How to build an effective marketing attribution strategy to avoid dependence on Google, Facebook, or Amazon
Building an accurate attribution model is more important for digital marketers today than ever before. According to the latest Forrester research, end-to-end B2B conversion rates are 0.75% on average. A lot happens between the first and last interactions with your business that can influence conversions. That’s why it’s essential to build a comprehensive attribution model using the highest quality data that illustrates your customer journey.
Truly understanding which marketing initiatives are responsible for sales and revenue can help you make targeted changes to improve performance and drive a greater return on investment.
Today, there are a number of major platforms that address marketers’ attribution needs in terms of quality, testing, and data management. But do the benefits of working with one-stop-shop platforms outweigh the problems they create in data management?
Here’s an overview of the most popular options:
Google has always been the forerunner in providing attribution technology. Google Analytics is a free tool anyone can use to track different touchpoints in the customer journey and attribute them to sales. Google Ads also has attribution capabilities for paid advertising.
Attribution 360 was originally an enterprise-level offering, but now a simplified version is available for everyone. It integrates with Google Analytics, Google Ads, and DoubleClick Search. This allows advertisers to analyze cross-channel performance data, as well as upload offline conversion data. You can assign whatever attribution model you want to your conversion events, as well as compare different models.
Facebook Attribution is the newest major option out there, designed to help you build a clearer picture of the customer journey. You can use it to assign credit to various audience touchpoints across your Facebook, paid, or organic efforts and then estimate the incremental impact of your marketing on Facebook, Instagram, Audience Network, and Messenger.
Using Facebook Pixel on your website further makes it simple to target previous site visitors with ads on the social media platform. You can then use data-driven attribution modeling to see how your efforts pay off.
Last year, Amazon introduced its own measurement solution for brands that sell on the platform. It allows them to measure the impact of display, search, social, and video channels based on consumer behavior on Amazon. It includes valuable conversion metrics like page views, purchase rates, and sales. Ultimately, you’re able to get a comprehensive understanding of how your marketing tactics across the web impact shopping decisions on Amazon.
On the surface, it looks like there are a number of easy solutions for marketing attribution today. Marketers just have to decide which major platform is the most valuable for their audience data needs. However, simply relying on big players like Google, Facebook, and Amazon creates some inherent issues that businesses should be aware of.
These platforms provide valuable data on consumer behavior at a granular level that you just can’t get elsewhere. But once you start relying on them to track your own audience behavior, you lose control over your data entirely. These platforms operate using what is called “walled garden” data management. They have complete control over audience data, making it impossible for businesses to utilize it for their own analyses.
Walled garden scenarios with Facebook and Google have been in the news for a while:
Now Amazon recently joined their ranks.
Working with walled gardens for marketing attribution can provide a lot of value, but it also encourages businesses to relinquish control of their own audience data. Features like Google’s tracking tags and Facebook’s tracking pixel give them control of your first-party data as well as the third-party data they provide. This might seem like a worthwhile solution given data management regulations like GDPR and the California Consumer Privacy Act, but managing your own consumer data is infinitely more valuable because you can perform your own unique analyses. Platforms like Google, Facebook, and Amazon are also under no obligation to give you full access to your audience data if they don’t want to.
With all that in mind, choosing an attribution technology should be about more than just selecting the best walled garden to work with. Forward-thinking marketers are prepared to use third-party tools that give them the freedom and flexibility they need to manage their own data while building a powerful attribution strategy across a clear customer journey.
Attribution modeling is an evolving art. As the volume and depth of audience behavioral data continue to grow, businesses are learning there are many different ways to attribute value to your marketing efforts. Choosing the most accurate attribution model is important, as it can drive decisions to optimize marketing campaigns and gain more sales.
Here’s an overview of the main options for attribution modeling in 2020:
This is the standard model used traditionally in Adwords and with many other platforms. All credit is assigned to the last touchpoint before converting. This model doesn’t consider any other interactions a customer made with your business before converting.
First click attribution gives 100% of the credit for a conversion to the first interaction with your business. If, for example, someone finds your business from a PPC ad, clicks through and converts, the ad would receive all the credit for the conversion.
This model also assigns 100% of the conversion value to a single interaction. If someone types in your URL then converts on your website, this model ignores that last click and instead focuses on the interaction that came right before it. It’s a valuable option for businesses that get a lot of direct traffic to their website.
Linear attribution splits credit for a conversion across all the interactions a customer has with your business before converting. Say they find your business on Facebook, sign up for your email list, then go directly to your site URL to convert. Linear attribution would give equal value to each of these 3 interactions.
Time decay works like linear attribution in that it assigns value to several different touchpoints across the customer journey. The only difference is that it assigns more value to the touchpoints closer to the point of sale. So it might assign 15% value to the first two touchpoints, 30% value to the third touch point and 40% value to the last. It’s possible to adjust how much value you assign to each touchpoint.
