Skip links

Large Lifestyle and Fashion Retailer

Jump To Section

A large lifestyle and fashion retailer was sitting on a gold mine of customer feedback. They had enormous amounts of data but struggled to convert it into meaningful insights.

Gleaning Actionable Insights from Customer Feedback

A large lifestyle and fashion retailer was sitting on a gold mine of customer feedback. They had enormous amounts of data but struggled to convert it into meaningful insights. Their customers had expectations that they were not able to quickly decipher or take action on. This situation could lead to a disconnect between the company and their customers creating a risk of diminished loyalty or increased attrition.

Altimetrik Data and Analytics team was engaged to bring structure to the data and derive meaningful insights that could help the client make informed decisions. Our team of data practitioners built a platform to structure the data, create a single source of truth, and based on various relevant parameters enable visualization for various stakeholders to drive decisions on customer service and product offerings. This led to the creation of loyalty programs and various other tailored customer initiatives.

  • 25% increase in customer satisfaction
  • Higher customer foot traffic in stores through efficient and targeted marketing campaigns
  • Tailored loyalty programs led to increased word of mouth referrals
  • Stronger brand portfolio to better suit the target demographic

Taking an incremental approach to applying data analytics created a sustainable model that provides customer insights that deliver great experiences.

Altimetrik Data and Analytics team took up the task to ensure that data collected across all touchpoints, channels, and areas into a standardized set of information and structures. Our focus and approach to developing data analytics started with data governance as a basis for a larger data solution. Therefore, improving the trust in data by implementing robust cleansing and data quality gates was foundational to making the data usable for development.

We began with a program to correlate their transaction processing (ERP) and feedback collection systems. Then, we built a data mart layer with SAP BW (for ERP data) and Sybase Platform (for MySQL sources)​. Using a BO universe semantic layer, we aggregated the data mart information for unified analytics solutions providing actionable intelligence​. Given the extreme complexity of the system, we piloted with two stores to prove the model. Once we validated the approach, we scaled it quickly to cover the client’s entire network.

Customer feedback became more clear and intelligible, which led to the creation of multiple targeted marketing campaigns and tailored loyalty programs and a strong brand portfolio. We measured a significant increase in customer satisfaction and store foot traffic within a very short period. Having the data and insights made it possible to increase data-based decisions and improved customer satisfaction.

“Understanding customer feedback in real-time is the best outcome a business can achieve, especially in the B2C sector. The challenge is to make humongous amounts of unstructured data flowing through countless sources intelligible and insightful. Altimetrik Data and Analytics Platform are posited to solve this challenge for organizations. We are helping several companies delight their customers by navigating through the data maze and discovering the power of data-driven ideas and decisions.”

Vipul Valamjee

Senior Engineering Leader – Product and Platform Engineering

admin

admin

Latest Reads

Subscribe

Suggested Reading

Ready to Unlock Yours Enterprise's Full Potential?

Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs.Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify.
Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indicationsUtilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes