The work you do on a daily basis produces valuable data that provides insight, clarity, and a competitive advantage for your dealership.

Your team grabs engagement stats from social media platforms like Instagram and Facebook. Clickthrough rates from email blasts. Traffic from web lurkers on your website through Google Analytics (have you migrated to GA4 yet?). And any traffic from your CRM system factors in there somewhere too. Inbound marketing data streams in from every source you’ve invested in, but what does it all mean to you?

Are you even collecting the right data? What about relevant data? Can you trust your data source?

The car biz is worth billions upon billions of dollars in the US alone, and all you want is to get a fair share of the pie… and perhaps a little extra. Smart marketing can give you an edge against the competition and help you figure out what methods are working well to compel clients and would-be customers to come back for a visit. And that requires a good grasp of the raw data piling in from all of those channels.

But looking at the raw data may not serve the purpose you’re after. As it is, it could mislead you, making you push all your chips into a marketing channel that’s not as effective as it appears. Not all data is equal, and it needs to be put through a filter to determine what stays and what goes.

Why is clean data important?

Silly question to ask, right? Clean data is crucial for marketing attribution because it directly impacts the accuracy of attribution models. Ultimately, it affects the insights and decisions that are derived from them, making up a solid data strategy that gives you a competitive advantage.

Attribution models aim to determine the effectiveness of your marketing efforts and how they contribute to outcomes such as sales, leads, or conversions. That’s dependent on what you’re trying to achieve with your campaign, of course.

Data analytics aren’t biased, they don’t take into account someone’s personality, life story, or personal information. It’s just the facts, which is the biggest value of all.

Think about problems that you’ve had contacting customers. A phone number doesn’t work, an email bounces back. Perhaps it’s a data entry issue or a customer that’s looking for information on a car but doesn’t want to give you a real way to contact them. Would you consider a customer profile with no real way to reach back out to them ‘clean data’? Probably not – and you wouldn’t want to use it for predictive analytics to set goals for sales next quarter, for example.

Dirty data can have a range of negative consequences, including incorrect analysis, lost productivity, inaccurate reporting, and poor decision-making when it comes to determining where to put your co-op dollars and budgeted ad spend.

Why attribution data may be incomplete

Among the countless data fields in countless spreadsheets, it’s hard to believe that anything is incomplete. But there’s data that’s missing, inaccurate, or insufficient to provide a true understanding of what successful marketing efforts have been. There are several reasons why attribution data may be incomplete:

• Limited data sources. Attribution data is typically created by tracking a customer’s journey across multiple touchpoints and channels, such as paid search, social media, email marketing, and so on. If not all marketing channels are tracked, then the attribution data may be incomplete, leading to an inaccurate picture of the full marketing impact.
• Limited tracking capabilities. Some non-digital marketing channels, such as bus benches or TV commercials, may be difficult to track accurately or at all, which can lead to incomplete attribution data.
• Incomplete user journeys. A user’s purchase journey may occur across multiple devices or platforms, and if these different touchpoints are not properly tracked, then the attribution data will be incomplete. It’s partially why there’s duplication.
• Technical issues. Technical issues such as data collection errors, data inaccuracies, or data loading problems can create gaps in the data.
• Data privacy concerns. Personal data protection laws such as GDPR or CCPA may restrict the amount of data that can be shared and collected, which can result in incomplete attribution data. Data governance can be quite difficult to handle on your own.
• Limited budget. Some dealerships may not have the budget to track every touchpoint effectively, making it difficult to obtain a comprehensive view of their customers’ journeys.

Will attribution data ever be 100% complete? No. There will always be limitations in tracking and collecting data along the customer journey. However, available tracking tools and strategies can help mitigate the impact of incomplete attribution data and improve the accuracy of attribution analysis.

What about integration issues?

One of the key issues that can arise with dirty data is data silos, where data is stored in multiple, disconnected systems without proper integration. This creates inconsistencies and discrepancies, leading to incomplete or missing data.

If different sources of data do not map to the same customer, it may result in attributing the same conversion to multiple campaigns. Think about the customer data in the CRM system. If it isn’t synced with Google Analytics, it could enter a ‘new’ customer into the data when attempting to analyze which channels are driving sales or leads. That’s going to give you false results, obviously.

Then there’s data that’s missing… A simple keystroke can be the difference between a marketing channel registering its attribution data or not. Or your marketing manager might’ve forgotten to integrate one of your channels into a report. A completely innocuous technical glitch could also lead to missing data. Or there’s all the data protected by privacy laws.

If you’re handling your user data on your own, you’ll need to constantly audit your data warehouse, which takes a lot of work.

Which data should be excluded?

The raw data needs to be parsed. Incomplete data should be removed. Duplications filtered out. Any leads with no contact method are best left falling to the cutting floor. But there’s also the case of campaigns that might’ve had the wrong KPIs tracked by mistake. Marketers work their magic based on trial and error, often met with varying degrees of success until the right formula is discovered.

It’s a complicated process, and a keen, finely tuned eye needs to be in charge of finding the dirty data.

Not all data makes the cut

A fact in marketing is that some data won’t be of any value, even if it looks fancy on a GA4 chart. Accuracy and quality are what’s most important to data integrity, and not all data is going to pass the test.

Who decides which data is valuable and can confirm its validity? Overall, attribution can be a nuanced and complex topic, and the devil is often in the details. Even with an extremely capable marketing team, wouldn’t you want to know how you can squeak out a better market share and direct your marketing dollars better?

Let’s optimize your campaigns and improve your ad spend! Contact us for a demo or more information.