tomers U in store S
The three models developed in Sections 4.2, 4.3 and 4.4 were combined together to form a coherent algorithm to enhance the purchasing of target itemsets. The steps of the proposed algorithm are detailed below:
Algorithm 1: Enhancing the purchasing of itemset (A, C) amongst frequent,
known customers U , in store S
1 Create a set, L, containing all itemsets (A, C) that are not frequent in S but
where its subsets A, C are frequent in S
2 Create a shortlist of L using Equation (4.15) and the extension of Lemma 1 3 For each A in the shortlist, create clusters as outlined in Section 4.3.1 4 Order the clusters based on Table 4.1
5 Run simulations as outlined in Section 4.4, re-prioritising the list based on
the shortest time to frequency, noting that there may be some practical constraints as to why some target items cannot be aggressively promoted, e.g. supply shortages, and regulations
This algorithmic approach is different from that proposed in [140], in that it first identifies target items, and then targets customers who are most likely to purchase
to target that will suit each of these groups. It was hypothesised that the approach taken in [140] enhances the potential for “false positives” and “false negatives” as it groups customers into clusters without verifying their purchase history for any affinity to the target product, which could lead to increased “false positives”. Further, each group is targeted with a different itemset, hence there may be some customers within a group that would have purchased items that were offered to other customer groups only, thus increasing the likelihood of “false negatives”. This hypothesis is tested in Chapter 5.
4.6
Summary
This chapter provided the foundation for this study, and is at the heart of this study’s unique contribution to the body of knowledge on MBA, ARM, and targeted pro- motions. In this regard, a summary of the key aspects discussed in this chapter is provided below:
• A generalised model for target selection, or decision-making, between two choices, e.g. (A → C) and (B → D) within the MBA context, was not readily available from past research.
• Given this, and the potential usefulness of such a model, the market target (mt) model was developed as part of this study using the research methodology outlined in Chapter 3. This model allows grocery retailers to decide quickly, and effectively between two choices for targeting purposes, and thus benefit from savings in time, effort, costs, and in some applications, lives.
• The mt model was developed following an attempt to use the uninorm, which showed promise in other multi-criteria decision making applications, but failed in this instance, due to its inability to compensate for high support or high confidence dominance. In this regard, the mt model proved robust when tested
for both the Apriori and High Support/Confidence Dominance conditions de- tailed in Section 3.3.5, and was thus selected as the model of choice for itemset targeting, i.e. decision-making between two choices.
• Targeting customers was done using a FCM clustering algorithm that was devel- oped based on the RFM framework, and taking into account both the customers’ purchase history, and loyalty to the store. Customers within each cluster dis- played similar behaviour, and which was different to other clusters, hence it was now possible to offer a tailored “treatment” plan for each cluster, thereby enhancing the purchasing of the targeted itemset without unnecessarily eroding the grocery retailer’s revenue.
• A simulation model based on Markov chains, was also developed to simulate the impact of various marketing campaigns on overall sales of the target item- sets. This provided a mechanism to predict the impact on sales, and to make adjustments, in advance, to optimise sales campaigns, thus potentially saving costs and time.
• The three separate models for itemset targeting, customer targeting, and sales simulation were combined to form a coherent algorithm for enhancing the pur- chasing of target itemsets.
• Thus, the unique contributions of this chapter are:
(1) The mt model for itemset targeting
(2) The customer clustering approach to segregate customers for targeted treat- ment
(3) The novel algorithm that combined the mt model, the customer cluster- ing approach, and the simulation tool to enhancing the purchasing of the targeted itemset.
Results and Discussion - Grocery
Retail
5.1
Introduction
The effectiveness of the mathematical models, and algorithm proposed in Chapter 4, were tested by conducting experiments, as outlined in Chapter 3, using real-life shopping data, and for completeness, synthetic market basket data. The overarching objectives of the testing were to establish whether: (1) the proposed models and algo- rithm obey the mathematical underpinnings that govern MBA, as outlined in Chapter 4, and (2) the models and algorithm achieve the underlying business objectives, i.e. they enhance targeted promotions.
To achieve the above objectives, experiments were divided into four tasks: (1) identify target itemsets, using the mt model, (2) identify target customers, using the proposed clustering approach based on FCM, (3) simulating the impacts on sales of the tar- geted item, and (4) comparing the proposed algorithm against the approach detailed in [140], and against what actually happened using the cross-retailer, consumer scan-
ner panel data.
The chapter commences by detailing the experimental process and conditions that was used as part of this study. This is followed by a presentation and discussion of the results of the experiments conducted on itemset targeting, using the mt model, the clustering algorithm, and the targeted promotion algorithm. Results from the simulation testing, and model comparison experiments are then discussed, before the chapter concludes with a summary of key points.
5.2
Experimental Process
Experiments were conducted based on the Knowledge Discovery in Databases (KDD) process and Systems Development Framework (SDF) as detailed in Section 3.3. The heart of the KDD process is the data mining phase which leverages models and algo- rithms to process data into information, [160]. In this regard, the Apriori and FCM algorithms form part of the data mining phase while the proposed market target model and the simulation forms part of the post processing phase, where patterns are interpreted to select the best information that contributes to overall knowledge [160]. Similarly within the SDF, the testing of the model, the algorithm and performing simulations are all part of the experimentation and observation phases.
The key process steps of this experimental approach, i.e. first identifying target items, followed by identifying suitable customers for these items, followed by touting these customers with incentives for these items, and then finally retaining these customers for the long term is in line with the CRM model proposed in [120] and detailed in Section 3.3. Thus, it is through the combining of elements from the KDD, SDF and CRM models that this study enhances targeted promotions.