Customer Transaction Data Analysis

Customer Transaction Data Analysis. Customer Bank Transaction Data Analysis Lenders face a growing challenge from heightened competition in the industry This is particularly evident in light of consolidation among major lenders and the rise of fintech companies disrupting the space The move has left many community banks scrambling as they often lack the technology resources to properly keep up.

ANZ Customer Transaction Data Analysis This analysis is based on a synthesised transaction dataset containing 3 months worth of transactions for 100 hypothetical customers It contains purchases recurring transactions and salary transactions The dataset is designed to simulate realistic transaction behaviours that are observed in ANZ’s real transaction data.

Using transaction data analysis to follow the clues your

Context The data provides customer and date level transactions for few years It can be used for demonstration of any analysis that require transaction information like RFM The data also provide response information of customers to a promotion campaign Highlight of this dataset is that you can evaluate the effectiveness RFM group by checking the one of the business metric.

Analysis of Customer Transaction Data Associations Based

Get a good clean dataset to work with There are a few things you need to be careful aboutAdd the required Categorisation Columns The problem with numeric fields such as balancesPerform the Basic Analysis Now we are ready to begin the basic analysis of the data – firstPerform the CrossReference Analysis It’s unlikely the basic analysis will tell us very muchPerform the Trend Analysis over Time We now extend our analysis to include the timeCheck your assumptions about the data It’s important never to do data analysis in aReview the Data Insights with Operational Staff All you really have so far is a set of potential.

Credit Card Transaction Data: Best Datasets, Databases

PDF fileFor transactional data this step often implies rolling up daily transactions into a weekly or monthly aggregate for analysis purposes For example EBP data which contains daily billpay transactions for all customers can produce an aggregation of monthly transactions for each customer per month These can include count.