The Rise of Chief Data Officers in Retail

In a world overflowing with digital breadcrumbs, the likes of Apple, Facebook, Google, and Twitter are voraciously scooping them up—hoarding personal tidbits like dragons guarding a glittering treasure.

This ever-growing data mountain leaves organizations scratching their heads, searching for the right path to navigate this uncharted territory.

Enter the Chief Data Officer (CDO), the fearless hero in our data story, who has emerged to meet this towering challenge and propel their companies to success.

The retail landscape sees CDOs perched high above, their sharp gaze zeroing in on the crucial goals of revenue generation and data monetization.

But their responsibilities soar even higher, empowering analytics and AI to deliver superior business results and foster data-driven decision-making.

The road ahead is far from a cakewalk, but no need to fear.

This post illuminates a trio of strategic beacons and tales of notable brands that have triumphed, harnessing the might of data.

So, let's go on this journey and unravel the secrets of the data age together.

Strategic Beacon #1: Pursuing the revenue growth horizon

In the dynamic world of retail, CDOs are entrusted with the mission of using data as the wind in their sails, propelling their organizations toward the horizon of revenue growth.

A journey that involves discovering fresh revenue streams, fine-tuning the gears of current business operations, and crafting compelling marketing stories.

In the search for new revenue streams, CDOs can harness data to uncover untapped customer segments, breathe life into innovative products and services, and venture into promising markets.

To optimize the driving force of their businesses, CDOs wield data like a compass, guiding them to:

• Unearth hidden inefficiencies,
• Slash unnecessary costs,
• And elevate operational efficiency.

When navigating the seas of marketing, data is the North Star—illuminating the mysteries of customer behavior, pinpointing the ideal audience, and shaping tailored customer experiences.

But the voyage doesn't end there—data monetization lies like a buried treasure on this map.

CDOs can unearth their riches by trading data with third-party vendors, conjuring subscription-based data services, or turning data into advertising gold.

By monetizing data, CDOs can unlock brand-new revenue streams to fuel their organizations' growth.

Revenue Growth Case Study #1: Sifter, an Egen Customer Success Story

Co-founded by Andrew and Thomas Parkinson (the founders of Peapod), Sifter is a revolutionary online grocery platform that caters to individual dietary and nutritional needs. Egen collaborated with the Parkinson brothers, leveraging their expertise to build a powerful, personalized, user-friendly grocery shopping experience.

Challenge:
Develop a cutting-edge online platform that provides personalized recommendations based on dietary and lifestyle preferences and syncs seamlessly with multiple large-scale retailers. The goal was to process data from over 25 million grocery SKUs within 12 hours and ensure secure third-party integration.

Solution:
Egen offered comprehensive solutions to accomplish the objectives set forth by Sifter, including:

• Cloud Strategy
• Infrastructure as Code
• Automated CI/CD Pipelines
• Semantic Search
• API Development
• PWA UI App
• Data Ingestion
• Data Pipeline Orchestration

Tech Stack:

• Data storage: Postgres (relational database), Elasticsearch (for read-heavy operations), Google BigQuery (for analysis)
• API layer: Python-based services using the Flask framework
• Data pipelines: Airflow
• Front end: Vue.js with Nuxt.js framework
• Version management: GitHub
• CI/CD pipelines: Azure DevOps and Azure
• Other technologies: Redis (for syncing services that don't interact with each other), Segment (for event tracking)

Results:
The collaboration with Egen led to the successful realization of Sifter's vision, achieving impressive outcomes:

1. Reduced data processing time by 33%: The planned 12-hour data processing time was reduced to just 4 hours.
2. Handled massive SKU data: A robust data platform was built to manage over 25 million grocery SKUs.
3. Near real-time syncing with 10+ large retailers: Seamlessly integrated with multiple large-scale retailers to provide users with an unparalleled shopping experience.
4. API-friendly shopping platform: A secure engine was developed to connect with third-party tools via API for a safe and personalized user experience.

Egen's expertise in building the Sifter platform brought the Parkinson brothers' vision to life, resulting in an innovative, personalized, and seamless online grocery shopping experience.

By delivering outstanding results and fostering a solid partnership, Egen contributed to the successful launch of the Sifter platform.

Revenue Growth Case Study # 2 The Home Depot

The Home Depot is the largest home improvement retailer, with annual revenues exceeding $151B. The company has over 2,300 stores and a significant online presence through HomeDepot.com. In recent years, The Home Depot has focused on creating a seamless, personalized shopping experience for its customers in-store and online.

Challenge:
The legacy IT system used by The Home Depot was inefficient and ill-equipped to handle the demands of personalized marketing. For example, customer transaction data took several days to process, and system changes took weeks to implement. Furthermore, recognizing customers at the point of transaction was a significant challenge.

Solution:
The Home Depot partnered with Google Cloud to build a new, hybrid solution that utilized Google Cloud's data platform, including BigQuery, Dataflow, DataProc, Cloud Storage, and Cloud Composer. This platform enabled the integration of first- and third-party data into an enterprise data and analytics data lake architecture, creating complete customer profiles and a 360-degree view of their shopping experience.

Results:

1. Increased transaction recognition: Leveraging Google Cloud's capabilities, The Home Depot now connects over 75% of all transactions to an existing household.
2. Faster customer matching: Before Google Cloud, matching customers to transactions took 48-72 hours. Now, this process takes less than 24 hours.
3. Personalized marketing: With improved customer data and analytical models, The Home Depot can offer personalized ads and experiences, making marketing efforts more efficient and enhancing the customer experience.
4. Improved data processing: Optimizing code for cloud-native functionality reduced data processing time from 3 days to 24 hours, resulting in lower cloud costs and better insight.
5. Privacy protection: Google Cloud's solutions help The Home Depot manage and protect customer data, enabling a personalized shopping experience while honoring privacy commitments.

