Startup with second round of funding, to develop a professional networking platform

To create an extensive database of prospects, individuals, corporations and small businesses including profit an non-profits. Customer acquisition plan through mails and social platforms (LinkedIn). Help with BRD and create plan for database architecture

Web scraping information and from LinkedIn. Keep cost minimize to build the database, so focus was on getting as much information from free sources rather than costly paid sources, also mostly unstructured, self-reported, incomplete or inconsistent data.

Built an extensive database, with a searchable dashboard with detailed industry categories, phone, address, corporate information including c-suites, LinkedIn profile of individuals and businesses & other tags like DNI, Profit/Nonprofit, location

Prospecting and targeting to acquire new customers for new business

Top advertising business organization that develops industry standards, conducts research, and provides legal support for the online advertising industry.

Publish an annual report of the total digital advertising marketing spend (previously commissioned to a big consulting agency)

Multiple sources of market activities, financial data and spend reports with limited sample information. Research materials, and survey data are used for augmenting gross market numbers. Data sourced were mostly unstructured, self-reported, incomplete or not verifiable.

Mdrk used a rigorous approach using statistical calibration, heuristics, survey data. Our agency build and test a proprietary algorithm for digital advertising market sizing.

Industry standard reports published (attached)

Top advertising business organization that develops industry standards, conducts research, and provides legal support for the online advertising industry.

Create a database of Direct-to-Consumer (DTC) brands from scratch through a data-driven research to understand DTC economy and create a watch list for disruptive brands.

DTC was relatively new evolving business model with no specific sources of information. Research materials, data and statistics were scrappy. Data curated manually from fragmented sources (providers) through independent research. Data sourced were mostly unstructured, self-reported, incomplete or inconsistent data.

Rigorous approach to data collection, governance and ETL, creating a seamless mapped, cleaned metadata labels with taxonomies. Our agency build and tested a proprietary algorithm for scoring and rank for publishing

Market sizing for strategic business decision for targeting and revenue growth opportunity. Also used for partnerships and new product evelopment

An award-winning interactive marketing, design, and technology agency. Digitally transforms the space between the brand and the audience

To build a solution for recruitment bias and correction for lift measurement using robust statistical algorithm

Online sample recruitment suffers from myriads of problems (confounding, tracking, and quality issues) and it is reflected in the measurements. There exists no out-of-box solutions due to data variations and other audience related segmentation problems, including reach, frequency, overlap, and targeting platforms.

Built proprietary algorithm using multiple robust statistical techniques and codified to deploy in the production systems by engineers.

Improved and help correct the sampling bias. Robust measurement and large-scale deployment

A top holding company retail media agency

The agency wanted to build a media planning, execution and delivery solution for their roster of retail brands, using customer data-driven intelligence i where people shop, and what brands they shop

There are no easy way to build a retail response targeting product using direct or indirect purchase or sales data from data vendors. Most of the audience targeting solutions are based past purchase history and scored data on audiences, which are dated, and based on infrequent sample data.

Built a product solution using real-time location data marred with retail store details, brand and category sales volume, and population dynamics down to the store zip code level.

A user-friendly interface allowed planner to build programmatic targeting strategy for delivery and execution on multiple platforms.

Built a Law Firm Segmentation and a predictive model for US legal spend

To glean insights and build benchmark on 20+ leading brand campaigns on the key KPIs. The insights to be used in a white paper commissioned by one of the top three advertisers' consortiums

Combining all the campaigns in one place for comparison posed a big data analytics challenge. Complex and granular data were unique for the interactive videos and standard measurements of KPI were not applicable.

Built cross-campaign measurements and benchmark KPIs. Developed customized algorithms and data queries. Applied advanced statistical measures to master report and helped published the industry benchmark.

White paper establishing the benchmarked KPIs for video campaigns released for the industry

An award-winning interactive marketing, design, and technology agency. Digitally transforms the space between the brand and the audience

To build a solution for recruitment bias and correction for lift measurement using robust statistical algorithm

Online sample recruitment suffers from myriads of problems (confounding, tracking, and quality issues) and it is reflected in the measurements. There exists no out-of-box solutions due to data variations and other audience related segmentation problems, including reach, frequency, overlap, and targeting platforms.

Built proprietary algorithm using multiple robust statistical techniques and codified to deploy in the production systems by engineers.

The company serves legal, business, risk, compliance, and healthcare markets

AMLaw Firm Segmentation based on Share of Wallet (Total Billings)

More than Five thousand firms and data from DnB and MDH not clean. Had to crowdsource data from Census, NALP, American Lawyer and American Bar Association

Built a Law Firm Segmentation and a predictive model for US legal spend