03 - browsing history
Rui
2025-04-14
How many people clicked product detail page
## [1] "Number of users that checked product detail page: 164"## [1] "Percentage of users that checked product detail page: 0.987951807228916"How long do people spend on the shopping task? (all users)
## [1] "Summary of browsing time (seconds):"## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 8.00 54.25 137.50 223.82 292.00 1338.00## [1] "Detailed statistics:"## mean median sd min max n
## 1 223.8193 137.5 242.4108 8 1338 166
## [1] "Number of users who checked product pages and spent >60 seconds browsing: 118"## [1] "Number of users who checked product pages and spent >30 seconds browsing: 150"Data Description for users who checked product pages and spent >60 seconds browsing
How many purchases people made?
## One_Purchase One_Purchase_Pct Two_Purchases
## More than 60s Users 39 33.05085 78
## More than 30s Users 58 38.66667 91
## Two_Purchases_Pct More_Than_Two More_Than_Two_Pct
## More than 60s Users 66.10169 1 0.8474576
## More than 30s Users 60.66667 1 0.6666667
## Total_Users
## More than 60s Users 118
## More than 30s Users 150data pattern: How many purchased product’s clinical results are seen. (Product-Level Analysis)
## === Analysis for >60s Users ===## Total purchases with claims: 54
## → Clinical image seen: 12 ( 22.22 % )
## → Clinical result seen: 2 ( 3.7 % )
## → Any clinical info seen: 14 ( 25.93 % )## === Analysis for >30s Users ===## Total purchases with claims: 64
## → Clinical image seen: 12 ( 18.75 % )
## → Clinical result seen: 2 ( 3.12 % )
## → Any clinical info seen: 14 ( 21.88 % )data pattern: How many people checked at clinical result text or image when it is present. (User-Level Analysis)
##
## === Treatment group ( 60 users) ===
## Viewed clinical result/image: 19
## Proportion: 31.67 %##
## === Treatment group ( 76 users) ===
## Viewed clinical result/image: 19
## Proportion: 25 %##
## === Summary Statistics for >60s Users ===
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 2.00 3.00 3.53 4.00 18.00
## Standard Deviation: 2.82
##
## === Summary Statistics for >30s Users ===
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 2.000 3.094 4.000 18.000
## Standard Deviation: 2.65
Proportion that choose Group I product
##
## === Treatment Analysis for >60s Users ===
## # A tibble: 2 × 7
## Group n mean_treatment_pa sd_treatment_pa se_treatment_pa ci_lower
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Control 98 0.510 0.502 0.0508 0.409
## 2 Treatment 97 0.557 0.499 0.0507 0.456
## # ℹ 1 more variable: ci_upper <dbl>
##
## Welch Two Sample t-test
##
## data: treatment_pa by Group
## t = -0.64811, df = 193, p-value = 0.5177
## alternative hypothesis: true difference in means between group Control and group Treatment is not equal to 0
## 95 percent confidence interval:
## -0.1879961 0.0950022
## sample estimates:
## mean in group Control mean in group Treatment
## 0.5102041 0.5567010
##
## === Treatment Analysis for >30s Users ===
## # A tibble: 2 × 7
## Group n mean_treatment_pa sd_treatment_pa se_treatment_pa ci_lower
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Control 122 0.508 0.502 0.0454 0.418
## 2 Treatment 118 0.542 0.500 0.0461 0.451
## # ℹ 1 more variable: ci_upper <dbl>
##
## Welch Two Sample t-test
##
## data: treatment_pa by Group
## t = -0.52817, df = 237.78, p-value = 0.5979
## alternative hypothesis: true difference in means between group Control and group Treatment is not equal to 0
## 95 percent confidence interval:
## -0.16164790 0.09329558
## sample estimates:
## mean in group Control mean in group Treatment
## 0.5081967 0.5423729
## Sample size needed per group for >60s users:##
## Two-sample comparison of proportions power calculation
##
## n = 1805.917
## p1 = 0.5102041
## p2 = 0.556701
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
##
## NOTE: n is number in *each* group## Sample size needed per group for >30s users:##
## Two-sample comparison of proportions power calculation
##
## n = 3350.158
## p1 = 0.5081967
## p2 = 0.5423729
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
