How Is Attribution Different Than Web Analytics, Marketing Automation, And Other Marketing Tools?

Many marketing tools, ones that most organizations already use, claim to do attribution or at least some version of it. It’s natural to want to use these tools for attribution as well since you’ve already committed to the rest of the tool. But unfortunately, when you take a closer look, they don’t really do what you need and you end up with the same problems.

Web and Other Channel-Specific Analytics

When it comes to attribution, one limitation of web analytics (e.g. Google Analytics) and other channel-specific analytics (e.g. Facebook Insights) is that it only tracks form conversions, which is still a proxy for revenue in a B2B situation. Not bad for a free or inexpensive tool, but it requires quite a bit of “hacking” to create a usable attribution solution through UTM parameters, event setting, and connecting spreadsheets full of channel-specific data in order to dig into the necessary granularity of each channel all the way through to revenue.
When hacking a solution, because the attribution is decentralized (done by each marketing channel separately), marketers face the challenge of double-counting credit. For example, if a visitor clicks on an AdWords ad on Monday, a Facebook ad on Wednesday, and then buys something for $100 on Friday, both your AdWords data and your Facebook data will claim 100% conversion credit because they don’t communicate with each other. When you bring both data sources into your spreadsheet and enter in that the conversion was worth $100, your report will show $200 of revenue — 2x your actual revenue — a big, and potentially embarrassing, problem.

Marketing Automation

Another set of marketing tech, marketing automation (e.g. Marketo, Eloqua, Pardot, Hubspot), focus on lead creation and typically look at attribution measurement from a broad channel or campaign perspective. They are great at telling marketers what channels impact the middle of the funnel (lead creation), but they don’t do a good job looking at bottom-of-the-funnel metrics (sales opportunities and revenue) with granularity needed for optimization, such as by paid search keywords, by specific blog posts, or by which events were contributing as the source.

Because they are so focused on lead creation, they also do a poor job with the top of the marketing funnel. Marketing automation tools don’t connect back to the first anonymous touch, which is vital in understanding the start of the customer journey. Without that first touch, would the visitor have come back to request a demo?

Without the initial step in the funnel or the bottom-of-the-funnel metrics, marketing automation doesn’t fulfill the multi-touch attribution needs of most B2B marketing teams.

Moreover, their tracking is often inadequate to capture the full B2B customer journey. (This is also true of most channel-specific analytics.) Most have cookies that expire within 30-90 days of the contact creation date. It’s fine for B2C marketers (and built with them in mind), where the decision process ranges from hours to a few weeks, but as B2B marketers know, the B2B customer journey is often longer than 90 days.

Marketers don't use marketing automation for their primary source for web analytics, SEO, or advanced A/B testing, so why would you for something as critical as revenue attribution?

Business Intelligence Visualization Tools

Business intelligence tools (e.g. Tableau, DOMO, Qlik) are great at helping marketers visualize lots of data at a time. However, because they don’t actually create data (e.g. use cookies to track visitor behavior and marketing activities) they are only as useful as the data that is inputted, which means you’ll still have to hack together data from several sources that aren’t designed to work together. For example, because attribution isn’t coming from a single source, you would not be able to tie together an anonymous visitor with a deal. You’ll also run into the same limitations as the other tools because you are relying on their data.

Finally, the biggest limitation of all of these marketing tools is that they don’t connect the marketing activity into a CRM. Because of that, it can’t connect to specific customers and therefore doesn’t connect to the sales department and revenue. This is often serviceable for B2C attribution because there isn’t a sales team process, but it doesn’t cut it for B2B attribution.

According to the 2016 State of Pipeline Marketing Report, the marketing leadership were more likely to report using down-funnel metrics like opps, pipeline, and revenue as the primary metric to measure marketing performance, while marketing practitioners were more likely to report using higher funnel metrics, like leads. Essentially, the more senior the marketer, the more important the bottom-of-the-funnel metrics are.

However, the data also shows that while CMOs may think their primary metric is revenue, their team is using other metrics, and that may be because they don’t have the right tools — only 21% of non-senior marketers believe they are using the right attribution model. To find out why the team is focusing on top and middle-of-the-funnel metrics, CMOs should ask them questions like:
- What attribution model are we using? W-shaped? Why are we using our current attribution model?
- Does our attribution model include first (anonymous) visitors?
- Does it treat prospects as accounts or as individuals?
- Do we look at digital channels such as paid social and offline channels such as
events/conferences with the same attribution model?
- What metric(s) does our attribution data help optimize for?

As a CMO, you might be measured based on revenue, but if your team is optimizing for other metrics (e.g. leads), your marketing will be misaligned and will underperform in the long run.

For B2B CMOs to accurately report on these bottom-of-the-funnel metrics, it is absolutely necessary for the attribution solution to seamlessly integrate with the CRM.
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