What Is Marketing Attribution?
Marketers use multiple channels (or touchpoints) and messages to convert customers. How conversions are defined varies from business to business, but we can define it as a customer completing a set goal. A click, a sign-up, a sale, or a video view can all be conversion goals.
With so many channels to use, manage and grow, marketers are confronted with waves of information that can be tricky to sort through – let alone gather insights from.
Do you know where your customers first engaged with your company? How did they discover that package they signed up for? Which channels deliver the best results and the highest value to customers?
To make their marketing spend as effective as possible, marketers need to know which tactics deliver the best results at the lowest cost. Some messages and channels deliver better results than others, and some will become less effective over time.
Marketers need to know which campaigns are bringing in business at any given moment, and this is where the science of marketing attribution comes into play.
Marketing Attribution Definition
The Simple Definition: Marketing attribution can be defined as an analytical methodology to determine how various tactics contribute to conversions.
The Long Definition: Marketing attribution is the analytical process of evaluating marketing tactics used to convert consumers on their path to purchase. It helps establish which channels and messages had the most significant impact on driving desired actions like sales, leads, or other conversions. By understanding attribution, marketers can optimize their strategies, budgets, and campaigns to focus on the most effective touchpoints.
Several different attribution models can be used to test how and why consumers interact with brand messages.
The History of Attribution in Marketing
To understand what attribution means in marketing, we have to look at how marketing has developed over time. In the 1950's, marketers used a cross-channel approach to a measurement called Marketing Mix Modeling (MMM). MMM was an early form of attribution that used statistical analysis like multivariate regressions on marketing time series data to create sales forecasts.
However, MMM had several shortcomings that led to the development of more advanced attribution models:
- Speed: Results were only available weeks after campaigns ended. Ideally, marketers need insights during a campaign so that they can make adjustments in real-time.
- Spending: It was difficult to measure brand impact and requirements, leading to overspending.
- Optimization: MMMs measure at an overall aggregate level, bucketing impressions into large groups, which do not allow marketers to optimize the message and targeting before making recommendations.
The gaps left in the market by MMMs led to the creation of attribution models, which gave marketers the ability to measure in much more granular detail, including the most effective targeting strategies, creativity, time of day, and messages.
Today, several popular marketing attribution models in use, such as lift studies, time decay, and multi-touch attribution, paint a much clearer picture than the MMM.
Why Is Marketing Attribution Important to SaaS Startups?
Marketing attribution can evaluate several datasets across online and offline marketing campaigns. If used effectively, these insights will help you reach the right audience, at the right time, with the right message.
You can use these insights as part of your Customer Relationship Management toolset. Anecdotal evidence and theories can be tested and monitored in real-time.
It’s advantageous to SaaS businesses at launch. Person-level attribution can provide helpful insight into the needs of their customers. Once a company knows what their customers are looking for, they can tailor their marketing messages to highlight the functionality their customers want from a SaaS provider.
The Benefits of Marketing Attribution
Although advanced attribution models can be time-intensive and expensive to perfect, the benefits outweigh the costs. Some of the benefits include:
Knowing What Is Working (and What Isn’t)
Customers aren’t bound by a single channel, and a single message won’t necessarily convince them to take action, which is why modern marketers use a variety of on- and offline tactics and elements when they launch their campaigns.
If you want to optimize your campaign, you need to know which combination of elements (website, social media, apps, emails, etc.) deployed during the conversion process had the most significant impact on your customer.
This knowledge will help you design a targeted marketing strategy at an optimal cost.
You’ll also be able to identify how your customers move through various channels and mediums, where they drop off or take action, and which elements they skip over entirely. This information will allow you to improve engagement at every point of the buyer journey.
Let’s say you are promoting your referral program online via a form that customers fill to obtain a free whitepaper you’ve advertised on LinkedIn. If you notice that customers are more likely to abandon the form after introducing this link, you may want to change tactics and send the request for referral in an email after the paper has been downloaded.
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Another example of channel attribution analysis could include following the route to conversion. A client that sees your CEO’s post on Linkedin is most likely to visit the blog linked in the post, read a few posts, and then complete a contact request form. A client that sees a blog post on Facebook might like the post but not click through to read more information.
Knowing this, you can put more spend behind Linkedin, change your call to action on Facebook, or both.
