How to be happier

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Our brain is constantly rewriting memories- therefore trying new things, going out of our comfort zones and developing ourselves as best we can, will help us to expand on memories. Bad memories are suddenly seen from a new perspective and become more positive; our already happy memories grow in their importance and significance to how we view ourselves as today. All experiences and memories make us who we are today, so it is important to look back on the past positively to help us develop as a person.

It’s also important not to rely on our brain to rewrite these memories. Addressing painful memories in which you have felt hurt or lied to can be painful. However, by facing them directly, and even the people who caused them can help you deal with the memories and the pain associated with them. By doing this the memories become less painful, and less significant to you, allowing you to give greater significance to positive parts in your life.

It is of great significance to create new memories. It is important to live for today, ensuring that all the new things you bring into your life help you develop into a more positive person. Trying to live without expectation for tomorrow can help benefit when that day comes as there is no opportunity for disappointment. It is important to remember that life is a journey, not a race. Making time to laugh, have fun and sleep in your day ensures that your needs are fulfilled. A lack of sleep makes you more vulnerable to negative emotions as it is harder to rationalise all the different ideas following through you day. Therefore enough time should always be put aside to rest and rejuvenate.

Remember that everyone has their own problems, and whenever you struggle you are not alone, or weaker than those around you. It is not a weakness to feel pain, or to be apprehensive to trust others because of the people who have lied to you in the past. But, we should use these memories to grow. By taking a negative memory and focusing on the positive can help us to have a whole new positive outlook on life. For example, do not let ignorant, rude people in life bring down your mood, but appreciate the positive things they have provided, for friends you would never have met without them.

When you focus on your problems you have more problems. When you focus on your possibilities you have more opportunities.

Read more Schezzer’s Blog articles at

Just because we can doesn’t mean we should!

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The attraction of new technology is often beguiling. David Mattin, Head of Trends and Insights at commentators, has long advocated that the failures of technologies such as Google Glass was because they failed this basic test.

Allowing the technology to dictate what is launched to the world can sometimes yield success, but as the evidence of the high percentage of failures shows, often the hype exceeds the hope.

David Mattin argues that innovation must always think about people first and, more importantly, develop things where the benefit in terms of ease of use outweighs the cost and hassle of using it. Things like “Amazon Dash” seem like a good idea to some people and there may be a niche but widespread use seems unlikely. By contrast, Alexa, which has many multiple uses, will appeal to more of the people more of the time. And that is key.

Mattin recommends that innovators and start-ups should always view technology through the lens of ‘deep human needs and wants’, not through the lens of technological possibility. That is easy to say but somewhat harder to do without conducting market research. But market research has an issue.

Designing research studies for products or mobile phone apps that do not yet exist, may require the need to get respondents to think about doing everyday tasks in new ways to how they do them at the moment. Sometimes that is easy – it is not a big step to tell an electronic assistant to order a pizza versus speaking to someone on the phone – and the benefit in terms of time saving from not having to find the phone, dial the number, wait for the call to be answered, and then negotiate with someone who is capable of totally independent thought and of unknown intellectual competence is clear and tangible!

The question is whether there is a more straightforward way to judge the likely marketplace success of the product or service you are looking to develop. And, of course, there is.

Marketplace success is driven by five key elements – known as the RAAVE drivers.

  • Relevancy – Do I need this?
  • Association – Is this something or a company I would like to be associated with?
  • Accessibility – Is this easy to use and can I afford to use it?
  • Value – Does the benefit outweigh price and hassle involved in using it?
  • Expectation – Will it do what it says on the tin?

If you want to know whether or not your next venture will be a marketplace success then judge it on research that tells you the answers to these five questions – both in absolute terms and relative to the competitors you are up against, both old and new.

If you out-score the competition on these five criteria then you will be likely to have a success on your hands.

Lag behind on one or more of them and you may have a problem.

Success is never guaranteed as the world is dynamic, but knowing your score on these five dimensions will ensure you stack the odds in your favour.

Read more Schezzer’s Blog articles at

Post-Recession Priorities for UK Consumers

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Back in September 2013, in an article published in Marketing magazine, Andrew Curry, a Director at The Futures Company, said that the post-recession consumer will, for some years to come, have a very different set of priorities. His key points were that they would be:

- More cautious attitudes towards debt
– Wanting increased social connectivity (and the support network that engenders)
– Have greatly increased intolerance of “rewards for failure”
– Looking for value, not cheapness
– Needing assurance (in an increasing uncertain world)
– Seeking “purchase decision process simplification”

RedRoute’s view, by contrast, was that these were not that different to the priorities they had before the recession and that, in fact, the historically low level of interest rates may actually encourage borrowing.

What we said instead, in our article on 25th November 2013, was that the best way to understand the future demand for your business is to use the five key drivers of customer preference as we explain further below.

Looking back at what has happened since the recession, we can now see that consumer debt has actually grown continuously since 2013, as shown by the chart below taken from the Guardian article by Phillip Inman and Caelainn Barr, published on 18th September 2017 (“The UK’s Debt Crisis”).

The following extract from their article gives the context to this rise:

In 2007 unsecured consumer debt – mainly on credit cards, store cards, loans and overdrafts – peaked at 45% of household income. In the years immediately following the financial crash, households were more inclined to kick their credit habit. Saving increased and borrowing declined, as the level of unsecured debt fell to 35% of income by 2012. But since 2012 households have increasingly failed to clear their credit and store card bills at the end of the month. High interest rates on those cards had sent their debts rocketing and the OBR [the Office for Budget Responsibility] now predicts unsecured household debt will reach 47% of income by 2021.

Bank of England figures show unsecured consumer credit jumped 4.9% in the past year when adjusted for inflation. The total increased from £192bn (in today’s money) in July 2016 to £201.5bn in July 2017.

