TURFBreathing new life into an old technique

Ray Poynter

Director, Virtual Surveys

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

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

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)

Gelati & Sons

Almond Banana Coffee Damson Elder Fig Grape Hazel

R1 1 1 0 0 0 0 0 0

R2 0 1 1 1 0 0 1 0

R3 0 0 1 0 0 0 0 0

               

Rn-1 1 1 0 1 1 0 0 1

Rn 0 1 0 0 0 1 0 0

There are 70 different ways to choose 4 flavours from these 8, which 4 maximise the reach?

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

Solver example 1

Number of flavours 4

Reach 95%

  1 1 1 0 0 0 1 0 4

  Almond Banana Coffee Damson Elder Fig Grape Hazel Reached

R1 1 1 0 0 1 0 1 1 1

R2 0 1 0 0 0 0 0 0 1

R20 0 1 0 0 1 0 1 0 1

Total 5 13 3 2 4 3 8 6 19

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

Different scenarios

# Flavours

Unduplicated Reach Flavours

1 65% Banana

2 80% Almond & Banana

3 90% Elder, Almond & Banana

4 95% Grape, Elder, Almond & Banana

5 100% Hazel, Grape, Elder, Almond & Banana

Sub-samples can easily be set up:Either as sample selectionsOr, as separate Excel pages, one per key sub-sample

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 buyo Probably buyo May or may not buyo Probably won’t buyo Definitely won’t 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 weeko 2-4 times a weeko Once a weeko 2-3 times a montho Once a montho Every 2-3 monthso Less often

Frequency, that’s why it’s 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

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

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 everyone’s needs, but with the lowest frequency

• Banana & Coffee has the highest predicted frequency

Solving for Frequency

# of flavours 4

Avg Frequency 7.1

  1 1 0 1 0 0 1 0 4

 Alm-ond

Ban-ana

Cof-fee

Dam-son Elder Fig Grape Hazel

Frequ-ency

R1 8 8 0 0 4 0 2 2 8

R2 0 3 0 0 0 0 0 0 3

R20 0 8 0 0 2 0 10 0 10

Total 27 87 12 17 15 16 50 18 142

Value to maximise

The system can be set up to report reach as well as frequency, along with sub-groups etc.

Frequency solutions

# Flavours

Average Frequency Flavours

1 4.4 Banana

2 5.8 Grape & Banana

3 6.6 Damson, Grape & Banana

4 7.1 Almond, Damson, Grape & Banana

5 7.4 Elder, Almond, Damson, Grape & Banana

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

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

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

Key TURF Questions

Why isn’t 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

Thank youQuestions?