micro level and to ensure that your measurement frameworks are measuring the right things in the right way.

This article was originally published on ClickZ

There is a trend, particularly in the web analytical arena, for some consultancies to simply create more and more metrics for businesses, rather than working on their clarity. Unfortunately, there is also a tendency for many people to suffer from ‘data blindness’ when confronted with rafts of metrics churned out by multiple tools and technologies. Advertising campaign management, web analytics, CRM, and other intelligence tools should enable businesses to interact better with their customers, but for many actually understanding all that information – particularly across wide portfolio websites – proves very frustrating.

This is why we and our customers place such value in effective visual dashboarding – known to some as data visualisation. But does everyone really understand what a dashboard is supposed to do? If we ask new clients whether they already use dashboards, many will say they do. But when we actually see these tools for ourselves, they often turn out to be Excel workbooks or similar spreadsheet style presentations riddled with tabs for all manner of different metrics. Sure, internal web analysts may spend many well intentioned days compiling these books to send around the business, but they rarely receive widespread adoption simply because most people don’t understand metrics in this dense form. This style of ‘dashboard’, if it can even be called such, is not targeted and really just represents dumped reporting. It’s an inadequate ‘one-size-fits-all’ approach that confuses most people and lacks any practical meaning.

At Foviance we know how important it is to understand business requirements before attempting to produce proper dashboards that help individuals understand key performance indicators (KPIs). In most cases, a dashboard wouldn’t attempt to present more than around ten KPIs, but would be sufficiently flexible to present different information for different people across a business with different information needs. We also try to help people understand the difference between metrics and KPIs – a metric is a simple measure that can be quantitavely assessed, but a KPI is an indicator that actually means something to individual people across a business when it goes up or down, and importantly it is something that can then be acted upon.

So an effective KPI framework or strategy helps people understand and challenge KPIs. These frameworks can be distilled down to a short list of things those people really need to measure that means something to them and which they are able to display and share in an immediately understandable way. How does last month’s figure relate to a previous year? How about month on month? Is that fluctuation typical or unusual? Is that good or bad?

We hone our dashboarding techniques through the use of ‘sparklines’ – inline charts that clearly show how the trends and variations of particular metrics. The technique and term was created by Edward Tufte, a noted American statistician and expert in information design. We have listened to Stephen Few, another respected expert in the field of effective visual communication who examined dashboard design and visualisation to see why some methods fail and others succeed.

We try to think of business dashboards like airplane cockpits – there is a huge amount of granular data we could present, but the most important thing is to clearly and immediately highlight critical indicators and common KPIs. A business that discovers it had 100,000 page impressions over the past week actually knows nothing unless it has also been alerted to the fact it had 10,000 or 1,000,000 the previous week. Good dashboards alert anomalies and dramatic trend tacks. The right people can then dive into annual averages assess significance and highlight possible actions. Initially though, the dashboard should clearly display from afar or at glance any issue of possible immediate concern – just like in a cockpit.

We’re continuing to develop ‘dashboarding’ as service at Foviance, and we’re making particular strides in this field right now. Talk to us about tactical dashboards, strategic dashboards, and how they can help different audiences across your own business with short and long term planning, as well as the unexpected.

This article was written as part of the crossing the channels of experience March newsletter

I’m increasingly convinced that the issues that most businesses face around the successful deployment of analytics in their business are not to do with their technologies but to do with their businesses processes. That view was reinforced this week when I was running a workshop with a group of students studying on a Masters Programme in Internet Retailing.

As part of the session I asked the class what they thought some of the key ingredients were in executing successful internet optimisation. They started off by throwing out items like “good data”, and “technology”, and then moved on with things like “flexibility”, “willingness to fail”, “good hypotheses”, “risk culture” and “strong business model”. I then asked the class how many of them worked in organisations that had those characteristics. No hands went up.

Often organisations are happy to spend money on new campaigns or large scale product development on their site without thinking or being explicitly clear about how they are going to measure the effectiveness of the campaign or the new piece of functionality. So how do they know that they have done a good job? The measurement piece also needs to be built into the campaign or product development process as well. Here is a simple framework to use as part of that process.