This marks a slowdown on the previous two years when growth hit 12% and inflation was almost non-existent, but maintains the trend for UK GDP growth to come almost entirely from an increasing population and consumer spending with borrowed money.

It wasn’t supposed to be this way. The government’s official forecaster, the Office for Budget Responsibility, once predicted that the UK’s economic revival would be built on the foundations of business investment, higher exports and an improvement in productivity that would lead to higher wages. It didn’t materialise. Instead a mix of low wages growth, government cutbacks on welfare and public services, and more recently the uncertainty created by the Brexit vote have forced millions of households to borrow to buy essentials.”

So old habits die hard, both for businesses and for consumers.

As for social connectivity, well in one sense – the use of social media – that was more-or-less a one-way bet. As the “internet of things” connects more and more devices in ever more ingenious and seamless ways, people become connected whether they want to or not! But things are changing.

Social media is increasingly being brought into disrepute and there is growing calls for further and further regulation. In a similar vein, the “sharing economy” is also coming under fire for being “unsocial” – with rulings against Uber growing around the world typifying the pressures.

With regular stories about “fake news”, “the need for authenticity and objective verification”, and stigmatism emerging with regard to “online cliques” with self-reinforcing narrow views, the future may look very different to the one envisaged back in 2013.

Our prediction is that, ultimately, Facebook and YouTube will be re-classified as publishing houses not software platforms, because that is the easiest route by which society will wield the pressure to get them to take responsibility for what appears on their services.

In the short term, whilst new regulations such as the General Data Protection Regulations (GDPR) which are being introduced from May 2018, are seeking to ensure companies of all sizes take greater care of personal data, people in general have already lost confidence in the security of data held online. That will lead to changes in behaviour by individuals which, encouraged by GDPR, will to lead them to be much more cautious about what they share online and what permissions they give.

Again, not quite the outcome anticipated. So, what about less tolerance of “Rewards for Failure”?

Unfortunately, the outcome on that score is not that good either. As Kara Scannell and Richard Milne reported in their article in the Financial Times on 9th August 2017, there have been hardly any prosecutions for malpractice resulting from the Financial Crisis and, worse still, the proposed “stronger regulations” on everything from Bankers Bonuses to Trading Rules and Financial Robustness requirements have gradually been quietly either reduced or completely eliminated. Meanwhile we see stories in the paper about earnings and payoffs for senior roles in the public sector that seem way out of line with those of the workers responsible for delivering the services.

To quote from the article by Scannell and Milne:

“One of the enduring mysteries of the 2008 financial crisis has been why the Justice Department made so few attempts to prosecute the individuals responsible for it, given the abundance of tangible evidence of wrongdoing by Wall Street bankers, traders and executives in the years leading up to the great unwinding.

The authorities …take pride in the “record-breaking” amount of money collected from the banks in the form of civil penalties. But forcing big banks to hand over their shareholders’ money in exchange for burying forever the evidence of wrongdoing is not nearly the same as holding people accountable for their behaviour.”

And not to mention any efforts to return the gains they walked away with which ultimately everyone else paid for.

On the quest for value for money, that is a perennial desire and it did not start in 2012. In fact, one of the things we find in our data modelling is that despite what everyone says, price elasticities do not “increase during recessions” except in a purely technical sense. What changes is the ability to spend at any level of price due to the reduction in real income.

If you want to predict how well your company will do in the marketplace in the future then the most predictive measure is what we call “Effective Net Preference”. ENP is a “balanced scorecard”-measure of the five key dimensions that influence customer purchasing behaviour (both consumers and businesses). These five dimensions are:

Relevancy (right solution)
Association (right image or brand identity)
Accessibility (perceived – and actual – obtainability)
Value (right cost-benefit trade-off)
Expectation (expected reliability that the brand promise will be delivered)

Value for money is one of these five.

Interestingly, though, each of the items listed by Andrew Curry are all part of one of these five dimensions. Consider:

Taking on debt is simply about “Accessibility” – what you can afford. And “simplifying the purchase process” is again about the preference to use services that are quick and easy. Which is why services such as Just Eat have been successful.

Social connectivity is about the need to be accepted socially – and one way to achieve that is to buy brands you are proud to be associated with. Feelings of injustice, by contrast, will mean you avoid companies that you believe do not “play fair” – and that is where brands like Uber are coming unstuck.

Meanwhile the need for assurance has been met by many online services from Trivago, to TripAdvisor to, to uSwitch, and ultimately to meerkats… who now personify the concept of trusted advisers.

So, if we analyse the winners and losers in the post-recession years we can see how the winners are the companies that have been delivering successfully across each of the five key drivers. The losers are those who have underperformed on one or more of those.

The companies that are under threat – and that includes Facebook and Google – are those who are not in control of their performance on all of those five drivers. Think Uber as a classic case in point. Expect Deliveroo, Airbnb, and other services that are not in total control of the service they provide as potential risks for the future.

The priorities for the post-recession consumer are Relevancy, Association, Accessibility, Value and Expectation – or RAAVE for short. If you want your customers to be your “raving fans” in the years ahead then concentrate on tracking your relative performance on these five dimensions and you will succeed. Ignore them and you trust your future success to decisions that are beyond your control.

For more evidence on the power of ENP and the RAAVE metrics contact us for further details.

Most Strategy is Actually Nothing of the Sort

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According to management consultant Alastair Dryburgh, most “strategy work” is simpler than many consultants and academics would have you believe.

When you engage in ‘strategy’ you are not re-inventing the company, redefining your industry, or creating the next Facebook.

You are simply looking at what’s going on in your markets to identify what customers (mostly your existing customers) are likely to be asking for in the future. You are looking inside the company to see where you are making money and where you are not. Often, you end up with something quite like what you already have, with some parts expanded, others shrunk or eliminated, and a few things added.

Recognise this and you will see that you probably already have all the strategic knowledge you need. The value is not in radical new concepts or techniques, but in the ability to see clearly and act accordingly.

