Multi-touch attribution and deduplicated reach have been the holy grail for marketers and advertisers for many years. Especially now – as traditional media (TV, Radio, Print) and digital media (CTV, Streaming, Digital media) converge in unified media buys. Because the advertising economy is based on eyeballs (ie, reach) and engagements (ie, touches), it’s important to understand incremental reach and engagement across every platform. This intelligence helps marketers assess ROI and conduct cross platform optimization.
From a technology perspective, delivering multi-touch attribution and deduplicated reach is no easy task. Technology providers must develop data models that can identify individual users, customers and households across all touchpoints and devices, then unite this data to tell a unified story.
Before we discuss the technical requirements of multi-touch attribution and deduplicated reach, let’s dig into what these capabilities are, and why they are important to marketers today.
Defining Multi-touch Attribution
Multi-touch attribution, also known as cross-channel attribution, helps marketers better understand customer journeys. It works by tying together touchpoints across platforms and attributing them to individual customers. Additionally, multi-touch attribution often assigns a value to each channel so that marketers can understand how each touchpoint contributed to conversion.
For example, consider a customer researching a TV purchase. They might start by googling TV brands and browsing options on sites like Amazon or BestBuy. Over the next few weeks, they see a few commercials on TV and pass an OOH ad on their commute to work. Later, they see a display ad for a Samsung TV while browsing their computer – which they choose to ignore. Later on, they see a native ad from a TikTok influencer showing off their new Samsung Smart TV. They click to the Samsung site to learn more. They are offered an on-site discount code in exchange for their email address. They add the TV to their cart but get distracted and don’t complete their purchase. A few days later they receive an email nudging them to complete the purchase.The TV arrives shortly thereafter.
Understanding customer journeys like these help marketers understand where to focus marketing spend. In this example, Samsung could use this customer journey intel to decide to scale up on influencer spend and scale back on display advertising.
Why Mutli-touch Attribution Matters
Holistic View of Customer Journey: Multi-touch attribution considers the entire customer journey rather than just the last interaction before a conversion. This holistic view helps marketers understand the various touchpoints that influence a customer’s decision-making process, from awareness to consideration and eventually conversion.
Accurate Allocation of Credit: Different touchpoints play different roles in a customer’s journey. Some touchpoints might introduce customers to a brand, while others might provide more detailed information or incentives that drive conversions. Multi-touch attribution models allocate credit to each touchpoint based on its actual contribution to the conversion, providing a fairer representation of their impact.
Optimizing Marketing Mix: By accurately attributing value to different touchpoints, marketers can make more informed decisions about where to allocate their marketing budget. This helps in optimizing the marketing mix by investing more in the channels and touchpoints that have a higher impact on conversions and ROI.
Improving Campaign Effectiveness: Multi-touch attribution allows marketers to identify which touchpoints and sequences of touchpoints are most effective in driving conversions. This insight can be used to refine marketing strategies, create more relevant and engaging content for each touchpoint, and enhance the overall customer experience.
Avoiding Overemphasis on Last Touch: Conversion doesn’t occur in a silo – users experience many marketing messages and interactions before deciding to make that final step. Multi-touch attribution helps marketers understand how each marketing activity contributed to the conversion, preventing overemphasis on the final marketing touch in the user journey.
Defining Deduplicated Reach
Deduplicated reach is the ability to recognize individual users or customers across touchpoints and devices to attribute those devices to that individual. In a world where consumers frequently jump between browsing sessions across phones, laptops and tablets, and across locations spanning home, office and on-the-go, deduplicated reach is more important than ever.
In the context of the TV-shopping example, deduplicated reach would help a marketer analyze the journey of a single user, regardless of device or location. The shopper in the example likely viewed the OOH ad from their car, the display ad on their laptop and the TikTok ad on their phone. They then received the email and completed the purchase on their laptop. Deduplicated reach tracks these movements and ensures that these interactions are attributed to one user, who interacted across all of these devices.
Deduplicated reach allows marketers to more accurately analyze audience behaviors and movements. When paired with mutli-touch attribution, it gives marketers a full, accurate view of how users complete the path to purchase.