• State the objectives
• Define the success indicators or KPIs
• Perform a gap analysis
• Create the measurement roadmap

State the objectives

Before you start, be very clear about what you are trying to achieve. State your objectives and make sure that they are not what I call “marshmallow objectives”. Marshmallow objectives are ones that are soft and squidgy. They are not firm and they don’t hold up to scrutiny. These objectives are difficult to measure. People often talk about making objectives “SMART Objectives”. SMART objectives are Specific, Measurable, Achievable, Realistic and Time bound. You should try and make your objectives as SMART as possible. If you come up with objectives like “to improve the user experience”, force yourself to be Smarter and ask yourself questions like: “why?”, “which users?”, “in what way?”, “by when?”

Define the success indicators or KPIs

Next define the measures of success or the Key Performance Indicators. These should relate directly back to the objectives. If the objectives are SMART then the definition of the success metrics should be relatively straight forward. Constantly ask yourself “What does good look like?”. If your are achieving your objectives, what will be happening in the business or on your site? Sometimes it can be difficult to measure objectives directly with significant cost (see below) then you may need to come up with other success metrics that are indirect measures of success.

Perform a gap analysis

Once you know what it is you want to measure, perform and analysis of what you can measure now versus what it is that you ideally want to measure. Where are the gaps? Do you have the right measurements systems in place already and are they configured the right way? For example, f you want to improve likelihood of someone booking a holiday once they have done the research on the site, you might set up a success metrics that is something about visitors propensity to book at a later date. You may need a survey in place to be able to measure one. So do you need to set up a survey from scratch or do you just need to ask a specific question on an existing Voice of the Customer programme?

Create the measurement roadmap

The final stage is to create the measurement roadmap. This is effectively the plan of how you are going to measure the things that you need to measure, or as I like to say, “Count the things that count”. A key part of this is prioritising the work that needs to be done, being clear which gaps you are going to plug in your measurement systems and then how that work is going to get done. This could range from just ensuring that there is a specific custom report developed through to the implementation of a new piece of software or the adoption of a new service.

The process I have outlined can take days to complete or it might just take a matter of minutes. It might be a very strategic piece of work looking at the business overall or it might be quite tactical, for example running an A/B test on a landing page. Whichever it is, it’s a way of putting measurement and analytics at the heart of your business processes. So anytime you’re planning to do something, ask yourself (or your colleagues): What does good look like? How will we know we’ve done a good job?

Jim Sterne, the author and Chairman of Emetrics, was in London last week and I had the opportunity to catch up with him at our offices. During our conversation we talked about the way that the market is developing and what the differences were (if any) between what is happening in the digital marketing optimisation space on my side of the Atlantic compared to his. As we were talking I kept thinking back to a simple model that I developed a number of years ago to describe where organisations are in the development of their digital marketing measurement and optimisation capabilities. These days it would be fashionable to call it a “maturity model”.

My simple maturity model has three main stages:

• Performance tracking
• Process optimisation
• Customer centricity

Performance tracking

In the first stage of their development organisations are focussed on performance tracking. The challenge here is to “get the right numbers right”. This is laying the foundations for further capabilities to be developed and the main activities revolve around deifying the needs of the business, setting success criteria such as Key Performance Indicators (KPI’s) and getting the right measurement technologies in place and getting them working properly. This will involve a lot of effort with specification and configuration issues.

At this stage in the model, organisations might be wondering whether it’s all worth it. The “effort to insight” ratio is high. The amount of value being gained from the data may seem to be low compared to the amount of heavy lifting required to get it. However, it’s vitally important that this bit is got right otherwise it’s hard to move from this stage of the maturity model to the next.

Process optimisation

Once the right numbers are right, organisations can start to use the data to make better decisions. The application of insight into the business creates an opportunity to optimise processes and to begin to create some return on investment in the putting the measurement capabilities in place. So the focus shifts from tracking to optimisation of the core digital marking processes such as acquisition and conversion.

