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Choice modelling - an example

Choice modelling - an example. Background. Bonlac changing processed cheese and natural cheddar offering from Bega to Perfect Cheese Previous research has: Explored an appropriate positioning for Perfect Cheese Identified the optimal pack design Further research is required to:

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Choice modelling - an example

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  1. Choice modelling - an example

  2. Background • Bonlac changing processed cheese and natural cheddar offering from Bega to Perfect Cheese • Previous research has: • Explored an appropriate positioning for Perfect Cheese • Identified the optimal pack design • Further research is required to: • Understand market response to the new range of Perfect Cheese in terms of: • Price sensitivity • Market share potential • Cannibalisation effects • In addition, feedback on sensory performance of Perfect Cheese products relative to competitors, in order to support positioning platform ( not discussed today)

  3. Pricing Objectives • To understand the impact of launching of Perfect in the Processed Cheese and Light Cheddar Block markets • Understanding initial impact (pre-trial) • Understand longer term impact (post-trial) • Understand the price sensitivity of each user group

  4. Sensory Objectives • To evaluate the Perfect cheese slice and block products relative to competitive offerings in terms of: • Acceptability (unbranded vs branded) • Sensory profiles • Relative to consumer ideals • Purchase intentions • Ability to support brand positioning expectations

  5. Pre-trial Discrete Choice Modelling Sensory Evaluation 1. All products unbranded 2. Perfect Cheese product branded Post-trial Discrete Choice Modelling Method • Central location test at Takapuna

  6. Sample Population • N=30 each of: • Light Slice users • Super Light Slice users • Cheddar Slice users • Reduced Fat Cheddar Block users • Sample population: • Females MHS, 20-65 years • Mix of household types (mainly families with kids)

  7. Pricing Methodology • 15 shelves - pre/post presented to each of 30 people in 4 user groups • Light Slices • Super Light Slices • Cheddar Slices • Light Cheddar Block • In each shelf range of prices consumers get to choose only one • Imitates shopping experience • Idealised situations (100% awareness of Perfect) • House-brands included

  8. Introduction…

  9. ( 4 of the 15 scenarios)

  10. Whoa there! - How did we get to this conclusion? • 3 brands of interest – Mainland/Chesdale and Perfect • The other 2, Pams and First Choice area at fixed, lower prices, prices • Decided to go with 3 price (low $1.99/medium $2.29 /high $2.59) points/brand • Why? • Therefore we have 33 =27 possible combinations • Decided to choose a sample of 15 to reduce respondent fatigue and to ensure we could measure all 2 order interaction effects • eg: does a high price of Chesdale result in different pricing response for Perfect than if it were a low price • This phenomenon is quite common so needs to be taken into account

  11. The design Discuss:

  12. The data - raw

  13. The data - how it’s needed for proc Phreg in SAS

  14. Some points • Note that we have decided to mode/post data together • Not how the data is agrregated now • Compare this to what we have: Preprice 1 Cumulative Cumulative PRE1 Frequency Percent Frequency Percent ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 1 8 26.67 8 26.67 2 7 23.33 15 50.00 3 1 3.33 16 53.33 4 12 40.00 28 93.33 5 2 6.67 30 100.00 Preprice 2 Cumulative Cumulative PRE2 Frequency Percent Frequency Percent ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 1 28 93.33 28 93.33 2 1 3.33 29 96.67 4 1 3.33 30 100.00 Preprice 3 Cumulative Cumulative PRE3 Frequency Percent Frequency Percent ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 1 4 13.33 4 13.33 2 3 10.00 7 23.33 3 15 50.00 22 73.33 4 6 20.00 28 93.33 5 2 6.67 30 100.00

  15. Some Points … • The variable T denote 1= choice, 2 = no choice • The resulting ‘doubling up” of all rows • The variable SET represents the appropriate scenario • For each scenario there are 10 =5*2 rows • Variables like CSD_MLD represents Chesdale’s effect on Mainland and so is in the relevant rows for Mainland but is Chesdale’s price • Remind me to give you a Splus function called SAS.DCM.FORMAT that helps format the appropriate design matrix for this data

