Central India

Recommendation & Personalisation across Madhya Pradesh

Recommendations that lift the metric you care about, measured by experiment, not by offline accuracy. Covering every district and PIN code in Madhya Pradesh.

Districts
52
PIN codes
769
Cities mapped
30

Recommendation & Personalisation in Madhya Pradesh

Offline accuracy and revenue are different things. We measure recommendations by experiment against the business metric, because that is the only number that pays anyone.

Madhya Pradesh runs on agriculture and soya processing, cement, automotive components, pharmaceuticals and textiles, agri-processing and a growing pharma footprint, both heavy on batch documentation. Where recommendation & personalisation earns its budget here usually follows directly from that mix.

Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move. Six weeks to something running in production, not six quarters to a strategy document.

नमस्ते , Namaste. We work in Hindi and English across Madhya Pradesh.

Madhya Pradesh coverage

State / UT
Madhya Pradesh
Region
Central India
Districts covered
52
PIN codes covered
769
Cities mapped
30
Working languages
Hindi, English

What is included

  • Event tracking design, since most projects start with inadequate data
  • Baseline popularity model to beat
  • Hybrid collaborative and content-based ranking
  • Cold-start handling for new users and new items
  • A/B testing framework with proper statistics
  • Business-metric reporting, not just offline accuracy

Questions

Do you cover all of Madhya Pradesh?

Yes, all 52 districts and 769 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.

Which Madhya Pradesh sectors do you work with most?

Across Madhya Pradesh the economy leans towards agriculture and soya processing, cement, automotive components, pharmaceuticals, textiles. Agri-processing and a growing pharma footprint, both heavy on batch documentation.

How much data do we need?

Less than people assume to start. A content-based approach works from day one; collaborative filtering improves as interaction volume grows.

How do you handle new products?

Content-based features carry new items until interaction data accumulates, with deliberate exploration so new items get a fair chance to be seen.

How do we know it is working?

Controlled A/B tests measured on revenue or engagement, with proper statistical treatment rather than eyeballing a dashboard.

Recommendation & Personalisation in Madhya Pradesh

Covering all 52 districts. Tell us what you are trying to change.

Or email bd@dtrasglobal.com · call +91 74118 77878