By leveraging Google Cloud's powerful technology and expertise, The Home Depot has successfully transformed its marketing strategy and customer experience, ensuring it remains a competitive force in the home improvement retail industry.

Strategic Beacon #2: Unleashing the Power of Analytics and AI

In the retail cosmos, CDOs harness analytics and AI to propel their businesses forward. They must ensure that data is bountiful, accessible, and comprehensible across their organizations.

By democratizing data, CDOs empower every stakeholder with the gift of insight, guiding them toward decisions illuminated by the glow of data revelations.

CDOs can also deploy data analytics and visualization tools to transform raw data into visually captivating stories. These striking narratives enable stakeholders to discern patterns, trends, and anomalies hiding in the shadows, ultimately leading to confident decision-making.

But the responsibility of a CDO doesn't end there.

They must also ensure that the data guiding their organizations is accurate, timely, and relevant. By establishing data governance policies that uphold data quality, CDOs keep their data up-to-date and ensure it adheres to the regulations and industry standards.

Case Study AI and Analytics: A Machine Learning Coupon Recommendation System for Retailers

In today's rapidly evolving digital landscape, businesses must establish trust and personal connections with their customers. Egen has been providing B2B retail analytics services globally for years.

Our recent project with a Texas-based retailer captures our expertise in creating personalized coupon recommendation systems using machine learning.

Challenge:
Our client sought to increase customer engagement and deliver an excellent shopping experience by implementing a user-friendly coupon recommendation system that store managers and marketing departments could use.

Solution:
Egen built an interactive dashboard with an intuitive user interface that featured drag-and-drop functionality for easy use.

Key features:
Actionable dashboard: Enables store admins to activate/deactivate promotions for single users or groups based on customer clusters, facilitating targeted promotions.

Insights & Analysis: Provide week-on-week sales and revenue comparisons due to active promotions for post-campaign analysis.

Technology Stack:

• Google Cloud SQL
• Google Kubernetes Engine
• Google Functions
• Kubeflow
• Python
• Flask API
• React

Implementation:
After obtaining access to the client's Google Cloud SQL, we used Kubeflow for model building, employing Market Basket Analysis, XGBoost, and Prod2Vec techniques.

Our recommendation system used XGBoost to predict customers' next purchases, clustering algorithms to create personalized coupons, and Market Basket Analysis and Prod2Vec for combo offers.

We converted the model into a REST API using Kubeflow's model endpoint, created a Google Function to call the Model Endpoint API, and built an interactive dashboard using React.

The system was hosted on Google Kubernetes Engine in the client's cloud environment.

Results:
The client reported that the dashboard helped store managers and marketing teams make informed decisions on targeted coupon offers, increasing store traffic and sales.

Ultimately, Egen's personalized coupon recommendation system strengthened the relationship between the Texas-based business and its customers, fostering trust and loyalty.

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Strategic Beacon #3: Cultivating a data-driven decision-making mindset

CDOs stand as guardians of data, ensuring that it is not only abundant but also accessible and understandable across their organizations.

Their mission is to knock down towering walls of data silos and foster collaboration among diverse teams. By democratizing the power of data, CDOs can guide their organizations toward well-informed decision-making.

They can also harness the capabilities of analytics and AI tools to unearth the intricate patterns, trends, and anomalies within the data, empowering stakeholders to confidently make sound decisions.

Data-Driven Decisions Case Study L'Oréal

L'Oréal, the world's largest beauty company, recognized the need to accelerate its digitalization to remain competitive in the rapidly changing retail landscape. As a result, the company leveraged Google Cloud solutions, including Cloud Workstations, to improve productivity and security for its global developers.

Challenge:
L'Oréal required an efficient, unified data warehouse to make data-driven business decisions and provide data access to thousands of employees. In addition, the company faced the challenge of aggregating and processing large volumes of data while streamlining the work processes of hundreds of developers working on various projects across different teams and countries.

Solution:
L'Oréal built a serverless, multi-cloud warehouse based on Google Cloud. Using Cloud Workstations, the company provided its global developers with a powerful, scalable solution that eliminated technical barriers. Additionally, L'Oréal Taiwan collaborated with Google Marketing Platform and Google Cloud to predict which customers were more likely to make in-store purchases and targeted them with advertising campaigns.

Results:
By adopting Google Cloud and Google Marketing Platform, L'Oréal Taiwan achieved a 2.5x increase in offline revenue and a 2.2x increase in return on advertising spend for Lancôme Taiwan.

In addition, using serverless, event-driven data platforms enabled L'Oréal to process 20TB of data monthly more sustainably and efficiently. Furthermore, the company was able to automate its cloud billing budgets, ensuring cost management for its large cloud environment.

With the help of Google Cloud solutions, L'Oréal successfully enhanced developer productivity and accelerated digitalization, positioning itself for growth and adaptability in the ever-evolving retail industry.

Conquer the Data Mountain. Propel Your Retail Business to Success.

Need a strategic partner to help you navigate the ever-evolving landscape of data, analytics, and AI? We have empowered numerous retail organizations like Sifter and Peapod to achieve significant business outcomes and growth.

Our team of skilled engineers is eager to collaborate with you to develop customized cloud foundations, data strategies, and architectures tailored to your business needs.

Join our long list of happy clients who continue to work with us and see the remarkable difference we can make for your business.

Get in touch with us here. Or, give us a call at +1-833-594-1809.