Google Business Profile and WhatsApp Business in Bengaluru Darshinis: Effect on Calls, Reviews and Footfall

When someone in Bengaluru searches "breakfast near me" or "biryani open now", the map results decide where they eat. For a darshini or a 30-seat family restaurant, a complete Google Business Profile (accurate hours, menu photos, replies to reviews) and a WhatsApp Business account (catalogue, quick replies, order confirmations) cost nothing but time. Yet many owners either never claimed their listing or set it up once and forgot it.

Most "digital marketing for small business" projects stop at asking owners whether they think online presence is useful. This BBA survey project goes one step further and measures outcomes. It has two parts:

1. Adoption survey of about 60 small eateries in Jayanagar, Basavanagudi, Malleshwaram and HSR Layout: whether they have claimed and maintain a profile, use WhatsApp Business, who manages it, barriers (time, language, fear of negative reviews) and perceived benefits.
2. Before-after comparison in 20 eateries that agree to share data: half complete a simple profile and WhatsApp Business checklist during the study (with the student's help), and half continue as they are. For eight weeks before and eight weeks after, the study compares profile views, calls, direction requests, new reviews and average rating, and average daily bills from the restaurant's billing software.

Analysis uses Excel (BBA 4.5) and SPSS (chi-square and Wilcoxon tests). The report follows BCU BBA 5.7 Survey Project requirements with a log book and viva.

Abstract

This study examines adoption of Google Business Profile and WhatsApp Business by small restaurants in Bengaluru and measures changes in online engagement and footfall after profile optimisation. An owner survey of about 60 eateries describes adoption levels, barriers and perceived benefits. A before-after comparison in 20 eateries, half of which complete an optimisation checklist, compares profile views, calls, direction requests, reviews and average daily bills over eight weeks before and after. The study offers evidence-based guidance for low-cost local digital marketing.

Introduction

Search and maps have become the starting point for many dining decisions, especially for people new to an area, students and office workers. Large chains employ agencies to manage listings, while small eateries depend on owners or family members. Free tools exist, but adoption is uneven, and owners need evidence that the time invested brings customers.

Existing studies versus this study

Earlier student and academic studies on small-business digital marketing mostly use attitude surveys: do owners think social media helps? Such studies cannot show effects. This project combines an adoption survey with platform performance data and the eatery's own bill counts, and compares eateries that optimised their profiles with those that did not over the same period, which controls for season and festivals.

Proposed study

• Survey adoption, management practice and barriers.
• Help ten eateries complete a standard checklist; observe ten comparison eateries.
• Compare eight-week before and after metrics within and between groups.
• Recommend a practical routine owners can follow in 15 minutes a week.

Feasibility

• Operational: the student visits eateries in the afternoon lull; owners share profile performance screenshots and aggregate daily bill counts.
• Technical: Excel and SPSS are taught in Semester 4; no paid tools are needed.
• Ethical: owner consent; no customer names or numbers collected; no fake or incentivised reviews, consistent with platform rules and BIS IS 19000:2022 on online consumer reviews.

Small restaurants compete with chains and cloud kitchens for local customers who decide where to eat on their phones. Many owners have unclaimed or outdated Google listings with wrong hours, few photos and unanswered reviews, and use personal WhatsApp for orders, mixing family and business messages. Owners are unsure whether spending time on these tools brings more customers, so adoption remains low. The study addresses the problem: what is the current level of Google Business Profile and WhatsApp Business adoption among small eateries in Bengaluru, what prevents fuller use, and does completing a basic optimisation checklist change calls, direction requests, reviews and daily bills compared with eateries that do not?

The study covers small, owner-managed eateries (darshinis, family restaurants, tiffin rooms, bakeries with seating) in four Bengaluru localities. Chains, franchises and delivery-only kitchens are excluded. Footfall is measured through bill counts shared by owners in aggregate. The eight-week window cannot capture long-term effects, and results may differ for other cities or cuisines. No paid advertising is studied.

Research design

Descriptive survey (Part A) and a quasi-experimental before-after study with a comparison group (Part B).

Part A — adoption survey

• Population: small eateries in Jayanagar, Basavanagudi, Malleshwaram and HSR Layout.
• Sampling: listing eateries along main roads of each locality, then systematic selection of every second eatery until 15 per locality agree (n ≈ 60).
• Questionnaire: eatery profile (type, seats, years, staff), owner profile (age band, education, smartphone use), adoption (profile claimed, last updated, photos, replies to reviews, WhatsApp Business use, catalogue), management (who, how often), barriers (5-point scale), perceived benefits (5-point scale).

Part B — before-after with comparison

• Sample: 20 eateries from Part A whose owners agree to share data: 10 optimisation and 10 comparison, matched roughly on locality, type and current rating.
• Optimisation checklist: verify listing; correct hours and holiday hours; add at least 15 photos (food, interior, menu board, entrance); add menu and price range; add attributes (veg-only, parking, UPI); set up WhatsApp Business with catalogue, greeting and quick replies; link WhatsApp to the profile; reply politely to existing reviews. No review solicitation with incentives.
• Metrics (weekly): profile views, calls, direction requests, website or chat clicks (from profile performance), new reviews and average rating (public), average daily bills (owner's billing software, aggregated).
• Windows: 8 weeks before optimisation and 8 weeks after, the same calendar weeks for both groups.

Analysis

• Part A: frequencies, cross-tabs; chi-square for adoption versus owner age and education.
• Part B: percentage change per eatery; Wilcoxon signed-rank test for before-after within each group (small samples, non-normal); comparison of median changes between groups (Mann-Whitney U); simple difference-in-differences table in Excel.

Timeline (10 weeks, alongside historical data)