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Fully Automated Analytics Platform

Find out how our Consultants helped a Market Research firm to get optimum value from adopting a fully automated analytics platform.

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A CASE STUDY

months go to market time reduction per release

percent reduction in UI design cycle time

days lead time improvement (DevOps)

increased uptime in 4 months (DevOps)

Automated Analytics Adoption by a Market Research Firm, leveraging the power of DevOps and Design Thinking

Client is a leading qualitative market research firm with over 40 years of experience in analytics driven brand understanding, competitive analysis, brand performance and strategy. They specialize in providing optimized direction to improve likelihood of success of advertising campaigns through multi dimensional near real time analysis with intelligence based on brand equity.

The Challenge

The Client wanted to move their existing customer-base onto a fully automated analytics platform. Their challenge was that customers were used to being hand held by the analytics team through manually created presentations and spreadsheets. Any change or turnaround took long cycle times. The other challenge was to help them transition from a high-touch mode of working to using the platform for insights they needed.

The engineering & business teams were struggling with identifying high value, high priority features with building out a stable platform and lack of a DevOps infrastructure made it even more challenging.

Approach and Solution

A two-pronged approach was proposed:

  • Creating a small architect team – with experienced developers. They were coached on the importance of DevOps and how to develop DevOps workflows. The coaching included identifying outcomes like improving build & deployment cycle time, improving time to demonstrate amongst several others. Consulting was extended to inspect and evaluate the implementation of DevOps infrastructure and prepare a codeline strategy.
  • Design Thinking approach with a combined business and engineering team to – Develop user personas, prioritize them, prepare a user journey map, do a pain-gain analysis and develop a value proposition canvas to prioritize features to be delivered. Also used the Lean Canvas as a means of developing platform roadmap and identifying new business models.

The Outcome

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Go To Market Time Reduction

Early and quick feedback cycles from customers with focused development effort led to reducing the GTM from 14 months to 6 months per release

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Improved Feature Prioritization

Common understanding on feature value, definition and priority

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Development Cycle Time Reduction

Improved UI design cycle time by 40% leading to downstream agility

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Cross Team Collaboration

between Sales, Marketing, Analytics, Product Management and Engineering teams

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Increased Value from DevOps

Lead time improvement from 15 days to 3 days, Uptime improved from 65% to 90% in 4 months

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