Directing Intelligence In automotive Industry

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D i r e c t i n g Intelligence in Automotive Industry

http://www.directingintelligence.com/


1. The Challenge. Churn Prediction And More... ................................................................. 3

1.1. After Sales Trends ........................................................................................................... 3

1.2. Objective review through data analysis. ............................................................................ 4

2. The Solution. Datactif Auto© Intelligent System. .......................................................... 5

2.1. Data Analysis Objectives .................................................................................................. 5

2.2. Datactif Auto Intelligent System ........................................................................................ 5

2.3. Churn Prediction Results................................................................................................... 5

2.4. Churn Prediction is NOT enough ....................................................................................... 5

3. New Challenge. Increase Visits for maintenance ........................................................... 6

3.1. Unsupervised Learning ..................................................................................................... 6

3.1.1 Hyper Clusters and Churn ............................................................................................ 7

3.1.2 Hyper Clusters & Life Time Cycle .................................................................................. 7

3.1.3 Transform analysis into Actions .................................................................................... 8



1. THE CHALLENGE. CHURN PREDICTION AND MORE...

An automotive company, faced a crucial challenge

for its After Sales Service business: How to reduce

churn for cars out of the warranty. We present here

our solution based on Artificial Intelligence Theory.

1.1. After Sales Trends

Repair works and maintenance contributes to an

automotive company the major part of its annual

profits with a still enormous potential as there is

over $55 billion per annum of unperformed and

underperformed maintenance by vehicle owners. In

US as in EU economic crisis had a negative impact

in cars after sales market, a market that was already

in a transition point between a promising potential

of further development and a difficult reality due to

client’s difficulties to perceive after sales service as

product but also because new cars are built to last

longer and require maintenance less frequently.

As such, the average age of vehicles on the road has

grown 14% since 2008 (IHS Automotive research,

“Cars On American Roads Is Older Than Ever,”

December 2013), with 86.4% of vehicles being out-

of-warranty as of Q2 2011(Experian Automotive’s

Vehicles In Operation (VIO) database, 2011).

Research shows, these out-of-warranty owners are

more likely to explore repair shop options rather

than return directly to the dealership. And in

general, those still visiting dealerships are there less

frequently for maintenance needs.

The average vehicle lifespan continues to increase.

Currently at a record 11.3 years, vehicle age is

expected to grow to 11.5 years by 2018 ((IHS). A

very large percent of these older vehicles will be

out-of warranty, challenging dealerships to compete

to retain their business.

Authorized Repairers vs Independents

Auto owners tend to leave dealerships for service as

cars age and go off-warranty. According to a

study by DME automotive, customers seeking basic

services (rather than major repairs), tend to defect

over time. These lost customers are estimated to

cost dealers a large percent of their revenue on older

cars. In fact, dealers lose an average of 60-78% of

revenue on three- to six-year-old cars and 82-92%

of revenue for cars more than seven years old.

Specially for Europe we have a domination of

authorized repairers in new cars and independents in

the old ones (BCG 2012)



While in the current economic climate customers

are more likely to defer expensive repairs on their

cars, they are more likely to repair them eventually

than to replace them. In this context Life Time

Cycle of each client is critical and specially Churn

as it affects the length of the service period and,

hence, future profit generation.

1.2. Objective review through data

analysis.

Before the economic crisis, we used to define as

churn the time period when a customer ceased

visiting authorized repair shops for the annual

service. For the majority of the customers churn was

related with the warranty (6 years in our case).

It was more or less correct but now due to the

economic crisis, churn definition must be

reconsidered, as a significant number of customers

stop maintenance service well before the sixth year,

a lot of them alternate service in authorized and

independent repairers and the majority of them,

prolongs the period between two maintenance

services (Figure 1).

Figure 1. Evolution of maintenance Life Time Cycle

in relation with car age and warranty



2. THE SOLUTION. DATACTIF AUTO© INTELLIGENT SYSTEM.

2.1. Data Analysis Objectives

i. Annual churn prediction for customers near to the

end of the warranty (car age >4 and <7)

ii. Evolution analysis of Customer Life Time Cycle

associated with churn.

Data Used : 5 years detailed historical data of

maintenance visits (2009 – 2013).

2.2. Datactif Auto Intelligent System

DATACTIF AUTO® uses machine learning

methodology and algorithms such as neural

network, fuzzy systems, genetic algorithms, Support

Vector Machines, etc… and contains visualization

methods that allows a global view on the domain

that is under analysis, and an analytical view to all

details offered by the existing data.

