Retail & E-commerce
Analytics that combine transaction history, store traffic, returns data, and review sentiment to understand what drives customer satisfaction, churn, and purchase motivation.
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Industry overview
Retail customer analytics systems that analyse transaction patterns, in-store behaviour, returns data, and review sentiment to identify what drives satisfaction, repeat purchase, and churn across the customer base.
At a glance
Retailers often know what customers bought but not why — or why they stopped buying. Transaction data shows what happened; behavioural analytics explains the dynamics behind it. Returns data reveals product quality or expectation issues that aggregate ratings miss. Review sentiment identifies specific friction points that affect purchase intent. Combined, these signals create a customer understanding that drives better decisions across buying, marketing, and operations.
We build customer analytics systems that combine transaction history with store visit data, returns reason codes, and sentiment analysis from reviews and support contacts. Churn propensity models identify customers who are showing early disengagement signals — reduced visit frequency, lower basket sizes, increased returns — before they are lost. Customer lifetime value segmentation identifies the top-value cohorts that merit retention investment. Store heatmapping from footfall data identifies which departments attract dwell time and which are being bypassed.
Key capabilities
Engagements are scoped to your business context — these are the core capabilities we bring to retail & e-commerce clients.
Transaction pattern and basket evolution analysis
Store footfall heatmapping and department dwell time analysis
Returns root cause analysis by product, channel, and customer segment
Review and support contact sentiment classification
Churn propensity modelling with early disengagement signals
Customer lifetime value segmentation and high-value cohort profiling
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