The process of optimisation is driven by the “test, learn and adjust” approach and requires more than just good data and some smart technology. It also needs the right organisational culture backed up by sounds business processes. These processes needed to be embedded into the organisation during the Performance Tracking phase and they guarantee data integrity. Nobody likes making decisions on dodgy data and you can’t optimise marketing processes without making decisions about what’s working and what’s not.

Customer centricity

During the Process Optimisation stage, organisations still tend to be quite “site centric”. They are focussed on how to optimise a series of processes, tweaking conversion rates, improving satisfaction scores and so on. The next stage in the model is moving from being “site centric” to being “customer centric”.

The main difference I think is the difference between asking what the conversion ratio was last week and asking how many new customers there were last week and what’s their expected value in the future. Customer centricity is about looking at the digital channel in the context of an organisation’s overall relationship with the customer rather than the other way around.

The main challenge here is to be able to get a multi-channel perspective on the customer. To do that you need multi-channel data, bringing together where possible data from offline systems and online systems.

The conclusion I came to from my conversation with Jim was that the difference between what is happening over here and what is happening in the US is essentially down to the distribution of where organisations are in the maturity model. There are some companies in the UK and the rest of Europe doing some really interesting stuff, it’s just that there are more of them in the US. But that’s OK, sometimes it’s best to go second, you can learn quicker that way!

Video interview with Jim Sterne

This post originally appeared in Applied Insights’ events section. Foviance acquired Applied Insights in November 2008, with Neil Mason joining us as Director of Analytical Consulting. As part of this acquisition, we’ve incorporated Applied Insights’ events list into our own.

This year Neil was invited by Jim Sterne to be the conference chairman and a keynote speaker the Emetrics Marketing Optimisation Summit in London.

As well as fronting up the proceedings over the two days and trying to keep the conference (and its speakers) on track, Neil delivered a keynote presentation looking at the development of web analytics and marketing optimisation practices within organisations called: “To Marketing Optimisation and Beyond!”

Last time I shared some thoughts about the process of selecting analytical systems, particularly web analytics systems. These included:

• Being clear about your goals
• Defining the KPIs and key metrics
• Defining the business processes
• Writing a challenging business requirements document

But selection is only half the battle to successful adoption, there is also the issue of actually implementing and getting business value from it. In the past I have seen systems implemented that work perfectly well but are not felt to deliver much in the way of value, simply because they are being underutilised (for whatever reason) relative to their potential. So, what are some tips for the successful implementation of analytic systems?

Write a plan

This may seem like a case of stating the obvious but the point that I would make is to write a type of business plan rather than a technical systems implementation plan. The plan needs to consider all the environmental and organisational issues as well as the technical issues.

On one level there are all the technical issues that need to be thought through such as how is the site going to be tagged or how are the log files going to be harvested? The devil is in the detail with these sorts of issues and careful consideration needs to be given to them upfront to avoid pain later.

One another level the success of the system implementation will also be judged on whether people are using it and whether they are getting any value from it. This is where the organisational and environmental factors come into play. Different types of organisations and functions are likely to have different attitudes to the adoption and use of analytical reporting systems. So what’s the plan to get them engaged on the level they need to be engaged at?

Find advocates

As part of the plan you may want to think about who your advocates are likely to be. These are the people who “get it” and will be willing adopters of the new system and the opportunities that it will offer them.

One of the mistakes I made in the past when trying to roll out a new analytical system was to try and roll it out on too wide a front. Too many people were involved and at the time it was like trying to push water uphill. Since then I have found that a useful approach is to find group of individuals or a function that are happy to be early adopters and work closely with them in the early phases to get the system actually being used in the business.

In the case of a web analytics’ system you may work with the advocates to get some of the more sophisticated features of the system up and running such as campaign tracking capabilities. Working with these advocates can then help you do develop useful case studies that you can use with the rest of the business in seminars, training courses and the like.

Deliver appropriate training

In order to get the most out of a new system everyone needs some training even if they are not going to be actually using the system itself. Training can often focus on how to “drive” the system itself, a bit like learning how to drive a car, but it’s also useful to coach people in what to do as a result as well, and that’s where the case studies you’ve built up with your early adopting advocates come in handy.

