Enhancing the Prediction of Missing Targeted Items from the Transactions of Frequent, Known Users

The ability for individual grocery retailers to have a single view of its customers across all of their grocery purchases remains elusive, and is considered the “holy grail” of grocery retailing. This has become increasingly important in recent years, especially in the UK, where competition has intensified, shopping habits and demographics have changed, and price sensitivity has increased. Whilst numerous studies have been conducted on understanding independent items that are frequently bought together, there has been little research conducted on using this knowledge of frequent itemsets to support decision making for targeted promotions. Indeed, having an effective targeted promotions approach may be seen as an outcome of the “holy grail”, as it will allow retailers to promote the right item, to the right customer, using the right incentives to drive up revenue, profitability, and customer share, whilst minimising costs. Given this, the key and original contribution of this study is the development of the market target (mt) model, the clustering approach, and the computer-based algorithm to enhance targeted promotions. Tests conducted on large scale consumer panel data, with over 32000 customers and 51 million individual scanned items per year, show that the mt model and the clustering approach successfully identifies both the best items, and customers to target. Further, the algorithm segregates customers into differing categories of loyalty, in this case it is four, to enable retailers to offer customised incentives schemes to each group, thereby enhancing customer engagement, whilst preventing unnecessary revenue erosion. The proposed model is compared with both a recently published approach, and the cross-sectional shopping patterns of the customers on the consumer scanner panel. Tests show that the proposed approach outperforms the other approach in that it significantly reduces the probability of having “false negatives” and “false positives” in the target customer set. Tests also show that the customer segmentation approach is effective, in that customers who are classed as highly loyal to a grocery retailer, are indeed loyal, whilst those that are classified as “switchers” do indeed have low levels of loyalty to the selected grocery retailer. Applying the mt model to other fields has not only been novel but yielded success. School attendance is improved with the aid of the mt model being applied to attendance data. In this regard, an action research study, involving the proposed mt model and approach, conducted at a local UK primary school, has resulted in the school now meeting the required attendance targets set by the government, and it has halved its persistent absenteeism for the first time in four years. In medicine, the mt model is seen as a useful tool that could rapidly uncover associations that may lead to new research hypotheses, whilst in crime prevention, the mt value may be used as an effective, tangible, efficiency metric that will lead to enhanced crime prevention outcomes, and support stronger community engagement. Future work includes the development of a software program for improving school attendance that will be offered to all schools, while further progress will be made on demonstrating the effectiveness of the mt value as a tangible crime prevention metric

Using Data Mining in Educational Administration - A Case Study on Improving School Attendance

open access articlePupil absenteeism remains a significant problem for schools across the globe with its negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96\%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, show that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools

Using Data Mining in Educational Administration: A Case Study on Improving School Attendance

The authors would like to thank the leadership and staff of Willen Primary School for permitting us to use their data and for their efforts in supporting this study, in particular, Ms Emma Warner (attendance officer), Ms Carrie Matthews (headteacher), and Ms Sarah Orr (deputy headteacher).Pupil absenteeism remains a significant problem for schools across the globe with negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, showed that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools

A product-centric data mining algorithm for targeted promotions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Targeted promotions in retail are becoming increasingly popular, particularly in the UK grocery retail sector where competition is stiff and consumers remain price sensitive. Given this, a targeted promotion algorithm is proposed to enhance the effectiveness of promotions by retailers. The algorithm leverages a mathematical model for optimizing items to target and fuzzy c-means clustering for finding the best customers to target. Tests using simulations with real life consumer scanner panel data from the UK grocery retailer sector shows that the algorithm performs well in finding the best items and customers to target whilst eliminating "false positives" (targeting customers who do not buy a product) and reducing "false negatives" (not targeting customers who could buy). The algorithm also shows better performance when compared to a similar published framework, particularly in handling "false positives" and "false negatives". The paper concludes by discussing managerial and research implications, and highlights applications of the model to other fields

Application of uninorms to market basket analysis

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The ability for grocery retailers to have a single view of customers across all their grocery purchases remains elusive and has become increasingly important in recent years (especially in the UK) where competition has intensified, shopping habits and demographics have changed and price sensitivity has increased following the 2008 recession. Numerous studies have been conducted on understanding independent items that are frequently bought together (association rule mining/ frequent itemsets) with several measures proposed to aggregate item support and rule confidence with varying levels of accuracy as these measures are highly context dependent. Uninorms were used as an alternative measure to aggregate support and confidence in analysing market basket data using the UK grocery retail sector as a case study. Experiments were conducted on consumer panel data with the aim of comparing the uninorm against three other popular measures (Jaccard, Cosine and Conviction). It was found that the uninorm outperformed other models on its adherence to the fundamental monotonicity property of support in market basket analysis. Future work will include the extension of this analysis to provide a generalised model for market basket analysis

