Pharma Retail Analytics

USING PRESCRIPTION-BASED PREDICTIVE MODEL TO BOOST CUSTOMER RETENTION FOR fast-growing pharmacy chain.
Data Nectar implemented a prescription-based predictive model to boost customer retention for a pharmaceutical retail chain.

The client was facing the challenge of keeping track of the high-value customers and defining the right approach towards connecting them proactively to deliver an enhanced customer experience to improve loyalty and brand’s perception.

Predicting a customer's purchase pattern based on the prescription was important for the retail chain to enable the right actions at the right time for engaging the customers. Prescription-based predictive modelling on top of a modern data warehouse enabled the retail chain to improve customer retention by 70%
70%
Improved customer retention
11%
Increased sales of critical and high value drugs
5x
faster availability of sales performance reports for stakeholders
Client based in India owns one of the fastest-growing pharmacy chain. With more than 35 retail stores across India and a team of 500+ employees, the client is fast emerging as a leader in offering healthcare and wellness products.

The client operates in a competitive pharmaceutical retail domain and with more than 5000 products ranging from common OTC drugs to advanced medications for chronic ailments to the life-saving drug as well. Thereby, retention of customers with high lifetime value was one of their key strategies to stay competitive and fuel the growth
INDUSTRY:
Retail (Pharmacy & Wellness)
LOCATION:
India
PRODUCTS:
5000+ branded, generic, life-saving and wellness products.
SERVICE
DELIVERIES
  • Enterprise-grade ETL
  • EDW(Enterprise Data Warehouse)
  • Dashboard & Report Development
  • Managed BI
  • Data Security
BUSINESS REQUIREMENT
The client’s pharmaceutical retail business caters high-value prescription drugs that are highly in demand from customers with chronic medical conditions. These medications are prescribed as dosage for a significant length of time, typically years to lifetime.

  • Aggregate ‘high-value’ patient’s data and reach them out at the right time, and with the right call-to-action to improve customer retention.
  • The high volume of Odoo-based POS transactional data didn’t provide a mapping of categories of high-value drugs, standard dosage, customer’s buying behavior, etc., important to execute an effective marketing strategy
  • Building Business Intelligence solution was inevitable to address business challenges by enabling the organisation to make appropriate customer engagement plans based upon the data driven insights.
TRACKING THE PATTERN
  • Analyse customer transactional history.
  • Aggregate Odoo-based POS to data warehouse.
  • Aggregators map patient’s drug requirement patterns to inform the retail stores about possible sales.
THE SOLUTION
The first step was the integration of Odoo POS (Point-of-Sale) of their 35 retail stores and automation of batch processes to load data into an enterprise data warehouse. Considering that the transactional system (POS) does not monitor customer’s continuity of purchase, data transformation has a key role to play here. The approach was to have predictive analysis around the POS systems data to gain insights based on the purchase history, durg type, prescribed dosage and customer demography. The system was designed to send notifications and alerts on predicted customer visits, transaction values, drug types, stock availability as well as sending out marketing or follow up messages to customers.

After making a detailed assessment of existing data management, data quality, and integration challenges, a seamless architecture for an end-to-end Business Intelligence solution was drafted. Implementation included data integration from Odoo POS transactional data across 35 retail stores, data transformation, data warehouse, dashboard and report development. We designed dashboards to highlight store-wise prediction of potential customers based on their purchase frequency, last purchase quantity(dosage), and standard prescribed dosage of drugs.

Data analytics and observations helped to :
  • Identify missing visits/ irregular purchase of specific medications
  • Make frequency-based demand predictions of life-saving drugs for store’s stock management
  • Fine-tune marketing campaigns & content creation
  • Initiate doorstep delivery with value-added services to boost retention for high worth customers
  • Predictive inventory replenishment alerts ensured the availability of drugs
KPIs
  • Customer footfall vs Sales
  • Patient demographics
  • Order volume & frequency
  • Buying pattern
  • Drug availability
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