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.

Posted by: Simon Foster
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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.

For more information please visit our site.

Posted by: Simon Foster
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July 30th, 2010

Data planning and market research - mind the gap

I once attended a research debrief to report the results of a survey into the communication effects of a direct mail campaign. The survey asked if the target group had received the direct mail piece and what they thought of it. The survey results were not good. According to the research, hardly any of the respondents could recall seeing the DM pack and even fewer claimed to have responded. There was disappointment; it was a big mailing and a strong offer, surely someone must have seen it and been motivated to respond. But all was not lost. In reality, away from the results of the survey, the campaign had in fact been very successful. I knew that the campaign was in the process of beating all its response, conversion and sign-up targets.  From a hard data point of view this campaign was on track to become one of the most successful DM campaigns ever run by the client.

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.

Posted by: Simon Foster
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June 29th, 2010

DRTV Campaign analysis using spot matching

If you are running a DRTV campaign it’s important to measure and analyse campaign performance in detail. Information gleaned from DRTV campaign analysis can inform subsequent DRTV campaign planning and performance.  The main thrust of analysis work in DRTV is to measure the variables that can be realistically controlled in media planning and buying. Typically, these are the following criteria:

  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

Posted by: Simon Foster
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June 28th, 2010

Marketing data analysis gets you closer to customers

Smart data analysis can be a major source of campaign insight and even competitive advantage for brands and advertisers. The customer data owned by a brand advertiser can reveal

  • 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.

For more information on our data analysis services please visit www.teqtonic.com

Posted by: Simon Foster
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June 8th, 2010

SAS Marketing Analyst Work

We are often looking for SAS marketing analysts to help us with quantitative marketing projects for our clients.  These projects are usually designed to help advertisers understand how customers are behaving within their databases or how their marketing investment has performed. So our projects are essentially about a) data discovery and b) marketing campaign evaluation.

Typically, to be considered for project work, you will need to:

  1. Be a strong numerate graduate - maths or statistics
  2. Ideally have a postgraduate qualification in maths or statistics
  3. Good working knowledge of SAS, Excel and PowerPoint
  4. Demonstrate that you can apply your numerical knowledge in a marketing context i.e. experience of handling marketing data sets like customer databases, campaign performance data or web traffic data.
  5. Be familiar with the techniques used to explore customer databases to generate insight that can be valuable to marketers and / OR
  6. Have  a good working knowledge of the techniques used to undertake quantitative analysis of market campaign performance
  7. Have experience of working with either advertisers, agencies, direct marketing agencies or digital agencies

From a more technical perspective our projects are likely to require the following skills:

  1. Discovery:  Data Audit, Data Prep, Classification, Segmentation, Predictive Modelling. Good working knowledge of relevant SAS modules (Business analytics, data mining etc).
  2. Evaluation: Descriptive Analysis, Multiple Regression, Time-Series Analysis - across either multiple media channels or individual channels like DRTV, DM or web. Good working knowledge of relevant SAS modules (Business analytics, forecasting).

If you think you have some of the qualities we value, please send your CV to Simon Foster on this email address: talk @ teqtonic.com

Posted by: Simon Foster
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