Why Conquest Data Matters

Conquesting new customers into the service drive can be one of the more difficult battles a Service Director can face when it comes to growing your service business. PPC advertising, Facebook, conquest emailing, and even direct mail can all be leveraged to drive new customer acquisition.

Using 3rd Party Data

Creating an effective conquest campaign begins with identifying who the conquest opportunities are. There are many vendors in the automotive data space who can provide types of conquest data – most of it, built around registration information.

Watching for Data Inaccuracies

Registration information is slippery. While vehicle registration can be a decent indicator of where a vehicle resides, it’s not fully encompassing. This type of data can include up-to-date vehicle information but often uses out-dated customer information.

Address Information

If the consumer data is not structured properly, it makes it difficult to conquest the vehicle. If a customer has multiple addresses the dataset may only include one residence and it is not necessarily be the primary address. Alternatively, some vehicle purchase record only have a PO boxes or business address. If a customer purchased his or her vehicle and then moved recently after the address will be invalid. Clean data yields marketing results. Dirty data lends itself to wasted advertising spend.
Unfortunately, the majority of vehicles sold today use whatever data is given at the time or sale or just pull from a consumer’s old profile. If the consumer has not bought a car recently, that profile could be over 3 years old.

The Problem

Most data companies are only 60-70% accurate on their best day. This means out of their entire database, 30-40% of the information is incorrect or outdated. It ranges from an incorrectly formatted mailing address, to a misspelling of a last name, or even as far as the wrong customer is connected to the vehicle purchase. If a dealership is wasting 30-40% of it’s marketing dollars on vehicles/owners that no longer exist — is that efficient or effective?
Let’s review some real case scenarios to understand the scale of this problem.

Dealership A Overview

Dealership A is a large point, in a densely populated city. It averages about 1,500 repair orders per month and has PMA that spans 15 square miles. This dealer is looking to conquest new service customers.

OEM Goals The OEM says there are 5,000 conquest prospects in the surrounding area.

Aftersales Campaign The tier-1 aftersales provider says we need to market all 5,000 conquest prospects in their PMA/AOR. The dealership agrees to a 5,000 mailer drop.

Campaign Performance Soon after the campaign starts, returned mail comes flooding back in droves. The dealership can’t understand why mail keeps coming back to them, Further, their active customers begin bringing in the mail that was intended for only new conquests.

Where the Aftersales Provider Went Wrong

The dealer sees returned mail and active customers bringing in mailers. What the dealer does not see is that 30-40% of that source data was incorrect from the start and they overspent by $2000 since the data only had about 3000 usable contacts. If this case is repeated over a 12-month period, $24,000 is wasted on prospects that have a 0% chance of converting. If the aftersales provider had a more thorough understanding of data, this problem could be prevented through simple data cleansing rules, and cross-referencing the dealers DMS.

Solving Conquest Data Problems

More Data

Over 40+ data sources have been compiled to identify the most up-to-date records. These sources range from home purchase history, vehicle service history, city municipalities, and self-reported data.

Scrubbed, Verified, Mailable

Aggregating the data is one piece, the next is making the data usable. The aggregated data is scrubbed against a dealer’s DMS and then against itself. Seems weird right? Why would I want to cross reference my data against itself. Well, in the case of the direct mail campaign with the 5000 prospects, 10% of those prospects shared the same mailing address. These are what we call duplicate records. Duplicate records can inflate the cost of any campaign making scrubbing a key component of producing a marketable record. A marketable record is now about 90% accurate.
Let’s take our dealer example from before:

Dealership A is a large point, in a densely populated city. It averages about 1500 repair orders per month and has PMA that spans 15 square miles. This dealer is looking to conquest new service customers.

OEM Goals The OEM says there are 5,000 conquest prospects in the surrounding area.

Aftersales Campaign The tier-1 aftersales provider says we need to market all 5,000 conquest prospects in their PMA/AOR. The dealership agrees to a 5,000 mailer drop.

DataClover Campaign Dataclover scrubs the data and sees there are only 3500 potential prospects.

Campaign Performance The dealer now may see 1 or 2 customers trickle in who have been at the store previously, how the majority of customers are all new acquisitions.

Not only is the dealer no longer receiving mail stamped “Return To Sender,” but they are seeing customers who have never engaged with them before. The mailer reached who it was intended for, and the dealer has been saved from the headaches of collecting returned mail and several thousand in wasted ad spend per month.

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