This article outlines the case for considering Permission as the 8th P in the marketing mix. It is slightly lighthearted as I wouldn’t presume myself worthy of adding the 8th P, but the recent rise in big data analytics and the resulting changes in data regulation (GDPR) mean that there may be a case for finding a place for Permission in the marketing mix.

Let’s start with the 4Ps of the original marketing mix

Everyone, or at least most people reading this article will be familiar with Jerome McCarthy’s 4 Ps of marketing. McCarthy, a US academic, proposed the concept of the 4 Ps marketing mix in his 1960 book Basic Marketing: A Managerial Approach  [1] – the foundation text of modern marketing. This work helped to give a managerial definition to marketing by outlining its scope of reference and providing a framework for marketing planning. For many years McCarthy’s four Ps helped to define what marketing is:

1. Product – developing the right type of product to meet the needs of the market
2. Place – distributing the product where the market will want it and be able to buy it
3. Price – pitching it at a price that will be suitable to the market and the firm
4. Promotion – communicating the product and its benefits to the market

The growth of services and the move to 7Ps

In 1981, in response to the growing service economy, Booms and Bitner [2] proposed a model of 7 Ps, comprising the original 4 Ps plus three more – people, process and physical evidence.

These are the three new Ps that Booms and Bitner added to the original four:

5. People – most services cannot be delivered without people. How well those people perform can define the quality of the service provided
6. Process – process is what delivers the service think carrying you from home to destination by public transport or going to the dentist, hospital or even the hairdressers
7. Physical evidence – makes the intangible service process more tangible and can confer quality – think pilot’s uniform or public services, fire, police and ambulance.

How has data and tracking changed marketing activities?

Over the last fifteen years marketing promotion and distribution (place) have been revolutionised by technology and eCommerce.

• Promotion – online advertising and email use personal data to drive promotional activity. Ad technology uses tracking data to identify prospects for products and services and predict when current customers might purchase again. This technology has become mainstream and many businesses now depend upon it.
• Distribution has been revolutionised by ecommerce which allows consumers to shop from home or work without entering a physical store location. Purchases are delivered to the door, again without having to leave work or home.

These developments have been underpinned by digital user tracking, both on site and within ad networks. Data from tracking cookies is used to build target audiences, target individual advertising messages, predict purchase behaviours and geo-locate prospects and customers.

Consumer tracking data is used by a number of large online advertising networks – these include well-known brands such as Facebook and Google and less well-known data brands such as Appnexus, Liveramp, OpenX, Rubicon and Pubmatic.  This tracking data, and in particular personal level tracking, has caught the eye of EU regulators and as of May 2018, a new stringent data regulation framework, the EU General Data Protection Regulation, comes into force.

GDPR puts the brakes on ad tracking and programmatic digital advertising

GDPR puts far more onus on those collecting individual level data than ever before. It practically demands that all data use is expressly consented or permissioned by the data subject (the consumer) and that responsibilities for what happens to that data extend along the chain of organisations using that data. To cut a very long story short, where a company is using a piece of data that can be used to locate or identify an individual person, it could be breaking the law if permission to do so has not been freely given.

This changes a lot in digital user tracking. The use of non-permissioned third party data which is used to expand the target audience of many brands using programmatic trading, effectively becomes a likely breach of the GDPR regulations.

So now in June 2018, having permission to talk to a consumer in digital space impacts three areas.

1. Acquisition –It will be harder to use PII to predict future behaviour and it will be harder to construct data pools of consumes who are demonstrating pre-purchase behaviour. It will also be harder to build cookie-based target audience pools.
2. Retention – the data of sending our blanket emails to non-consenting recipients are at an end. Over the last few months you will have received a request for consent email, or even a piece of direct mail, from most organisations that have been regularly visiting your inbox.
3. eCommerce – tracking is used both onsite and in ad networks for eCommerce. Whilst onsite tracking is less of an issue, tracking across third party ad networks is. eCommerce sites use ad networks to run re-messaging campaigns – trying to persuade consumers who have to abandoned a purchase to return and complete the transaction

In marketing mix terms – the GDPR regulation is having a direct impact on place (distribution) and promotion (advertising).

So, is Permission now the 8^th P in the marketing mix?

In my view it is.  We’ve had a revolution in digital marketing that’s been driven by data, We now have a regulatory environment that changes everything for marketers using individual level communications.

Moving forward, there can’t be a marketing meeting or action that doesn’t talk about permission – whether it’s about product or service usage, personal level communication, distribution targeting or even pricing if that pricing is based on individual level behaviours and data.

How can Permission not be the 8^th P in the marketing mix?

