A leading solutions provider reduces manual effort for data processing teams by using AI
The customer is a leading data solution and intelligence provider. They specialize in hunting, harvesting, aggregating and transforming data. Their core focus is on collecting, refining and interpreting information and managing data into diverse database systems. Their clients include some of the world’s leading B2B brands whose work is bolstered by the the customers proprietary data management systems and data products.
The existing customer workflow was subject to errors in the output fields across multiple stages of data extraction and processing which led to dependency on manual effort to clean and supplement the data resulting in high turnaround time for completion of a project.
There was significant manual effort involved in segregating datasets. The average volume per project usually went up to thousand a range, and monthly volumes ranged from a quarter to half million records.
At stage 1, of the process, the scraped data often didn't match the input given. Manual intervention then became necessary to ensure accurate matches of records.
In stage 2, the time taken to standardise the output was significant. Further scrapping was sometimes required to ensure the limit and quality set by the client was met.
To solve these issues, the customer needed a solution that could:
Blox provided automation solutions to streamline and automate content validation and standardization process. The solution leverages machine learning modules as part of the overall workflow automation and is designed to increase the agent and process throughput.
In this stage, the uploaded excel file is assessed by the tool to determine how accurate the name and company name output is with the given input.
At this point, the output file that is generated post scrapping consists of a list of names and associated publicly available details including address, location, numbers, etc. The Blox tool verifies, and standardizes the information with the actual requirements.
70%
time saved in matching and validating company and contact names (Stage 1)
60%
time saved in standardizing the output (Stage 2)
55%
55% overall time saved in the end to end workflow
There is enough evidence to show that structured data helps companies improve operational efficiency and better ROI. Using the right ML and NLP tools to understand, analyze and organize unstructured data can help break data silos within companies. There is also enough evidence to show that in the last few years use of AI by marketers has increased to 84% from 30%.
Blox helped the customer level up by providing them with automation solutions that would improve and simplify critical processes post data scraping and reduce time manual effort by automating the validation process required to ensure the end clients were able to identify and market to the correct set of people for their campaigns.
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