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Discrete Choice Survey: Analysis of Grocery Purchase Behavior

““Labor and logistics costs are much cheaper in India than in western markets, so e-grocers here can make money faster.”–Hari Menon, CEO, BigBasket

Motivation

• A 2014 U.S. Department of Agriculture (USDA) report notes that “the growth in India’s online retailing for food and groceries is a function of the rise in total Internet users from 120 million to 213 million in the past year as well as a fall in mobile handset prices and a rise in smartphone penetration.”

Motivation

• The year 2015 started with a plethora of investments happening in the ‘hyper local’ space, be it food delivery, home services or logistics. On the same front, a number of new grocery delivery firms were also seen sprouting and scaling up. The veterans in this space BigBasket, ZopNowetc. too garnered attention from VC investors.

Problem Statement

• The objective of this survey is to identify the important levels of various customer specific factors which effects grocery purchasing behavior along with the business model specific characteristics

• Observe and analyze the overall Offline vs Online grocery puchasing behavior

Survey Methodology

• The findings in this survey are based on respondents with online access.

• An online survey methodology allows for tremendous scale and reach

• Though it provides a perspective only on the habits of existing Internet users, not total populations

• Survey responses are based on claimed behavior rather than actual metered data.

• For few cases, an offline mode survey also have been done based on an assistance for online form filling.

Survey Methodology (Respondents Profile)

Customer Specific Variables

• Gender• Age• Family Size• Marital Status• Duration of belonging in the same neighbourhood• Average distance to Supermarket from home• Average distance to Local Kirana Store from home• Frequency of Online shopping• Frequency of payment through Mobile app• No. of vehicle owned• No. of Air conditioners owned• Frequency of International trips

Choices

• 3 distinct Online Models and 2 offline model including Supermarket and Local KiranaStores

Namely;

1. Online Model 12. Online Model 23. Online Model 34. SuperMarket5. Local Kirana Store

Business Models (Inventory Based Model)

Business Models (Market-Place Model)

Business Models (Market Place Model)

Business Models (Offline)

Business Models (Offline)

Situations

Grocery Purchasing behavior can vary based on the situation and need of purchasing

In this survey, three situations have been chosen

1. Grocery purchase for whole month, once2. Daily or more than thrice weekly grocery

purchase3. Grocery purchase on an immediate need

Sensitivity towards different Business Models

Categories with a high price-to-weight ratio, which may have lower shipping costs, and those with high profit margins that allow room for discounting are well suited to e-commerce. People are having different preferences towards different business models which in turn offers different services. The variable choice of the customer can be captured from this survey which also significantly varies based on the situation

Sensitivity towards different Business Models (Daily Purchase)

Sensitivity towards different Business Models (Monthly Purchase)

Sensitivity towards different Business Models (Immediate Purchase)

Discrete Choice Model

• Random Utility Theory provides the theoretical basis of Discrete Choice Analysis.

• The utility model of nth alternative depends on deterministic score given the levels of attributes that define the alternative whereas there lies a random unobservable error terms.

• The difference between unobservable term for most preferred choice and any other choice follows distribution of logit.

• For multiple choices, the distribution is called multinomial logit model

Discrete Choice Model

• Grocery choice behavior = f (all customer specific variables + Business Model characteristics latent in choices)

• Log Likelihood = Sum( Frequency of each unique combination of customer specific variables)*(Probability of each unique combination of customer specific variables)

• Using Multinomial logit model we have determined the coefficients for each levels of the each attributes

Discrete Choice Model (Result)

Total

coefficients sd t p(>|t|) Base

Online Model 2:(intercept) -3.807846351 1.889072 -2.01572 0.043829

SuperMarket:(intercept) -5.993513225 2.930777 -2.04503 0.040852

SuperMarket:genderMale 2.20034957 1.117362 1.969235 0.048926 Female

Online Model 2:distance.from.SupermarketMore than 1 km less than 3

km 1.445452633 0.475261 3.041385 0.002355

Less than 1 km

Online Model 1:Distance.from.KiranaMore than 1 km 2.551142319 1.160456 2.198396 0.027921

Less than500m

Online Model 1:VehicleCar 3.82812851 1.608863 2.3794 0.017341 Bike

Online Model 2:ACOne 2.13350485 0.881012 2.421651 0.01545

AC more than 2

Online Model 2:ACTwo 1.878824548 0.888186 2.11535 0.0344

AC more than 2

Online Model 2:ACZero 2.348758812 0.890112 2.638722 0.008322

AC more than 2

Online Model 2:situationsImmediate -1.022749738 0.439637 -2.32635 0.02

Situations daily

Online Model 2:situationsOnce 1.56656093 0.480714 3.258823 0.001119

Situation daily

Test for IIA:

Hypothesis that choices are IIA is rejected

Discrete Choice Model (Result)

OFFLINE (Inside nest)

coefficients sd t p(>|t|) Base

SuperMarket:situationsOnce 1.748844795 0.885561 1.974844 0.048286 Daily

ONLINE-OFFLINE (Nested)

coefficients sd t p(>|t|) Base

ONLINE:distance.from.SupermarketMore than 1 km less than 3 km 1.037404458 0.386945 2.681013 0.00734

Less than 1 km

ONLINE:Mobile.paymentOnce or twice in a month -1.647103608 0.70406 -2.33944 0.019313

More than 5 times

ONLINE:VehicleNone 1.146245417 0.508432 2.254472 0.024167 Bike

ONLINE:ACOne 1.93297661 0.797121 2.424948 0.015311

AC more than 2

ONLINE:ACTwo 1.790803076 0.808948 2.213743 0.026846

AC morethan 2

ONLINE:ACZero 1.71970502 0.78863 2.180622 0.029211

AC more than 2

ONLINE:situationsImmediate -0.761843714 0.377687 -2.01713 0.043682

Situation Daily

ONLINE:situationsOnce 1.08072278 0.405436 2.66558 0.007686

Situation Daily

ONLINE (Inside Net)

coefficients sd t p(>|t|) Base

Online Model 2:Age25 to 34 -2.153900342 1.022597 -2.1063 0.035178 Age 18-25

Online Model 2:distance.from.SupermarketMore than 3 km -4.591049651 2.106395 -2.17958 0.029289

Less than 1km

Online Model 2:VehicleCar -4.410087986 1.806954 -2.44062 0.014662 Bike

Discrete Choice Model (Nested Model)

Discrete Choice Model (Nested Model)

Discrete Choice Model (Nested Model)

Discrete Choice Model (Nested Model)

Discrete Choice Model (Nested Model)

Discrete Choice Model (Nested Model)

Will clicks replace bricks?

Not anytime soon. Online shopping has a number of benefits, but physical stores also have strong key advantages over e-commerce—especially for fast-moving consumer goods.

Strategies For E-commerce Success

While brick-and-mortar stores dominate the grocery shopping experience, e-commerce is a growing business that is still in its nascent stage in many parts of the world

• Establish credibility and exceed expectations• Functionality and ease of use should be top

priorities in website and app design• Know the market: Demographics, operating costs,

the competitive landscape and consumer preferences

• Understand shopping occasions and consider specializing

• Consider alternative approaches to ordering and distribution

REFERENCES

• Online Groceries in India: Will Consumers Bite? – knowledge@wharton

• THE FUTURE OF GROCERY- Nielson survey

• After many shutdowns, online grocery in India finally takes off – yourstory.com

Thanks!

ANY QUESTIONS?For any query mail at us bodhisattwapm2017@email.iimcal.ac.injayantam2017@email.iimcal.ac.in