In reaction to the Mifid II regulation, the client was seeking to increase the efficiency of the research team’s activities and design new revenue models to meet strategic goals through the use of web-analytics. Research Analysts needed to gain readership insights through:
- Improved engagement measurements
- Coordination with sales to identify new opportunities
Due to regulatory changes, the organization needed to shift how it managed the research function’s revenue and costs of unbundled services
- We tagged the research website to capture tracking details using open source tools and in-house data capture to gain readership insights
- We redesigned the website dashboard reports used by the Analysts to highlight critical usage patterns revealed by newly captured web-tracking data
- We improved and streamlined the dashboard reports used by Sales and Management to highlight upsell opportunities more efficiently
In reaction to regulatory requirements to retain and classify payment records, the client needed help creating a cash transaction classification system for regulatory reporting and compliance, looking specifically to solve for following challenges:
- Migrating away from using Excel that applied the classification rules with a manual process.
- Implementing scripts to allow for processing a greater amount of data
- Needing to improve operational scalability rather than rules being created small chunks at a time
- Improving repeatability, decreasing processing time, and reducing error
We developed a text mining and reporting engine in R that efficiently runs against the client’s full data warehouse
- We automated code in R to be applied against the data warehouse for fast and accurate classification
- We built a front-end UI to allow business users to create and modify rules to refine business logic and improve resulting classification rate
- We created reporting on summary statistics and classification rate, including the impact of the addition of new rules
- We created a next generation text mining process to prioritize the new candidate classification rules
Fulcrum created a scalable and sustainable solution to comply with regulations, which provided significant time-savings from manual labor, increased accuracy of classification, and introduced a mechanism to easily create and integrate new rules.
The client was seeking to improve incremental customer spending through personalized product promotions delivered via email, as well as integrating delivery with digital coupons on loyalty cards, all while tracking user experience end to end -- from email delivery, to open, to click to add coupon/redemption.
- We built an offer bank of product-specific promotions to individuals based on prior purchase and cross sell optimization models we built
- We delivered a personalized set of promotions via email and built an interactive pop-up micro site to add promotions to the customer’s loyalty card in real time
- We developed online dashboard reporting on ROI and individual promotion performance
Fulcrum developed tools to formulate and offer granular, personalized offers based on product opportunities, a big data platform hosting the end-to-end of the process, and implemented testing to determine the optimal means to drive more trips, build bigger baskets, and yield higher margins.
The client was seeking to transform a labor-intensive pricing process to a seamless, sustainable and transparent operation. Key challenges included:
- Lack of standardization in reporting across types of policy (e.g., auto, GL, etc.) which made it difficult to analyze account-level performance at a glance
- Manual data pulling, and copying/pasting during the data preparation created high human-error risk
- Difficulty tracking changes and updates to quotes
- We mapped out user stories to better understand various user needs and experiences
- We developed a platform-flexible front-end pricing tool that accounted for user needs (e.g., metrics, reports, etc.) utilizing real-time data ingestion
- We developed automated scripts for consistent data pulls and to reduce labor hours
- We built a job management framework to provide pricing governance and monitoring
Fulcrum’s development of a reliable and sustainable process resulted in an 80% reduction of manual labor hours, increased pricing transparency, and yielded faster quote turnaround.
A Retail Bank wanted to understand the attractiveness of their local markets and how they fare against market competition but faced the following challenges:
- Public data is available to answer these questions - but come from multiple sources and need aggregation
- Some data sources are extremely large and most have quality issues
- Manual analysis for any given store, branch, or collection of local markets is time consuming and prone to error
- Exploratory analysis requires technical expertise and access to data and computing resources - business users can't easily answer "what if" questions
- Fulcrum built an automated process to retrieve, clean, and combine dozens of distinct public datasets to create a master database of local market performance and demographic data for the entire US across 8 years of historical data
- We automated creation of detailed reports for the retailer and reduced the time needed to create a tailored presentation from weeks to minutes
- We built an interactive application to allow business users to explore the data and create custom charts to answer questions related to company performance
Fulcrum’s web application aggregates, normalizes, and allows instant visualization of massive public datasets for business analyst use.