Consumer Insight

This article, written by Neil Mason, was originally published on Clickz.com on 07/12/10 and is republished here with permission.

“Insight”. It’s a word that most of us probably use every day. Client companies demand “insight”, agencies and consultancies strive to deliver it. But what is it and how do you know when you’ve got it? Or have created it? Is it one of those words these days that we use glibly like “analytics” when we really mean “reporting” because it sounds better or more sophisticated? Read more…

These days we live in an experience economy. Many organisations look to compete through some kind of service or product differentiated strategy rather than purely on price. But it’s a complex landscape – organisations have to work across multiple channels and deliver a joined up experience – across the web, the call centre, stores and other touch points. Consumers are no longer tolerant of organisations that don’t.

So how are businesses coping with the need to deliver a multi-channel, integrated customer experience? Well the evidence from a recent report conducted by Foviance in association with Econsultancy here in the UK, suggests that whilst organisations recognise that is something they need to do, they are a long way from delivering on that need.

The report is based on a survey of over 500 businesses. It conforms that the majority of organisations do recognise the link between business performance and customer experience but are struggling to develop a strategy for multi-channel customer experience management and then delivering on that strategy…

A more in-depth version of this article has  been republished with permission by ClickZ on the Foviance website if you’d like to read more. Also, please download your own copy of the Multi-channel Customer Experience Report today.

This article was written as part of the Foviance December 2010 newsletter

By Billie Andersen

Even if you’re not a Facebook addict or regular Twitter user, you’ll know how difficult it is to escape social media. Why? Because social media is revolutionising the way that people consume content.

Social media is opening new channels of communication between brands and customers and there is a lot of potential in the social web that marketers can tap into. For example, a study earlier in the year by Penn State University showed that 20% of all tweets mentioned a brand name. Sales and marketing professionals need to be aware of these significant media consumption trends so they can tailor and target their messages as effectively as possible across a changing landscape. Read more…

This article, written by Neil Mason, was originally published on Clickz.com on 22/12/09 and is republished here with permission.

It’s hard at this time of year not to get a bit reflective at the year that’s gone by and to think ahead to the year that’s about to be. But it only struck me though as I sat down to write this column that I am just about to complete my first decade working in digital marketing analytics. I got started when I moved to work at an online auctions business in 2000 having spent a (large) number of years working in “offline” marketing analytics and consumer insight. I remember that when I got to this online business that the head of marketing told me to forget everything that I had learned in the offline world as this was “new media” and that “things were different” now. Read more…

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 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.