References

1. Jerome McCarthy Basic Marketing – A Managerial Approach Hardcover – 1960 (Irwin – original publisher)
2. Booms, B. H. and Bitner, M. J. Marketing Strategies and Organizational Structures for Service Firms Marketing of Services 1981 – American Marketing Association – Chicago

The post What is the marketing mix in 2018? first appeared on Marketing IQ.

Warm Direct Mail – mailings to your active customer file: In our experience, warm direct mail, i.e. DM sent to your customer file should deliver a response rate of between 1% and 5%. The average figure is around 3.5%.

Cold Direct Mail – DM send to prospects via a “cold” list: Response rates here are lower as the consumers you are mailing are less familiar with you and your brand. Typically 0.5% to 1.5%.

The DMA in the UK cites a response rate of 4% and claims that overall 7% of recipients will take some kind of action as a result of receiving direct mail.

The DMA in the US has produced a lot of information in its 2015 Response Rate Report and cites response rates of 3.7% for a house list and 1% for a cold prospect list.

The post Direct Mail Response Rates first appeared on Marketing IQ.

The most common forms of multivariate analysis in marketing are cluster analysis and hierarchical analysis. Cluster analysis uses statistical techniques to allocate customers into segments based on how similar, or dissimilar, they are to each other. So for example, if you had 10,000 customers and you were clustering by income and home ownership, you would be able to define groups of customers with similar levels of income and home ownership status, or those with high income and low home ownership status, or those with low income and high home ownership status. The number of clusters generated depends on how you set up your cluster analysis and of course, what patterns actually lie within your data. You can set up your analysis to produce either a large or small number of clusters, but most marketers can’t practically service more than about fifteen clusters.

Hierarchical analysis breaks customers down into sub-sets of the whole customer base. Results of hierarchical analysis are often shown as dendrograms or tree diagrams. In a tree diagram, all customers belong to the ‘root’ and segments of the customer base are called ‘nodes’, nodes are connected to the tree by ‘branches’.  So for example, all customers can be divided into males and females. Then the males and the females can be divided by age, and then by income and then by spend. You are then able to see what proportion of the whole base is composed of customers with certain characteristics.  Here are some examples of customer segments defined using hierarchical analysis:

1. Spend more than £250 per year and are aged 18-34 and female and do not have children
2. Spend more than £500 per year and are aged 25-44 and male and do not have children and earn between £20,000 and £30,000 and have a mortgage
3. Spend more than £1000 per year and are aged 35-54 and have children and have a mortgage and live in the South East

Whichever technique you use, it is likely that you will see a small number of segments account for disproportionally large amounts of sales revenue or sales potential. When you have identified these segments you can leverage what you know to develop tailored offers, messages and targeting. Over and above this you can identify customers who have the characteristics of high performance segment membership, but are not spending at the rate they could be. You can use this information to target your marketing messages to the sales prospects with the highest untapped potential.

The post How is multivariate data analysis used in marketing? first appeared on Marketing IQ.

CRM has never been perfect. Traditionally, the term CRM has meant email, direct mail, SMS and phone. These are ‘push’ communication channels. Brands push their message out to their customer base. Push communications have always had a problem; they are by nature interruptive, as such they risk being seen as intrusive or irrelevant at time of receipt. This is just one of the reasons why many forms of DM based CRM are still referred to as ‘junk mail’ by consumers. Other reasons for ‘junk’ status are that these communications are often not requested, they’re irrelevant, they’re not green, and they leave your customer with the feeling that you are trying to persuade them to do something they may not want to do. In short, people like being in control. By pushing your message into your customers’ lives you threaten that control and risk being ‘junked’.

The advent of social media offers us the opportunity to overcome these issues and move towards a more perfect world in CRM. With its ability to aggregate, assemble and cluster groups of like minded individuals social media allows us to address and overcome the junk issues listed above. Social media gives brands an opportunity for a radical re-think of what CRM is, how it works and how we deliver it. Let’s look more closely at the sources of “junk mail” categorisation and examine how social media may make CRM a more involving experience:

1) Lack of control: Junk mail is called junk mail because it’s not requested. In the social media world consumers control the dialogue; they do the requesting and they are in control. As a brand you are not imposing yourself on the customer. You are simply there for them when they want to engage with you. This is a different dynamic to traditional CRM. It puts the customer in control of the conversation and that’s where they want to be.

2) Irrelevance: Junk mail is called junk because it risks being irrelevant at the time of receipt. Here’s where social media really scores. If you allow the consumer to control the conversation then they are likely to contact you only when they have something important to say. Consumers will either like product, dislike a product or need more help with it. If you are dealing with these issues for customers at a time of their choosing then you are more likely to maximise the relevance of your communication.