Alastair Dryburgh is chief contrarian at Akenhurst Consultants and author of the book “Everything You Know About Business is Wrong”

Tesco Woes Predicted by NPS as Early as 2011

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Back in 2011 Tesco was still riding high with over 31% market share but even at that stage it was the only one of the big 4 supermarkets to have a negative NPS value. Moreover, the 2011 NPS scores for each of the top 4, as measured at the time by research company, proved to be a very reliable to their long term market share performance, as can be seen below.

The problem with NPS, however, it that it is does not, of itself, tell you what the problem is. For that you need underlying diagnostics. In Tesco’s case we can find such evidence in the general YouGov tracking study, BrandIndex™. As can be seen in the chart below, the tracker showed how perceptions of value had been falling like a stone throughout the whole of 2011 and by 2012 Tesco was being rated the worst of all the major supermarkets.

Tracking your NPS but only having individual customer feedback to help diagnose what the big issues are means that companies can fail to get the big picture and take the right strategic steps to correct the problems.

That is where RedRoute’s AIME Tracker gives companies a vital edge. It tracks the 5 key aspects that determine whether customer prefer your service to the competition. Its overall metric, Effective Net Preference (ENP), not only explains movements on NPS but also predicts them. So you know what the causes of your low NPS are, whether it makes a difference to your sales, and in what direction it is likely to move in the future. It is based on the five dimensions shown below:

More colloquially, these five dimensions can be thought of being customer perception of whether your offer provides:

- The Right Solution from
– The Right Brand for
– The Right Effort for
– The Right Value in
– The Right Way

To find out more, come along the Customer Experience Summit in London on October 15th and 16th and see how our analysis of the Tesco situation and how you can avoid similar problems when using managing your NPS programme.

Steve Messenger
RedRoute International Ltd

Measuring the Marketing Benefit of Social Media

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In the July 2013 edition of Marketing Richard Medley of Nexus Communications highlighted the fact that Facebook ‘Likes’ were a debased currency. Marketers employing social media tactics just to maximise their number of ‘Likes’ were deluding themselves and the only value in the data generated was in proving that the campaign had happened, not that it had been successful or effective.

In fact, so much in a similar vein has been written on this topic it has become amusing. It was recently described as being as popular a topic as teenage sex. Everyone says they are doing it but how often and how well no one knows. So as practitioners in the field (of Measuring Marketing Effectiveness – not teenage sex) we thought we would inject some realism.

The problem of measuring the effectiveness of social media is like that of experiential marketing – it is easier to measure that it has happened than to measure what effect it has had. Sure there are some immediately measurable impacts, especially if there are sales links or incentives to participate, but that is not quite the same thing as measuring what impact it has had on underlying long term brand preference. Worse still, unlike advertising where bad ads are usually either ignored or just do not get noticed in the first place, bad social media and/or bad experiential can actually damage a brand’s reputation.

So we need a way to judge how comments are influencing underlying brand preference – and a way of doing it directly from the data so that we can do it in real time. The answer is to combine skills and expertise from traditional market research with knowledge of how to measure effective brand preference and data that shows how underlying brand preference affects sales. How can this be done?

At RedRoute we know that our Effective Net Preference (ENP) Score, based on brand perceptions across the five key dimensions that most influence likelihood to purchase, explains underlying sales trends more accurately than other metrics such as Net Promoter Score, Brand Equity measures and so on. This is because it includes perceptions not just about brand image and whether the product “does the job” but a holistic set of five key factors that influence actual ‘preferred choice’ behaviour. These factors are: Relevancy; Identification; Accessibility; Value; Confidence. For more information about these please visit our web site.

How do we know these work? Because we have predicted individual choice behaviour as tracked by loyalty cards and through customer sales programmes for 12 months ahead with very high degrees of accuracy, and not just for one or two people but for millions. In other words, “been there, done it, got the t-shirt”.

Into this framework has stepped social media data – and when you look you find you can classify social media comments using the same types of code frames as we use for verbatim comments collected via traditional market research. We have the same positive, negative and neutral spectrum. And we have the same five dimensions as mentioned above. And they give the same read on what is happening to underlying consumer preference. The only difference – it is more immediate and because of that, more valuable and more actionable.

Tracking social media comments enable you to see over time whether a particular social media campaign has moved the needle on any of the five key metrics – and whether positively or negatively. Amplification measures enable you to scale the likely impact onto overall effective brand preference. The only remaining step is to prove the relationship between Effective Preference and sales. That can be achieved via both market research surveys (which directly show the links between, for example, a brand’s ENP score and that brand’s share of wallet) or by modelling actual sales data using ENP as one of the explanatory drivers.

So this is the real way to measure the value of social media to long term brand building. And as our clients know, it works.

ENP is probably the most important brand metric that you have never heard of.

But master it and you will master the fortunes of your brand.

So you may well hear more about it in the future.

Using Market Research in Customer Targeting: An Updated Approach

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Those outside it frequently castigate the Direct Marketing (DM) industry for over-communicating with potential and existing customers (“junk mail syndrome”). Strenuous efforts to minimise mailing volumes and maximise response rates are therefore legion amongst DM practitioners. This article argues that it is time for a step-change in thinking about targeting that matches the UK’s current change from being a producer-led to a customer-led economy. DM practitioners need to move from using generic data for targeting that is provided by the market to finding and demanding the exact data that they need for the exact customers they need it for. The industry is the customer in this respect and must itself become fully customer-centric in meeting its own needs if it is to help its clients achieve the promised returns from CRM. We explain below one step-forward in this respect that we have been using for the past 5 years with great success. To help illustrate our key points we have used learnings gained from our work with a major European Gardening retailer over this time.