Understanding what works (and what doesn’t) means that you are saving time and money.
Scaling up Successful Efforts
Marketers can understand how campaigns are performing and the role they play in customer journeys – as they unfold. Adjustments can be made in real-time.
If a podcast sponsorship delivers higher returns than a social media campaign, you can ramp up your spending on podcasts and lower the social media spend. If campaigns underperform over weekends and holidays but peak on a Monday, you can time your spending so that you generate the bulk of your impressions on Mondays.
Channel attribution will collect and integrate all of the relevant data from different channels so that you can quickly determine what motivates your customers, how they behave, and what they expect from you at various touch-points and across devices.
Image: Marketing Attribution determines which channels deliver the most value
If you notice that a customer will seek out information on their mobile device during the initial interest phase (e.g, reading reviews or other forms of social proof) but will complete a form, download a white paper, or request a meeting on their desktop, you can change your marketing promotions according to devices to match their user journey.
Knowing how different channels perform and which messages will help you build virality into your communication channels so that your existing assets will keep delivering, even once you’ve reduced spending.
Cutting Budget for Failed Experiments
According to Forrester, 67% of companies have used attribution to assist with future marketing decisions. The attribution process fundamentally gives marketers insight into performance as it unfolds, along with a real insight into drivers of customer behavior.
Attribution modeling provides insight into the cost of acquiring a conversion, including acquisition cost, the cost of delivery, the value of a customer (e.g., whether a customer goes on to make larger purchases or sticks to low-cost sales items), the quality of the leads generated and more.
Because of these insights, it’s easy to shut down experimental channels or messages that aren’t delivering without wasting your budget any further.
You can spend more money on channels that deliver the most significant number of high-value conversions. Over time, this can significantly improve your bottom line and reduce wasted advertising dollars.
6 Marketing Attribution Models
Marketing attribution measures different campaign elements to determine which advertisements or messages were the most effective. You can choose from several marketing attribution models. The process attributes a value to a touchpoint or several touchpoints in the customer journey amounting to 100%.
Last Click Attribution
Last Click (or last-touch attribution) gives full credit to the final marketing touchpoint before a conversion, like the last clicked ad or keyword. It completely ignores any other prior marketing interactions the customer had with the brand. While simple to implement, last-touch attribution has limitations in that it fails to account for the impact of other touchpoints earlier in the customer journey.
It’s the most commonly used attribution model and is generally applicable to mass marketing campaigns, e.g., promoting a big online retailer’s annual sale. 100% of the success is attributed to the last click.
Companies using this methodology would spend the bulk of their budget on retargeting or branded search campaigns and very little on brand-building efforts.
First Click Attribution
First Click (or first-touch) attribution gives all conversion credit to the initial marketing touchpoint that introduced the customer to the brand, such as the first clicked ad or keyword. Like last-touch, it oversimplifies the customer journey by ignoring any other subsequent interactions. First-touch can be useful for top-of-funnel brand awareness campaigns, but doesn't provide insight into what drives conversions further down the funnel.
It is most commonly used during brand-building or awareness campaigns where the aim is to find new audiences.
Linear Attribution
Linear attribution is a multi-touch attribution model that gives equal credit to every single marketing interaction along the customer's path to conversion. So if there were 10 total touchpoints, each one would receive 10% of the credit. While simple to calculate, linear models don't account for the fact that certain touchpoints may have more influence than others. They also provide limited insights into which channels or messaging are most impactful.
It is limited as it doesn’t provide insight into which channel had the most significant impact.
Time Decay Attribution
The time decay attribution model gives the most credit to the marketing interactions that occurred closest to the time of conversion, with credit decaying for older touchpoints further back in the customer journey. The logic is that more recent touchpoints had the biggest influence on the final conversion decision. However, time decay models can overvalue bottom-of-funnel tactics while undervaluing earlier brand awareness and engagement efforts.
Position-Based Attribution
The position-based or U-shaped attribution model assigns the most credit (typically 40%) to the first and last marketing touchpoints, while distributing the remaining credit evenly across any middle interactions. This approach recognizes the importance of both initial brand discovery and final conversion drivers. However, it still oversimplifies by treating all middle touchpoints as equal contributors.