The U-shaped (or position based) attribution model prioritizes both the first and last interactions before a sale. It’s based on the idea that the first time someone discovers your business and the last contact point before they convert are the most valuable for driving conversions. In this case, 40% of the credit is given to the first and last touchpoint while 20% is split between the other interactions in the middle. This results in a U-shaped graph for attribution.
A custom attribution model involves assigning whatever value you want to the various touchpoints in a sales funnel. No one understands the inherent value of different pieces of marketing material more than those who created them. If you have touchpoints in the middle of the funnel that you think are more valuable for driving conversions, you can assign a custom percentage of value to it.
Algorithmic attribution is a more advanced modeling strategy that relies on machine learning technology to assign a value to your various touchpoints. It uses historical performance data, including won and lost deals, to create a unique model with custom weights for your customer journey. Also known as data-driven attribution, this strategy is able to make more nuanced inferences and changes to your attribution model based on your unique audience. To use this attribution model, you need advanced technology designed specifically for this purpose.
So, which attribution model is right for your business? The only way to know is by testing out different models and comparing their performance. It’s important to utilize technologies that offer the flexibility to test and compare varying modeling strategies.
That said, the data you feed an attribution model is just as important as the model itself. Marketers need access to nuanced, high-quality data regarding audience behavior and the customer journey across marketing channels in order to get a full picture of their marketing operations.
Luckily, there are a number of technologies out there that can help marketers create their own beautiful landscapes instead of relying on the walled gardens. QuanticMind, for example, helps you integrate and manage your data and help you employ data-driven attribution models. Some options are integrated marketing solutions, some are for attribution only, while others focus on tracking specific marketing channels. Assuming you plan to avoid relying on walled garden data giants, some third-party options include:
The list continues to grow. If you want to invest in technology that rivals the capabilities of Google or Facebook attribution, here are a few key features to look out for:
Unquestionably the most important element you need to build an accurate attribution strategy is data. Your marketing attribution technology should be able to manage your first-party audience data while simultaneously integrating with third-party providers. Tracking all customer interactions across numerous platforms is important for building a clear picture of the customer journey. This includes on-site, search, social media, email, and even offline conversions.
If you use certain technologies for data management as well as analysis, then they should have processes in place to accommodate privacy regulations such as GDPR.
Competitive intelligence is the ability to monitor your marketing performance compared to other major industry players. This can include news monitoring, tracking trends in news mentions, and analyzing how your PR initiatives perform compared to others. This can be based on keywords for search advertising, news topics, social signals, or other important factors. Advanced technologies can also provide recommendations to improve your marketing strategy based on historical performance and the current industry landscape.
The best technologies offer flexibility in the types of attribution models you can use to analyze marketing performance. Customization allows you to build any kind of attribution model that makes the most sense for your business. You can also analyze your data using a variety of different models (last click, linear, U-shaped, etc.) and compare results.
Data-driven attribution is widely considered the most nuanced option with the most potential to deliver accurate results. Investing in an attribution technology with machine learning capabilities will allow you to test the performance of data-driven attribution versus other models.
Effective attribution isn’t just about building the best model. It also involves having the analytical capabilities to truly understand the impact of individual touchpoints on your marketing goals. Important tracking features include:
Most importantly, your technology should be able to make connections between specific marketing initiatives, use cases, and their value for ROI. You need to be able to build a clear picture of how financial investment and marketing initiatives impact sales and revenue. Comprehensive data about the customer journey and historical performance allow the tool to accurately attribute cost and revenue data to specific bidding decisions.
Building a comprehensive, accurate attribution model is essential for advertisers to understand the impact and value of their marketing initiatives. But these insights are only valuable if marketers are prepared to make quick, targeted changes to their strategy based on performance. The competitive landscape is constantly changing. Touchpoints in your sales funnel that were once responsible for significant revenue can quickly become less valuable over time. Marketers need to stay on top of these changes if they want to maximize the value of comprehensive attribution long term.
That’s why predictive advertising is the perfect solution for search engine marketers today.
And this is where QuanticMind comes into play. Our automated bidding technology collects all important data points to assign value to clicks and identify the most profitable price to target your PPC ads.
It also offers more precision than any stand-alone attribution technology by using decimal conversion values, allowing it to attribute a conversion to multiple clicks. This illustrates how specific keywords, audience targeting, and bids impact the path to purchase.
Based on these nuanced attribution insights, QuanticMind uses automation to make new bid decisions. This ensures your paid search initiatives are always optimized.
Businesses today have a lot of options when choosing marketing attribution technology. In summary, the keys to success are:
Lastly, consider investing in technology that can help you build a nuanced attribution model as well as automate campaign optimizations based on these insights.