##
## NOTE: n is number in *each* groupProportion that choose Group I product - first choice
##
## === First-Purchase Treatment Analysis for >60s Users ===
## # A tibble: 2 × 7
## Group n mean_treatment_pa sd_treatment_pa se_treatment_pa ci_lower
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Control 57 0.526 0.504 0.0667 0.393
## 2 Treatment 60 0.55 0.502 0.0648 0.420
## # ℹ 1 more variable: ci_upper <dbl>
##
## Welch Two Sample t-test
##
## data: treatment_pa by Group
## t = -0.2547, df = 114.64, p-value = 0.7994
## alternative hypothesis: true difference in means between group Control and group Treatment is not equal to 0
## 95 percent confidence interval:
## -0.2078824 0.1605139
## sample estimates:
## mean in group Control mean in group Treatment
## 0.5263158 0.5500000
##
## === First-Purchase Treatment Analysis for >30s Users ===
## # A tibble: 2 × 7
## Group n mean_treatment_pa sd_treatment_pa se_treatment_pa ci_lower
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Control 73 0.548 0.501 0.0587 0.431
## 2 Treatment 76 0.526 0.503 0.0577 0.411
## # ℹ 1 more variable: ci_upper <dbl>
##
## Welch Two Sample t-test
##
## data: treatment_pa by Group
## t = 0.26298, df = 146.79, p-value = 0.7929
## alternative hypothesis: true difference in means between group Control and group Treatment is not equal to 0
## 95 percent confidence interval:
## -0.1409097 0.1841685
## sample estimates:
## mean in group Control mean in group Treatment
## 0.5479452 0.5263158
## Sample size needed per group for >60s users:##
## Two-sample comparison of proportions power calculation
##
## n = 6954.243
## p1 = 0.5263158
## p2 = 0.55
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
##
## NOTE: n is number in *each* group## Sample size needed per group for >30s users:##
## Two-sample comparison of proportions power calculation
##
## n = 8341.146
## p1 = 0.5479452
## p2 = 0.5263158
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
##
## NOTE: n is number in *each* groupTreatment effect for those users that have at least seen clinical image/results once
Control group is all the purchases made by people in the control group
##
## === Clinical Info Filtered Analysis for >60s Users ===
## # A tibble: 2 × 7
## Group n mean_treatment_pa sd_treatment_pa se_treatment_pa ci_lower
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Control 98 0.510 0.502 0.0508 0.409
## 2 Treatment 32 0.688 0.471 0.0832 0.518
## # ℹ 1 more variable: ci_upper <dbl>
##
## Welch Two Sample t-test
##
## data: treatment_pa by Group
## t = -1.8184, df = 55.864, p-value = 0.07437
## alternative hypothesis: true difference in means between group Control and group Treatment is not equal to 0
## 95 percent confidence interval:
## -0.37262691 0.01803508
## sample estimates:
## mean in group Control mean in group Treatment
## 0.5102041 0.6875000
##
## === Clinical Info Filtered Analysis for >30s Users ===
## # A tibble: 2 × 7
## Group n mean_treatment_pa sd_treatment_pa se_treatment_pa ci_lower
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Control 122 0.508 0.502 0.0454 0.418
## 2 Treatment 32 0.688 0.471 0.0832 0.518
## # ℹ 1 more variable: ci_upper <dbl>
##
## Welch Two Sample t-test
##
## data: treatment_pa by Group
## t = -1.8904, df = 51.07, p-value = 0.06438
## alternative hypothesis: true difference in means between group Control and group Treatment is not equal to 0
## 95 percent confidence interval:
## -0.36971077 0.01110422
## sample estimates:
## mean in group Control mean in group Treatment
## 0.5081967 0.6875000
## Sample size needed per group for >60s users:##
## Two-sample comparison of proportions power calculation
##
## n = 118.7819
## p1 = 0.5102041
## p2 = 0.6875
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
##
## NOTE: n is number in *each* group## Sample size needed per group for >30s users:##
## Two-sample comparison of proportions power calculation
##
## n = 116.207
## p1 = 0.5081967
## p2 = 0.6875
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
