Archive for 2010

December 28th, 2010

2011 marketing predictions: The death of mass marketing has been greatly exaggerated

No doubt there will be many a New Year marketing prediction over the next few days.  The most common theme is likely to be that mass marketing will decline and be replaced by new and emerging channels and techniques. This year, I’m not going to make any such prediction. This year I’m standing in defence of mass marketing and mass media. I predict that mass marketing as a concept will be as strong this time next year as it is now.  I predict that marketing’s big beasts, the jumbo jets, supertankers and super-trucks of marketing otherwise known as TV, print and outdoor will not die in 2011 nor any time soon.  This year I’m flying the flag for the future of traditional mass marketing and the media channels that enable it.  Why? Because I think mass marketing has been tied down by too many critics for too long. Here then is my defence of mass marketing:

  1. Critics of mass marketing argue that it can’t work because it’s so “expensive”. This has to be a flawed argument. How can something not work simply because it is expensive? Things don’t fail because they’re expensive.  In fact, things that are expensive are, in my experience, likely to be of better quality and deliver a better experience. Yes, mass marketing is expensive from a capital perspective, but that’s because it delivers mass audiences - usually millions of consumers several times over in a campaign - at a very low unit cost. In other words, mass marketing delivers mass value. Here’s an example: If you are buying TV audience at £5 per thousand reaching 20m viewers five times then yes, it is going to cost £500,000  - but you will have delivered your message to a huge chunk of the UK population in a medium that builds brand credibility like no other.  The issue is not simply the overall cost of the activity, but whether or not the activity is delivering the brand or sales shifts required.  Unfortunately, not many of mass marketing’s critics understand how this type of value works. How many of these critics have examined the cost structures of mass marketing channels like TV and print?  How many of them know that it costs a tiny fraction of 1p to reach a consumer for 30 seconds on TV? How many of them realise that TV can be less expensive on a unit of audience basis than many online display, search or affiliate channels?
  2. Critics of mass marketing argue that it can’t work because it is “wasteful”. “It’s not targeted” the critics complain, “it reaches people who are not in your target audience” or “you are buying wastage”. But do they realise that the whole point of mass marketing is to sell products that large segments of the population want to buy? Food, drinks, home appliances, cars, computers, toys, mobile phones, holidays, credit cards, bank accounts, mortgages, furniture and so on. Mass marketing isn’t wasteful when used with products that almost everyone might want to buy in the near future.
  3. Some critics of mass marketing argue that it simply “doesn’t work”. But how many of these critics have pored over the results of the many tests, research projects, case studies and evaluation papers designed to quantify the sales effect of mass media? How many have studied the works of marketing academics and thought-leaders like Simon Broadbent, John Phillip Jones, Byron Sharp, Erwin Ephron, Giep Franzen or Colin MacDonald? How many of them understand the relationship between a £500k TV adspend and a 10% category share gain? Here’s an example. If a brand has a 10% share of a £200m category its share is worth £20m. If a mass media campaign costing £500k helps the brand increase share by 10% from £20m to £22m, then the adpsend of £500k has secured £2m in sales.
  4. Of course if points 1-3 fail to help you win the argument, you might want to ask one of mass marketing’s critics which brands they consume in different categories. Do they drink an unknown brand of soft drink, use an unknown make of PC, contract with a mobile phone network no-one has ever heard of or fly on that airline whose name no-one can remember?  No, they drink Coca-Cola, they use Apple, Dell or IBM, they make phone calls through O2, Orange and Vodafone and they fly BA, BMI, EasyJet or Virgin.  If these critics use a well known brand at least some of the time then somewhere along the way, mass marketing has done its job.
  5. If point 4 doesn’t work, you could invite a critic of mass marketing to tell Simon Cowell that TV and newspapers aren’t effective communication vehicles and see what he says. You might need to stand well back.

And finally, earlier this month the Advertising Association/Warc reported that UK advertising enjoyed its best year since 2004.  “In Q3 TV, out of home and internet were the top performers posting growth of 15.8%, 12.4% and 11% respectively. Direct mail posted a 7% rise, its first growth since Q1 2006″.Although the base was low in 2009 and the future remains “clouded by economic factors”, UK advertising expenditure is expected to increase by 2.3% in 2011.

Not quite dead yet then…. Here’s to a successful year for the big beasts of marketing in 2011.

November 1st, 2010

Today’s vintage number

Today’s date is the wonderfully symmetrical number 011110. A classic but highly perishable vintage that shouldn’t be overlooked.  However, the real rarity is yet to come; next year’s perfectly formed 111111 (a pattern that cannot repeat in any other year except the expired 000000). Strong candidates for future vintage status are 211112, 101112, 121212, 021120, 111222, 221122, 031130, 131131.  Others? (do bear in mind 311113 is a non-starter)

October 27th, 2010

What is predictive modelling in marketing?

