Bacancy

Bacancy represents the connected world, offering innovative and customer-centric information technology experiences, enabling Enterprises, Associates and the Society to Rise™.

12+

Countries where we have happy customers

1050+

Agile enabled employees

06

World wide offices

13+

Years of Experience

05

Agile Coaches

14

Certified Scrum Masters

1000+

Clients projects

1458

Happy customers

Overview

ShopSphere Retail, a growing omnichannel fashion brand based in the United States, was grappling with an increasing rate of unexplained customer churn. Despite offering seamless shopping experiences across online platforms, a mobile app, and physical stores, customer loyalty was declining—and traditional analytics tools failed to uncover the root cause. The real challenge was their inability to track customer behavior cohesively across channels, leading to fragmented insights and missed opportunities for retention. That’s when Bacancy stepped in to transform their customer engagement strategy.

Technical Stack

  • Python
  • TensorFlow
  • Apache Kafka
  • PostgreSQL
  • Power BI

    • Industry

    Omnichannel Retail

  • region

    • Region

    United States

  • project-size

    • Project Size

    Confidential

Highlights

Unified customer journey insights across web, app, and offline.

Reduced churn rate by 38% using predictive AI models.

Improved re-engagement with intelligent channel-based campaigns.

Real-time behavioral alerts using AI stream processing.

2.2x increase in loyalty program participation.

Challenges & Solutions

Disconnected customer data across platforms

  • Solution: Bacancy implemented an AI-powered Customer Data Platform (CDP) leveraging entity resolution algorithms and graph neural networks to connect scattered touchpoints. This enabled a 360-degree view of each customer’s multi-channel behavior, creating a unified identity and journey path.

    Problem:- Customer interactions on the website, mobile app, and physical stores were stored in isolated systems, making it impossible to create a unified view of the customer journey.

Inability to predict churn due to inconsistent behavioral signals

  • Solution: Our AI experts built a multi-source behavioral churn prediction model using sequence modeling (LSTM networks) and Bayesian probability mapping. The model processed interactions across time and channels to calculate real-time churn probabilities and recommend preventive actions.

    Problem:- Traditional churn models failed to account for fragmented engagement signals, like browsing on mobile but abandoning in-store purchases, which leads to missed churn indicators.

Lack of actionable insights to re-engage at-risk customers

  • Solution: Bacancy deployed a real-time AI decision engine using reinforcement learning to recommend the most effective re-engagement strategy, such as email, app push notifications, SMS, or personalized in-store offers, based on user habits, sentiment analysis, and past response data.

    Problem:- Even when churn risks were suspected, the marketing team lacked tools to target the right customer at the right time on the right channel.

No mechanism to track and improve channel-switching experiences

  • Solution: We integrated AI-based journey analytics using Markov Chain models to trace common switch-points and drop-off nodes. Combined with heatmaps and predictive insights, this allowed the UX and operations teams to fine-tune the experience and optimize channel transitions.

    Problem:- Customers often began their journey online and completed it in-store (or vice versa), but the system didn’t capture cross-channel drop-offs or pain points.

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13+

Years of Business
Experience

1458+

Happy
Customers

12+

Countries with
Happy Customers

1050+

Agile enabled
employees

Core Features

  • AI-driven customer churn alerts
  • Channel-specific behavior analysis
  • Automated intervention engine
  • Customer journey pattern clustering
  • no.-of-resources

    • No. of Developers

    05

  • time-frame

    • Time Frame

    7 Months

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