Marketing research, customer targeting, one-to-one marketing, usage & attitude study, database marketing, customer motivation, consumer behaviour

Targeting the Right Problem

In 1898 the British Navy found that for every 1000 rounds fired by its battleships only about 17 ever hit the target. Then Admiral Percy Scott studied the techniques employed by his gunners and discovered the most accurate were those who were better at allowing for the pitch and roll of the ship. Using this knowledge, he designed a new automated aiming mechanism that took their hit rate to over 50%.

Direct marketing is facing a similar dilemma. The information age has reduced the cost of delivering messages and increased exponentially the means of delivering them. The resulting deluge of communication has seen consumers donning their hard hats and rushing for cover. With eleven million tonnes of paper being dropped on UK consumers every year it is little wonder many are opting-out.

Current practice is a game of “Battleships and Cruisers”. Using a “Test & Learn” methodology dating back more-or-less to the era of the game itself, a never-ending series of “hit-and-miss” strikes uncover what works and what doesn’t. Admiral Scott would think we had learned nothing in 100 years and would probably have a point.

Modern armaments take Scott’s principle even further. By working backwards from the target, detailed intelligence ensures 98% accuracy, not 98% inaccuracy. DM practice needs a similar leap of thinking and making better use of market research is key. Depth of customer understanding is something each company can differentiate itself on; everything else is technology-driven and hence copy-able by competitors. This makes it the best (or only) way of achieving sustainable competitive advantage.

Taking the Consumer’s View

Working backwards from the target takes much greater effort. It needs close links with “special forces” close to the target who can uncover individual customer needs, pinpoint when, where, and how to contact them, and determine the best message to get the job done.

Using insights from market research would seem the obvious solution yet current attitudes within the DM industry would appear to be against this. In a recent edition of Marketing Direct , Holly Acland says she was told by the MD of one major DM company: “Market research is useful but it’s only the mental picture stuff. If I’m managing the customer, and the revenue that customer represents, I can do that without knowing the ‘why?’”. One only wonders how.

Acland concluded that market research has a valuable role in influencing the tone of the copy, influencing product development and measuring customer satisfaction but little else, quoting another senior DM executive as saying “There’s a limit to the amount of data you can collect and the number of people you can collect it from. So there’s a degree of assumption and therefore inaccuracy in applying the model.”

We disagree. Market research will tell you where to dig, what tools to use, and how rich a deposit lies beneath the surface. Done correctly, it identifies new measures of the customer, even from comparatively small samples. This article shows how this can be done. We begin with the underlying model.

Using Market Research to Understand Customer Motivations

Like Dick & Basu and Huddleston et al, we believe behaviours are driven by interactions between an individual’s attitudes and circumstances. Using this framework to model customer-specific data from loyalty card schemes we have found consumer purchase behaviour can be predicted using 5 common-sense motivations:

• The Relevancy of the Product or Service on Offer
• Identification with the Brand or Company providing the service or product
• The Accessibility of the Brand or Product
• Its perceived Value for Money
• Their Confidence they will be satisfied with what they will get

We present below examples from our work for a European gardening retailer where, with the respondents express permission, we have combined usage and attitude data, from a sample of over 3000 cardholders, with their loyalty card transactions.

a) Relevancy
Relevancy drives the level, mix and timing of engagement with a category and, unsurprisingly, factors such as age, life stage and other geo-demographics correlate with usage of a gardening store. Chart 1 shows category engagement (number of visits to the store over a 12-month period) versus customer age. The highest engagement in terms of both customer numbers and number of visits per year, occurs in middle age.

Chart 1 : Number of Visits per Year by Age of Customer

Interestingly, those under 25 and between 64 and 74 have disproportionately high engagement, implying these customers may have a higher interest in gardening than the average for their age group. Chart 2 supports this, where combining transactional records with data from the usage and attitude survey shows a strong and statistically significant relationship between the number of visits made to the gardening store and the customer’s claimed level of interest in gardening.

This is significant because it illustrates how U&A studies uncover attitudinal drivers that will predict category participation. It also enables us to find spend and visit behaviours that distinguish differing levels of interest.

Chart 2 : Number of Visits per Year by Interest in Gardening

This means we can understand the impact of customer attitudes without surveying the entire database and without needing to develop a projective model for attitudinal segment membership that is based overwhelmingly on geo-demographic overlaps. The U&A study delivers all the information we need to relate each individual’s attitudes and circumstances to their recorded purchase behaviour.

It also means we can find the measures we need to create from the data already on the database to identify customers with differing motivations, and what else is needed to reliably predict their engagement with the category and our brand on-going. And we can quantify how much better these models will be compared to existing models.

Chart 3 : Seasonal Visit Pattern Differences for Customers who “Dislike” Gardening

Lastly, it means we can use the model for “What if..?” modelling to evaluate the relevancy, appeal and hence “size of the prize” of potential new propositions.

b) Brand Identification

Many companies have conducted Usage & Attitude studies and have a market segmentation model that separates people according to their likelihood to consider using their brand. Couple this with information from the database and we can now derive how much they are likely to use our brand in specific circumstances.

Chart 4 presents an indirect example of this. It shows that there is a correlation (other things being equal) between number of store visits and number of loyalty cards held.

Not surprisingly, those holding many cards are less loyal but, more interestingly, those who claimed not to hold any loyalty cards also visited less. However, to take part in the survey they had to be cardholders. This lack of “top-of-mind” awareness of the card suggests a lower brand-engagement that appears to be reflected in behaviour.

Differences between more- and less-committed users enable us to tailor the message accordingly and better predict likelihood to respond. For customers who are more price conscious we may emphasise the price discount aspect of an offer and provide reassurance on quality. For more attitudinally loyal customers we may do the reverse. Combining research and database information gives greater confidence in and known reasons for response that can be leveraged in marketing communications.

Chart 4 : Numbers of Visits per Year by Number of Loyalty Cards Held

c) Accessibility

Accessibility measures the external circumstances that determine the consumer’s ability to take advantage of the proposition. For instance, I may get a mailing about a Mercedes but may not have the means to afford it, or may live far from a Mercedes dealer making servicing inconvenient. These factors are similar to those determining Relevancy, but either work at a more detailed level (e.g. the second Mercedes example); or relate to barriers affecting a particular individual (e.g. disability).