In many cases systems can “push” reports out to end users via email or on an intranet, which is great. But do they end users always know what these reports are telling them and what they should be looking for? I’ve argued in the past that an analyst adds value by adding interpretation but if this isn’t possible all the time at least think about how to coach end users in how to interpret the data that’s being delivered to them.

Demonstrate value and celebrate success

The proof of the pudding is in the eating and as I mentioned last time you only really know what you’re going to get with a new analytics system once you’ve got it in and up and running. It’s important that the value of the investment is demonstrated to the business. This may take different forms depending on the business but there should be some early winds that you can point to in order to demonstrate the success of the implementation. These may be new approaches to campaigning that are being implemented or improvements in conversion in certain areas. Whatever they are, add them to your library of case studies and use them to show the business value every opportunity you get!

Next week I’m heading over to Search Engine Strategies in New York to take part in the ClickZ track. I’m really looking forward to catching up with some old colleagues and hopefully meeting new ones. I’m also looking forward to taking a look at the search analytics scene and will reporting back on what I find next time round. Till then…

There was a bit of research publicized recently about organizations’ satisfaction with web analytics systems. It reported that 62% of the respondents from these large companies were happy enough with their web analytics package that they would recommend it to others. 62% seems like a pretty good number to me but what about the other 38%? Are they not getting what they need from their package and if not, why not?

I’ve worked with a number of companies over the years helping them to select and implement analytical software including web analytics packages. Indeed I went through the painful process myself, implementing a tag based ASP solution across a dozen sites across Europe and integrating it in with other information and operational systems. As a result of the work I do with clients, I have heard lots of stories about why they want to move from their existing package to another one.

Some of the common themes that I come across are:
The system doesn’t deliver any value to the business…
The system sits in isolation and it’s difficult to get it to integrate with other data…
The system doesn’t appear to work properly and deliver what’s needed…
Nobody really knows how to use it…
These themes are usually the symptoms of a poorly thought through selection and implementation process for the existing system and may not always be a problem with the system itself. Often these issues stem from a lack of clarity about what is really needed in the business and also an underestimation of the associated effort required.

There can be a tendency in the process of selecting analytical systems to be led by the technology and the feature set. As I mentioned in the last article, funky overlays and pretty dashboards might make the data look good, but you also need to ensure that you can get at the right sort of data you need in the right sort of way.

Here are some thoughts about how to minimize the risk of being one of the “38%”.

• Be clear about your goals
• Be very clear about what you are trying to do online and the reason for the investment in the web channel. From this all else flows.
• Define your KPIs and main tracking metrics

Once the goals are visible, it’s possible to identify what the channel Key Performance Indicators (KPIs) are. In addition, there will also be a number of other important tracking metrics as well, which are not as strategically important as KPIs. It’s important to recognize that KPIs are not always metrics that come out of a web analytics package. In fact, in my experience they rarely are.

Define the business processes

As well as defining the important metrics, it’s important to map out and define the important businesses processes. How are campaigns managed? How is the site development process managed? The tools should help the business processes and you shouldn’t need to change your business processes to fit the tool, unless it is clearly a much better way of doing it.

Write the business requirements document

This is the document that goes out to potential vendors. The point of the document is to clearly set out your needs and to invite the potential supplier to articulate how their system can meet them. In my view this document should enable the best potential suppliers to shine through, so don’t be too prescriptive in approach. The kind of huge document that requires huge amounts of detail input on every single aspect of the system does not help at this stage. You hate writing them, the vendors hate responding to them and then you hate reading them. And even then, there is no guarantee that you will be able to see the wood from the trees anyway.

Keep the requirements document concise, clear and open. Invite vendors to make an effort to differentiate themselves. A vendor once told me that the best brief they had had from a prospect was “This was what we are trying to do, tell me how you can help us do it better” and that was it.

Get it down to two or three

From the various responses that you get, you need to whittle it down to two or three potential vendors to come in and pitch for the business. Don’t invite them all. Look for quality not quantity in the response documents that you get back from them. First of all, have they answered the brief? Have they thought about what you asked them to think about? Or have they merely cranked the document out of the proposal machine? Do they demonstrate an understanding of the business and how they can help? Does it look like they want your business more than the next person?

