12 reasons to select OWOX BI ML Funnel Based Attribution
Vlada Malysheva, Creative Writer @ OWOX
The goal of attribution is to evaluate the effectiveness of advertising channels and campaigns and identify which paid traffic channels lead to conversions. Knowing this, you can redistribute your advertising budget to effective channels, stop spending money on inefficient channels, and eventually get more conversions.
Your success in executing your sales plan and growing your business depends on the quality and validity of your attribution model. The problem is that most conversions happen as a result of more than one session: before buying, the user visits the site several times. Therefore, to objectively distribute the value of revenue or profit for a specific order, you need to evaluate each session, not just the last one. Most standard attribution models don’t take into account all user actions before an order and give all its value to one channel in the chain. Or they evaluate channels according to a conditional rule and not according to real merits.
At OWOX BI, we’ve created our own machine learning (ML) Funnel Based Attribution model that takes into account all user actions online and offline plus real revenue data from your CRM and shows the mutual influence of channels on conversions and user promotion through the funnel.
In this article, we describe what you can do with OWOX BI Attribution.
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What is attribution and why do I need it?
To begin with, let’s refresh your theoretical knowledge.
Attribution is the distribution of value from a conversion between channels that advanced the user through the funnel. It helps answer to what extent each channel influenced the user’s promotion through the funnel and the revenue you received in the end.
An attribution model defines the logic of distributing this value — for example, to advertising channels and campaigns that helped attract users. Ideally, your attribution model should be reliable (taking into account the objective contribution of each effort) and transparent (making it possible to understand and verify the results of the calculation).
Who needs attribution and why?
Attribution is needed by those who manage the budget for marketing channels and want to distribute it effectively in order to reduce costs, increase revenue, and fulfill the sales plan.
Learn what attribution is, how it helps companies, and what errors and difficulties marketers and analysts typically encounter when working with attribution.
Standard attribution models in Google Analytics
- First Click. All value derived from the conversion is attributed to the first source that led the user down the path to conversion.
- Last Click. All value goes to the last channel the user came into contact with before the conversion.
- Last Non-Direct Click. All value is assigned to the last channel in the chain. If it’s direct traffic, the value is attributed to the previous source.
- Linear. The value is distributed equally among all sources in the chain.
- Time Decay. The value is distributed among channels on an incremental basis.
- Position Based. The channel that introduced the user to the brand and the one that closed the deal receive 40% of the value each. The remaining 20% is divided equally between all channels in the middle of the funnel.
Attribution models in advertising services
Different attribution models are available in different advertising services and analytical systems.
|Last Non-Direct Click||Position Based||Probabilistic||Post-Click||Post-View||Cross-Device||Online-Offline|
|Google Display & Video 360||✔||✔||✔||✔||✔||✔|
|Google Search Ads 360||✔||✔||✔||✔||✔|
|Google Analytics 360||✔||✔||✔||✔||✔||✔||✔|
Most services use the last non-direct click model; some provide post-view, cross-device, or other models. But these models can’t be compared across services: Facebook measures advertising in its own way, while Google Ads has a different approach. As a result, it’s impossible to get an overall picture of all your advertising tools.
Learn the basic principles, pros, and cons of the best-known attribution models to choose the model that best suits your business.
Standard attribution models are not appropriate when:
- most orders on your website are made in more than one session — that is, there are two or more visits in the chain before the transaction
- you have a large number of advertising sources
- you need to evaluate the mutual influence of each channel on funnel promotion
- you want to understand how advertising channels work in a bundle
- you have a complicated and inconsistent sales funnel.
To assess the mutual influence of all sources, you need to combine data from different advertising services, Google Analytics, and your CRM and use complex attribution models: the Data-Driven model in Google Analytics 360, Markov Chains, Shapley value, or customized algorithms. But these models also have their limitations: a minimum amount of data required for calculations, the inability to consider post-view conversions or combine data from your CRM, hidden calculation logic, complex and expensive implementation, etc.
ML Funnel Based Attribution from OWOX BI helps you avoid all these limitations.
How ML Funnel Based Attribution works
The OWOX BI attribution model assesses the effectiveness of your advertising campaigns, taking into account the contribution of each channel to the customer’s promotion through the conversion funnel. With this model, you can:
- correctly allocate your advertising budget, taking into account the real contribution of channels to conversions and their mutual influence
- reduce Cash Reserve Ratio (CRR) while increasing revenue
- increase the number of new customers while saving your budget
- increase the number of target users reached by media campaigns while saving your budget.
Funnel Based Attribution in OWOX BI is based on Markov chains and machine learning. The Markov chain is a sequence of events in which each subsequent event depends on the previous. Attribution based on Markov chains uses a probabilistic model that calculates the probability of transitions between steps of the funnel, allowing you to evaluate the mutual influence of steps on conversions and find out which steps are the most significant.