##
## NOTE: n is number in *each* groupLoterry choice
## === First Choice Analysis for >60s Users ===
## Proportion of lottery_choice = 1 (Keep the product): 0.1724 ( 17.24 %)## === First Choice Analysis for >30s Users ===
## Proportion of lottery_choice = 1 (Keep the product): 0.2177 ( 21.77 %)Check the browsing pattern
##
## === Browsing Behavior Summary for >60s Users ===
## → Checked ingredients text: 18.8 %
## → Checked product reviews: 50.43 %
## → Clicked 'Show More' button: 17.09 %
## → Clicked 'How to Use' button: 5.13 %
## → Viewed clinical image: 13.68 %
## → Viewed clinical result: 2.56 %##
## === Browsing Behavior Summary for >30s Users ===
## → Checked ingredients text: 14.77 %
## → Checked product reviews: 41.61 %
## → Clicked 'Show More' button: 14.09 %
## → Clicked 'How to Use' button: 4.03 %
## → Viewed clinical image: 10.74 %
## → Viewed clinical result: 2.01 %Compare the purchased product characteristic
##
## === Group Comparison (First Purchase) for >60s Users ===
## Control.Control Treatment.Treatment Difference.Treatment
## claim_benchmark 4.5714 4.5167 -0.0548
## lottery_binary 0.1607 0.1833 0.0226
## avg_awareness_pa 9.5296 8.9153 -0.6143
## avg_loyalty_pa 5.9030 5.7401 -0.1630
## avg_quality_pa 7.0081 6.9053 -0.1027
## Rating 4.3777 4.4342 0.0566
## Reviews 1014.8772 879.1833 -135.6939
## h_price 54.9123 55.7667 0.8544
## treatment_pa 0.5263 0.5500 0.0237
## p_value
## claim_benchmark 0.8290
## lottery_binary 0.7494
## avg_awareness_pa 0.2227
## avg_loyalty_pa 0.1830
## avg_quality_pa 0.2240
## Rating 0.1225
## Reviews 0.5433
## h_price 0.7809
## treatment_pa 0.7994##
## === Group Comparison (First Purchase) for >30s Users ===
## Control.Control Treatment.Treatment Difference.Treatment
## claim_benchmark 4.6197 4.5921 -0.0276
## lottery_binary 0.1972 0.2368 0.0397
## avg_awareness_pa 9.7512 9.1301 -0.6211
## avg_loyalty_pa 5.9306 5.7992 -0.1314
## avg_quality_pa 7.0480 6.9483 -0.0997
## Rating 4.3865 4.4364 0.0499
## Reviews 1048.1644 958.6974 -89.4670
## h_price 55.8630 56.5132 0.6501
## treatment_pa 0.5479 0.5263 -0.0216
## p_value
## claim_benchmark 0.9005
## lottery_binary 0.5626
## avg_awareness_pa 0.1831
## avg_loyalty_pa 0.2437
## avg_quality_pa 0.1914
## Rating 0.1191
## Reviews 0.6440
## h_price 0.8141
## treatment_pa 0.7929##
## === Group Comparison (All Purchases) for >60s Users ===
## Control.Control Treatment.Treatment Difference.Treatment
## claim_benchmark 4.5417 4.5464 0.0047
## lottery_binary 0.1667 0.1753 0.0086
## avg_awareness_pa 8.9447 9.0021 0.0574
## avg_loyalty_pa 5.7456 5.7524 0.0069
## avg_quality_pa 6.9280 6.9348 0.0068
## Rating 4.4427 4.4380 -0.0048
## Reviews 838.8980 735.2680 -103.6299
## h_price 54.7857 57.0103 2.2246
## treatment_pa 0.5102 0.5567 0.0465
## p_value
## claim_benchmark 0.9802
## lottery_binary 0.8749
## avg_awareness_pa 0.8779
## avg_loyalty_pa 0.9408
## avg_quality_pa 0.9133
## Rating 0.8728
## Reviews 0.4749
## h_price 0.3406
## treatment_pa 0.5177##
## === Group Comparison (All Purchases) for >30s Users ===
## Control.Control Treatment.Treatment Difference.Treatment
## claim_benchmark 4.5966 4.6017 0.0051
## lottery_binary 0.2017 0.2119 0.0102
## avg_awareness_pa 9.1922 9.1206 -0.0715
## avg_loyalty_pa 5.7998 5.7894 -0.0105
## avg_quality_pa 6.9641 6.9579 -0.0063
## Rating 4.4449 4.4443 -0.0007
## Reviews 883.4754 796.0763 -87.3991
## h_price 55.8934 56.9407 1.0472
## treatment_pa 0.5082 0.5424 0.0342
## p_value
## claim_benchmark 0.9762
## lottery_binary 0.8473
## avg_awareness_pa 0.8393
## avg_loyalty_pa 0.9048
## avg_quality_pa 0.9141
## Rating 0.9797
## Reviews 0.5029
## h_price 0.6219
## treatment_pa 0.5979Among people in the treatment group, do those who purchased products and saw any of their clinical claims rate the claim’s credibility higher than those who purchased but didn’t see the claims? - Yes, but not significantly
##
## === User-Level Claim Benchmark vs. Clinical Seen (Treatment Only) - >60s Users ===
##
## Welch Two Sample t-test
##
## data: claim_benchmark_user by clinical_seen_user
## t = -0.72779, df = 32.852, p-value = 0.4719
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.7117398 0.3367398
