#6 - Separating signal from noise in measurement with Power Digital's Ben Dutter

Happy Thursday! We’ve got a great listen for you today. Ben Dutter is the Chief Strategy Officer at San Diego-based marketing agency Power Digital.

Laser-focused on what drives (or doesn’t drive) business outcomes, Ben is a measurement pragmatist who’s advised 100s of brands on improving their investments in marketing strategies with a few concepts we’ll detail below.

Listen to Ben’s expertise on full display below:

🎯 The TL;DR on what you’ll learn from this chat 🎯 

1. Defining incrementality, and how it slices through attribution noise to land on what is really driving sales for your brand

2. How “triangulation” helps his brands solve for what drives impact and what doesn’t

3. The importance of a properly-scaled data infrastructure and data hygiene in measuring success, and ways brands of any size can better tie P&L to ads performance

🎥 Using the Movies to explain Incrementality 🎥 

Ben draws an important distinction between ads that can be tracked versus ads that actually caused a purchase, and he often sees brands conflate a trackable event with a purchase decision, especially with a last-touch attribution model (which assigns credit to an ad that is shown right before a purchase occurs).

“Attribution - especially last touch attribution - is a bit like giving all the credit for the movie sales to those posters that are outside the movie theatre,” he said. “I don’t know anyone who goes to the movie theatre who hasn’t already decided what movie they’re going to see…Just because someone had a touchpoint of a trackable ad, doesn’t mean it convinced that person to buy.”

Incrementality is really just a fancy word to describe what actually drove someone to make a purchase: Would the shopper have purchased if they hadn’t otherwise seen your ad?

“Just because you can see and track, doesn’t mean you’ve caused anything.”

📐Triangulation: An art-and-science approach to better measurement 📐 

“Triangulation” is an art-and-science measurement system increasingly adopted by marketers that uses three pillars to interpret marketing performance or evaluate the value of a new channel:

1. Trackable metrics (performance data from platforms like GA or Meta)

2. Business metrics (P&L data)

3. Validation metrics (trial & experimentation data)

Triangulation, in many ways, de-risks any reliance on a single data source above to make a decision. An example of this is that brands’ measurement tools can often be disconnected from the P&L, and even tell entirely conflicting stories. Email could be generating revenue “up-and-to-the-right” for your business for several consecutive months, but if you aren’t seeing an increase in repeat purchases or lifetime value from Shopify corresponding to that email performance, you should probably taper investment in the channel.

❎ Don’t Cart-Before-The-Horse your Data Stack ❎ 

In Ben’s view, regardless of the size of your brand, a clean, accessible data infrastructure is the most important thing to making quick pivots in your marketing decisions.

He believes every eCommerce brand should have in-house talent that can wrangle an Excel spreadsheet, and have software that can tie your P&L, COGS and OPEX by product to your Shopify data.

But, he says it’s important to align the tech that you use to handle and access your data with the maturity of your business. If you’re a $1M/yr. brand, you probably don’t need to invest in a Customer Data Platform or a data science team to make business decisions. That may impact the speed of your decision-making, which is crucial.

“Having data that is fast and based on solid, clean information is a game-changer,” Ben told us. “Don’t overcomplicate it. I’ve seen brands successfully scale to $100M with Shopify and free tools.”

🔎 Main Takeaway 🔎 

Ground your marketing peers in the P&L of the business. And look at your business data more holistically when evaluating channel and marketing mix investments. In over-indexing on just platform metrics or web analytics tools like Google Analytics, you’re not getting the full story of a marketing strategy’s contribution to your bottom-line.

⭐️ North Star Metric & Key Data Tools ⭐️ 

North Star Metric:

• Contribution Margin (by product) = sales per product - variable cost per product

Key Data Tools:

• StoreHero - business intelligence tool for SMBs that unifies sales, marketing data and costs into a single view

• ChatGPT and Google Sheets - a scrappy but entirely legitimate way to run causal analyses (i.e. did this new campaign in a new market actually cause more shoppers to purchase?)

» For the full run-down of insights from Ben Dutter and from our other expert guests, don’t forget to check out our Youtube channel.