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TURF Breathing new life into an old technique Ray Poynter Director, Virtual Surveys

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A typical research problem Gelati & Sons make ice cream In a typical store they sell 8 flavours and they have lots of data about how well they sell They have a new contract to supply a national supermarket But they are only allowed to offer 4 flavours Which flavours? The simple answer? The best selling 4 The research answer TURF – Total Unduplicated Reach and Frequency

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TURF – a bit of background Dates back to the late 80s Many research companies offer it in their toolkit Only a handful of papers over the last 20 years Rarely used these days BUT With a dusting of Internet-based data collection And exposure to Excel-based modelling A powerful tool for portfolio management

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Why TURF? Consider the matrix below, with 3 flavours The data shows whether a flavour is bought by each respondent 234Customers 100R5 001R4 011R3 011R2 111R1 CoffeeBananaAlmond Almond + Banana = 4 happy customers (total unduplicated reach = 4) Almond + Coffee = 5 happy customers (total unduplicated reach = 5)

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Gelati & Sons AlmondBananaCoffeeDamsonElderFigGrapeHazel R111000000 R201110010 R300100000 Rn-111011001 Rn01000100 There are 70 different ways to choose 4 flavours from these 8, which 4 maximise the reach?

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Solver Excel Add-in Check you have the Solver Add-In enabled Choose a cell to maximise The Reach value in our case Create constraints Each flavour is either in or out (integer values in the range 0 to 1) The number of flavours must equal the number requested Solver will then search for the best solution

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Solver example 1 Number of flavours4 Reach95% 111000104 AlmondBananaCoffeeDamsonElderFigGrapeHazelReached R1110010111 R2010000001 R20010010101 Total51332438619 Number of flavours wanted Reach achieved, the value maximised Solver adjusts these values constraining them to be 0 or 1 Constrains the number of 1s, to number wanted

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Different scenarios # Flavours Unduplicated ReachFlavours 165%Banana 280%Almond & Banana 390%Elder, Almond & Banana 495%Grape, Elder, Almond & Banana 5100%Hazel, Grape, Elder, Almond & Banana Sub-samples can easily be set up: Either as sample selections Or, as separate Excel pages, one per key sub-sample

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Simple to Collect Each respondent sees all the scenarios, randomised Gelati & Sons Almond Ice Cream 2.95 How likely are you to buy this ice cream some of the time? o Definitely buy o Probably buy o May or may not buy o Probably wont buy o Definitely wont buy If definitely or probably buy Gelati & Sons Almond Ice Cream 2.95 How often will you probably buy this ice cream? o 5-7 times a week o 2-4 times a week o Once a week o 2-3 times a month o Once a month o Every 2-3 months o Less often

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Frequency, thats why its not TUR Only people who are going to buy the product have a frequency greater than 0 Definitely buys have a frequency Probably buys have a frequency only if you are counting probably buy as people who are buying Frequencies need converting to a common base In our example we might use the values as purchases per year Frequencies may need re-scaling Ideally using calibration data or norms Rough rule of thumb Square root of definite buy frequencies Cube root of probably buy frequencies

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Choice and Frequency The questions were monadic So, what do we do if we have a respondent who says If Almond is offered I will buy 4 per year If Banana is offered I will buy 12 per year If we offer him Almond and Banana? If the products are comparable? As in this example Usually safe to assume he/she will buy 12 products Some unknown mixture of Almond and Banana If necessary, keep the ratios, e.g. Almond 3, Banana 9 If the products are not substitutable? e.g. some last longer, or are twice as big Then more complex assumptions have to be used

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Simple example, re-visited 42 7 0 8 12 15 Banana & Coffee 700R5 003R4 083R3 0124R2 8155R1 CoffeeBananaAlmondp.a. 38 0 3 8 12 15 Almond & Banana 25 7 3 3 4 8 Almond & Coffee Almond has more people who would buy, but they would buy less Almond & Coffee meets everyones needs, but with the lowest frequency Banana & Coffee has the highest predicted frequency

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Solving for Frequency # of flavours4 Avg Frequency7.1 110100104 Alm- ond Ban- ana Cof- fee Dam- sonElderFigGrapeHazel Frequ- ency R1880040228 R2030000003 R20080020100 Total2787121715165018142 Value to maximise The system can be set up to report reach as well as frequency, along with sub-groups etc.

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Frequency solutions # Flavours Average FrequencyFlavours 14.4Banana 25.8Grape & Banana 36.6Damson, Grape & Banana 47.1Almond, Damson, Grape & Banana 57.4Elder, Almond, Damson, Grape & Banana

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Improving the interface By using customised VBA and Solver a more complete solution includes: Selection of sub-groups Dynamically switching between Definite Buys only and Definite plus Probably Buy Stepwise solution of 1 to N products, reporting reach, frequency, cumulative reach and cumulative frequency Dynamically switching between Reach and Frequency Ability to temporarily exclude products Ability to force specific products Ability to weight key sub-groups, e.g. to make it much more likely that longstanding customers will have a product they definitely like

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The client experience Whilst traditional TURF approaches provide useful insight, it has often been static and dull What-if modelling allows the client to really understand the dynamics Extensions include: Adding Value weights to the products Forcing specific items to be selected Asking for the next best solution Identifying the disenfranchised Modifying the rules so a solution that finds 2 products for each respondent Multiple ranges, e.g. in chilled food the best ranges of Indian, Chinese, Mexican, and Italian

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Definites versus Probables Should the analysis be based on Probably Buy or on both Probably and Definitely Buy? Cases vary but: Which option is closest to sales data? Try it both ways, see what the difference is If you are getting enough definites use these If you are using frequency then either use only definites or down weight the probably frequencies

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Key TURF Questions Why isnt TURF used more? Perhaps because it is a specific tool for a specific problem and is not readily converted into a general tool How might technology impact TURF? HB might remove the need for each respondent to evaluate all the scenarios When is TURF applicable? Flavours Products in a vending machine Travel and ticket options Pack and size variants (with care) Courses (including conferences) Menus and bundles

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Thank you Questions?

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