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Welcome back!

We hope everyone’s ready to start the week with some fresh marketing strategy! Today, we’re moving past one-touch and leveling up to slightly more complicated attribution models. But don’t worry— linear attribution models (which we’ll cover today) aren’t too difficult to understand or implement.

Let’s dive in! 🏃‍♂️🚀

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Strategy

What is it?

While first-touch and last-touch models are somewhat reductive models that give all credit to a single touchpoint in a customer’s journey toward conversion, linear attribution models do the opposite, assigning equal credit to each touchpoint throughout the customer journey.

The driving principle behind this model is the idea that every interaction a customer has with your brand is equally important to their decision to convert.

Let’s say a customer wants to buy a new smartphone, and before finally making the big purchase, they interact with the brand through 5 different touchpoints.

First, they watched an online unboxing video of a smartphone on YouTube.

Then, they clicked on a banner ad for the smartphone while browsing the web.

They may have spent some time on the brand’s website, comparing features and specifications to learn more.

Having tracked the potential customers’s website visits, the company sends a follow-up promotional email about the new smartphone launch, maybe even with a special offer or personalized discount.

In this scenario, say the smartphone is one grand. Then, each touchpoint receives 25% credit, or $250 for the sale. Assuming each campaign in the journey contributed equally, the cost can then be compared to the return to estimate ROI.

Tools like Google Analytics can track website activity across channels if UTM parameters are used or if the user logs in with the same account on multiple devices.

Who should use it?

What goals and qualities of a company make it well-suited for a linear attribution model?

Brands that value brand awareness as much as direct conversions can benefit a lot from this model— for example, a luxury brand might value name recognition as much as customer loyalty and direct conversions.

Linear attribution models also make sense for companies with multiple marketing channels and relatively short, straightforward, and fairly consistent customer journeys.

For example, customers at an e-commerce company selling cheaper consumer goods often make quick decisions based on a few touchpoints, which makes linear attribution well-fitted and easy to use.

Or, a B2B software company that sells project management tools might have a typical customer journey of reading a sponsored blog post about project management best practices, attending a webinar demonstrating the software’s features, requesting a free trial, having a strategic call with a company rep, then making a purchase.

The model also works well if your brand uses many marketing channels that all significantly contribute to conversions. A fashion company that uses social media, email campaigns, and paid ads might find linear attribution helpful in understanding the contribution of each channel.

Most importantly, small businesses and startups without big data science teams or large budgets for complex attribution modeling can implement linear attribution pretty easily.

On a similar note, a small brand starting with a one-touch attribution model for simplicity then deciding to switch to a more complex attribution model to accommodate a growing marketing campaign might find linear attribution to be a practical gateway.

Implementation in action

Let’s use an example of a hypothetical outdoor gear e-commerce company, OutdoorGear to illustrate how a linear attribution model would be implemented.

OutdoorGear decides to track a customer’s journey over the 30-day period before purchase. First, they would have to identify all touchpoints leading to a conversion (defined as a completed purchase) and determine how they’ll be tracked.

They decide on: 1) Social media ads (Facebook, Instagram), 2) Google Search ads, 3) Email marketing, 4) direct search 5) organic search

After setting up tracking codes and their analytics platform to capture these interactions to collect user IDs and the product viewed.

Looking at one customer’s journey (User ID: OG123):

Day 1: clicked on a Facebook Ad

Day 7: opened a promotional email

Day 12: clicked on a Google Search Ad for “OutdoorGear tents”

Day 15: direct visit to website and completed purchase for a $200 tent

A linear attribution model would assign each of these touchpoints with a conversion value of $50 (200/4).

For simplicity, let’s assume costs are the same.

If the attributed value for each channel after 1,000 conversions is as follows (keep in mind these values might be different because each customer may not go through all of these touchpoints, and most will have a different combination of touchpoints— but for each individual customer’s journey attribution is split evenly among touchpoints):

Facebook Ads: $45,000 (20.45% of attributed revenue)

Email: $52,000 (23.64% of attributed revenue)

Organic search: $38,000 (17.27% of attributed revenue)

Google Search Ads: $48,000 (21.82% of attributed revenue)

Direct visits: $37,000 (16.82% of attributed revenue)

Now, OutdoorGear can see that email marketing is their top-performing channel, so they might ramp up efforts toward that campaign. Since touchpoints are usually different costs, in that case, OutdoorGear would look at the ROI (revenue/costs) to compare channels.

Reviewing this data consistently and making adjustments accordingly would leverage a linear attribution model to improve OutdoorGear’s marketing strategy.

Looking Ahead

Now that we’ve gotten a taste of a simple multi-touch attribution model, we can dive into some of the most complicated ones in the next couple of weeks.

If you feel like you’re losing sight of the core purpose, remember that picking the right attribution model is key to optimizing ROI by allocating your marketing budget to the most effective channels/touchpoints.

Stay tuned!

Attribution models

Week 1: Last-touch attribution
Week 2: First-touch attribution
Week 3: Linear attribution (this week!)
Week 4: Time-decay attribution
Week 5: Position-based attribution

Stay tuned!

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