Archive for 2010

July 30th, 2010

Data planning and qualitative 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.

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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

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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

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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

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April 13th, 2010

What is Social Media?

We’re often asked to define social media either directly (what is social media?) or indirectly (we need a social media strategy), so I thought I’d provide a list of the key platforms that make up what we call “social media”:

  1. Article directories that publish original user generated content (UGC).
  2. Blogs that feature original content and allow comment from users.
  3. Blog aggregators like Technorati that allow members to bookmark, tag, syndicate and recommend blog content to other people.
  4. File sharing sites with community and comment functions like YouTube and Flickr.
  5. Forums that allow users to post within a special interest community such as Crackberry.com for Blackberry users.
  6. Microblogging sites like Twitter and all associated sites like Tweetdeck that carry and syndicate content to their users.
  7. Review sites for products and services (like Travelocity) that carry user generated content (UGC) and reviews.
  8. Social bookmarking sites like Delicious and Digg that allow tagging and tag sharing so that other people can explore the same tags.
  9. Social networking sites like Facebook and LinkedIn that allow communities to manifest themselves online.
  10. Wikis - online encyclopedias that can be edited by anyone - like Wikipedia

It’s worth noting that there are two critical components in social media, the 2 C’s: Content and Community. These are the two sides of the social media coin.

On the one side, content lies at the very heart of social media. Content populates all the components above, all of which would cease to exist without content. In the case of social media, content is often user generated (User Generated Content or UGC) as opposed to being generated by a professional publishing house.  If you are thinking social media in any way, you must be thinking content; without content there is no social media.

Community is the other aspect of social media.  Communities gather around people of similar types (alumni, workplace, school, product users etc) or common interests (stamp collecting, trainspotting, FX trading) in the same way that “birds of a feather flock together” in many other aspects of social sciences. Communities are also important because they create a demand for content as members seek opinions or seek to make their own views known. Without communities there would be less of the subtle “friction” that causes many types of content to be generated and consumed.

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April 6th, 2010

What is the value of a brand in the online world?

For the last ten years we’ve heard no end of tales about the triumph of the Internet over mass marketing.  Some robust sources like Wired have informed us that brands are in decline and some, like the American Marketing Association have even declared that “Brands are Dead“. Jonathon Salem Baskin announced in his book that “Branding Only Works on Cattle”. Through the publishing power of web 2.0 consumers are now empowered to make or break brands by the power of their aggregated reviews. One false move in the product or service department, coupled with no satisfactory attempt to remedy the situation can result in a cataclysmic descent in a brand’s fortunes. Ergo, today’s brands exist on a product quality, consumer service knife-edge. Given this new consumer-empowered situation, many a marketer may ask the question, “What is the value of a brand in the online world?”

Before we get to the answer we have to have a working definition of what a brand is. Surprisingly, many business-people (not necessarily marketers) still think that a “brand” is a “logo”.  That’s not true.  Crucially, a brand is not a thing, it’s a set of perceptions that exists in the minds of consumers. A brand is a collection of perceptions about pricing, quality and consumption experiences. Brands are defined in consumers’ minds by the recommendations, criticisms, tastes, and “jobs well done” they themselves have experienced or heard about.  On top of all this, the brand logo is the “brand mark”. To use an analogy based on roads, the brand mark is the sign that says “M1″, but it’s not the motorway.  The product is the motorway, the brand mark is “M1″ and the brand itself is what people think and feel about the M1 as means of transport relative to other options.

Now let’s swing back to the Internet. The Internet is of course a glorious place where the consumer reigns supreme and the truths about products and services are revealed to all.  In this Utopian dream, companies, brands, corporates and institutions can no longer ‘hoodwink’ the consumer. Consumers can “talk directly” with brands and have a customised one-to-one relationship based on “digital conversations”.  All things from the past are now aged and obsolete

Well that’s one point of view. The Internet is also something else. It is a ‘cesspit of false information’. With no barriers to entry and nearly frictionless production and distribution, it’s easy for false information, lies, doctored images, and other forms of deception to infiltrate the Internet. Now that’s not my point of view, it belongs to Eric Schmidt, the CEO of Google and the engineer of its post invention growth.   Yes, the Internet is also the home of spam, cloaking, deception, bank account thefts, fraud, consumer scams, false and misleading reviews, viruses, trojans, hacking and many other types of cyber crime.

So, where does this leave brands in the digital age?  I’d argue that the truths represented by brands coupled with the complexities of increased choice and the realities of the darker side of the Internet mean that brands are more vital now than ever before.  The world is getting more complex because more choice is being offered.  Many sources of products and information can’t be trusted.  Consumers use brands to help simplify decision making.  Brands are ‘the solution not the problem’ according to Eric Schmidt at Google, ‘brands are how you sort out the cesspool’.

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