![]() We use this concept to validate our data in order to see the switching tendency of a household between items. We summarize these loyalty values at item level by aggregating the mean(GL1) or median(GL2) at the household level to get the final GL based loyalty scores.Īpproach V: Information Entropy of purchase behaviorĮntropy is defined as the average amount of information produced by a probabilistic stochastic source of data. Here we define a loyalty variable as an exponentially weighted sum of past purchases of a Household, that will capture the bias of the households towards a particular product against others. We use these methods introduced by Guadagni and Little (1983) and modify it to cater to our needs, this is more rigorous and computationally extensive than the previous approaches and attempts at reading between the lines so to speak. This method can be extended to consider all available POS data including that without household informationĪpproach IV: GL Probability (GL - probability) Essentially we would not like to consider loyalty for say Plain Yogurt and Flavored yogurt in the same substitutable group since the purchasing intent might be different.Īnother variant of this method takes a mildly altered form which takes into account the number of different items choices available in a substitutable group while calculating this proportion. ![]() Loyalty in this method is always calculated at a substitutable item group level since this would strongly reflect the loyalty of a household amongst similar behaving items. Here loyalty is split among products indicating a share of loyalty amongst products purchased by a household (share of requirements) over time. It captures much of the previous works done on loyalty variables, being a unique case of the Guadagni and Little loyalty variable. This method is conceivably the simplest measure of loyalty for an item, swift to execute, scale and understandable to decision makers. This method looks at the proportion of choices allocated to item by a household Hi, typically called the share of requirements for item by the household.Į.g.: A household that makes 10 trips into the Yogurt category over the time period of the analysis and buys ‘XX Greek Yogurt’ in 8 of those trips would have 80% household loyalty to that ‘XX Greek Yogurt’ item This can be useful in categories where the repurchase rate is low (such as automobile or electronics category) but might be misleading in cases such as grocery. However, this can be used as a quick trial measure to get a direction of relative loyalty values of the items in question. The measure is really simple and easy to compute though it lacks the rigor to capture the hidden patterns in the transactional data. it measures 100% commitment towards an item. Given that a household has bought a certain item, what is the probability they will never buy anything else except that one item the household favors, i.e. Ideally the accuracy of the loyalty score greatly depends on the extent of capturing the discussed purchase paths of the respective households in the POS dataĪpproach II: Loyal Household Probability (L - probability) in a household purchase pattern: both A and B will have a loyalty score of 0 as per the above method which is clearly misleading. We adhere to these conditions when adopting any loyalty measurement approach.įigure 6: Purchase occurrences for a HouseholdĬonsider the purchase pattern above where a household might purchase the product Tide in the 1st, 3rd and 4th purchase thus implying a 50% re- purchase probability for Tide.Īlthough very convenient, yet many times it does not give much insight into the data, e.g. Repeat Purchasing Behavior, 1973) which includes a set of six necessary and collectively sufficient conditions. These loyalty scores can be enhanced further by the online web based transaction data coupled with surveys which are conducted for the said items.įor this purpose, we propose to work with the definition proposed by Jacoby and Kyner (Kyner, Brand Loyalty vs. For instance, when making space for new product on the shelf, we would not want to drop a low selling yet a high loyalty item.Īs part of Assortment Analytics team in Walmart Labs Bangalore we have used the transactional/scanner (POS) data for our exploration of the various techniques for measuring item loyalty. Within Assortment Planning, item loyalty feeds into critical shelf placement decisions i.e. ![]() Figure 2: Shelf decision based on item loyaltyĬonventionally, Household based loyalty has been looked at as a component leading to certain business advantages, such as reduced promotional costs, acquiring of new customers and scoping out of cross/up-sell opportunities for steadfast customers. ![]()
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