Introduction |
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1 | (1) |
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PART ONE How can marketing analytics help you? |
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1 | (34) |
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9 | (11) |
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Measures of central tendency |
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9 | (2) |
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11 | (3) |
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14 | (1) |
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15 | (1) |
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Relations among two variables: covariance and correlation |
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16 | (2) |
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Probability and the sampling distribution |
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18 | (1) |
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18 | (1) |
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19 | (1) |
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2 Consumer behaviour and marketing strategy |
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20 | (9) |
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20 | (1) |
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Consumer behaviour as the basis for marketing strategy |
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21 | (1) |
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Overview of consumer behaviour |
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22 | (2) |
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Overview of marketing strategy |
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24 | (3) |
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27 | (1) |
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28 | (1) |
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29 | (6) |
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29 | (1) |
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Insights tend not to be used by executives |
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29 | (1) |
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30 | (1) |
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31 | (1) |
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Ultimately, an insight is about action-ability |
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32 | (2) |
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34 | (1) |
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PART TWO Dependent variable techniques |
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35 | (108) |
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4 Modelling demand and elasticity |
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37 | (27) |
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37 | (1) |
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Dependent equation type vs interrelationship type statistics |
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38 | (1) |
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Deterministic vs probabilistic equations |
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38 | (1) |
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38 | (6) |
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Results applied to business case |
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44 | (1) |
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44 | (3) |
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47 | (4) |
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Highlight: Segmentation and elasticity modelling can maximize revenue in a retail/medical clinic chain: field test results |
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51 | (1) |
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The problem and some background |
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52 | (1) |
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Description of the dataset |
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53 | (1) |
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53 | (2) |
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Then: elasticity modelling |
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55 | (6) |
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61 | (1) |
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61 | (1) |
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Conclusion: why is elasticity modelling so rarely done? |
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62 | (1) |
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63 | (1) |
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5 Polynomial distributed lags |
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64 | (9) |
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64 | (2) |
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66 | (2) |
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68 | (3) |
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71 | (1) |
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72 | (1) |
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6 Using Poisson regression |
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73 | (6) |
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When to use Poisson regression |
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73 | (1) |
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74 | (1) |
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75 | (2) |
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77 | (1) |
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78 | (1) |
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7 Logistic regression and market basket analysis |
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79 | (23) |
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79 | (1) |
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80 | (1) |
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81 | (1) |
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Results applied to the model |
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82 | (3) |
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85 | (1) |
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86 | (4) |
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Using the model - collinearity overview |
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90 | (4) |
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94 | (2) |
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Highlight: Using logistic regression for market basket analysis |
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96 | (1) |
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96 | (1) |
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96 | (1) |
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96 | (1) |
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97 | (1) |
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How to estimate/predict the market basket |
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97 | (1) |
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98 | (2) |
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100 | (1) |
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101 | (1) |
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8 Survival modelling and lifetime value |
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102 | (21) |
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102 | (1) |
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Conceptual overview of survival analysis |
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103 | (1) |
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104 | (2) |
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More about survival analysis |
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106 | (2) |
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Model output and interpretation |
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108 | (4) |
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Note: The only way to do churn modelling |
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112 | (2) |
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114 | (1) |
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Highlight: Lifetime value: how predictive analysis is superior to descriptive analysis |
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115 | (1) |
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115 | (1) |
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115 | (1) |
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116 | (2) |
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118 | (4) |
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122 | (1) |
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9 Panel regression and same store sales |
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123 | (10) |
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123 | (3) |
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What is panel regression? |
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126 | (1) |
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Panel regression: details |
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126 | (2) |
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128 | (1) |
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Insights about marcom (direct mail, email and SMS) |
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128 | (1) |
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Insights about time period (quarters) |
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129 | (1) |
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Insights about cross-sections (counties) |
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130 | (1) |
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Brief note on modelling same store sales |
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130 | (1) |
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131 | (1) |
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132 | (1) |
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10 Introduction to forecasting |
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133 | (10) |
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133 | (1) |
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134 | (1) |
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134 | (1) |
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Dummy variables and seasonality |
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135 | (2) |
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137 | (4) |
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141 | (1) |
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142 | (1) |
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PART THREE Interrelationship techniques |
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143 | (78) |
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11 Simultaneous equations |
