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Introduction to conjoint analysis 2021

Draft materials for an e-Learning course being developed by Ray Poynter, NewMR and The Future Place.

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Introduction to conjoint analysis 2021

  1. 1. Introduction to Conjoint Analysis Ray Poynter NewMR ray@new-mr.com March 2021
  2. 2. Agenda • What sort of problems? • Why? • How? • An example • The bigger picture • Design considerations
  3. 3. What sort of Problems? • In designing a computer, what is the right combination of screen size, memory, price, operating system etc? • Looking at a financial product, what combination of features and prices will be most successful? • Thinking about a range of soft drinks, what sizes, containers, flavours, sweetening should be used to create a range? • Designing a holiday, how important is travel time, connections, evening activities, daytime activities? • What is the premium that a brand of tyres can charge compared with others for a variety of options?
  4. 4. What sort of problems? Multiple Features / Attributes • Tangible • E.g. Computer: Screen, Price, Brand, Size • E.g. Car: Number of doors, Engine size, Brand, Finance option Multiple Levels • Tangible and discrete • E.g. 13 inch, 15 inch, 17 inch • E.g. 2-door versus 4-door, versus 5-door
  5. 5. Uses for Conjoint • Modelling different product combinations • Understanding why products are liked • Looking at segments based on ‘needs’ • Calculating the value of attributes • NPD and portfolio management • B2B, consumer, expert • Not often used with ‘emotional’ messages/issues • Nor when attributes interact – I may like vodka, I may like pasta, but vodka-flavoured pasta I might not like
  6. 6. Why use Conjoint? Two key reasons 1. It allows multiple options to be tested efficiently • If we have just 3 attributes (brand, price and colour) • If the attributes have just 3 levels each – there are 27 possible products • If there are 5 attributes, each with 4 levels – there are 1024 possibles 2. People can’t describe their own motivational structure • Asking people to say how important screen size is versus memory size, versus prices for a computer does not provide useful or predictive results • Showing people options and asking them to make a choice is much more predictive
  7. 7. Life is a Trade-Off A 4 Days $15,000 Covers main needs B 2 Weeks $10,000 Covers main needs C 4 Days $20,000 Covers all needs D 2 Weeks $15,000 Covers all needs Thinking of research projects. How do you prioritize the attributes Speed, Price and Quality? On a 1-10 scale, how important are: • Fast • Cheap • Good People can’t access their own motivations
  8. 8. Different mental models A 4 Days $15,000 Covers main needs B 2 Weeks $10,000 Covers main needs C 4 Days $20,000 Covers all needs D 2 Weeks $15,000 Covers all needs Some people might look at the total picture and make a choice. Some might filter and choose. For example, 1st decide that it has to be 4 days, and then pick one of two options. How they choose does not impact the model or the results.
  9. 9. Evaluating Conjoint 120GB Disk 15 inch Screen 1MB RAM 120GB Disk 17 inch Screen 2MB RAM 80GB Disk 15 inch Screen 2MB RAM 80GB Disk 17 inch Screen 1MB RAM 120GB Disk 19 inch Screen 512KB RAM 80GB Disk 19 inch Screen 512KB RAM Card Sort Pair-wise Comparisons (Discrete Choice) 120GB Disk 15 inch Screen 1MB RAM 80GB Disk 17 inch Screen 2MB RAM or 120GB Disk 15 inch Screen 1MB RAM Not Buy Def Buy 1 2 3 4 5 6 7 8 9 10 11 Ratings Conjoint 120GB Disk 15 inch Screen 1MB RAM 100GB Disk 17 inch Screen 1MB RAM or or 80GB Disk 15 inch Screen 2MB RAM None of these or Choice Based (Discrete Choice)
  10. 10. More complex options are possible If these were the only ways to get to work, which would you choose? Car Fuel: $1.50 per gallon Parking Fee: $8 per day I’d Walk Bus Picks up every 30 minutes $1.00 for one- way fare I’d Bike Biking lane on 80% of route I’d choose another method
  11. 11. Conjoint – Key Outputs • Calculating the utility values of all the levels of all the attributes • We call these partworths • For all the groups of interest (e.g. younger vs older) • Or, for each individual participant • Conducting ‘needs’-based segmentation • Creating ‘What-if’ models to allow scenarios to be investigated
  12. 12. How many tasks? This is a task Too few tasks makes modelling hard Too many tasks is difficult for the participants Current advice is to use about 10 tasks
  13. 13. Not Enough Tasks With 10 tasks per participant, there is often not enough data to calculate all the utility values for each participant for each level of each attribute Options • Build aggregate models (e.g. one for men, one for women, one for total) • Use latent class (with choices as the dependent variable) – builds aggregate models based on similarities in the data • Use HB (Hierarchical Bayes) to estimate individual models
  14. 14. An Example Study • A simple example using QuestionPro’s Conjoint option • Use this link to take the test survey https://www.questionpro.com/t/AGXJqZlsQY
  15. 15. Attributes and Levels Operating System • Apple Mac • Windows Memory / Ram • 4GB • 8GB • 16GB Disk Size • 512 GB • 1 TB • 2 TB Screen Size • 13 inch • 15 inch • 17 inch There are 54 possible combinations 2 Operating Systems * 3 Memory options * 3 Disk sizes * 3 Screen size options
