Archive for June, 2010

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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