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145 | (11) |
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145 | (1) |
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What are simultaneous equations? |
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146 | (1) |
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Why go to the trouble of using simultaneous equations? |
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147 | (1) |
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Desirable properties of estimators |
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148 | (3) |
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151 | (3) |
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154 | (1) |
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155 | (1) |
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12 Principal components and factor analysis |
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156 | (6) |
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Interrelationship techniques |
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156 | (1) |
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157 | (2) |
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159 | (1) |
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Similarities between PCA and factor analysis |
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159 | (1) |
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Differences between PCA and factor analysis |
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159 | (1) |
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160 | (1) |
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161 | (1) |
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162 | (22) |
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162 | (1) |
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Introduction to segmentation |
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163 | (1) |
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What is segmentation? What is a segment? |
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163 | (1) |
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Why segment? Strategic uses of segmentation |
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164 | (2) |
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The four Ps of strategic marketing |
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166 | (2) |
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Criteria for actionable segmentation |
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168 | (1) |
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168 | (1) |
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169 | (6) |
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Highlight: Using segmentation to improve both strategy and predictive modelling |
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175 | (1) |
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175 | (1) |
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Segmentation is a strategic, not an analytic, process |
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175 | (1) |
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Why would segmentation improve predictive modelling accuracy? |
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176 | (1) |
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Segmenting variables for model improvement |
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177 | (1) |
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177 | (2) |
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Interpretation and insights |
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179 | (2) |
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What if there was no segmentation? |
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181 | (1) |
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182 | (1) |
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183 | (1) |
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184 | (37) |
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184 | (1) |
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Metrics of successful segmentation |
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185 | (1) |
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General analytic techniques |
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185 | (8) |
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Segmentation techniques summary |
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193 | (1) |
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194 | (3) |
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197 | (4) |
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201 | (2) |
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Comments/details on individual segments |
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203 | (7) |
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210 | (4) |
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Highlight: Why go beyond RFM? |
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214 | (1) |
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214 | (1) |
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214 | (2) |
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What is behavioural segmentation? |
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216 | (2) |
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What does behavioural segmentation provide that RFM does not? |
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218 | (1) |
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219 | (1) |
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220 | (1) |
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PART FOUR Focus on media and loyalty |
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221 | (58) |
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15 Modelling marcom value |
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223 | (7) |
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223 | (1) |
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223 | (3) |
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226 | (3) |
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Checklist: You'll be the smartest person in the room if you... |
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229 | (1) |
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230 | (9) |
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230 | (1) |
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231 | (1) |
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231 | (1) |
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232 | (1) |
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233 | (4) |
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237 | (1) |
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238 | (1) |
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239 | (14) |
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239 | (1) |
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Is there a range or spectrum of loyalty? |
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240 | (1) |
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What are the three Rs of loyalty? |
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241 | (1) |
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Why design a programme with earn-burn measures? |
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242 | (4) |
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246 | (2) |
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A note on programme valuation |
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248 | (2) |
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250 | (2) |
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252 | (1) |
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253 | (10) |
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Structural equation modelling (SEM) |
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253 | (4) |
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257 | (4) |
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261 | (1) |
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262 | (1) |
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19 The customer loyalty journey |
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263 | (16) |
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263 | (1) |
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264 | (1) |
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265 | (1) |
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266 | (1) |
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267 | (1) |
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The experiences questionnaire |
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268 | (9) |
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277 | (1) |
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278 | (1) |
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PART FIVE More important topics for everyday marketing |
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279 | (24) |
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281 | (11) |
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281 | (1) |
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Sample size equation: use the lift measure |
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282 | (2) |
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A/B testing and full factorial differences |
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284 | (2) |
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286 | (5) |
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291 | (1) |
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21 Introduction to Big Data |
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292 | (11) |
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292 | (1) |
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292 | (2) |
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294 | (1) |
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What does it mean for analytics? For strategy? |
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294 | (1) |
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295 | (1) |
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Surviving the Big Data panic |
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295 | (2) |
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297 | (1) |
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Big Data - exotic algorithms |
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298 | (3) |
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301 | (1) |
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302 | (1) |
Conclusion: The finale |
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303 | (8) |
References |
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311 | (2) |
Further reading |
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313 | (2) |
Index |
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