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Oracle Data Cloud Blog

Data gives automakers more shots on the goal

This week's blog is contributed by Mike Schumacher, Senior Director: Data Science, Oracle Data Cloud.

It’s hockey season, and if you ask any player their winning strategy they’ll likely respond with, “we just need to get shots on goal.” In other words, keep it simple, and keep hammering away at the target. The Hockey-town USA adage transcends sports; it’s useful for marketing teams too. But for auto marketers it’s more complicated…who is the target?

Today’s audience allocation is skewed away from car buyers.

In any given quarter, about 1.5MM US households will buy a new vehicle vs. about 90M reachable households who won’t. Let’s assume your job is to allocate a digital marketing program across these two audiences. What’s the right balance?

In typical 2015 Tier 1* campaigns, just 3% of the reached audience were upcoming** car buyers while the remaining 97% were non-buyers. Yes, you read that right. Surely that’s unintentional and unacceptable, even among “branding” campaigns. In this era of predictive analytics and big data, when those who will buy in the next 90 days are identifiable and addressable, why is 97% of your reached audience missing your most relevant audience?

Campaign Reaching 10MM Households:

 

Current Allocation

Easily-Achieved Alternative

Will buy in the next 90 days = 1.5MM

300,000

1MM

Will not buy in the next 90 days = 90MM

9.7MM

9MM

Total Exposed Households

10MM

10MM

* We looked at large, national brands that sell models across the vehicle segments.

**Upcoming = next 90 days. Switch it to 180 days and the percentage grows to 7%.

The allocations seem logical but are actually suboptimal choices.

When logging into your DMP, you’ll see thousands of audiences for potential targeting. Most brands will start with obvious choices, like, “In-Market for [Your Car]” or “In Market for [Your Competitor].” But what audiences should you select next?

Typically brands then select demographic audiences (e.g.: Male 24-35) followed by broad-reach publishers. This is precisely where things break down. If you want to reach car buyers, you need to use data-driven audience recommendations. In doing so, you’ll reach more actual car buyers while simultaneously adding in some similar non-buyers who aren’t quite ready to buy.

Here’s a better way to select your audience.

Example: ½ ton pickup buyers.

Highest Concentrations of Eventual ½ Ton Pickup Buyers

After “In-Market” Audiences within Oracle Data Cloud:

Performance Rank

Audience

1

Motorcycle Owner (164799)

2

ATV Owner (164854)

3

Bought Coleman Products (377256)

4

In-Market for Texas Acreage (75158)

5

In-Market for an RV (12737)

6

Bought Carthartt Clothes (369356)

7

Occupation = Farmer (150885)

8

Bought Teva,Keen Products (369375)

9

Owns a Boat (44082)

10

Bought Wrangler Clothing (369378)

*Note the absence of demographic audiences*

Note the completely intuitive but rarely targeted audience recommendations. The highest-ranking demographic audience was 1,096th! Let the data guide you in finding audiences with high concentrations of eventual buyers by asking your data provider for help.

Take Control of your targeting and sell more cars.

Brands that devote more attention to car-buyers will drive more sales by influencing those with the inclination and means to buy in the near term. Those brands will also organically target the non-buyers who look like today’s buyers and set the stage for future growth. This is a better strategy than serving huge volumes of poorly targeted ads in the name of branding, simplicity or “efficiency." You don’t have to do it like you’ve always done it. Let your competition make that mistake.

Stay up to date with all the latest in data-driven news by following @OracleDataCloud on Twitter and Facebook! Need data-related answers for your next marketing campaign or client partner? Contact The Data Hotline today. (What's The Data Hotline?)

Photo: Andrey Yurlov/Shutterstock

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