Problem A Food and Beverage company in the B2B space was facing multi million in lost sales annually
Goal The aim that was set forth was a gain in annual savings against lost sales by 5%
Approach We worked on developing a model to predict customer churn. Created a report for the sales team that looked at the granularity of customer and product and develop a monthly alert tool, that would notify the sales team members on highlighting customers that showed early signs of churn
Action We worked with the sales team to develop an action plan to touch base with these potential churn candidates and identify ways to continue business relationship.
Outcome A YoY analysis revealed that our client was able to consistently achieve a 10% annual savings against lost sales via customer retention.
Predictive Sales model
Problem Ecommerce B2B company has low visibility in small or growth accounts and looking for help to enhance customer retention and experience
Goal To improve sales on low touch accounts by 10%
Approach We worked with the Ecommerce team to categorize and segment customer buying behaviors based on the product and end use. We developed a machine learning tool to track the customer browsing pattern and combine with their buying activity.
Action We use these findings to devise a customer outreach program to enhance customer experience on their Ecommerce purchasing and offering targeted promotions.
Outcome Our client was able to achieve a 20% improvement in their customer retention through this program and create a better visibility for the commercial team on customer needs and ability to proactively improve their bottom line
Industrial IoT Analytics
Manufacturing Analytics
Problem Industrial parts manufacturing company facing high defect rates in their manufacturing process.
Goal To reduce defect by 10%.
Approach We worked with manufacturer on collect the process data, applied analytics to investigate the process parameters to pinpoint the root cause of quality defects. We established a dashboard for process engineers that provided near real time analytics.
Action We use these findings to work with the manufacturer to re-define the operating envelop of process parameter range, that optimized the process and reduced the defect rates. Additionally, cross factory analytics helped the company get a better visibility on the entire process streamline across various plants and realize significant savings. Furthermore, the AI driven approach provided prescriptive analytics to the process and quality engineers, indicating them on fixing a step, even before the problem happened.
Outcome Our client was able to achieve $150,000 in savings per plant through reduced defect rate. Furthermore, by having a cross plant view on process analytics, the company was able to save time on the RCA process that they spent dealing in cross plant investigation - from spending several months to completion in a few weeks time.
Predictive Maintenance Analytics
Problem Industrial process skids management company struggling with maintenance planning of the skids for their customers and saw increasing downtime on their systems.
Goal To reduce downtime by 15% from existing levels, and incorporate a predictive maintenance program.
Approach We developed analytics approach to create an asset monitoring dashboard. The continuous monitoring was combined with developing process analytics to understand system process, health and established protocols for maintenance for the skids.
Action We implemented our domain expertise on system operation and health, combined with the process analytics to develop operating thresholds, that would alert the operator, when a system operation parameter goes beyond its operating envelop. The continuous data collection and asset tracking enabled building a complex predictive service model, that provided timely notifications to the operators, that helped them plan out the scheduled downtime and maintain the asset.
Outcome Our client was able to reduce the downtime by 20%. Additionally, the predictive maintenance suite enabled the operators to plan out the scheduled on system maintenance and achieved cost optimization on fleet management.