3) Environmental issues: Junk mail is called junk because prospects and customers think it’s not green. The statistics around DM paper wastage are staggering and the DM industry should move forward from denial to recognition. It has been estimated that the UK is subject to more than 500,000 tonnes of waste paper through DM every year. Even if it’s recycled we should be thinking about the energy costs of this mammoth recycling task. Whilst all social media has some costs, they are minuscule compared to the environmental costs of paper manufacture, printing and recycling of millions of tonnes of DM. In 2011 brands must be seen to be environmentally aware and social media allows this to happen by reducing your dependence on less environmentally friendly paper-based forms of communication.

Social media gives us the opportunity to reverse the drive train in CRM. It’s time we used the internet to move from putting things into peoples’ homes to inviting people into our brands. It’s time we stopped trying to control the customer. It’s time we put the customer in control of us. It’s time we moved from push to pull. There nothing new here, marketing theory dictates that companies should be responsive to customer and consumer needs. The problem has been that until the advent of easy to use social media networks being open and responsive was easier to say than do.

By moving into social media CRM we open up our relationship with consumers. This sends positive signs. Companies that are prepared to openly discuss issues between themselves and their customer base will be perceived as accessible, caring and confident in the way they provide products and services. These are all valuable brand attributes.

Of course running CRM in social media where all comment can be seen by others requires marketers to have a high level of confidence in the brands and services they are delivering. But rather than being seen as a hurdle to be overcome, this should be seen as a useful litmus test of a company’s relationship with its markets. If as a brand you don’t feel confident enough to open up your CRM in the social media environment then that tells you something about the prevailing relationship you have with your customers. If thinking about social media raises negative issues then you should use this as an opportunity to clarify and address those issues.

And if you are confident that you can press the social media button now, then your openness can only serve to increase the confidence customers and consumers place in your brand.

The post Is Social Media CRM’s new platform? first appeared on Marketing IQ.

Here’s an example: Let say you sell 10 products. It may be the case that all purchasers of product 8 are: 1) in a certain geodemographic group, 2) married with more than one child and 3) own more than one car. All these factors can be analysed and combined to predict the likelihood of any consumer in your database buying product 8. Usually this combined measure is referred to as a ’score’ i.e. a figure which represents the presence or combination of certain variables in the consumer record. Once you have developed your scoring model you can rank all customers by their score. When you’ve stripped out those who have already bought product 8, you are left with a set of high potential prospects.

Predictive modelling can also be undertaken based on transactional information about past purchases. Going back to the 10 products, it may be the case that 80% of people who buy product 7 have previously bought products 2, 5 and 6 and in that order. So we can say that people who have bought products 2, 5 and 6 (in that order) but who have not yet purchased product 7, are much more likely to buy product 7 than everyone in your database. Again a score is attached to these behaviours and that score can be used to rank your prospects in terms of untapped sales potential.

Of course as well as predicting purchase behaviour, these techniques can be used to predict risk. In credit assessment for example, it may be the case that those customers who have certain demographic characteristics combined with a certain type of past purchase behaviour are highly likely to default on a credit agreement. This is sometimes referred to as credit scoring. If you are rejected for credit at a bank or in a shop it will be because your data has been analysed and your credit risk score is deemed too high or low to meet the criteria of the lender.

These predictions can help you target your communications very efficiently and also help you control commercial risk in customer behaviour. What’s interesting about these techniques is that they help both the marketing department and the finance department. Marketing delivers customers who are both highly likely to convert to sales or high lifetime value whilst at the same time, producing customers who are less likely to cause problems for the finance department. Overall, this means that the resources of the business are being better utilised.

The post What is predictive modelling in marketing? first appeared on Marketing IQ.

At the most basic level your database should contain a name and address for each record. The name and address can give you valuable information. The postcode in the address opens up the potential for geodemographic analysis using tools like ACORN or MOSAIC. These tools work by grouping consumers into clusters of similar people based on the types of neighbourhoods they live in. The principle behind these systems is simple; birds of a feather flock together. The owners of these segmentation systems undertake research into the clusters they have developed. For example, Cluster 1 may contain people who are known to be affluent pre-retirement couples with children who have left home. Research may show that these people are three times more likely to drive a certain car, purchase certain electrical products or take holidays to certain destinations. So from just the address record you can build a much wider picture of the record in question.

