Enhancing Customer Service Through Real-time Analysis of Dwell Time in US Bank Branches via Wireless IoT Data

The Challenge

The challenge is to utilize the presence data captured by various network access points in bank branches to perform real-time analysis of customer and visitor behavior, specifically their dwell time during operational banking hours.

The Solution

Capture: Wireless access point data is collected to track customer presence.

Session Duration Determination: Defining a session duration for identifying new visits.

Filtering In-house and Staff Devices: Removing data from in-house and staff devices.

Cost-efficient Snowflake Execution: Employing methods to keep the Snowflake execution cost low despite ingesting data almost every minute.

Steps

Configuration of Cisco network devices to post minute-by-minute data to a bank webservice endpoint hosted on AWS.

Capturing JSON data from the bank webservice endpoint API calls, storing it in AWS S3, and ingesting it into Snowflake stage via SnowPipe.

Analyzing total device durations by day in business hours, excluding bank and staff devices, using the 80th percentile value as the session duration, averaging around 70 minutes.

Employing an Alteryx ETL pipeline for other network data capture in Snowflake.

Balancing data speed with Snowflake cost; allowing minute-by-minute ingestion into Snowflake stage, and performing gold layer data tasks thrice daily during business hours.

The Result

The implementation facilitated reliable ingestion of high-frequency, low-volume wireless device data without queuing. The bank can now effectively analyze dwell time, peak days, and peak timings across multiple branches, aiding in resource planning for improved customer service.