In retailing, a strong Accessibility factor is distance to store. Chart 5 shows this for our gardening retailer.

Chart 5 : Impact of Distance to Store on Number of Visits per Year

Existing awareness of the proposition, from TV advertising for example, is another Accessibility factor. Data from advertising research (Chart 6) shows how differing segments changed their spend according to their exposure to the retailer’s TV advertising (other things being equal).

Chart 6 : Customer Response to Advertising Pressure

This has important implications for integrated campaigns where mailings and other direct communications targeted towards customer acquisition are much more successful if harmonised with above-the-line campaigns. These generate awareness of the proposition; and the direct marketing component acts as the call-to-action.

d) Value for Money

Value for Money is the consumer’s judgement on whether the perceived benefit outweighs the perceived cost. Any lawnmower will cut the lawn but choosing which one to buy will also depend on the size of your lawn and how much you like mowing.

To understand which measures are most predictive of the consumer’s view of Value for Money, we typically consider 3 aspects: the individual’s attitudes to value; the relative price compared to competitor solutions; and the consumer’s view of the total benefit the solution provides relative to competing alternatives.

Market research is especially valuable in uncovering which behavioural characteristics best reflect these opinions. If I would not consider buying a Mercedes because of the lack of servicing facilities nearby (a perceived cost), I may become more likely to buy if offered a “free collect and return service”. This may be worth more to me than it costs the dealer to provide. Knowing the relative importance of differing value drivers in the eyes of each consumer gives us valuable clues as to their likelihood to respond to our new “free backseat satellite TV” offer.

Chart 7 : Percentage of Annual Spend that is Spent on Discounted Items

Charts 7 and 8 present examples of how perceptions of Value for Money and attitudes to life influence behaviour. Chart 7 shows a clear relationship between the percentage of the respondent’s total actual spend on discounted products and their claimed level of price consciousness. This allows us to compare many other behavioural variables against this dimension in order to understand which are most relevant.

It could be claimed that the behavioural data alone is all that is needed to do this and that the market research information is merely confirming what we already know. Tesco, for example, claimed that they used their Clubcard data to identify products that were signatures of people who shop exclusively on price. Accordingly it was identified that buyers of Tesco Value margarine were such consumers and targeting them with price offers was extremely successful. No doubt.

However, this is probably simply a better indicator of disposable income rather than perceived value for money. Someone might never buy Tesco Value Margarine but might readily switch store if they knew the cost of their average basket was going to be 10% cheaper at a supermarket nearby. Being price sensitive doesn’t necessarily mean someone shops exclusively on price.

Tesco does not know the income level of each of its customers but we do know that income is a key driver of Accessibility. We could therefore design far more effective and profitable ways to retain the loyalty of the Value Margarine buyers than price promotions. By working with suppliers we could offer retrospective non-price bonuses for staying jointly loyal to their brands and to our store. Couple this with promotions on “treat” items such as mid-priced desserts and we could generate long-lasting loyalty whilst growing their spending with us.

Chart 8 : Customer Profitability versus Attitude to Home

Identifying and validating such opportunities is where mixing market research and transactional data comers into its own. Chart 8, shows that attitudes to life influence perceptions of Value for Money separately from price sensitivity / price consciousness. Customer profitability data shows some consumers are willing to pay more to get the result they want. This helps us to predict their likely price sensitivity.

Following the Tesco view, this relationship should break down for the most price sensitive consumers but, as Chart 9 shows, the relationship still applies. Just because someone is more price sensitive than the average does not mean other considerations are ignored when judging Value for Money. Using our approach, Tesco’s customer management strategy could easily be outmanoeuvred.

Chart 9 : Customer Profitability and Attitude to Home for Price Conscious Customers

e) Expected Satisfaction

Expected satisfaction, or confidence, comes from previous experience, word of mouth recommendation, comments in the press and so on. For existing customers, current satisfaction is a good indicator, other things being equal, that they are willing to continue to deal with your company.

One concept appropriate to CRM is Ipsos’s Customer Delight Principle (Keiningham and Vavra). This asserts that customers’ satisfaction levels influence customers’ loyalty only when more extreme levels of satisfaction are reached (“Dissatisfaction” or “Delight”). And small changes in satisfaction between these extremes have much less impact.

In our view, this is because only these extremes cause customers to fundamentally reappraise. Dissatisfied customers dismiss future communications either because they doubt the company’s competence or feel the company does not care enough about its customers. Delighted customers consider the company is “good” because, even if things go wrong, they care enough to fix the problem.

Unfortunately delighting customers costs money, possibly lots of money. Modelling combined market research and transactional data helps evaluate the trade-offs.

Chart 10 illustrates these effects for two samples of customers using Customer Satisfaction as a measure of consumer confidence. In the first sample, 2258 respondents, we compare overall level of satisfaction with numbers of visits to the store in the twelve-month period prior to completing the satisfaction questionnaire. In the second sample, 2862 respondents, we compare with number of visits to the store in the twelve-month period after completing the questionnaire.

For the first sample, apart from the highly dissatisfied customers, there is little correlation between visit frequency in the year prior to completing the survey and satisfaction at the end of it. For the second sample, there is a statistically strong and significant correlation between their subsequent visit behaviour and their satisfaction at the start of it. Moreover, the largest differences in behaviour for this group occur for the Dissatisfied and Delighted customers for whom the index value compared to their neighbouring groups show the widest variation (7% – 10%). Amongst the “Merely Satisfied” groups the differences in behaviour are much smaller.

Interestingly, the visit frequency for the Highly Dissatisfied group who completed the questionnaire at the start of the year was not as low as for those who did not. It is therefore possible that by having had the opportunity to “Have their say” some of their negative attitudes were calmed and the potential negative impact on their behaviour moderated.

