making customer data actionable with predictive analytics in the automotive market

Using Predictive Analytics to Achieve

Relevancy and Improve Sales

DAN SMITH

CHIEF MARKETING OFFICER, OUTSELL, LLC

Dan Smith | CMO | Outsell, LLC | [email protected]

Dan Smith | Outsell | CMO | [email protected]

Consumer Expectations


Consumers Expect:

•An engaging brand experience

•On their own terms

•Via any (and all) of their devices


The Industry Challenge


In 2005, Shoppers would visit 4.5 dealerships within a 20 mile radius*

*Building A Groundbreaking Video Strategy Guaranteed To Sell Cars

Phil Sura & Peter Leto, presented at 2013 Digital Dealer Conference

TV Ads

Radio

Newspaper

Car Shopping Was Linear


Now more than 50% gather information 6 months prior to purchase.

In 2012, shoppers visited 1.4 dealerships within a 100 mile radius

It’s Not Linear Anymore

(2)

(1) J.D. Power & Associates

(2) Building Groundbreaking Video Strategy Guaranteed To Sell Cars

Phil Sura & Peter Leto, presented at 2013 Digital Dealer Conference

1

2


• Collectively, the ownership period currently stands at 4.75 years, up from about 3.2

back in 2002*.

• New vehicles = 6 years

• Used vehicles = 4.2 years

• Average service interval has increased over the past year from 140 days to 145

days**.

• Oil changes up to 13k miles

• Spark plugs up to 100k miles

• Increasing reliance on service prompts

Purchase and service intervals are at a record high

Source: *Polk Finds Average Age of Light Vehicles Continues to Rise, August 6, 2013, HIS

**DMEautomotive Research, September 4, 2013


The Case for Relevancy


50%

Will

Disengage

Source: DM News


60% of

marketing is

Not Relevant!

Source: DM News


2X

$$$

Source: DM News


The 40 yr. old woman with the

18 yr. old boy gene


Dear Ford,

Seriously? I own a Mustang. You know I do. I follow Mustang on Twitter and

liked it on Facebook. I've clicked through emails you've sent me to go search

your site for Mustang paraphernalia and badges, check out the current

model, and "customize" my dream Mustang (three times!). I've clicked

through Mustang ads (and Camaro ads, not that you'd know that). I've never

once expressed any interest in an SUV. Why on earth would you email me

about the Escape? It may be all new, with cool features and whatever, but

clearly I'm interested in sports cars, not sports utility vehicles. You may want

to reconsider your segmentation strategy. Thanks for thinking of me, though.

Source: DM News

?


Relevancy Matters:

72% of auto shoppers are open to influence prior to making a

purchase decision

52% said that ongoing dealer communications had a direct

influence on the purchase of their next vehicle

Targeted email prompted 28% of shoppers to begin their

vehicle purchase journey.

Digital Drives Auto Shopping; Google, November 2013


How to Get Relevant


Monitor Every

Customer Interaction

Detect In-Market

Shoppers

Missed Signals =

Missed

Opportunities

Relevance + Timing

= $uccess

Anticipate Service

Needs

Understand

Interests &

Preferences

Predict Behavior

and Measure

Loyalty

4-6 years is a long time. Analytics is the solution.


The power of Your Data

• Dealer data is rich but

underutilized

• DMS, CRM & Web

• Collectively dealers

maintain 10X more

customer data than

OEMs


What’s the Solution?


Predictive Analytics

The analysis of

current and

historical facts to

make predictions

about future

events


Get Smarter With Predictive

Analytics

• Gold standard for driving

relevance

• Requires a specialized skillset

• Could double your results


The “Pregnancy–Predictor” Model


Aggregate all online and

offline consumer

information

Use predictive analytics

to understand past and

present consumer

behavior

Relevant and timely

communications

• Multi Channel

• Integrated

Engaging each customer and prospect in a cross-channel dialog that

builds upon their past and current behavior

Goal: Effective Customer Engagement


Predictive Analytics & Business Intelligence

Business Intelligence

Reports, metrics, dashboards up to this point in time

User-driven to explore data and interpret results

Based on experience and gut-feel


Automatically discover important patterns

Learn from historical data and create predictive models

Consistent, objective, efficient, fact-based

Deploying Predictive Models

Leverage current and historical data

Make predictions on current and future cases

Deploy to enhance outcomes

Reactive

Proactive


What are Predictive Analytics?

