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Inference Labs has set on a major resource planning data reliability complication for a major FMCG client to ensure standard outline reporting and insight cohort using automation (AI):
40% of all business initiatives fail to achieve their targeted benefits due to poor data quality according to Gartner. On average, organizations suspect that 29% of their current customer and prospect data is inaccurate in some way. Only 15% of the companies are confident of the quality of their CRM data. So, data taken from ERP/CRM applications possess many issues these days due to increase in complexity of data understanding.
Our client has faced similar issue with business planning software. The data silos that are shared by the client’s inventory and sales management systems have various issues related to Raw data duplication, duplication with different unique identifiers, data mismatch, concerns with data refresh.
Client was worried about the inefficiencies of such processes which is creating a lot of effort, time. Inference has come up with an AI automated and visualization model that captures the data, fine tunes it and provides the best visualization.
Automated the poor data pipelines, inefficient processes and reporting.
Raw data shared by the inventory and sales management systems had duplication wherein the same record sometimes repeated two or more times. Inference developed data handling systems which were trained to identify duplicate records and purge it from raw inputs received from the systems
The data collection system was incorrectly tagging same records with as separate which was resulting in reporting of abnormal total sales volume. Inference has identified an appropriate level of data was identified and the data handling systems were trained to identify and purge duplicate records
The system was not capturing all information entered by field agents leading to a difference in the values reported by the field systems and ERP systems. Inference developed data validation engine which compared KPIs between fields and ERP systems thus altering to differences and filling gaps automatically.
The data capturing systems refresh cycle was out of sync with business requirements and reporting cycle thus causing a lot of data to not be included in daily insight reports. An automated reporting system was built in sync with previous solutions to automate the reporting cycle and cause least deviations in KPIS reported from multiple sources.
We have developed an AI model that can automatically detects, cleans the data that came from the business planning software and resolved the multiple data quality issues to ensure quality reporting and insight generation.
For the insight generation, we have developed the sales score report which is the scoring mechanism for evaluating sales and performance of sales executives and visit score report which measures effectiveness of merchandising activities performed by merchandisers on daily, weekly and monthly basis. For the developed reports, we described the stockiest analysis along with recommendation activities to improve store performance, grade and route adherence report to calculate time spent by employee in market and transit time to move between assigned stores
We are very proud about our crew as they worked (data ingestion & report churning) on nights for the reason that client submits the data daily after 10pm.
Upon successful implementation of our automated architecture & insight – report generation, client was very happy to see 4X growth in deliverable reports while ensuring over 70% of work hours saving from this effort.
Do you have the similar business at your organization, why don’t you discuss with us? Let’s talk business. If you’re interested in implementing such amazing and accurate automated engine at your organization, just mail us your requirement immediately or else you may lose millions of dollars doing the same thing others did.
We are always there to fine tune your processes that develops a framework for overall organization’s business.
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