  16. Some more code data temp; set hold.cslmodel; PR_Mld2 = PR_Mld**2; PR_Prf2 = PR_Prf**2; PR_Anc2 = PR_Anc**2; PR_FC2 = PR_FC**2; PR_Pam2 = PR_Pam**2; DMld = POST*Mld; DPrf = POST*Prf; DAnc = POST*Anc; Dpam = POST*pam; Dfc = POST*FC; DPR_Mld = POST*PR_Mld; DPR_Prf = POST*PR_Prf; DPR_Anc = POST*PR_Anc; DPR_Pam = POST*PR_Pam; DPR_FC = POST*PR_FC; . . . DPam_Prf =POST*Pam_Prf; Note: coding up the pre/post effects DPam_Anc =POST*Pam_Anc; and quadraticprice effects DPam_FC =POST*Pam_FC ; run;

  17. Analysing the data Saving this data: data hold.cslmodel; set temp; run; Now we are ready to start finding the correct model: ** trial and error to obtain the ‘correct’ model proc phreg data =hold.cslmodel outest =betas nosummary; strata set; model t*t(2) = CsD Mld Prf FC Pam PR_CsD PR_Mld PR_Prf PR_FC PR_pam PR_CsD2 PR_Mld2 PR_FC2 PR_pam2 DCsD DMld DPrf Dpam Dfc DPR_CsD DPR_Mld DPR_Prf DPR_FC DPR_Pam DPR_CsD2 DPR_Mld2 DPR_Prf2 DPR_FC2 DPR_pam2 DCsD_Mld DCsD_Prf DCsD_FC DCsD_Pam DMld_CsD DMld_Prf DMld_FC DMld_Pam DPrf_CsD DPrf_Mld DPrf_FC DPrf_Pam DFC_CsD DFC_Mld DFC_Prf DFC_Pam DPam_CsD DPam_Mld DPam_Prf DPam_FC /ties =breslow; freq freq; run;

  18. Analysing the data… • The final model: procphregdata =hold.cslmodel outest =betas nosummary; strata set; model t*t(2) = CsD Mld Prf FC Pam PR_CsD PR_Mld PR_Prf PR_FC PR_pam PR_CsD2 PR_Mld2 PR_FC2 PR_pam2 DPrf DCsD_Prf /ties =breslow; freq freq; run;

  19. Analysing the data… • Output: Analysis of Maximum Likelihood Estimates Parameter Standard Hazard Variable DF Estimate Error Chi-Square Pr > ChiSq Ratio CSD 1 62.03446 10.46407 35.1451 <.0001 8.734E26 MLD 1 53.51747 11.68024 20.9936 <.0001 1.747E23 PRF 1 12.41570 1.09299 129.0359 <.0001 246643.1 FC 1 1.15688 0.16214 50.9094 <.0001 3.180 PAM 0 0 . . . . PR_CSD 1 -48.72340 9.27818 27.5772 <.0001 0.000 PR_MLD 1 -41.00351 10.42944 15.4568 <.0001 0.000 PR_PRF 1 -5.23230 0.49984 109.5801 <.0001 0.005 PR_FC 0 0 . . . . PR_PAM 0 0 . . . . PR_CSD2 1 9.65004 2.03784 22.4241 <.0001 15522.40 PR_MLD2 1 7.85671 2.30708 11.5972 0.0007 2583.017 PR_FC2 0 0 . . . . PR_PAM2 0 0 . . . . DPRF 1 2.78607 1.13648 6.0098 0.0142 16.217 DCSD_PRF 1 -0.88705 0.48077 3.4042 0.0650 0.412

  20. Turning this into something meaningfull

  21. Presenting the data

  22. Presenting the data

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