Predictive analytics employ a variety of techniques from statistics, modeling

and data mining to analyze current and historical customer data to develop

models that predict likely preferences, future events and next actions.

Why use Predictive Models?

• anticipate needs

• detect preferences

• improve message timing

• increase relevancy

• engender loyalty

• improve sales

Introduction to Predictive Analytics


© Outsell, LLC | Strictly Outsell Confidential

Predictive Analytics enables dealers and brands to

precisely target customers with tailored communications

based on their likelihood to:

• Purchase or Service within a given

timeframe

• Respond to an offer

• Defect to another brand

• Advocate for your brand

• Prefer a specific vehicle class, model,

feature or price point

• Spend a certain amount over their

lifetime




Propensity Value Preference

Defection Response In-Market

Typical Model Types

Example: Segment Intender

identifies customers who have high propensity to

purchase within a specific vehicle segment.

Example: Price Point

identifies customers who are highly likely to

prefer a vehicle within a specific price

range.

Combining models allows you to precisely target consumers with highly-relevant

messaging and offers.


Business Objective: I want to identify which consumers are in market for a

vehicle so I can target them with relevant and timely offers

In-Market Timing Model

• Customers/prospects/both?

• Purchase/Lease?

• Today/tomorrow/next month?

• Brand?

I want to predict which customers will purchase in the next 90 days


I have data (vehicle purchases, ROs, demographics, offers,

responses) from a variety of sources.

I’d like to predict the likely future behavior of a customer. I‘ll use

historic data that has examples of that behavior:

Age Education Marital Gender Occupation Historic Response to Offer

21 College Single Male Engineer Yes

23 HSgrad Single Male Administrator No

29 HSgrad Married Female Bus. Owner Yes

Build a model (find the patterns) then use the model to predict that

behavior for new records:

Age Education Marital Gender Occupation Predicted Response to Offer

24 HSGrad Married Male Engineer No

27 College Single Female Bus. Owner Yes

31 PhD Married Male Bus. Owner Yes

Developing Predictive Analytics Models


Business Objective: I want to predict which customers will purchase in the

next 90 days

Source: Outsell Insights

Analytical Framework

Cutoff

Date

1/1/2014

Decision Period

1/1/2014-3/31/2014

• Identify purchasers

and label as 1’s

• All other label as 0’s

Customer Behavior and Profile Data

5 years sales

2 years RO’s

24 months Clicks/Opens

- 60

months - 24

months

- 1

month

+ 1

month

+ 3

months


Source: Outsell Insights

• Every consumer has a unique “score” that captures the essence of

what is being modeled

• The “score” is essentially the “probability” of something happening

scaled in a predefined way

Example:

Customer 1234 has a score of 900 which translates to a

90% probability of purchasing in the next 90 days

Customer 1357 has a score of 600 which translates to a

60% probability of purchasing in the next 90 days

Output of Modeling Process



Model scores can be combined to develop a

complete picture of consumer intentions

• Scores can be ‘layered’ to identify prospects who are:

• In-market

• For a specific type of vehicle

• With specific features

• And financed in a specific way

Targeting Power


Benefits of Predictive Analytics

Source: Aberdeen Group, September 2011


Case Studies


Consumer click behavior is one of the most valuable sources of data in

understanding consumer intentions and crafting targeted marketing

communications

45% of purchase likelihood within 90

days is explained by click behavior* *Opening an email, clicking on inventory, valuing trade-in

Source: Outsell

Timing Model Case Study



Case Study: Analytics-driven Dealer Communications

Source: Outsell

• Customers that engage with dealer communications are

4x more likely to purchase from the dealer

• Over 50% of customers and prospects engage with dealer

communications within 6 hours of receiving a targeted

communication

• Customers engage with dealer campaigns 7 times before

coming in to purchase a vehicle


Questions & Answers

Contact Info

Full Name:

Company:

Job Title :

Email:

Dan Smith

Outsell, LLC

Chief Marketing Officer

[email protected]

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making customer data actionable with predictive analytics in the automotive market

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