## sample estimates:
## mean in group 0 mean in group 1
## 4.479167 4.666667
##
## Kolmogorov-Smirnov test for distribution difference:
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: user_level$claim_benchmark_user[user_level$clinical_seen_user == 0] and user_level$claim_benchmark_user[user_level$clinical_seen_user == 1]
## D = 0.1875, p-value = 0.4833
## alternative hypothesis: two-sided
##
## [1] "Group-level summary statistics:"
## clinical_seen_user claim_benchmark_user.mean claim_benchmark_user.sd
## 1 0 4.479167 1.2202125
## 2 1 4.666667 0.6513389
## claim_benchmark_user.n
## 1 48
## 2 12
##
## === User-Level Claim Benchmark vs. Clinical Seen (Treatment Only) - >30s Users ===
##
## Welch Two Sample t-test
##
## data: claim_benchmark_user by clinical_seen_user
## t = -0.36129, df = 29.251, p-value = 0.7205
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.5895750 0.4124917
## sample estimates:
## mean in group 0 mean in group 1
## 4.578125 4.666667
##
## Kolmogorov-Smirnov test for distribution difference:
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: user_level$claim_benchmark_user[user_level$clinical_seen_user == 0] and user_level$claim_benchmark_user[user_level$clinical_seen_user == 1]
## D = 0.18229, p-value = 0.5178
## alternative hypothesis: two-sided
##
## [1] "Group-level summary statistics:"
## clinical_seen_user claim_benchmark_user.mean claim_benchmark_user.sd
## 1 0 4.578125 1.2574185
## 2 1 4.666667 0.6513389
## claim_benchmark_user.n
## 1 64
## 2 12
Among all people, do those who purchased products and saw any of their clinical claims rate the claim’s credibility higher than those who purchased but didn’t see the claims? - Only slitely higher
##
## === Comparing Treatment Users Who Saw Clinical Info on Purchases vs. All Others - >60s Users ===
##
## Welch Two Sample t-test
##
## data: claim_benchmark_user by group
## t = -0.59197, df = 25.095, p-value = 0.5592
## alternative hypothesis: true difference in means between group Other and group Seen is not equal to 0
## 95 percent confidence interval:
## -0.6172202 0.3415792
## sample estimates:
## mean in group Other mean in group Seen
## 4.528846 4.666667
##
## Kolmogorov-Smirnov test:
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: compare_df$claim_benchmark_user[compare_df$group == "Other"] and compare_df$claim_benchmark_user[compare_df$group == "Seen"]
## D = 0.21474, p-value = 0.2837
## alternative hypothesis: two-sided
##
## [1] "Summary statistics:"
## # A tibble: 2 × 4
## group mean sd n
## <chr> <dbl> <dbl> <int>
## 1 Other 4.53 1.40 105
## 2 Seen 4.67 0.651 12
##
## === Comparing Treatment Users Who Saw Clinical Info on Purchases vs. All Others - >30s Users ===
##
## Welch Two Sample t-test
##
## data: claim_benchmark_user by group
## t = -0.30057, df = 21.035, p-value = 0.7667
## alternative hypothesis: true difference in means between group Other and group Seen is not equal to 0
## 95 percent confidence interval:
## -0.5278790 0.3945457
## sample estimates:
## mean in group Other mean in group Seen
## 4.600000 4.666667
##
## Kolmogorov-Smirnov test:
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: compare_df$claim_benchmark_user[compare_df$group == "Other"] and compare_df$claim_benchmark_user[compare_df$group == "Seen"]
## D = 0.21296, p-value = 0.285
## alternative hypothesis: two-sided
##
## [1] "Summary statistics:"
## # A tibble: 2 × 4
## group mean sd n
## <chr> <dbl> <dbl> <int>
## 1 Other 4.6 1.37 137
## 2 Seen 4.67 0.651 12
Missed clinical exposures
##
## === Clinical Exposure Summary for >60s Users ===## → Number of interactions with claims AND NO clinical info seen: 87
## → Number of interactions with claims AND clinical info seen: 25
## → Proportion seen (seen / (missed + seen)): 22.32 %##
## === Clinical Exposure Summary for >30s Users ===## → Number of interactions with claims AND NO clinical info seen: 97
## → Number of interactions with claims AND clinical info seen: 25
## → Proportion seen (seen / (missed + seen)): 20.49 %Treatment effect for those users that have at least seen clinical image/results once
Control group is all the purchases made by people in control group that have at least one group 2 product click