A variation called the W-shaped model adds another 30% credit allocation to a key middle touchpoint like lead creation or opportunity stage. This provides more nuance than basic U-shaped models by highlighting influential mid-funnel interactions.
The theory is that customers in the midpoint of the consumer journey can be considered an active lead.
Value is then assigned evenly across the remaining touchpoints.
This model is helpful to understand the touchpoints that deliver and convert leads while also taking into account touchpoints that influence engagement along the way.
Algorithmic Attribution
Algorithmic or custom attribution models leverage machine learning to analyze large datasets and automatically determine the optimal credit weightings for each touchpoint based on their impact on conversions. Rather than using predefined rules, algorithmic models can uncover unique patterns in customer journeys to better allocate attribution. However, they require very robust data and are often more complex to implement compared to rules-based models.
Algorithmic attribution is best suited for businesses with long, multi-channel customer journeys and the ability to integrate massive datasets across multiple sources. When configured properly, these AI-driven models can provide a highly accurate, granular view of marketing impact.
How Do You Choose the Right Attribution Model for Your Business?
Choosing the suitable attribution model for your business depends on several key factors:
- Sales Cycle Length: Shorter sales cycles may be better suited for simpler models like last-touch or first-touch attribution. Longer, more complex cycles likely require multi-touch or algorithmic models.
- Marketing Channel Mix: If you rely heavily on offline marketing like TV, radio, or print ads, you'll need an attribution model that can integrate those offline touchpoints.
- Data Capabilities: Algorithmic models require very robust customer data integrated across multiple sources. Simpler models can work with more limited datasets.
- Resources: More advanced attribution models also require more technical resources and expertise to implement and maintain properly.
If you use a mix of offline and online methods, it’s essential to choose an attribution model that can correlate both for accurate insight. You may want to opt for a mix of MMM and multi-touch attribution methodologies to get the complete picture.
You will likely have to use several models in tandem to gain real insight into the effectiveness of your marketing efforts.
A typical SaaS startup would require a Customer Relationship Management System (CMS) to track their customer relationship marketing efforts (and sales), as well as a Marketing Automation Platform (MAP) to track touchpoints.
You may also benefit from dedicated marketing attribution software that can be configured to the attribution model of your choice. Many of the leading marketing clouds and campaign management tools now include built-in attribution capabilities. This allows you to choose from predefined models or create custom weighted models tailored to your unique customer journey.
When evaluating attribution software, look for features like:
- Flexible model configuration options (rules-based and algorithmic)
- Unified data integration across online and offline sources
- AI-driven insights and recommendations
- Data visualization and reporting dashboards
- Closed-loop integration with CRM, marketing automation, and ad platforms
Marketing attribution models are essential tools that improve our understanding of who our customers are, how they relate to our brand messages, where they like to interact with us, and how we can reach them with our campaigns. But attribution goes beyond just understanding - it allows marketers to continuously optimize their strategies, channels, messaging, and budgets based on what's driving real business impact.
When leveraged effectively, attribution can:
- Increase marketing ROI by prioritizing high-performing channels and tactics
- Improve customer experience by aligning messaging across the most influential touchpoints
- Inform product development by highlighting features that drive conversions
- Maximize ad spend efficiency by cutting wasted budget on underperforming campaigns
- Enable true closed-loop reporting by tying marketing efforts directly to revenue
During your campaign, you can deploy any of the principal attribution methodologies, including:
- Last click attribution
- First click attribution,
- Linear attribution
- Time decay attribution
- Position-based attribution
- Algorithmic attribution
Whichever methodology you choose, marketing attribution models will help you figure out what’s working (and what isn’t), which communication methods to scale up or scale down. They will ultimately cut wasted spend and improve your bottom line.
However, keep in mind that no single model is perfect - most organizations leverage a combination of models to get a comprehensive view. For example, you may use algorithmic attribution to analyze overall marketing impact, but layer position-based models to optimize specific channels or campaigns. The goal is to choose the right model(s) to answer your key business questions.
it's important to have an attribution strategy and governance process in place. This should include:
- Clearly defined goals, KPIs, and use cases for attribution
- Established processes for data integration, modeling, and reporting
- Cross-functional stakeholder alignment on insights and actions
- Continuous monitoring and recalibration as needed
With a strategic attribution program, marketers can move beyond just descriptive insights to true data-driven optimization across the customer journey.
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