  16. 16. Concepts Per Task • The most typical configuration (these days) for Conjoint Analysis is 4 concepts or options, usually with a ‘None of of these’ • ‘None of these’ is a common option, but it is not a requirement • Showing 4 concepts together fits quite badly on a mobile phone • For this example, 2 concepts per task have been chosen • Better for mobile • Better for teaching
  17. 17. 6 tasks are going to be asked If the participant chooses the left option Apple Mac, 4GB, 1TB & 13 inch are all marked as ‘chosen’ Windows, 8GB, 2TB & 15 inch are all marked as ‘rejected’ The algorithm works out the value for each of the levels for each of the attributes This is DCM – discrete choice modelling
  18. 18. The Example Data 176 completed surveys – convenience sample 57% 34% 7% 2% Very easy Easy Neither difficult nor easy Difficult Ease of survey completion 13% 27% 36% 13% 11% Extremely… Very familiar Moderately… Slightly familiar Not at all familiar Familiarity with Conjoint
  19. 19. Aggregate Partworths Memory Partworths 4GB -90 8GB 10 16GB 81 Disk Partworths 512GB -35 1TB 3 2TB 32 Operating System Partworths Apple Mac -24 Windows 24 Screen Size Partworths 13 inch -19 15 inch 11 17 inch 8 Shows, on average, how much is each level of each attribute contributing to the total?
  20. 20. Aggregate Importances Attribute Importance Gap (Max - Min) Importance Memory 171 54% Disk 67 21% Operating System 48 15% Screen Size 30 9% Total 316 100% In Conjoint, the importance of an attribute is the difference between the value of the most preferred level minus the least preferred level. In this example, Windows had a partworth of 24, Apple Mac had -24, so the importance is 48. The total of all the Max – Min values was 316, so 48 is 15% of the total Aggregate importance.
  21. 21. Aggregate Importances Attribute Importance Gap (Max - Min) Importance Memory 171 54% Disk 67 21% Operating System 48 15% Screen Size 30 9% Total 316 100% In Conjoint, the importance of an attribute is the difference between the value of the most preferred level minus the least preferred level. In this example, Windows had a partworth of 24, Apple Mac had -24, so the importance is 48. The total of all the Max – Min values was 316. So 48 is 15% of the total Aggregate importance.
  22. 22. What-if Modelling
  23. 23. What-If Modelling Operating System Model Operating System Memory / Ram Disk Size Screen Size Aggregate Utility Share Estimate Concept 1 Windows 8GB 1 TB 15 inch 48 60% Concept 2 Apple Mac 8GB 1 TB 15 inch 0 40% The aggregate Utility show Windows is preferred to Apple iOS. The Aggregate Utility is the sum of all the partwords for the levels that make the Concept. The What-if Model shows 40% of the sample prefer Apple iOS. Share is often an easier measure to interpret, and more relevant.
  24. 24. What-If Modelling Trading of size with specification Model Operating System Memory / Ram Disk Size Screen Size Aggregate Utility Share Estimate Concept 1 Windows 16GB 2 TB 17 inch 145 52% Concept 2 Windows 8GB 1 TB 15 inch 48 34% Concept 3 Windows 4GB 512 GB 13 inch -120 14% The aggregate utilities suggest the high spec, larger computer is heavily preferred. Also, that the low-spec, smaller machine is heavily disliked. However, the modelling shows: There are people for whom 17 inch is too negative There are people for whom 13 inch is a strong positive
  25. 25. What else might we check? Using sub-groups • Do men/women, young/old, North/South etc have different values/choices Grouping people by their choices • People who prefer Apple to Windows, who are they • People who prefer a 17-inch screen, what else do they prefer?
  26. 26. Brand and Price The example does not include Brand or Price – for simplicity • Brand is linked to operating systems (the Apple iOS is only available from Apple, all the other brands would offer Windows) – this is an interaction • Prices would vary by country – which was unnecessary in a simple example This example is a measure of configuration preferences One of the main reasons to use conjoint is to investigate brand and price
  27. 27. Typical projects Perhaps there is no such thing as a typical Conjoint Study, but here is try … • Attributes – 4 to 8 • Levels – 3 to 5 per attribute • Options per task 4 • With a ‘None of these’ • 10 tasks per participant • 250 to 400 participants • Analysis either a) aggregate and sub-group, or b) with Hierarchical Bayes
  28. 28. Hierarchical Bayes (HB) • A very advanced statistical technique • In general, it deals with situations where there is a lot of missing data • In conjoint analysis it allows an approximation of a model per participant – even when there were too few tasks per participant • Most advanced conjoint analysis studies use HB
  29. 29. Design Considerations • Conjoint is an advanced technique – it is best to have the support of an expert • At the design and analysis/interpretation stages • Just because you can run the software, doesn’t mean you can safely and reliably conduct a study • It only works in cases where the appeal of a product or service can be ‘approximated’ by the sum of the appeal of the parts • It tends to be the main focus of a study – you don’t (normally) add a conjoint onto a study looking at other things • The questions you ask participants should be as understandable and as realistic as possible
  30. 30. Want to Learn More?
  31. 31. Introduction to Conjoint Analysis Ray Poynter NewMR ray@new-mr.com March 2021

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