For many years CRM has been a “direct” channel delivering one way communications to customers. Now, with the advent and maturity of social media networks brands have the opportunity to engage in more balanced and cohesive discussion with customers and consumers. Social media with its wide accessibility and easy to use functionality offers brands a platform on which to engage with consumers on their terms. This in turn offers brands a sea change opportunity in the way they manage customer relationships.

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.

Earlier this week an edition of Coronation Street (the UK’s leading and longest running soap) featured a kitchen shot which included a packet of, I think, ‘Honey Nut Cheerios’. Take note, that’s not ‘Cheerios’ or ‘Honey Nut Loops’ but ‘Honey Nut Cheerios’. If like me you find these sub-text jokes by ITV’s production teams rather amusing you’ll be sad to know that their days are almost certainly numbered. But all is not lost; the commercial opportunites around product placement are set to mushroom and that’s good news for advertisers.

From February 28th we can expect to see more popular high street brands making star appearances on the breakfast tables of some of the UK’s leading programmes.  The opportunities are of course almost endless. Many TV programmes feature kitchens, kitchen tables,  cars, clothes, shoes, TVs, mobile phones, T-shirt logos, food products and drinks to name but a few. As the year progresses we are likely to see more branded products appearing more overtly in programme content. This raises two questions for advertisers and marketers.

First, how do brands make it work? I see product placement as being a bit like sponsorship where the route to impact, acceptance and increased brand salience is through careful targeting of content, environments and associations. A brand that makes the right association with a point of view, a cause (even if only fictional) or a personality may well benefit. A brand that gets it wrong may suffer the indignity of product mis-placement.

The second question is how do we evaluate it? This one is tricky because the returns are unlikely to be short term or immediately obvious. Product placement is not as intrusive as advertising; the advertiser is not granted sole use of the screen for the ad slot they’ve purchased. Product placement sits on screen within another set of messages that are being received in different ways by viewers. This carries a potential risk of intrusion which needs to be measured and managed as carefully as any awareness or sales upside.

In the US where product placement has been around for some time, there are media tracking companies who use image recognition software to measure how long a product has appeared for. They then attach audience size and cost data to provide a value for the reach and length of exposure obtained for the brand.  It seems that in the main product placement evaluation is limited to calculating the media value of brand exposure and to measuring recall and awareness.

One interesting metric could be changes in online brand searches as a result of product placement views or mentions. I know from working with advertisers that just one mention of a product in a property makeover programme for example can drive a significant spike in online brand search activity and e-commerce sales. It may be that the granularity of search data may allow it to emerge as a useful currency for measuring the short and medium term effectieveness of product placement.

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.

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.

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.

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.

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.

For more information please visit our site.

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 responsd, 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.

1. Day of Week
2. Time of Day
3. Channel
4. Programme type
5. Time length of ad
6. Position in break
7. Position in programme
8. Diminishing returns (audience size)

How does DRTV Spot Matching work?

The established way to undertake these analyses is to use a technique called “spot matching”.   In simple terms, spot matching involves matching two files with each other. The first file is the DRTV spot schedule which contains spot transmission times, programme, channels, audience size and timelength. The second file is the response file which contains information about inbound response, the time of calls, and often the outcome of those calls e.g. whether it resulted in an action with value (i.e. became a qualified lead) or the call failed (i.e. caller not interested, hoax, timewaster etc).

How are the response files matched and reported?

The files are matched using a response curve.  It is generally accepted, from numerous research studies, that around 75% of DRTV calls occur within 15 minutes of spot transmission, and around 50-60%% occur within around 7-8 minutes.   By overlaying the response curve across every spot, it is possible to allocate calls to spots throughout the whole DRTV transmission schedule. When call volumes have been “attached” to each spot transmission, it is then possible to establish the call response rates for each spot.   This then enables reporting by time of day, day of week, channel, timelength etc.

Establishing Financial ROI

By multiplying spot audience volumes by the cost per thousand (CPT) rate at which the DRTV audience was bought,  we can establish the cost of each spot. Because we know the call volumes attached to each spot we are able to report cost per call by spot.  If the advertiser has a notional value that they can attach to a call with a positive outcome then it is possible to establish ROI based on the cost of call from each channel, time band, day of week, programme genre etc in order to report a ROI based on prospect value.

For more information on our analytics services visit www.teqtonic.com