##
## === Clinical Info Filtered Analysis for >60s Users ===
## # A tibble: 2 × 7
## Group n mean_treatment_pa sd_treatment_pa se_treatment_pa ci_lower
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Control 87 0.575 0.497 0.0533 0.469
## 2 Treatment 32 0.688 0.471 0.0832 0.518
## # ℹ 1 more variable: ci_upper <dbl>
##
## Welch Two Sample t-test
##
## data: treatment_pa by Group
## t = -1.1409, df = 58.116, p-value = 0.2586
## alternative hypothesis: true difference in means between group Control and group Treatment is not equal to 0
## 95 percent confidence interval:
## -0.3106608 0.0850861
## sample estimates:
## mean in group Control mean in group Treatment
## 0.5747126 0.6875000
##
## === Clinical Info Filtered Analysis for >30s Users ===
## # A tibble: 2 × 7
## Group n mean_treatment_pa sd_treatment_pa se_treatment_pa ci_lower
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Control 103 0.602 0.492 0.0485 0.506
## 2 Treatment 32 0.688 0.471 0.0832 0.518
## # ℹ 1 more variable: ci_upper <dbl>
##
## Welch Two Sample t-test
##
## data: treatment_pa by Group
## t = -0.88818, df = 53.702, p-value = 0.3784
## alternative hypothesis: true difference in means between group Control and group Treatment is not equal to 0
## 95 percent confidence interval:
## -0.2787136 0.1075971
## sample estimates:
## mean in group Control mean in group Treatment
## 0.6019417 0.6875000
## Sample size needed per group for >60s users:##
## Two-sample comparison of proportions power calculation
##
## n = 286.1081
## p1 = 0.5747126
## p2 = 0.6875
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
##
## NOTE: n is number in *each* group## Sample size needed per group for >30s users:##
## Two-sample comparison of proportions power calculation
##
## n = 490.0154
## p1 = 0.6019417
## p2 = 0.6875
## sig.level = 0.05
## power = 0.8
## alternative = two.sided
##
## NOTE: n is number in *each* groupUser-Product click by Group
##
## === User Interaction with Treated Products - >60s Users ===
## Total users: 117
## Users who interacted with at least one treated product: 102
## Proportion (%): 87.18 %##
## === User Interaction with Treated Products - >30s Users ===
## Total users: 149
## Users who interacted with at least one treated product: 121
## Proportion (%): 81.21 %##
## === User Interaction with Treated Products by Group - >60s Users ===
## # A tibble: 2 × 4
## Group total_users users_with_treated proportion_treated
## <chr> <int> <int> <dbl>
## 1 Control 57 50 87.7
## 2 Treatment 60 52 86.7##
## === User Interaction with Treated Products by Group - >30s Users ===
## # A tibble: 2 × 4
## Group total_users users_with_treated proportion_treated
## <chr> <int> <int> <dbl>
## 1 Control 73 61 83.6
## 2 Treatment 76 60 79.0##
## === User Interaction with Untreated Products by Group - >60s Users ===
## # A tibble: 2 × 4
## Group total_users users_with_treated proportion_treated
## <chr> <int> <int> <dbl>
## 1 Control 57 47 82.5
## 2 Treatment 60 47 78.3##
## === User Interaction with Untreated Products by Group - >30s Users ===
## # A tibble: 2 × 4
## Group total_users users_with_treated proportion_treated
## <chr> <int> <int> <dbl>
## 1 Control 73 57 78.1
## 2 Treatment 76 57 75##
## === User Interaction with BOTH Treated and Non-Treated Products - >60s Users ===
## Total users: 117
## Users who interacted with BOTH treated and non-treated products: 79
## Proportion (%): 67.52 %##
## === User Interaction with BOTH Treated and Non-Treated Products - >30s Users ===
## Total users: 149
## Users who interacted with BOTH treated and non-treated products: 86
## Proportion (%): 57.72 %##
## === User Interaction with BOTH Treated and Non-Treated Products - >60s Users ===
## # A tibble: 2 × 4
## Group total_users users_both proportion_both
## <chr> <int> <int> <dbl>
## 1 Control 57 40 70.2
## 2 Treatment 60 39 65##
## === User Interaction with BOTH Treated and Non-Treated Products - >30s Users ===
## # A tibble: 2 × 4
## Group total_users users_both proportion_both
## <chr> <int> <int> <dbl>
## 1 Control 73 45 61.6
## 2 Treatment 76 41 54.0