To calculate probabilities, all steps of the conversion funnel specified in the settings of the OWOX BI attribution model, plus the site login step, are presented as outcomes in the Markov chain. After that, the probability of a transition between these outcomes is calculated:
The graphic above is a simplified example for ease of understanding. In real cases, there can be even more transactions.
Read more about calculating the OWOX BI Funnel Based Attribution model in our Help Center.
If you want to see how OWOX BI Funnel Based Attribution works, sign up for a demo. Our colleagues will show you real use cases and tell you how Funnel Based Attribution can be useful to your business.
What you need to do to work with ML Funnel Based Attribution
- Collect raw user behavior data from your website in Google BigQuery. To do this, you can use data streaming from OWOX BI or BigQuery Export with Google Analytics 360.
- Import cost data to Google BigQuery from advertising services. To do this, you can use OWOX BI to complement statistics on Google Ads campaigns in Google Analytics with cost data from other advertising services, then import all this information into Google BigQuery. You can also use OWOX BI to directly import raw data from Facebook to BigQuery.
- If you wish, you can complement data in Google BigQuery with information from your CRM/ERP, call center, and internal accounting systems. This will allow you to use offline sales and actual CRM revenue data in your attribution calculations. You can also use custom events as funnel steps: calls, emails, personal meetings, etc.
- In your OWOX BI project, create an attribution model and run calculations.
Advantages of OWOX BI Attribution
Most of our customers use OWOX BI to objectively evaluate existing advertising campaigns and plan future ones. This is necessary to execute a revenue plan with planned return on ad spend (ROAS).
Here are the key reasons why we believe OWOX BI is the best choice for marketers.
1. Consider full data, ROPO sales, and revenue data from your CRM
Our attribution model is based on complete data: media advertising impressions and post-view conversions, user activity on your website, cost data from advertising services, transaction data from your CRM, and any other online and offline events. As a result, you see the whole picture of users’ interactions with your business and can take into account the impact of all marketing efforts on business indicators.
Revenue in Google Analytics and revenue from purchased goods in your CRM often don’t match. With OWOX BI, you can measure the contribution of online marketing to real sales by taking into account revenue from physical stores and data on completed orders from your CRM.
2. Objectively assess your advertising channels
Unlike standard attribution models, the OWOX BI model takes into account every step of the user before the order — not just the last click. The harder it is to pass a step, the more value a session gets that helped make it happen. And by knowing the value of sessions, you can calculate the value of campaigns, taking into account their contribution to promoting users through the funnel.
3. Set up a unique funnel for your business
You can grow faster than your competitors by exploiting your business’s unique advantages. With OWOX BI, you can create a funnel that meets the structure of your business.
In addition to the actions of users on your website, you can add any custom events from your CRM, call tracking system, and other systems as funnel steps. For example, you can upload data about calls, meetings, or emails to Google BigQuery. Combine all business efforts aimed at converting users into one funnel to more accurately evaluate their effectiveness.
4. Learn how each channel works at different stages of the funnel
It’s not enough to get a comprehensive evaluation of your advertising campaign. Your assessment must be divided into stages of awareness, interest, and conversion using the AIDA model.
For example, with OWOX BI, you might see that campaigns almost don’t form Awareness:
As a result, campaigns designed to influence the next stages of the funnel may look ineffective. An ROAS assessment of existing campaigns may show you how to redistribute your budget between them but won’t reveal the lack of effort at the upper stage of the funnel. OWOX BI gives you an understanding of where to focus your effort.
5. Evaluate only managed channels
No algorithm can replace your experience. For example, you may know that your brand campaigns are exhausted and that their high ROAS doesn’t mean you should be investing more in them.
In OWOX BI, you can easily specify the channels that won’t participate in your evaluation:
As a result, you get an assessment of only those campaigns you can objectively manage. It’s important that you can do this at any time in a convenient interface without contacting developers or your support team.
6. Evaluate acquisition channels for different user cohorts
OWOX BI calculates the value of each session. This allows you to customize attribution models for different user cohorts, calculate ROI/ROAS for new and returning users, and compare cohort profitability.
For example, mobile operators can evaluate the contribution of advertising campaigns to the sale of additional services to current customers (the “current customers” cohort). And companies from the fashion retail niche can evaluate first-time buyers and next-time buyers separately to find out which channels are better at driving new customers to the business.
Also, by knowing the cost of a session, you can calculate how much you spend and how much you earn on each product group. With this information, you can evaluate the effectiveness of advertising for different regions, landing pages, mobile app versions, and applications.
Learn how to group costs and revenue by any session properties using OWOX BI.
7. Transparent algorithm
It’s important not only to evaluate campaigns but to make evaluation transparent for businesses. Any black box assessment will be questionable and won’t allow you to find errors.
