Leveraging Machine Learning Techniques for Vehicle Auction Industry: Online shopping has grown in popularity over the years. Nowadays many shoppers turn to online shopping sites for shopping. By recommending those content that is relevant to the online shoppers we are minimizing the time they spent online and maximizing the business success of online shopping sites. Many online sites use recommendation systems nowadays and they leverage content based and or context based collaborative filtering machine learning techniques for this purpose. We have leveraged the power of few machine-learning techniques like collaborative filtering, neural networks, Bayesian learning for relevant content vehicle recommendation and time series forecasting for vehicle auction at Manheim. My talk will focus on some of these techniques and their uses on relevant content recommendation.

1. Leveraging Machine Learning Techniques for
the Vehicle Auction Industry
Raji Balasubramaniyan, PhD
Senior Data Scientist
Manheim, Inc.,
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2. Overview
• Automobile auction
– Manheim
• Introduce the ML use cases
– Churn rate
– recommendations
– Forecasting
• How to approach a problem?
– Tools and algorithms used
• QA

3. Manheim, Inc., Automobile auction
Providing auction services for the physical sale of
automobiles as well as online tools to connect wholesale
vehicle buyers and sellers.
Leader in wholesale
vehicle auction
industry. 85% vehicle
auction business
happens at Manheim.
We have over 100
location across US and
Canada
About 15 million cars
goes through auction
every year

4. ML use case 1: Predicting Churn rate
• What is Churn?
– Churn rate, refers to the proportion of members who leave during a
given time period
• Motto: Make customer happy
– If the customer is happy, he/she wont churn.
• Why it is important?
– It helps us predict and analyze the parameters that drives the
customers away helps sales force team to focus on those parameters
and coach the customer
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5. Predicting Churn rate: The approach
• Step 1
– Create profile for current and cancelled members by collecting their
behavior data for last 6 months
• Activity, Transactions, Messages, Response time etc.,
• Step 2
– Segment the customer according to their behavior
• Unsupervised clustering
• Step 3
– For every segment perform supervised learning, to select parameters
that influence current members Vs. cancelled members
• Logistic regression, Neural net
• Step 4
– Include sentiment analysis add another score
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6. Algorithms: Unsupervised K-means clustering
• Given a set of observations (x1, x2, …, xn), where each
observation is a d-dimensional real vector consists of each
members parameters, k-means clustering aims to partition
the n observations into k (≤ n) sets S = {Successful Seller,
Successful Buyer, Buyer at risk, Seller at risk, undecided} so as
to minimize the within-cluster sum of squares (WCSS).
In other words, its objective is to find:
• where μi is the mean of points in Si.
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7. Algorithms: Logistic regression
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If P is viewed as a linear function of an explanatory variable, or a linear
combination of explanatory variables, then the logistic regression function can be
written as
Where
α1…αn are parameters influencing the churn

8. Algorithms: Neural net
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Given a specific task to assign a user in a group, given 5 groups, learning
means using a set of factors to find f* ∈ F which solves the task in
optimal sense.
Our training data consists of N dealers from each group from 5 groups.
x1 :Activity
x2 : Number of messages
x3: Response time
xn : etc
w1
w2
w3
wn
wnå xn
Output
Our cost function is the mean-squared error,
which tries to minimize the average squared
error between the network's output.

9. Algorithms :Sentiment analysis
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Sentiment refers to the use of natural language processing, text analysis and
computational linguistics to identify and extract subjective information in source
materials. We used Naïve-Bayes model.
We have two training groups G ={ ‘Cancel’, “Member”}, D= Messages
Example tk= {“like”, “love”, “hate”, “bad”, “worst” , "interesting-to-me" : "not-interesting-to-
me”,…..k-terms}
Goal is to find best group for a message D using maximum a posteriori (MAP)
group Gmap
tk is a term;
Dm is the set from ‘Members’;
Dmk is the subset that contain tk;
Dc is the set from ‘Cancelled
Member’;
Dck is the subset that contain tk.

