Data-driven attribution and mixed marketing models will take your analytics to the next level.

#Attribution is one of the most important things in today's #marketing mechanism. It's mostly because there are numerous #digitalmarketing channels available, and customers are virtually everywhere, companies must know which channels will provide the greatest return on investment.

Using attribution models helps marketers better understand which parts of their marketing effort are driving the most leads to that part of the sales funnel.

What is marketing attribution?

The goal of marketing attribution is to determine how marketing tactics contribute to sales and conversions.

Detailed definition:?Marketing attribution evaluates the marketing touchpoints that consumers encounter on the way to purchase.

Attribution is used to determine which channels and messages influenced conversions, or whether the desired action was taken. Multi-touch attribution, lift studies, time decay, and other attribution models are popular today.

These models provide insight into how, where, and when consumers interact with brand messages, enabling marketing teams to customize campaigns to meet the specific desires of individual consumers, thereby enhancing return on marketing investments.

Marketing Attribution Models: Types and Differences?

Last interaction -?With the last interaction model, the total value of a conversion is attributed to the last touchpoint of a customer’s conversion path.

Last non-direct interaction?- With the last non-direct interaction model, the total value of a conversion is attributed to the last touchpoint of a customer’s conversion path, that isn't direct traffic.

Last Google Ads interaction?- With the last Google Ads interaction model, the total value of a conversion is attributed to the last touchpoint of a customer with Google Ads campaign, which led to the conversion.

First interaction?- A first interaction model is the exact opposite of the last interaction model. In this model, the total value of a conversion is assigned to the first touchpoint in a conversion path.

Linear?- the linear model of attribution gives equal credit or value to each channel in a conversion path.

Time Decay?- With the time decay attribution model, channel value varies according to where a channel occurs in a conversion path.

Position Based?- As a hybrid/combination of the first and last interaction attribution models, position-based attribution is when the first and last interactions in a conversion path are attributed the most value.

Data-driven attribution will be the future of attribution

"Data-driven attribution allows us to assign the right credit to every touchpoint. With automated bidding and data-driven attribution, we've seen an 18% reduction in cost of sales over last click." - Lara Harter

This approach incorporates facets of other attribution models, but its strength lies in the fact that the weight assigned to different engagements is uniquely determined by the company using it.

Data-driven attribution allows marketers to spot new trends in how users engage with their content and campaigns. You can clearly see what's working and what's not by using data-driven attribution instead of analyzing data yourself.?

For data-driven attribution with MCF, there are certain data requirements

For data-driven attribution with MCF, you must be a paying member of Google Analytics 360.?

Additionally, you must have a Google Ads account with at least 15,000 clicks on Google Search and at least 600 conversions within 30 days

Attribution modeling's disadvantages

• Since cookies will be gone in 2024, tracking everything 100% is impossible

Although 3rd party cookies will stay within Google chrome, at least until 2024, it's still a matter of time until DDA will also die with it.

I mean, it's not like we didn't know it, it's just that we got a promise that DDA is "The solution" to all of our cookie problems.

Those who support multi-touch attribution believe 70 percent of customer touchpoints are better collected than 0%. There is a problem with this argument in that, no matter how you slice it, you'll overvalue the channels that can actually be measured.?

• But wait, what about View through conversions?!

We have many touch points with our users, other than driving them directly to our website through clicks, including video campaigns, display, podcasts, and much more.

Did our user just watch a video on YouTube, or was served an ad through DV360's display ads? What should we do about these users? Should we ignore them?

Ok Jonathan, we got you, attribution sucks, what's next?!

Marketing mixed modeling:

Market Mix Model (MMM) is a method of quantifying the impact of several marketing inputs on sales or market share. By using #mmm, you can determine how much each marketing input contributes to sales and how much you should spend on each.

The MMM helps determine the return on investment of each marketing input. Therefore, a marketing input with a higher return on investment (ROI) is more effective as a medium.

Instead of a weak chain of trackable customer actions, marketing mix modeling examines inputs (budgets and channels) in relation to outputs (revenue and brand awareness).?

With MMM, we analyzed the data top-down.

The first thing we'll do here is to gather all the aggregate data, which is the checks you wrote for paid media.?

All your paid media spend will be taken into account, both digitally and offline, as well as all these econometric factors that are included in many of these models, such as seasonality, unemployment, weather, coupon use, promotions, discounts, competitive activity, what your competitors are doing in the market, and even consumer sentiment.

Our model will take into account all of those inputs and find out how they influenced the sales at the bottom, and then we'll look at how each of these pieces can affect them.

What's needed for Marketing mix model to work?

First and foremost, we need a big chunk of historical data, to be exact, we need 18-24 months in order for it to work.

Another thing is a large marketing budget, In my opinion, the biggest problem with marketing mix modeling is that it only makes sense when your company reaches a certain scale. For each channel and campaign to have an impact, you must create variability in your marketing mix.

Finally, for the conclusion

As with everything we do in marketing, I would suggest to A/B test those two models and try to compare which one is more accurate.

But, do this as fast as possible, and get ready for the doom day (When cookies will be gone).

As far as I can tell, MMM is a much better option for the long term, but, don't count on me, I mean, what do I even know????