Client
One of the leading global search engine giants, our client has over 100 million unique users performing over 5 billion searches each month. Receiving traffic from over 1.5 billion devices, the client not only reaches large audiences in popular industries but also accounts for one third of all online retail searches. Having partnered with other search engines as well as major ecommerce platforms, the client was seeing a lot of growth that they wanted to optimize.
Challenge
There was a requirement to monetize search results through marketing analytics of core and syndicated data across devices for both advertisers and publishers, all while offering the right content to the end user. Mindtree had to enable the customer through their entire analytics journey, covering the entire spectrum of what has happened (or is happening) through to what will happen - going through descriptive, inquisitive, predictive as well as prescriptive analytics.
There was also an endeavour to have high visibility engagement through competitor analysis, finding business value for customers, a Machine Learning-based approach, as well as statistical decisions to measure advertiser and supply impact. Mindtree also had to set up revenue forecasting, MBRs, churn analysis, KPI forecasting, and ad hoc analyses.
Solution
To get started on this transition, Mindtree started by ramping up 170 minds of different skill sets (Data Scientists, Engineers, Marketers, Analysts) within 20-30 days and completed the entire transition in the span of 45 days. Once this was done, customized learning paths were created using Yorbit - covering aspects of technical and business foundations in collaboration with the customers. To this, we also added support for multiple tracks across finance, sales, marketing, and engineering. As the last step, we implemented automation, led through the adoption of DevOps and automation initiatives.
Benefits
Through advanced analysis for generating data-enabled insights, Mindtree delivered the following benefits:
- Seamless transition to multiple modes of analytics
- 12% efficiency gains through automation within the first quarter of transition
- Complete automation of 7% of the reports
- Improvement in platform performance through modernization (from SSAS to a modern databricks-based platform) to handle 5 TB of data