10. The Result
• Every dealer will be assigned to a group
• He / She will have 3 different health score (1-Churn rate)
– 0-30 days health score (Calculated using last 30 days data)
– 30-60 days health score (Calculated using last 30-60 days data)
– 60+days health score (Calculated using last 60-120 days data)
• Sales force will be alarmed to see if a successful user turned
to fall in risk category. They will look into the parameter which
forced them to be in risk category
– Example : Last 30 days less Activity
• Marketing team will take risk category users and aim
promotion schemes to them
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11. ML use case 2: Recommendation
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What is recommendation system?
Recommender systems are a subclass of information
filtering system that seek to predict the 'rating' or
'preference' that a user would give to an item.
Goal
Suggest relevant content to the users

12. Recommendation: The Approach
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• Step 1
– Segment customers according their transaction patterns
• Step 2
– For every segment create user profile per customer
• Step 3
– Match user profile with vehicle profile and arrive at matching score
• Step 4
– Rank the relevant content
• Step 5
– Combine profile matching and ranking and provide recommendations

13. The approach: Segment the customers
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Segment the customers according to their behavior
• Franchise dealer, Independent, Wholesaler
K-means or any clustering technique could be used for this
purpose
Our objective is to find best group every dealer
belongs to.
where μi is the mean of points in Si. and
S = {different customer segments}

14. The approach :Creating user profile and
Matching
• Create user profiles by collecting the dealer transaction pattern for a
period of time
• For every user profile perform vehicle filtering using content based
collaborative filtering
– User – Item collaborative filtering: Relevant content recommendation
• Customers who bought car X also bought car Y
– 2010 Honda Accord Vs 2010 Toyota Camry
– User- User collaborative filtering : You may also like these
• Dealer A and Dealer B how much their profiles match
Similarity or Co-rating matrix is used to arrive at relevant content
matching correlations
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15. The approach: Ranking scores using regression
Customer need score
Once we have filtered the profiles that are relevant to the users, rank/sort
the vehicles according to some goal to provide more relevant content on top
• Example: Suggest items that makes more profit for the customers in
the retail market, in this case regression goal is profit.
Where
α1…αn can be Buying price from auction, retail selling price,
Detailing work done on the cars etc.,
Result
Suggest relevant cars to the dealers when they login to the site

16. ML use case 3: Forecasting
• How many transaction a buyer is going to make in next few
weeks?
– Given the past year transaction history for a buyer, how many cars the
dealer will buy in future few auctions or online.
– Which year, make and model the dealer buy?
– In which auction, region he will buy.
• How many users are going to Churn in next few months?
– How many will move from risk category to successful category
– How many will move to risk category
– How many non active moved to active category
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17. Synopsis : Time series and ARIMA
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A time series can be viewed as a combination of signal and noise, and
could have different patterns like, and it could also have a seasonal
component.
• Mean reversion
• The trend will tend to move to the mean over time
• Sinusoidal oscillation
• Etc.,
An ARIMA model can be viewed as a “filter” that tries to separate the
signal from the noise, and the signal is then extrapolated into the
future to obtain forecasts.
ARIMA models are, the most general class of models for forecasting a
time series.

18. The Approach :ARIMA
Auto Regressive Integrated moving average model for calculating the forecast,
A non seasonal ARIMA model is classified as an"ARIMA(p,d,q) model,
where:
p is the number of autoregressive terms
d is the number of non seasonal differences needed for stationarity
q is the number of moving average terms.
A seasonal ARIMA model is classified as an ARIMA(p,d,q)x(P,D,Q) model, where
P=number of seasonal autoregressive (SAR) terms
D=number of seasonal differences
Q=number of seasonal moving average (SMA) terms
According to signal type, we developed automatic forecast parameter prediction algorithm, that choses
different p,P, d,D and q,Q values and selects the one which has lowest RMSE value using 80-20 rule.
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19. Manheim | Proprietary and Confidential 19
perioid− Example4−c(0, 0, 0),S(1,0,0)
Weeks
count
0 20 40 60 80 100
400005000060000700008000090000
80/20
Weeks
count
0 20 40 60
400005000060000700008000090000
One Example

20. Summary
• We used various ML techniques and implemented them for
vehicle auction industry use cases.
• Choosing the algorithm determines the success of the results
and depending on the use case, various algorithms can be
used
• Extracting , Cleaning and normalizing the data forms the
crucial layer in determining the use case success
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21. Acknowledgement
• Dr. Stephane Pinel
• Sonar Team
• Manheim
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22. Q &A
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