Similar results exist for their levels of spend, average spend per visit and likelihood to respond to CRM communications. So customer satisfaction should be a key element in our targeting models even though we may not be able to survey the satisfaction of everyone on the database. We must instead find measures of customer behaviour that indicate whether they are likely to be “Delighted”; “Dissatisfied”; or “Merely Satisfied”.

Chart 10 : Relationship Between Confidence in the Brand and Customer Behaviour


The proposed approach has several key advantages. It is easy to see and understand the key drivers and define the measures one needs. It evaluates the CRM proposition from the consumer’s perspective and intrinsically identifies the drivers of acceptance of a proposition not just its potential applicability.

Crucially, it is more predictive of actual consumer response than simple attitudinal segment flags (see Chart 12). For all the talk about how Tesco has leveraged its Clubcard data, the truth is transactional data are retrospective. Incorporating market research puts the customer’s opinions at the heart of your targeting. New and existing propositions; known and potential requirements; prospective and existing customers can all be explored in a consistent and marketing-oriented way, and valuable insights uncovered that would otherwise be missed.

Whether your customer data is derived from on-line or off-line transactions, or even if it only contains quite basic information about your customers, the same approach can be used. Bringing together the behavioural and circumstance data from your database with the attitudinal and circumstance data from market research will produce customer insights that will enable you to increase the returns from your CRM programme.

Armed with this knowledge, communications can be tailored to individual customer groups based on their potential receptiveness to your proposition and couched in terms they find most motivating and the proposition refined to fit individual customer needs more closely.

Other initiatives, such as gathering potential leads from contact databases, web-site referrals and so on, can be analysed and profiled using the same techniques. You can better judge their potential and, more importantly, justify the investment needed in your CRM capabilities to exploit them. One client, with a direct sales force and call centre operation, used the model to better predict what type of proposition would be most motivating and whether a prospect was likely to convert. This helped prioritise staff time on the best opportunities and increased conversion rates by 38%.

RedRoute CVM Model

RRI’s CVM model provides a structured framework for modelling the interactions between the behaviours, attitudes and circumstances. A database containing information on each dimension for a large sample of customers is compiled to both segment customers and develop predictive models of behaviour. By revealing which behaviours most closely correspond (given each customer’s circumstances) to particular attitudes, we can better predict their likely response to new propositions and their future needs.

Having obtained a respondent’s explicit consent, their market research data is either directly cross-matched with their behaviour and characteristics from the client’s database or data fusion is used to “marry” respondents from survey research with surrogate donors from the database. This is carried out confidentially by the research agency to maintain respondent anonymity. Naturally there are potential limitations based on the nature of the survey questionnaire and the ability to forge the links to the database. This therefore has to be undertaken expertly. Usually a mixture of both approaches is used to obtain a dataset covering around 10% of the customer base.

Diagram 1 : Motivational Modelling

For our gardening retailer this enabled motivational models explaining various aspects of garden shopping to be developed. The actual results are confidential but Chart 11 shows the type of model developed to explain visit frequency. As can be seen, a number of the key drivers are attitudinal, enabling the client to produce more tailored propositions. A marketing campaign can then be as motivating for a keen gardener as it is for someone who just wants to clear the weeds from their front garden.

In addition, understanding which measures of customer behaviour are most predictive of differing customer attitudes then enables us to project attitudinal data with confidence across the database without having to survey to every customer.

Typically we find some highly predictive attitudinal or circumstance information, usually between 3 and 6 key items, cannot be proxied using data already on the database. In this case the client can put mechanisms in place to obtain this information continuously and cheaply and do so just for those customers for whom the information will make a difference to our effectiveness. This saves money by avoiding the need to run regular large surveys. We also phrase the questions so as to provide direct links to the database. For example, not just “Are you interested in gardening?” but “How many times would you say you visit a garden store each year?”.

Chart 11 : Drivers of Visits to a Gardening Store


Naturally a company will only wish to enhance their targeting in this way if the rewards are worthwhile. The immediate benefits achieved by our retail gardening client, namely a step-change in response of more than 50%, generated additional profits and a project ROI in excess of 500 that more than justified their investment.

Chart 12 shows how the five factors described above contributed to this improvement (excluding “Confidence“ which was a later refinement). To make the communications more relevant and target customers who were more likely to respond, the model placed greater emphasis on Relevance and on targeting people with greater Brand Identification. They were less price sensitive and had the potential to uplift spend. More value-driven messages were directed at price-sensitive customers who were most likely to both switch and repeat. The result was more sales-effective and more profitable marketing campaigns, increasing ROI’s from 2:1 to 10:1.

Chart 12 : Response from the Motivational Model versus Client’s Original RFM Model

Conclusion: The Marriage of Market Research and Database Marketing

Using market research in developing one-to-one targeting models is both feasible and, if done correctly, highly productive. It requires considering the five key drivers of response and using the data to model the relationships between attitudes, behaviours and circumstances. In so doing it provides a new “ABC” of targeting effectiveness.

It tells you how to mine your data; what to look for; what extra data to collect and what themes and messages would be most beneficial in driving response rates.

This brings increased predictability and more easily justifiable database investment – typically doubling the returns from the CRM marketing budget and providing evidence to justify continued investment in database marketing that even Finance Directors cannot argue with. We have presented some straightforward but powerful examples of how circumstances and attitudes affect customer behaviours and ways in which this can be used to better meet consumer needs and increase company sales and profit performance.

However, when it comes to the marriage of market research and database marketing, one phrase says it all: “My wife doesn’t understand me!” It’s often difficult for Database Marketers to see how to use research to improve their ROI’s because it often covers only a few hundred people, the data are anonymised and they cannot usefully be projected across thousands of people in a database that is required for targeting.

