Determination of Factors Affecting Purchase of Pre-Owned Cars

Determination of Factors Affecting Purchase of Pre-Owned CarsAmbuj Gupta (04)avneesh munjal (18)Balram sundas (19)kunal malhotra (33)sahil Jain (50)

1Research Objective and Executive SummaryObjective: To understand the mindset of consumers while buying a pre-owned car and categorize different variables into Components and FactorsIndia represents one of the worlds largest car markets. Various reasons viz. easy availability of finance, rising income levels etc. have been cited for a stupendous growth of the industry over the past few years. While many still prefer to buy cars first-hand, there has been a positive impact on the used car market as wellAccording to the latest study on the sector, the Indian used car industry possesses a significant potential, with overall market expected to grow at a CAGR of around 22% during 2011-2014 to reach 3.9 Million Units by 2014-end.The growth of the sector can be attributed to the rise of organized sector. Big players such as Mahindra First Choice and Maruti True Value are coming to the fore and taking the market by a stormAs the demand for pre-owned vehicles continues to outstrip the supply, Original Equipment Manufacturers (OEMs) have been witnessing a huge opportunity in the businessIn Kolkata, the market is not as booming as it is in other Metro Cities of India. This can be attributed to the fact that the average per capita income of Kolkata is less compared to other Metro cities of India. Despite this, dealers such as Ritchie Motors at Park Circus, Bhandari Motors at Ballygunge and MA motors in Salt Lake City have made a name for themselves in the field2Research Methodology3Data Gathering and Questionnaire DesignWe selected non probabilistic sampling-convenience sampling

We began with interviewing the one of the pre-owned car dealers in Kolkata to get a slight insight into the attributes that consumers look for while buying a car

The process of gathering data started with the primary research and floating an online form, which included questions pertaining to the preferences made by users for purchasing pre-owned cars

Respondents were asked to rank the independent variables cost of purchasing, brand name, cost of servicing, to learn driving, peer pressure, frequent buying, warranty coverage and brand ambassador on a Likert scale of 1-5 with 1 being the lowest and 5 being the highest

4Questionnaire (1/3)

Questionnaire (2/3)

Questionnaire (3/3)

Response GatheredCost of purchasingBrand NameCost of servicingTo learn DrivingPeer PressureFrequent BuyingWarranty CoverageBrand Ambassador453113414451344111131151343231314535553244451341345524313432313145355532444513413455243133325111453113414451344133325111444513413455243134323131453555324445134134552431333251114531134144513441333251114531134144513441111311513432313144451341345524313332511145311341445134411113115134323131Data Processing and AnalysisNull Hypothesis- H0:Population Matrix is an identity matrix (It means independent variables are uncorrelated)Alternate Hypothesis- H1: Population Matrix is not an identity matrix (This means independent variables are correlated and we can group them into factors)We ran a Factor Analysis test on SPSS for analyzing the responses that we gatheredKaiser-Meyer-Olkin measure of sampling adequacy:Cost of purchasing0.589Brand Name0.689Cost of servicing0.395To learn Driving0.394Peer Pressure0.465Frequent Buying0.600Warranty Coverage0.464Brand Ambassador0.531KMO0.524 On SPSS we found out the value of KMO test to be 0.524 which is greater than 0.5, so we can go ahead with Factor Analysis. Our Null Hypothesis is rejected and we accept alternate hypothesis which means variables are correlated.9Data Processing and AnalysisTotal Variance ExplainedComponentInitial EigenvaluesExtraction Sums of Squared LoadingsTotal% of VarianceCumulative %Total% of VarianceCumulative %13.09838.71938.7193.09838.71938.71921.96824.59763.3171.96824.59763.31731.39917.48780.8034.92211.52092.3235.4355.43597.7586.1201.49899.2567.060.744100.0008-2.281E-016-2.851E-015100.000Extraction Method: Principal Component Analysis.We can see from the table that cumulative variance is 63.317 which is greater than 60% in 2nd factor itself , so out of 7 factors we will broadly consider 2 factors to which independent variables will be correlatedData Processing and Analysis

From the scree plot we can see that there are 2 Eigen values which are substantially greater than One and explaining almost 65% of total variance which reinforces our point of considering Two factors.11Data Processing and AnalysisComponent MatrixaComponent12Cost of purchasing.870-.002Brand Name.868.014Cost of servicing.712-.144To learn Driving.273-.039Peer Pressure.090.962Frequent Buying.883-.196Warranty Coverage.030-.875Brand Ambassador.465.464Extraction Method: Principal Component Analysis.a. 2 components extracted.From the component matrix by observation, Factor loading of the above variables can be observed. We can see Cost of purchasing, Brand Name, Cost of servicing and Frequent Buying can be categorized as Factor 1-Budget ConstraintAnd Learn Driving, Peer Pressure, Warranty coverage and Brand Ambassador can be categorized under Factor 2-Status Symbol12Data Processing and AnalysisThe 4 independent variables Cost of purchasing, Brand Name, Cost of servicing and Frequent Buying are strongly correlated to Budget constraint and weakly correlated to Status symbol while Learn Driving, Peer Pressure, Warranty coverage and Brand Ambassador are strongly correlated to status symbol while weakly correlated to budget constraint.13ConclusionVarious variables were gathered and understood through primary and online surveys and those were later factorized into finally two components namely, Budget Constraint & Status SymbolThe research draws attention to the fact that the major customers or consumers of the used cars segment are the young and the middle aged people with age group 18-54 contributing close to 90% of the total sampleOne very interesting insight for the major players in the industry was that more than 90% people consult with friends and family before buying a new car, which reaffirms our theory, that car buying is not an individual decisionOnly 40% of the customers refer to an automobile website or an online classified before buying a new carAlso, the customers claimed that overall condition of the car is usually the make or break factor for them, followed by mileage promised. Profile of the previous owner played the least role in decision makingIt is recommended that:Marketers focus on the value proposition of buying a pre-owned car and highlight itTarget the whole family not just the decision-makerHighlight the cost effectiveness of buying a second hand carStores such as Maruti True Value and Mahindra First Choice should highlight how people will get the car with proper documentationTHANK YOU