Estimating_In_Market_Timing_Auto_Industry

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In Market Timing – Vehicle Purchase

Analytics Consulting

Technology Consulting

Business Intelligence


Objective : Identify In-Mark Timing for Customers

In Market Timing – Vehicle Purchase (Passenger Segment)

Identifying the time when customers would reenter the marketplace so as to

develop effective marketing campaigns.

Target marketing coupled with right timing when the customer will be in the

market "looking around" for the right deal.

Predict the time to next car purchase a function of customer demographics,

geographic, behavioral patterns, service oriented, societal factors, current

vehicle and or last purchase date

Solution: Survival Modeling approach for addressing the in-market timing

Identifying the occurrence and timing of a given event (Next Purchase).

Estimates the probability distribution that the customer will look for a new

vehicle within the next time period t (called survival time), given that the last

purchase took place t units of time ago.


Benefits : Survival Modeling

Survival Modeling predicts the timing of customer in the market.

It also indicates how various factors such as Mileage, Year of Birth, Income, Battery

Replacement, Total Problem Count, and others impact the occurrence and timing of

customer coming back to market.


Overall Methodology

The Survival model building process follows the following approach and steps:

Owner data base consists of two types of customers, Sample customers from both

types

Customers who have made single purchase and

Customers who have made multiple purchases.

We divide the entire dataset into development set and validation set (70/30)

Derive graphical representation of interaction of various variables (customer

demographics, transactions, service data, vehicle systems) with time to re-purchase

Enables to realize the overall shape of survival & hazard function and also

whether the variable significantly influences the probability to re-purchase or

not

Impact on re-purchase probability for different values of the same variable

With the reduced set of variables and knowledge of hazard and survival function

curve shape, build a model which gives probability of every customer to re-purchase

in a particular month and re-purchase in the time up to any particular month


Modelling Phases

Sampling

Variable reduction

Survival and Hazard

Function

Estimating Customer In-Market Timing

Explanatory Data Analysis


Sampling - Variable Creation


Sampling - Variable Creation (Cont.)

Sampling Strategy

Survival time - The time between the last event in the history period and the time until the first event

in the target period. For censored cases, the last time at which customers were observed.

Choice - Choice variable is used to distinguish the censored cases (customers who have done next

purchase within the analysis period) from observed cases. Choice =1 for customers who has done

next purchase within the analysis period, else it is 0.

Defining the time periods

Analysis Period - The period from the starting time to the ending time ("today") is the experimental or

the analysis period

Target Period - Target period is used to define the choice variable. If a customer has bought again in

this period than the value of choice variable is 1.

History Period – History period is used to define the independent variables.

The "target" period used to define the choice variable and the "history" period used to define the

predictors, are not consecutive. As customers who buy a car will lookout for another car only after

a gap of some time.



• The final step is to estimate the probability of a customer to re-enter the market at a

given point of time.

• With each customer in the base being scored for predicted in-market probabilities for a

specified time (that is, number of months since the origin of time), each customer will

have P0 through P6 as its predicted in-market probabilities. P0 is the predicted in-

market probability at the beginning of month 1, which is the origin of time. P1 is the

predicted in-market probability at end of month 1; P2 is the predicted in-market

probability at end of month 2, and so on.

• Two sets of probability scores will be generated

• Number of customers by decile during specified months

• Number of customers by decile up to specified months


Model Lift


Valiance Solutions Private LimitedA-146, Opposite TCS building,Sector 63, Noida, U.P - 201306India.

Contact Person: Vikas KamraOffice No: +91 120 4119409Contact No: +91 8750068961

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Estimating_In_Market_Timing_Auto_Industry

Automotive

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