About InsightsQ
An analytics and AI consultancy built on one idea: data should help people decide, not leave them more confused.
Achyutanand Jha
Founder of InsightsQ.
Achyut’s interest in data began in college, where he analysed field hockey matches and wrote blog posts about what the numbers showed. He liked finding the pattern that the commentary missed. That habit carried into a career in analytics.
Over more than eight years he has worked inside online retail, marketplaces and software companies, in roles that ranged from analyst to data scientist to analytics engineer. He has built dashboards and models, fixed tracking, designed warehouse tables and automated reports that used to eat days of someone’s month.
Outside work he follows football and basketball and enjoys a good quiz. He holds a B.Tech in Computer Science Engineering from Apeejay Stya University.
Where the experience comes from
Our team has worked for Fortune 50 and Fortune 500 companies, and for fast-growing businesses at the other end of the scale.
- Retail and e-commerce
- Conversion and experience reviews for a large online retailer, loyalty and rewards analytics, customer review and Q&A analysis, and last-mile operations reporting.
- Marketplaces and resale
- Product ranking from predicted click-through, recommendations, cancellation-risk models and buyer and seller cohorts.
- Software and apps
- Event tracking audits, warehouse models built from messy backend JSON, and predictive and market basket models for a B2B events platform.
- Advertising and media
- Ad slot pricing for publisher sites and automated weekly client reporting.
What we believe about data
Data is only worth having if people can trust it and use it.
That means being clear about where a number came from, honest about what it can't tell you, and careful with the people it describes. We'd rather help you collect less data and understand it properly than collect everything and trust none of it.
Our advice is shaped by what suits your business, size and team, not by what any software vendor would like you to buy. The same goes for AI: we use it where it earns its place, and say so plainly where it doesn't.
Toolkit
- Pipelines and warehouses
- Airflow, Airbyte, Supermetrics, BigQuery, AWS Redshift, PostgreSQL, MySQL
- Languages
- SQL, Python, R
- Dashboards
- Power BI, Tableau, Looker, AWS QuickSight, Excel
- Tracking and web analytics
- Mixpanel, Google Analytics, Adobe Analytics
- Modelling and AI
- Regression, random forests, support vector machines, k-means and k-prototypes clustering, time series forecasting, text analytics and topic modelling, market basket analysis, language-model tools
- Methods
- Marketing mix modelling, A/B testing and experimentation, KPI design, requirement gathering