Microevolution of Helicobacter pylori during prolonged infection of single hosts and within families

Our understanding of basic evolutionary processes in bacteria is still very limited. For example, multiple recent dating estimates are based on a universal inter-species molecular clock rate, but that rate was calibrated using estimates of geological dates that are no longer accepted. We therefore estimated the short-term rates of mutation and recombination in Helicobacter pylori by sequencing an average of 39,300 bp in 78 gene fragments from 97 isolates. These isolates included 34 pairs of sequential samples, which were sampled at intervals of 0.25 to 10.2 years. They also included single isolates from 29 individuals (average age: 45 years) from 10 families. The accumulation of sequence diversity increased with time of separation in a clock-like manner in the sequential isolates. We used Approximate Bayesian Computation to estimate the rates of mutation, recombination, mean length of recombination tracts, and average diversity in those tracts. The estimates indicate that the short-term mutation rate is 1.4×10−6 (serial isolates) to 4.5×10−6 (family isolates) per nucleotide per year and that three times as many substitutions are introduced by recombination as by mutation. The long-term mutation rate over millennia is 5–17-fold lower, partly due to the removal of non-synonymous mutations due to purifying selection. Comparisons with the recent literature show that short-term mutation rates vary dramatically in different bacterial species and can span a range of several orders of magnitude

Evolutionary History of Helicobacter pylori Sequences Reflect Past Human Migrations in Southeast Asia

The human population history in Southeast Asia was shaped by numerous migrations and population expansions. Their reconstruction based on archaeological, linguistic or human genetic data is often hampered by the limited number of informative polymorphisms in classical human genetic markers, such as the hypervariable regions of the mitochondrial DNA. Here, we analyse housekeeping gene sequences of the human stomach bacterium Helicobacter pylori from various countries in Southeast Asia and we provide evidence that H. pylori accompanied at least three ancient human migrations into this area: i) a migration from India introducing hpEurope bacteria into Thailand, Cambodia and Malaysia; ii) a migration of the ancestors of Austro-Asiatic speaking people into Vietnam and Cambodia carrying hspEAsia bacteria; and iii) a migration of the ancestors of the Thai people from Southern China into Thailand carrying H. pylori of population hpAsia2. Moreover, the H. pylori sequences reflect iv) the migrations of Chinese to Thailand and Malaysia within the last 200 years spreading hspEasia strains, and v) migrations of Indians to Malaysia within the last 200 years distributing both hpAsia2 and hpEurope bacteria. The distribution of the bacterial populations seems to strongly influence the incidence of gastric cancer as countries with predominantly hspEAsia isolates exhibit a high incidence of gastric cancer while the incidence is low in countries with a high proportion of hpAsia2 or hpEurope strains. In the future, the host range expansion of hpEurope strains among Asian populations, combined with human motility, may have a significant impact on gastric cancer incidence in Asia

Investigation of NRXN1 deletions: Clinical and molecular characterization

Deletions at 2p16.3 involving exons of NRXN1 are associated with susceptibility for autism and schizophrenia, and similar deletions have been identified in individuals with developmental delay and dysmorphic features. We have identified 34 probands with exonic NRXN1 deletions following referral for clinical microarray‐based comparative genomic hybridization. To more firmly establish the full phenotypic spectrum associated with exonic NRXN1 deletions, we report the clinical features of 27 individuals with NRXN1 deletions, who represent 23 of these 34 families. The frequency of exonic NRXN1 deletions among our postnatally diagnosed patients (0.11%) is significantly higher than the frequency among reported controls (0.02%; P  = 6.08 × 10 −7 ), supporting a role for these deletions in the development of abnormal phenotypes. Generally, most individuals with NRXN1 exonic deletions have developmental delay (particularly speech), abnormal behaviors, and mild dysmorphic features. In our cohort, autism spectrum disorders were diagnosed in 43% (10/23), and 16% (4/25) had epilepsy. The presence of NRXN1 deletions in normal parents and siblings suggests reduced penetrance and/or variable expressivity, which may be influenced by genetic, environmental, and/or stochastic factors. The pathogenicity of these deletions may also be affected by the location of the deletion within the gene. Counseling should appropriately represent this spectrum of possibilities when discussing recurrence risks or expectations for a child found to have a deletion in NRXN1 . © 2013 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97220/1/35780_ftp.pd