INBOX INSIGHTS, May 19, 2021: Attribution Model, Google Analytics, Benchmarking - Trust Insights Marketing Analytics Consulting

Attribution Model, Google Analytics, Benchmarking

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Multiyear Attribution Modeling

In a recent talk we gave at the MarketingProfs B2B Forum, Jorge asked, “How do you best attribute marketing spend when you have a multiple year sale horizon? I use incremental sales against baseline, yet is not always clear, since there is so many touchpoints and cost associated.”

This is a fairly common question from folks who have very long sales cycles, and it’s a case where tools like Google Analytics may not be as helpful as we would like. Why? Google’s attribution window in Google Analytcs 3 is 90 days; after that, some data always falls away. In Google Analytics 4 with BigQuery enabled, you can track over longer periods of time.

However, the better answer to this question is to store attribution data in your CRM itself. Depending on your CRM software, it may already have this information available as an audit trail or log for each of your contacts, prospects, and deals. As long as you have a robust enough system that correctly logs each touchpoint with a customer, you’ll have multi-year data from which to build attribution models.

What kinds of models can you build from that sort of data? A multitude! The most common models would be binary or multi-class classification models to help understand what enables a deal to close, comparing closed won deals to closed lost deals and all the touchpoints and factors involved. Channel-level attribution is also another popular analysis, looking at what channels helped push deals over the finish line versus what channels had little impact:

The challenge for most organizations is getting that information into the CRM in a clean, compatible way. The most proficient organizations have tight integration among the call center, digital properties, and sales interactions (phone calls, events, etc.) so that every possible touchpoint is recorded, and then you construct an attribution model from that data. The least proficient organizations have no process for logging data, and thus what’s in the CRM is incomplete and haphazard at best.

The other challenge for many organizations building these kinds of attribution models are the resource requirements to do them well. To build a custom attribution model from your CRM data requires a substantial amount of time – it’s not uncommon for projects to take 6-12 months or longer at larger enterprise organizations. Because these models are custom-built, they often require large computational capabilities, and can cost hundreds of thousands of dollars or more, depending on how they’re being built. However, once they’re done and operational, organizations see substantial performance increases; Google has cited conversion impact increases of 3X or more in select case studies.

What’s the key takeaway here? Attribution modeling is built on a foundation of good data, strong processes, and organizational buy-in. If you aspire to intelligent, multi-touch attribution models, start today by making sure the data you collect is clean and solid, then construct a road map of what you’ll need to make your model happen.

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In this episode of In-Ear Insights, Katie and Chris discuss recent privacy changes, iOS 14.5, why Google wasn’t freaking out like Facebook was, and how Google can promise to intelligently append/fill in missing third-party data where other adtech platforms cannot. Find out why you’ll be spending more money in Google Ads in the coming years, and what you should do to prepare.

Watch/listen to this episode of In-Ear Insights here »

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Data Diaries - Interesting Data We Found

This week, we turn our eyes to the TSA Passenger Throughput data to see how the economy is recovering. We like the TSA numbers because they’re not speculative; they’re real people, traveling every day by airplane. The data is also daily, which is a much faster cadence than many other economic indicators, and thus we can see anomalies and trends faster.

What we see is very straightforward; let’s look at some of the details.

This is, of course, the start of the pandemic and shutdowns, when the world pretty much came to a halt.
This is the Thanksgiving 2020 holiday weekend.
This is the 2020 holiday season.
Vaccines first start rolling out to high-risk groups in the USA; we see this as the first major inflection point towards recovery.
Vaccines become more broadly available in the USA.
Vaccines become broadly available and accessible to USA people ages 12+.

What’s important here is that air travel is now at the bottom end of pre-pandemic levels, and is showing a strong, steady increase over time, accelerated by the rollout of vaccines in the USA.

What does this tell us? At least in the USA, the pandemic is tapering off enough that we’re starting to approach pre-pandemic travel levels. As air travel is both business and leisure, it’s a good indicator of people’s comfort with what was normal prior to March 2020. We anticipate the trend (blue line on the chart) to continue increasing, putting air travel back towards “normal” by mid to late September 2021; this trend may accelerate depending on how quickly vaccines for younger children become approved.

What does that mean for you? It means that you have a scant few months to plan your marketing for what will be one of the fastest economic surges since World War II. Why? Consumers are ready to buy again, ready to do all the activities they’ve had to postpone for 14 months or more. Businesses are eager to meet demand and tap as much elastic demand as possible. Be ready for when things really pick up!

Methodology: Trust Insights used data from the United States Transportation Security Administration daily checkpoint travel numbers. The timeframe of the data is March 31, 2019 – May 17, 2021. The date of study is May 18, 2021. Trust Insights is the sole sponsor of the study and neither gave nor received compensation for data used, beyond applicable service fees to software vendors, and declares no competing interests.

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