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What NOT to Do When Measuring Digital Marketing Success

What NOT to Do When Measuring Digital Marketing Success

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To help you successfully launch a new, more accurate way of measuring attribution, here are four don’ts you need to avoid when setting up a new model.

One of the most challenging and common roadblocks for modern marketers is calculating attribution. Regardless of how many tools and strategies marketers have in their arsenal, it’s of no use unless there’s a concrete attribution model in place. But sometimes setting up attribution feels a bit like cleaning out your garage: you know it has to be done, but it’s complex and you don’t know where to begin.

Coming up with a plan to accurately assign a value to your marketing efforts and calculate which tactics are generating results — and knowing you’ll be held accountable for this data — is a lot of pressure. How do you make sure you’re getting it right?

To help you successfully launch a new, more accurate way of measuring attribution, here are four don’ts you need to avoid when setting up a new model.

1. Don’t trust the first click or the last click

The most common trap marketers fall into is relying on the first click or the last click as an indication of digital marketing success. Unfortunately, the issue with trusting what someone has clicked on first or last often means you’re not getting an accurate picture of the full purchase journey.

Think about the last time you purchased something online. You might have seen an advert, clicked on it, and then completed a purchase. But it’s more likely your buyer’s journey went something like this:

You saw an advert, Googled the item, read a blog post on the company’s site, read reviews on a third-party site, watched a product video, shopped around to see if you could find a better price, got distracted by something else, saw another advert and finally completed the purchase.

Everyone goes through different types of purchasing routes, especially online, and it’s rarely linear. Marketers must factor in all engagements leading up to a purchasing decision and recognise that using first-click or last-click attribution simply doesn’t act as an accurate representation of real marketing success.

2. Don’t invest in tools until you’re ready to use them

Attribution technology is only as powerful as the data you feed into it. If you’re still not sure what data to analyse, investing in attribution software probably won’t get you anywhere. While these tools can give you incredible insight, you have to be ready to use them.

To get started with more a sophisticated attribution model, you only need three things (that you likely already have): a CRM (like Salesforce), a marketing automation tool (like Marketo), and good old-fashioned Excel (or any other spreadsheet).

Most CRMs can show you plenty of useful data, such as the age and details of a particular deal, and your marketing automation platform hosts all your campaign data. If you can integrate the two, you’ll have plenty of useful “closed-loop” data. Simply export your data into a spreadsheet and begin identifying the stories in your data.

Remember, you can’t break anything (especially if it’s in a spreadsheet), so dive in and get your hands dirty. The more you dig into the data, the more comfortable you’ll feel.

Once you’ve determined what data you’d like to use in your attribution model and how to best analyse it, marketing attribution software can help take your measurement and analysis to the next level and make sure you’re getting the most from your investment.

3. Don’t change your model too often

Consistency is the name of the game when launching a new attribution model. Any changes you make should be small, iterative and done over a specific period - not large and sudden. Moreover, it’s important to set a specific timeframe for testing your model (i.e., 60 or 90 days). If one aspect is working well then rinse and repeat. If another isn’t, then tweak as needed.  

If you’re unsure about which model is best suited for your organisation start with an even-weighted approach, where each touchpoint has equal value. By doing this you can incorporate every factor that has influenced a customer in making their purchase.

Keep in mind you can always decide to modify this later if you know for a fact that one event holds more influence than another. For example, you might determine that a client’s choice to attend a webinar was more influential in their decision to buy than a client reading a top-funnel blog post. But to draw any meaningful conclusions across your customer base, consistency is critical.

4. Don’t use marketing jargon with business leaders

Despite what you think, even the most educated and experienced marketing and sales teams can sometimes speak different languages when it comes to measuring success. Therefore, it’s crucial for marketers to identify an attribution model that allows them to articulate marketing success in a way their sales team and their business leaders can actually understand.

So instead of sharing engagement metrics in hope of impressing business leaders, focus on how your efforts are impacting the bottom line by shortening the sales cycle, increasing deal size, or expanding the number of contacts within an account. A more advanced attribution model can you help you draw these conclusions.

Bottom line: Marketing measurement can be complex, but it’s not an exact science, nor is it a one size fits all. What works for one organisation won’t be right for another. Marketers shouldn’t be afraid to test the waters and try new approaches. Looking at data from a different perspective can be incredibly useful. At the end of the day, it can help you make more informed decisions and optimise your campaigns for wider business success.

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Drew Sollberger

Drew Sollberger

    Drew Sollberger is the director of systems and operations at Spiceworks with a focus on measuring revenue impact. With deep experience in CRM and marketing automation platforms, Sollberger specializes in evaluating, implementing, and managing marketing and sales technology to optimize the path to revenue, accelerate the sales ​cycle, and return key metrics that enable predictable forecasting and planning.

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