Predictive modelling is a term with many applications in statistics but in database marketing it is a technique used to identify customers or prospects who, given their demographic characteristics or past purchase behaviour, are highly likely to purchase a given product. In this context, ‘predictive’ does not simply mean predicting the future; it means identifying the quantitative factors that can be used to predict buyer behaviour. Predictive modelling is a powerful data analysis technique that can be used to target email and direct mail activity, and to some degree behavioural targeting in online media.

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.

October 19th, 2010

On a clear day: Measuring ROI in Social Media

Measuring ROI in social media is a big concern for marketers as they consider moving budget away from traditional media channels and into social media activity.  But before they can invest in social media, marketers need to get an idea of what it can contribute to their brand.  This has driven a debate about measurement in social media but unfortunately much of the discussion is focused on measuring social media for social media’s sake. What we should be asking is how do we measure the delivery of marketing objectives when we run activity across the social media platform. When we look at it this way we focus on measuring marketing outcomes versus marketing objectives and the answers become much clearer.

As a start point, everyone needs to recognise that social media is a media channel. It is not a marketing discipline. It is not a marketing objective. It is not a marketing strategy. So we might use the social media channel to raise brand awareness (objective) by targeting affluent new car buyers in social media (strategy), we might use social media to increase consideration (objective) by informing new car buyers about the unique benefits of the car we are selling (strategy) or we may use it to increase sales (objective) by communicating a last minute ‘walk-in’ trade-in deal (strategy). The metrics we use to measure social media should therefore relate directly to the objectives and strategies that we managing through the social media channel.

So, before we can measure social media we need to understand what we want social media to deliver from a marketing perspective. Only then can we select the right types of measurement and metrics to get the job done properly. Here are three examples of how we might measure social media activity against the delivery of three different marketing objectives:

  1. Objective: Raise Awareness: There are a number of good tools for measuring online brand awareness, ad awareness, product awareness and salience. Ad Index from Dynamic Logic allows you to play ‘spot the attitude difference’ between web users who have been exposed to your messaging and those who have not. You can ask exposed and non-exposed groups bespoke questions about your brand and campaign activity which allows you to contrast and compare the differences between the two groups. Brand sentiment can be measured using sentiment trackers like Sentiment Metrics; through without bespoke surveys these may include a range of external references to your brand, not just your own social media activity.
  2. Objective: New Customer Acquisition: If we want to use social media as a new customer acquisition tool then we should be using customer acquisition metrics. Microsoft’s Atlas can be used to track the online behaviour of your social media users across all touch points in the sales funnel. Bespoke tracking URLs in your social media pages can be used to identify visitors to your site originating in your social media pages. This type of tracking means you can ultimately relate customer value back to your social media activity.
  3. Objective: Increase Retention / Loyalty: Here we can combine online tracking, data collection and customer data analysis to understand the contribution of social media. We can collect prospect and customer data in social media pages or in pages that link directly to social media. Fusing data collection with online tracking means we can find the data source of known named customers and measure their progress and value in the sales funnel and through cross sell and up-sell. The results from this type of activity may not be instant; customer value from market source can take a year or more to establish, but once it’s in place you will be able to see how social media is building sales revenue for your business.

The message is that we can’t measure social media for social media’s sake. We should always be measuring how social media performs against a given marketing objective. If we are clear about this, the techniques and metrics for measuring and evaluating social media ROI become much easier to identify, select and implement.

October 18th, 2010

How is multivariate data analysis used in marketing?

‘Multivariate’ means ‘many variables’ and in the context of marketing it usually means analysing multiple variables from customer records to get a deeper understanding of the customer base. This increased understanding of customer behaviour permits the development of customised offers, relevant creative messaging and more accurate media targeting - particularly with techniques like email and behavioural targeting. Very strong offer targeting will significantly increase your response and sales conversion rates.  Any company that has a database of more than around 5,000 records should be using multivariate data analysis to analyse customer data and improve marketing performance.

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 dendograms or tree diagrams. In a tree diagram, all customers belong to the ‘root’ and segments of the customer base are called ‘nodes’, nodes are connnected 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.

October 12th, 2010

What can a database record tell us about customers?

Your customer database is a potential fountain of opportunities to improve campaign targeting, creative messaging and return on marketing investment. Good database analysis can have a huge positive effect on your business. Your database can tell you who your customers are, where they live, what kind of people they are, what they buy, how they pay, what they might buy next and how you should advertise to them to maximise sales. Let’s look at each of these in turn.

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.

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