But the full name and address have even more potential. They can be used to match your customer file with an external data file containing more information about the same person. This data can come from many sources, but more often it comes from lifestyle surveys. If a customer in your database has completed a lifestyle survey then you can buy supplementary information to significantly expand what you know about that person.Here’s an example. You may only know the name, address and age of a customer. But if that record can be matched with a respondent to a lifestyle survey then you can see the answers to tens or even hundreds of other purchase preference questions that person has shared. For example, you may be able to see what type of car they own, when it was bought, when they intend to replace it. They may even tell you what type of car they are considering next.

If you have transactional data then you are able to undertake an analysis of the types of products and services bought by the customer. From this data you would be able to say that a customer owns products X, Y and Z and you will probably know when they bought those products. You will be able to see how the often products are purchased and the preferred means of payment. If there is cyclical behaviour in the purchase pattern you may be able to predict when this customer is likely to purchase those products again.

With these high levels of customer understanding you are able to take a lot of the guesswork out of marketing. You can be much more focussed in terms of selling specific products to specific individuals. As a result you response, conversion and customer value rates are likely to improve significantly.

The post What can a database record tell us about customers? first appeared on Marketing IQ.

So why was the recall in the research so low and the actual response so high? I can think of three explanations:

First, we were targeting a large group of the population. It was possible that even though the hard data results were good, we were drawing our DM response from portions of the population that simply hadn’t been included in the sample.   If we had a 25% response then that was a record-breaker from a DM planning point of view, but it still meant that the vast majority of the target – 75% – hadn’t responded. Those that had engaged with the mailing were far more likely to recall it than those who had not. So if our sample happened to comprise of 85% or 90% of those who did not respond, then the recall results would be much lower than the response actually experienced.

The second explanation is more intriguing. Could it be that even though 1 in 4 of the target had responded, those that did respond had failed to make the connection between the what they’d actually done and what the research was asking them? In this scenario the sample is accurate and reaching our 1 in 4 respondents, but those who had responded forgot that they had done so when asked in research. Had they failed to connect the research question to the campaign and to their response behaviour?

The third explanation is that some of the respondents deliberately disconnected their actual behaviour from the answers they gave in the research. In other words, they did respond, but they didn’t want to say so.  They were using the research as a communication channel to share a point of view along the lines of ‘I’m not going to tell you exactly what I did. What I am going to tell you is that I didn’t like being perceived to be in your target audience, or perceived to be the sort of person who would buy the sort of product you were offering’.

Whatever the explanation, this taught me an important lesson; market research and behavioural data can say very different things. Asking people what they did, or think they did, can be very different to what they actually did. If market research tells you something, take it as an indicator not a fact. If it’s something big, do more digging around the research before you act on it.  But if hard data tells you something, whether it’s good or bad, whether you like it or not, you can be sure that it reflects changes in actual behaviour, the ultimate measure of marketing success or failure.

The post Data planning and market research – mind the gap first appeared on Marketing IQ.

• Exactly who buys a given product or service
• Detailed information about the characteristics of those buyers
• Which other products and services they buy
• Which product and service offers they find most attractive
• Which buyers buy more of certain types of products
• How you can find more buyers with the same characteristics

These data analysis techniques can be applied to all types of customer data – whether it’s for a retail business, an online business or a call centre based business. Insight from data analysis can be applied across a wide spectrum; from adding inspiration to a creative brief through to changing a company’s entire business strategy.

You may think the claim that data analysis can change the destiny of a business is rather grandiose. But I can can think of two examples of breakthrough data insight from the same category that ended up contributing millions in additional brand revenues.

Sainsbury’s  – Sainsbury’s agency AMV were tasked with increasing the then ailing retailer’s sales by £2.5bn over a three year period. A seemingly impossible challenge until viewed as a data question. The AMV team calculated that £2.5bn equated to £833m per year which in turn equated to £16m per week.  It still looked like a big number until the AMV team considered that Sainsbury’s handled 14m customer transactions per week.  Then the target equated to just £1.14 per transaction. The brief to increase sales by £833m per week could be redefined as increasing each existing transaction by just £1.14. Now the target not only looked attainable, but this data insight led to the idea that lots of small changes could make a big difference.  From this insight came the campaign idea that consumers should “Try something new today”. By asking customers to ‘try something new’ they were able to persuade customers to spend at extra £1.14 every time they shopped.

Tesco – The Tesco Clubcard is now legendary as both a customer loyalty card and a source of information about customers.  Up until the introduction of the loyalty card, many retailers didn’t know who their customers were. And if they didn’t know who they were it was difficult for them to gather the data that allowed them to understand individual customers better. With the Club Card this all changed. Tesco were able to develop individual data driven relationships with their customers.  They were able to understand customer needs better and in doing so they gained competitive advantage over their rivals.

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