Overview
High digital abandonment rates are typical for brands across domains; driven mainly by the experiential issues faced by the site users during their journey. Identification of these friction points can be burdensome for businesses as they struggle to digest the new wealth of granular data generated along their customers' digital journey.
Many teams are still basing many of their experience decisions on instinct which is certainly not the most optimal way of improving website design. With AIDE (Automated Insights for Digital Evolution), you can now capture every in-page interaction and micro-gesture to understand site user’s journey to identify frustrations, and errors that impact conversion and self-serve rate. AIDE can enable businesses to troubleshoot faster to resolve issues and recover potential revenue and leads.
Addressing the “Why” and not just the “What” is how AIDE differentiates from conventional platforms like Adobe
Moving away from monitoring surface-level metrics such as clicks, conversions and bounce rates, to a reason behind those results is the new way of analyzing clickstream data feed
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True friction v/s Focus on exit point “Exits on the page” DOES NOT necessarily mean the visitor faces the friction on that page. Millions of journey touchpoints mined using AI recognize interaction patterns which leads to abandoning the journey
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Cross-channel interaction feed Customer interactions before landing on the digital channel and post exiting the digital channel provides a granular level of insights to understand customer’s decision-making behavior
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Context identification Uncovering root cause is not possible using only clickstream data feed. Integration with Call, Chat, Store and other unstructured data domains helps to piece together root cause of friction/dissonance
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Data as a Product Automated pipeline to derive meaningful business features at scale using the semi-structured clickstream feed to support multiple downstream AI/ML use cases
AIDE is build using Amazon RDS, Redshift, EMR, Lamda, E2 and S3
Highlights
- Sensorize & Detect: An automated AI/ML pipeline to derive meaningful business indicators using the click activity across customer journeys. Deviations from expected behavior across digital journeys get captured by applying pattern recognition algorithms to the key digital indicators
- Locate & Reveal: A suite of supervised machine learning algorithms to identify drivers of key customer journey outcomes (drop-off, clear cart, etc.) and measure relative impact at a page & click level on a customer’s experience. NLP module to perform sentiment analysis and entity extraction on the voice of customer data such as chat; feedback etc. to identify the root cause of the friction and generate actionable insights
- Prioritize: Quantify the insights with respect to loss in revenue or incremental overhead costs to prioritize hypotheses for improving website design
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