The route outlined is harder than profiling a set of targets and picking a list. Unfortunately, because modern CRM systems mean we can now bombard consumers with communications many are now being used to do just that. Consequently there has been an explosion in the number of messages being sent to consumers with no corresponding increase in response per communication. In fact, one could make the case that response per message is now lower than when the industry was simpler!

So for the benefit of all concerned, and for long term consumer trust, what is needed is to replace what some commentators have termed “Carpet Bombing” with a situation where we know how each communication we send fits into the life of the consumer, what they are going to think when they get it, and where the expected response is 98% rather than 2%.

Nowadays at RedRoute, provided we have a respondent’s explicit consent, we often analyse cross-matched survey and customer data for clients. This enables us to understand both the “What?” and “Why?” of consumer behaviour and the learnings directly impact on our client’s database marketing strategies. Such analyses will only increase in the future as more and more digital communications are used for customer contact and tracking. The benefit of taking this more considered approach will be a step-change in targeting accuracy, and an improvement in effectiveness that we hope even Admiral Scott would be proud of.

The Big Data Debate

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One of the big assertions that have been made by people working in the social media Analytics space is that social media data can be used to predict individual purchasing behaviour.

At an intuitive level this is appealing. If my friend recommends a product then if I have the same need I will be more likely to consider that product as a solution to my need. Furthermore, I may signal that I have that need by Tweeting that I have (or indicate I am likely to have) such a need.

But from a brand owner’s perspective there is still a fundamental conceptual issue here, as well as a whole host of practical issues. The most obvious practical issue is “Which database do I use to manage the customer?”. The only set of universally-correctly linked set of data is the one that is owned by the individual themselves. Only they know the myriad of on-line identities that they have chosen for themselves.

Brand owners are very aware that whilst systems such as Facebook and Twitter have millions of users, there are still people out there (and we have to remember that on a global basis it is still the majority) who do not, even though they may be economically active in many other ways.

This leads to a second practical issue that is often glossed-over but which is a fundamental prerequisite – how can I link together data from disparate systems and know that I am linking the right data to the right individual? There are many ways it can be done but it adds time, cost and complexity – all of which undermine the very concept of achieving ‘close to real time response’.

What is more, however, these issues are common to every marketer – there are, at the end of the day, only one set of individuals (even if they may well have multiple on-line personalities). So in principle it makes no economic sense for each company to create ‘a single view of the customer’ because at best whatever they create will always only be a partial view. Each company that is trying to sell goods/services to an individual is only interested in information that influences their needs for those particular types of goods/services. The volume of data about other areas is just noise that needs to be simplified – at least in their view, that is. Yet as the social network data experts will tell you – seemingly unrelated data can be connected.

My purchase of an airline ticket may well be of Interest to the coffee shop chain that I use because they will know that at some point I will be going to the airport. So they can trigger a communication to ensure I use their outlet/brand of coffee at the terminal.

Which brings us back to my original assertion – what we are describing is a multi-dimensional view of the individual, not a “single customer view”. But generating that multi-dimensional view can only ever be done by an independent direct marketing (data) company. There are already many out there – and the strongest players will, ultimately, prove to be those like Nectar and Tesco who already have millions of individuals on their databases.

Unfortunately there will be many discussions ahead about data privacy, data linking, data cleaning, meta measures, and many other detailed considerations and that the average marketing director will find extremely tiresome. All they want to do is get their message across to the right people, at the right time, in the right way. Nevertheless these detailed issues will have to solved / handled if the concept of the “connected world” (as trumpeted by companies such as IBM) is to become a reality in any meaningful way. Needless to say, for the best solutions to these problems we should look to companies such as Amazon as the most likely source of the innovation rather than IT manufacturers. Their views on the world are fundamentally different.

Meanwhile, back in the real world of today, what should the marketer do to achieve his/her more immediate objectives?

The first thing to do is to make sure that you partner with a good direct marketing company – and one that spans both online and offline communications. Without this you run the risk of running “silo communications” and that totally undermines the principle of a customer-centric approach.

In addition, make sure the agency has their own consumer database, one where they are bringing together data on consumers from a variety of sources, even if those sources are not necessarily complete. You do not need them to have a full picture of the consumer, just enough to cover the key dimensions. If you need advice on who to choose, please let us know.

Secondly, if you have your own internal CRM customer database then supplement it with appropriate meta-data measures (even if they are only fused or projected) taken from as wide a cross-section of sources as are relevant. For example, in the DIY sector, data on house moves is always useful, as is data on leisure activities.

Better still, although it is harder to achieve in the short term, is to become a trusted adviser. For example, if you manage an airport, do not just focus on information about your own location but become a mine of information about all airports in that region of the world. Establishing a reputation for providing accurate, authoritative, advice about a whole category will make web site attract people back time-and-again and that will, in turn, create deeper customer loyalty and greater revenues.

Thirdly, use econometric modelling at both a strategic and operational level to maximise the returns from your marketing activities.

At the operational level this means creating appropriately-extended customer propensity models from all the measures in your customer database. In particular, calculating an “Effective Net Preference” (ENP) score for each and every customer. This will give you an instant understanding not just of their likelihood to respond to your offers but also what you need to change about your offer (other than a cheaper price) to increase that likelihood and reduce their likelihood to defect.

At the strategic level, it means understanding how much money to allocate to different types of marketing activities by understanding the levels of reach and frequency they generate. This is important because online communities generate lots of seemingly actionable data but often for only very small proportions of the customer base. To know how to optimise the media mix you need to step back and look at the net reach and frequency different types of activities are able to deliver – individually and when working in concert. Econometric modelling both describes the mechanisms involved and also measures and correctly allocates their net sales effects – providing you with the ROI’s from each of the differing parts of your marketing mix.

With this framework in place you can then, overtime, improve the data sources you have, improve your customer and prospect targeting, improve your offer(s), and improve the effectiveness of your marketing communications programmes.

