Media

Retail Data Monetization

While experiencing strong growth, our client was interested in complementary, adjacent and incremental data products Needed assistance to understand market opportunities, technology approaches to support, work plan and business case Was looking for help in identifying new products to sell, how to package the products, and to which markets/clients to pursue

Solution

Initially engaged to define all the data monetization opportunities spanning multiple lines of business. However, after training the models we discovered a large market opportunity. Developed a prioritized target list and focused drill-down on the optimum opportunity, including: Solution Topology, Functional, Taxonomy, Value Proposition, Organizational Model, High Level Workplan.

Result

Gained 10 client commitments within 8-week pilot

Identified a 150 million annual revenue stream

Created the base AI model and pilot platform

Indicative AI Models & Methods

Predictive Analytics for Market Opportunity Identification

AI-Driven Demand Forecasting

Natural Language Processing for Market Analysis

Implementation ease:

High

Indicative Effort:

240 – 300 days

Data Fusion and Consumer Choice Modeling

The company and its client advertisers needed a data-driven approach for audience targeting for linear TV that competed with digital solutions. Advertising budgets were increasingly shifting from traditional TV to digital and the client needed to compete.

Solution

A solution that enabled the creation of granular audience segments that created digital-like ad targeting capabilities across any advertising vertical i.e. CPG, retail, finance, transportation, travel, etc. Data Fusion concepts (from Aerospace Engineering) used to integrate and enrich traditional television viewing data with consumer preferences i.e. using Consumer Choice Modeling

Result

$29M in revenue in the first year of commercial product launch

$200M+ in fourth year of business

Indicative AI Models & Methods

Data Fusion Techniques

Consumer Choice Modeling

Segmentation Algorithms for Audience Targeting

Implementation ease:

High

Indicative Effort:

220 – 280 days

Advanced Optimization Science

Client was needed a technologic advancement in advertisement operations and data. They had manual processes for ad placements on log files and they had the Inability to optimize placements based on various campaign objectives. This was a problem because they had a very large number of campaigns (8,000+) running concurrently.

Solution

A set of methods and technology to integrate data from disparate systems into in a centralized advertising management system, Enablement of end-to-end management of advertising inventory and optimized scheduling and Development of an Ad Traffic System driven by data science algorithms

Result

By optimizing ad placements, liability was decreased by $25M/year and incremental sales revenue for advertising products was captured by the additional available inventory for sale

Optimization algorithms continues to yield incremental returns since its inception

Indicative AI Models & Methods

Machine Learning for Ad Placement Optimization

Data Integration Techniques

Optimization Algorithms for Scheduling

Implementation ease:

High

Indicative Effort:

200 – 260 days

Retail Data Monetization

Solution

Result

Gained 10 client commitments within 8-week pilot

Identified a 150 million annual revenue stream

Created the base AI model and pilot platform

Indicative AI Models & Methods

Predictive Analytics for Market Opportunity Identification

AI-Driven Demand Forecasting

Natural Language Processing for Market Analysis

Implementation:

High

Indicative Effort:

240 – 300 days

Sales & Marketing Analytics

The client is undergoing a digital transformation to become a data-driven company. Its survival depended on it moving to a knowledge-based enterprise that could achieve market leadership and introduce new products into the marketplace quickly.

Solution

We were engaged to develop a roadmap for digital transformation that relied heavily on building data analytics and commercialization strategy with AI implemented across the enterprise.

Result

Three new product launches with in a year

50% reduction of IT labor

20% increase in upselling products within call center

Indicative AI Models & Methods

Virtual AI Assistants for Reporting and Customer Interaction

Predictive Analytics for Churn and Performance

Buyer Intent Analysis and Audience Segmentation

AI for Product Market Fit Analysis

Implementation:

High.

Indicative Effort:

260 – 330 days

Data Commercialization Platform

A large global media company was collecting massive amounts of customer data, but they had not monetized the data in anyway. Their capacity was limited to observational decision making based on the raw information available, given that they lack the capability to process that sheer volume of data in real-time. They wanted to build a new data analytics platform that they could share with their clients. This would provide new revenue streams as well as improve their customer value proposition.

Solution

The client looked for us to envision, design and deploy a comprehensive analytics and data science architecture. This architecture allowed them not only to process and analyze the telemetry data received in real-time or near-real-time, but it also enabled and empowered the clients teams to create new and innovative solutions for their customers.

Result

Identified four new revenue streams, and customer segments, within 3 months.

Revenue to cost will exceed 25x

Optimization algorithms continues to yield incremental returns since its inception

Indicative AI Models & Methods

Machine Learning for Data Monetization and Analysis

Optimization Algorithms for Revenue and Pricing Strategies

Customer Choice Modeling

Implementation:

High

Indicative Effort:

250 – 320 days

Data Fusion and Consumer Choice Modeling

Solution

Result

$29M in revenue in the first year of commercial product launch

$200M+ in fourth year of business

Indicative AI Models & Methods

Data Fusion Techniques

Consumer Choice Modeling

Segmentation Algorithms for Audience Targeting

Implementation:

High

Indicative Effort:

220 – 280 days

Advanced Optimization Science

Solution

Result

By optimizing ad placements, liability was decreased by $25M/year and incremental sales revenue for advertising products was captured by the additional available inventory for sale

Optimization algorithms continues to yield incremental returns since its inception

Indicative AI Models & Methods

Machine Learning for Ad Placement Optimization

Data Integration Techniques

Optimization Algorithms for Scheduling

Implementation:

High

Indicative Effort:

200 – 260 days

Media

Retail Data Monetization

Solution

Result

Indicative AI Models & Methods

Implementation ease:

Indicative Effort:

Data Fusion and Consumer Choice Modeling

Solution

Result

Indicative AI Models & Methods

Implementation ease:

Indicative Effort:

Advanced Optimization Science

Solution

Result

Indicative AI Models & Methods

Implementation ease:

Indicative Effort:

Retail Data Monetization

Solution

Result

Indicative AI Models & Methods

Indicative AI Models & Methods

Implementation:

Indicative Effort:

Sales & Marketing Analytics

Solution

Result

Indicative AI Models & Methods

Indicative AI Models & Methods

Implementation:

Indicative Effort:

Data Commercialization Platform

Solution

Result

Indicative AI Models & Methods

Indicative AI Models & Methods

Implementation:

Indicative Effort:

Data Fusion and Consumer Choice Modeling

Solution

Result

Indicative AI Models & Methods

Indicative AI Models & Methods

Implementation:

Indicative Effort:

Advanced Optimization Science

Solution

Result

Indicative AI Models & Methods

Indicative AI Models & Methods

Implementation:

Indicative Effort:

Privacy Policy

Terms of Services

All rights reserved ©2024

Services

Industry Cases

Our Company

Services

Industry Cases

Our Company

Privacy Policy

Terms of Services

All rights reserved ©2024