Test it out if you can

In an ideal world you should test at least your preferred system, or preferably two systems side by side to see them in action. Analysis and reporting systems are “experience products”, you only really know what you are going to get when you’ve already got it. Having real hands on experience will be invaluable in helping you decide whether this is the one from you and you are going to be one of the “62%” of organizations.

Selection though is only half the battle. The other half is getting the system in, working properly and being used to its potential. Next time, I’ll being sharing some thoughts on successful implementations. Till then…

There have been a couple of interesting threads running on Eric Peterson’s web analytics forum over the past few days. One was on setting Key Performance Indictors (KPIs) and the other one was on ways of reporting web analytics data in a meaningful way for business users. These are of course related topics because if you know what’s important to measure, then it makes it easier to focus and report that data in a meaningful way.

My fellow columnist Jason Burby has written extensively about setting KPIs for different types of web properties and it’s worth reviewing those articles. I just wanted to add some thoughts and my perspective on the subject of developing KPIs and the dissemination of e-business insight around the organisation.

In an ideal world, all organisations will have clear goals and objectives. Flowing out from those goals and objectives will be the organisation’s Key Performance Indicator’s (KPIs). These KPIs are the measures by which the organisation knows whether it is being successful or not. In a similar way the e-business channel would have its own goals and objectives which would be aligned to the overall corporate goals. From the e-business’s goals and objectives would come it own KPIs which would have some resonance with the corporate KPIs.

For example, take a multi-channel retail banking operation with branches, a call centre and an online banking presence. One of its corporate objectives might be to improve the efficiency and cost-effectiveness of its customer service operations. This might have a corporate KPI attached to it like the cost per customer transaction.

In terms on the online channel, this corporate objective might manifest itself into a desire to increase the use of online self-service facilities. A KPI for this might be the number of transactions carried out on the online bank. By measuring the volume of transactions being carried out on the online bank against its targets, the bank will know whether it’s being successful not in improving the adoption of self-service facilities and reducing its cost per transaction.

One of the potential traps when developing KPIs is to have too many of them. It’s important to remember that KPI stands for Key Performance Indicator and so the focus of measurement and attention is going to be lost in there too many metrics being tracked. My view is that there should only be a handful of KPIs at a time, ie about 5 or 6. A small number of KPIs are easier to remember and to keep at front of mind. Too many KPIs become unwieldy and result in a lack of focus on the main objectives. If you have 30 different measures each with the same perceived priority, then there is a danger of “paralysis by analysis” as you wade through a sea of metrics trying to understand what is happening to the business.

Selecting KPIs is a strategic process but that doesn’t mean that they then become set in stone. They are dynamic metrics that can change from year to year as the goals and objectives of the business change.

I believe that there is a tendency amongst e-businesses to focus on metrics that come out of web analytics packages and call them KPIs. I heard an interesting comment at a conference earlier this year from a guy who used to run Amazon in Europe. He said “Be careful of what you count. What you count is what you get”. I think that this is a powerful piece of insight as it links the way that organisations are measured into the ways that organisations (and the people within organisations) will then behave. Are you more interested in increasing page views or are you actually more interested in improving customer satisfaction?

Another tendency is often to focus on KPIs that are outcomes rather than the causes of those outcomes. Shouldn’t the focus be as much on the drivers, as on the results? For example, on a commerce site a KPI is often “Sales”. The sales figures will also typically be part standard financial reporting pack. However, sales are an outcome or a result, what is it that is driving sales and should some of those drivers be KPIs?

So, what makes a good KPI? One succinct answer from Craig Schiff, a business performance management consultant, is that the qualities of good KPIs are that “they are strategic, they are actionable and they will make a difference in improving the business”. How do your KPIs stack up against these criteria?

Once KPI’s are agreed, understood and measurable, then I believe the process of data dissemination becomes a lot easier. The organisation is focussed around the key issues and knows what data they should be looking at or looking for. There are still obvious challenges in finding ways and means of bringing web data to life and that’s something I’ll be taking a look at next time round.