DATACTIF AUTO® uses both supervised and un-

supervised learning methods in order to solve

prediction problems.

For customers churn prediction in our case, we used

supervised learning method and more specifically

the polynomial kernel with the support vector

machines (SVMs) that represent the similarity of

vectors (training samples) in a feature space over

polynomials of the original variables, allowing

learning of non-linear models.

2.3. Churn Prediction Results

We verified our prediction model with real data

provided by maintenance visits occurred between

1/1/2014 and 31/7/2014 with the following results :

cars >4 and <7 : Average Accuracy = 86.8%

cars >=7 years : Prediction Accuracy = 84.2%

2.4. Churn Prediction is NOT enough

In fact the most important is to discover those that

they are deciding not to come any more for service

in the authorized dealers and they could change

their mind !

And of course find the way to make them change

their decision.


http://en.wikipedia.org/wiki/Support_vector_machine

http://en.wikipedia.org/wiki/Support_vector_machine


3. NEW CHALLENGE. INCREASE VISITS FOR MAINTENANCE

Our goal is to find customers near the end of the

warranty period, ready to stop visiting authorized

repairers for service and make them change their

mind.

3.1. Unsupervised Learning

In order to find hidden information into data and in

same time to have a macroscopic point of view

on the relationship between customers and

maintenance, we used unsupervised learning neural

networks method with self organizing map.

As data and input variables, we used the same as in

the SVM prediction model.

A self-organizing map (SOM) is a type of artificial

neural network that is trained to use unsupervised

learning to produce a two-dimensional, discretized

representation of the input space of the training

samples, called a map.

For the visualization of the result we used the

technique called U-matrix or unified distance matrix

that visualizes the distance between adjacent units

in the SOM. It represents the map as a regular grid

of neurons as illustrated in the figure bellow.

In order to interpret the map, and in particular, the

characteristics of each cluster, we used the

component levels that show the distribution of

values across the map, according to one variable at

a time (figure bellow)

U-matrix visualization and clusters' description

.



3.1.1 Hyper Clusters and Churn

Based on extracted values of features for each

cluster and on clusters similitude’s analysis, we

could define 4 Hyper Clusters (Figure bellow).

3.1.1.1 Why Hyper Clusters

Because an enterprise needs groups of customers as

big as possible in order to design cost efficient

strategies (stock logistics, price policy and discount,

promotional campaigns, etc...).

3.1.1.2 Hyper Cluster enriched with life style

In the context of a Customer Centric knowledge

model, association rules allows to relate clusters

with any kind of information provided from both

internal or external data such as demographics

qualitative and specially data from social media

Group A: LOST. Customers

who stop service after warranty

expiration

Group B: TRANSITIONALS.

Customers with cars out of

warranty. Their attitude depends

on their economic situation.

Sensible to offers and price

policy in general.

Group C: LOYAL. Customers

with cars out of warranty, good

and loyal, doing their service

regularly.

Group D: New Customers in

WARRANTY.

3.1.2 Hyper Clusters & Life Time Cycle

Based on real visits of cars that did maintenance

service (not churn customers) we observe that

Hyper Cluster D is more important for car ages

under 6 years, Hyper Cluster C for car ages between

6 and 10 and Hyper Cluster B for car ages over 10

years (Figure 4).



Considering past years' history, we observe that

from 2009 to 2014, there is a gradual movement

from Hyper Cluster A to Hyper Clusters C and D,

this movement allows us to predict that in 2015 we

will have Hyper Cluster D as the most dominant.

So Hyper Clusters allow us a macroscopic point of

view on LTC evolution also related with Churn.

Figure 5. Evolution of Hyper Clusters

3.1.3 Transform analysis into Actions

At the end of 2013 we knew who from our

customers will come for maintenance service in

2014 and who will not, allowing us to organize after

sales service in a structural way (logistics, technical

advisors availability in each authorized dealer,

etc...).

We knew also who from our customers that will not

come for maintenance service (churn) are able

to change their decision and how based on Hyper

Cluster description and according to their evolution

through time.

Based on this knowledge, the Automotive Company

has designed after sales marketing strategy that

increased significantly the visits for service and as

consequence company's profitability


Directing Intelligence In automotive Industry

Data & Analytics

Transcript of Directing Intelligence In automotive Industry