Retailer Satisfaction and Distribution Effectiveness of a Regional Dairy Brand in Pune (SIP, Excel + Tableau)

Fresh dairy is one of the hardest FMCG categories to distribute: milk, curd and paneer have a shelf life of days, need chillers at the outlet and must arrive before the morning rush. For a regional brand competing with large cooperatives and national players, the retailer — the kirana, the dairy parlour, the bakery and the local supermarket — decides which brand sits at eye level and which one the shopkeeper recommends.

This Summer Internship Project is carried out at a fictional regional dairy company, Pawana Greenfields Dairy Pvt. Ltd., Pune, which sells through eight distributors to about 1,200 outlets across Pune and Pimpri-Chinchwad. The intern surveys 150 retailers on six service dimensions — margin, credit terms, delivery punctuality, handling of expired/leaked stock, salesman service and trade schemes — and combines the survey with the company's secondary sales and fill-rate data.

Survey data is analysed in Excel and SPSS (chi-square, ANOVA, t-test, correlation and multiple regression), and distributor- and beat-level performance is visualised in Tableau dashboards the sales team can keep using after the internship. The output is an SPPU GC-13 SIP report with specific recommendations on beat planning, return policy and scheme design.

Abstract

This summer internship project studies retailer satisfaction and distribution effectiveness for a regional dairy brand in Pune. A structured survey of 150 retailers, stratified by outlet type, measures six service dimensions and overall satisfaction, while the company's distributor-wise sales, fill-rate and stock-out data are analysed in Excel and Tableau. Statistical tests identify which service dimensions drive retailer satisfaction and whether satisfaction differs across outlet types and distributor territories. The findings support recommendations on delivery scheduling, expiry-return policy and trade schemes.

Introduction

In the Indian dairy market, cooperatives and national brands have strong distribution networks, and regional private brands compete by offering better margins and closer relationships with retailers. Because fresh dairy has a short shelf life, late deliveries or a slow return process for expired and leaked packs directly cost the retailer money. A dissatisfied retailer gives less chiller space, pushes a competing brand or stops ordering altogether.

Organisation and existing system

The host company's sales team tracks primary and secondary sales in a distributor management system and Excel sheets, but retailer feedback is anecdotal — collected informally by salesmen. Management knows which distributors sell more, not why outlets in some beats are more loyal than others.

Literature gap

Channel-satisfaction literature, from service-quality models to studies of FMCG retailers in India, shows that margins, credit and service all matter, but very few studies focus on perishable dairy, where delivery timing and expiry returns are critical. Company-level internship studies seldom combine a retailer survey with the firm's own sales data and a reusable dashboard.

Proposed study

• Measure retailer satisfaction on six dimensions and overall.
• Identify the dimensions that most influence overall satisfaction.
• Compare satisfaction across outlet types and distributor territories.
• Build Tableau dashboards on distributor fill rate, stock-outs and beat-level sales.

Feasibility

Access to outlets is through company salesmen's beats; Excel, SPSS and Tableau Public are free or available in the institute; the eight-week SIP period is enough for one round of retailer visits and analysis.

Pawana Greenfields Dairy has seen flat secondary sales in several Pune territories even though its trade margin is slightly higher than that of competing brands. Salesmen report complaints about late morning deliveries, slow credit notes for expired curd and paneer, and irregular scheme communication, but the company has no structured measurement of retailer satisfaction and cannot tell which of these issues matters most or where it is concentrated.

At the same time, distributor-level data on fill rate and stock-outs sits in separate Excel files that managers rarely review together. As a result, decisions on distributor performance, beat planning and trade schemes are made on intuition.

The problem addressed by this SIP is: to measure retailer satisfaction with the company's distribution service, identify the service dimensions that significantly influence overall satisfaction, compare satisfaction across outlet types and distributor territories, and present distribution-performance data in a form that supports better sales decisions.

Organisation: Pawana Greenfields Dairy Pvt. Ltd. (fictional host), fresh-dairy portfolio — pouch milk, curd, buttermilk and paneer.

Geography: Pune Municipal Corporation and Pimpri-Chinchwad, across eight distributor territories.

Respondents: owners or managers of retail outlets currently stocking the brand — kirana stores, dairy parlours, bakeries and small supermarkets. Large national modern-trade chains, with centrally negotiated terms, are excluded.

Data: primary retailer survey and three months of internal secondary sales, fill-rate and stock-out data shared by the company in anonymised form.

Limitations: eight-week window, one city, retailers may soften criticism when a company intern asks the questions, and internal data is limited to what the company chooses to share.

Research design

Descriptive research design with an analytical component, using primary data (retailer survey) and secondary data (company sales records, industry reports).

Population and sampling

• Population (N): ≈ 1,200 active outlets on the company's distributor beats.
• Sampling method: proportionate stratified random sampling by outlet type — kirana ≈ 55%, dairy parlour ≈ 20%, bakery ≈ 15%, supermarket ≈ 10% — with outlets selected randomly (Excel RAND()) from each stratum of the beat list, then visited along the salesman's route.
• Sample size (Yamane): n = N / (1 + N·e²) = 1200 / (1 + 1200 × 0.08²) = 1200 / 8.68 = 138, rounded up to 150 (e = 8%, acceptable for a time-bound SIP). Replacement outlets are drawn from the same stratum if a shop is closed or refuses.

Questionnaire design

Questionnaire is administered face-to-face in Marathi, Hindi or English (10 minutes). Pilot: 15 outlets; Cronbach's alpha ≥ 0.70 for each dimension.

Hypotheses and tests

α = 0.05.

Secondary-data analysis

Three months of distributor-wise primary and secondary sales, fill rate (quantity supplied ÷ quantity ordered) and stock-out incidents are cleaned in Excel (Power Query) and joined to survey scores by territory, then visualised in Tableau.

Timeline (8-week SIP)