It is not exactly rocket science, more a methodical approach that remains consistent over time but without becoming repetitious. This are the benefits that our clients derive from using our services and if you would like to know about it then please let us know.

The Penny Falls: Marketing Mix Optimisation in a Digital World

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Look around these days and it seems that many companies – even some of the world’s largest – are finding it hard to know exactly how much to spend on each type of communication. In most cases, however, we find the difficulties are rooted in not being able to “see the wood for the trees”.

Discussions about integrated campaigns, digital media, on-line marketing, multi-channel, multi-layered, multi-faceted, and multi-mixed campaigns get lost in the vast web of competing possibilities for perpetual interconnections.

What is needed is some rational simplicity.

Most people lead “busy” lives and whilst they are, in principle, accessible to every possible means of marketing communication – off-line, on-line, above-the-line, below-the-line, through-the-line and, no doubt, “down-the-line” – to develop the optimum media mix the marketing director must still think in terms of reach and frequency.

Put simply, the role of most communication budgets is to use media (of all kinds) to address three key questions “do they know my brand exists?”, “do they know its features and benefits?”, “do they consider it to be a viable solution for their needs?” If we can achieve ticks in these three boxes then we can move to the higher levels of gaining trial, conversion and loyalty.

The use of differing media, including digital, should be viewed in terms of how effective each will be in helping you achieve those initial 3 key communication objectives.

So the first step is to think about reach.

If all communication channels are worth the same in terms of the connection you could make with your target group then if you were to spend one pound on social media marketing what would be the probability that you would gain an additional buyer? How does that probability compare with having spent that same pound on radio advertising instead? And would the efficiency of both be heightened if you spent a second pound on pay-per-click?

Now add to this the value of repetition. Is one contact via social media worth three contacts via radio? If so, then that changes the cost ratio.

You may be thinking at this point, “but it will vary depending on the strength of the creative”. If you are then you need to take a step back for a moment. All we are comparing here is the relative effectiveness of the channels (singly or, where required, in combination) in delivering the same message. In other words, the equivalent of hearing the communication via the radio versus getting the same message from a friend.

We would picture all this as like using an old-fashioned penny falls machine on the pier in Brighton.  You can choose a number of slots into which you can place your coin – but you can’t be absolutely certain how it will bounce all around on its way to bottom. But what you can be sure of – because you can see it – is which slots are the most likely places to use to get the biggest return – the places where you are most likely to get a payment after inserting the fewest amounts of coins.

And that, in reality, is all you really need to know.

As a real life example, think for a moment about choosing how you might want to travel to the airport to catch your plane. You can drive yourself, go by taxi, by rail, by coach or get a friend or relative to drop you off and pick you up.

In this situation you probably wouldn’t go on-line to work out your personal mathematically-optimum solution as to which of these you would choose. But if you heard a radio ad that highlighted the benefits of driving yourself you might consider that option. And then you might go on-line to compare prices, canvass some opinions, and make a booking. Or you might just think the information provided in the radio ad was enough to convince you.

So in this situation the best marketing strategy is to first get the maximum number of people to consider the mode of transport we wish to advocate. That is likely to favour “display”-type advertising rather than “search”. Both will have an effect, but one is more efficient at it than the other.

Understanding these relative probabilities – and how they work singly and in combination across multiple streams – is at the heart of econometric analysis for marketing. The answers tell you how many coins you need to put into each slot at the top in order to maximise the pay-out you get at the bottom. How they bounce around on the way to the bottom will always be a bit of lottery. What econometrics does is stack the odds in your favour so you can be sure you will get the highest payback from the media budget in your pocket!

Forget Big Data – Think Real Time Analytics

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The puzzling misnomer about so called ‘big data’ is that it is not that big. It may contain data in formats that IT people have traditionally found hard to handle but in terms of data volumes the amounts involved are small by comparison with the volumes that have been used by supermarkets for more than 15 years already.

The second rather puzzling aspect is that Big Data attracts so much attention. The mechanisms for processing and analysing such types of data, albeit in smaller volumes, have been used by market researchers for at least 30 years. What has changed is the volumes of such data and their mechanism of collection.

Of much greater interest, as well as greater challenge, is real time analytics.

Techniques developed by economists in the late 1970′s based on the application of control theory to economic and social data suffered from the omission of attitudinal data.

Data sources which now paint a continual picture of consumer behaviour, consumer circumstances and consumer attitudes are about to render such techniques all powerful.

The application of control methods and feedback loops has moved on unrecognisably since the 1970′s. We see this manifest itself in every defence industry application used around the world. And in the car you drive every day.

Rapid response to changing conditions is essential to the effective management of the these systems. These techniques are being easily adapted to enable one-to-one response to changing customer needs and situations.

When you book a ticket you spark a whole series of interactions that will eventually take us into the realms of the “Minority Report” era.

There is, however, still much work to be done before we get there. The IT guys still struggle to discern mathematically tractable measures from analogue data like tweets and photos that captures things are predictive rather than just descriptive.

But the process of solving this is well under way and it will be completed within 5 years if not sooner.

RedRoute’s customer panel technology, for example, provides effective and systematised methods of tracking and predicting the impacts of changing consumer sentiments.

Our Effective Net Preference measure adds the dimension of consumer attitudes that was missing from earlier attempts to understand and predict the trends in consumer needs and behaviours. Our clients already benefit from this everyday to help them outperform the market.

Going forward these techniques will become ever-more automated and continuous. Leveraging real time feedback and analytics to enable businesses to respond to changing consumer needs.

Predicting the future is always hazardous. The problem is that ‘we don’t know what we don’t know”.

We can, however, react quickly when it changes and that is the benefit that control theory and feedback loop analytics provides.

Will it be a substitute for creative genius? No, never. The human mind is still light years beyond the power of computers to ever be truly creative.

In the meantime, however, these techniques will enable us to at least react more quickly to better anticipate and meet consumer needs.