In OWOX BI, you can see how value is distributed across campaigns for each transaction a user has interacted with:
Errors and incorrect conclusions obtained due to attempts to apply machine learning on insufficient data can be costly for businesses. Our algorithm automatically checks and controls the statistical significance of calculations. In addition, OWOX BI gives your analysts full access to a table of confidence intervals for each segment. Thanks to this, you can be sure of your results and understand how they’re obtained.
8. Machine learning in the OWOX BI attribution model
The effectiveness of online marketing is increasingly dependent not on the technologies used but on the quality of the data on which models are trained. Therefore, attribution on its own loses out to models built using market data.
The OWOX BI machine learning model considers data from tens of thousands of projects.
For an attribution model to be effective, it also needs to meet the following requirements:
- You need to consider information about interactions with the upper part of the funnel: media advertising impressions. To do this, we’ve developed OWOX BI Post-View Pixel, which allows you to measure media ads at the user level. We’ve also integrated OWOX BI with Google Ads Data Hub, which allows BigQuery to request information from Campaign Manager (former DoubleClick Campaign Manager) and some other sources so that businesses can consider and transparently assess the contribution of media advertising to the upper stages of the funnel.
- The model should consider the expert opinion:
- Which channels are managed?
- What is the capacity of these channels?
- What is the role of a given channel in the funnel?
- What is the conversion window?
The OWOX BI attribution model meets all these requirements.
9. No limits on the minimum amount of data
Data-Driven attribution in Google Analytics 360 demands high data requirements: 400 conversions of each type with a path length of at least two interactions. OWOX BI, by dynamically grouping events into funnel stages, provides meaningful results with smaller amounts of data and suits many more companies. At the same time, the statistical significance of calculations is automatically controlled at the level of user cohorts, so you can be sure of the results.
10. Get ready-made reports in OWOX BI Smart Data
In OWOX BI Smart Data, you can build reports based on attribution data without the help of analysts or any knowledge of SQL. Once the attribution model is calculated, Smart Data automatically reports on income, the number of conversions, ROI, ROAS, and CRR in the context of the added events. Besides, you can create your own reports using a convenient Report Builder. Simply select the parameters and key figures you want to see in your report. The service will instantly provide you with an understandable chart and table.
Here are some examples of out-of-the-box data attribution reports you can get in Smart Data:
This report allows you to compare the results of calculations using different attribution models. In our case, these are Last Non-Direct Click, which is used in Google Analytics, and ML Funnel Based Attribution from OWOX BI. You can see attributed revenue and ROAS by channel, campaign, user type, region, city, and device. Campaigns that are overrated have a negative difference. Underrated campaigns have a positive difference/value. For example, in the screenshot above, we see that the yahoo/cpc channel was underestimated and it’s worth putting more effort into it.
The following report shows how the value of sources and channels is distributed by funnel steps. In the example above, we see that the largest number of purchases (the light green stripe) are made offline.
In addition to events, you can add other parameters to reports such as the type of users (new or returning):
The report above shows how expenses, funnel steps, and purchases are distributed across customer cohorts. With it, you can determine which channels and campaigns attract transactions from new customers and allocate budgets for them. These campaigns help you increase your customer base.
11. Use attribution data to manage bids and audiences
With OWOX BI, you can create automatically updated audience segments based on any of your data and download these segments to Google Ads. In addition, you can automatically send the results of attribution calculations to Google Ads and Facebook to manage bids considering the real effectiveness of your advertising.
12. Mature product, guaranteed results
In pioneering the development of funnel based attribution, we’ve gathered unique experience that we share with our clients in dozens of articles and lots of documentation. Additionally, we’re ready to guarantee a level of data collection and processing quality above 96% in our SLA.
With OWOX, you get not just the beautiful idea of using machine learning in marketing but step-by-step guides, practical recommendations, and examples of successful use cases.
Attribution use cases:
- Fabelio found out what online customers are doing in their retail stores
- ROPO analysis: How useful is it for omnichannel marketing analytics
- How to Monitor Multiple Business KPIs in a Single Dashboard: Success Story of MatahariMall
- 9 Steps to Know Everything: Building Marketing Analytics for Comfy
- How to improve the advertising-to-sales ratio by 10%
Learn how OWOX BI attribution can help your business. Sign up for a product demo to evaluate the capabilities of our products.
How can I implement a funnel-based attribution model in my marketing strategy?You can implement a funnel-based attribution model in your marketing strategy by using machine learning algorithms to process data on user behavior across the funnel. This can be done by partnering with a machine learning vendor or building your own attribution model in-house.
What are some benefits of using a funnel-based attribution model?The benefits of using a funnel-based attribution model include a better understanding of how customers interact with your brand, identifying the most effective touch-points in the funnel and making data-driven decisions regarding your marketing strategy.
What is a funnel-based attribution model in machine learning?A funnel-based attribution model in machine learning is a method that uses a machine learning algorithm to assign credit to marketing touch-points along a user's conversion journey based on their position in the funnel.