Why Deduplicated Reach Matters
Accurate Audience Measurement: Deduplicated reach provides a more accurate representation of the actual audience size. Without deduplication, the same individual might be counted multiple times if they encounter the same ad through different channels or devices. This inflates the perceived reach and can lead to skewed insights about the campaign’s effectiveness.
Avoiding Overexposure: Advertisers want to avoid bombarding the same individuals with the same ad repeatedly. Overexposure can lead to ad fatigue, where viewers become disinterested or annoyed by the repetition, negatively impacting the effectiveness of the campaign. Deduplicated reach helps advertisers monitor and control the frequency of ad exposures to prevent overexposure.
Optimizing Ad Spend: Accurate measurement of deduplicated reach allows advertisers to make better decisions about allocating their advertising budget. They can identify which channels or platforms are reaching the most unique individuals and adjust their spending accordingly, optimizing the campaign’s overall impact.
Insights into Audience Behavior: By understanding how many unique individuals have been reached, advertisers can gain insights into the diversity of their audience. This information helps them refine their targeting strategies and tailor their messaging to different segments, improving the relevance and resonance of their ads.
Campaign Evaluation: Accurate deduplicated reach measurements contribute to more precise campaign evaluation. Advertisers can determine the true reach of their campaigns, compare it to their goals, and assess the overall success of the advertising efforts. This information is crucial for refining future campaigns and improving ROI.
Data-Driven Decision Making: With accurate deduplicated reach data, advertisers can make data-driven decisions about their marketing strategies. They can identify trends, assess the effectiveness of different channels, and make adjustments to optimize future campaigns based on real insights rather than unreliable metrics.
Data Modeling for Multi-Touch Attribution and Deduplicated Reach
From a technology perspective, multi-touch attribution and deduplicated reach are difficult to execute. Sorting through massive amounts of data to attribute it by user and device, then clustering those users and devices to households or businesses is no easy task. AdTech and MarTech companies that enable these capabilities not only have to collect the data associated with user interactions, but also parse the data to make sure the marketer can understand and act on it.
Technical Requirements:
- Recognizing one user/customer/household across all touchpoints and devices
- Attributing all interaction events to individual users
- Profiling individual users with all interactions across devices and touchpoints
- Comparing profiles across devices, touchpoints, locations, etc
The data models that power mutli-touch attribution and deduplicated reach must be able to identify where each data point originates from. This includes identifying touchpoints, devices, data sources. Then, the data must be grouped by or associated with individual users.
Data Architecture:
Enabling a 360 degree view of user behavior is the most important capability of the data architecture for multi-touch attribution and deduplicated reach. To achieve this, the data model must attribute behavioral data to individual users, devices and households.
The most important components of the data architecture are identity resolution and the ability to group data in an efficient, performant manner. Identity resolution should connect all devices and touchpoints to collect information on each user’s behaviors and interactions. To make that information actionable, the data should be collated to group the events and individuals under each device and/or touchpoint.
Building the Architecture to Enable Multi-Touch Attribution and Deduplicated Reach
If building complicated data models for mutli-touch attribution and deduplicated reach sounds like a daunting task – you’re in luck! Aqfer offers fully white labeled data architecture solutions to get these capabilities up and running in no time. Our Build-It-Together approach allows our clients to bring new offers to market more quickly, more affordably, and with less risk. Aqfer’s solution is built and maintained by experts in marketing and advertising technology, customized for your customers and use cases.
The Aqfer Approach to Multi-Touch Attribution and Deduplicated Reach
Aqfer’s suite of identity management solutions offers a complete and unified view of consumer behavior to power multi-touch attribution and deduplicated reach.
Our flexible and customizable graph manager builds and manages complex identity graphs. These graphs are updated in real-time as users interact across devices and channels. Graph manager also stores and supports probabilistic edges and inferred entities via customizable code or integration of third-party identity graphs to augment your own identity graphs.
Click here to learn more about our Marketing-Data-Platform as-a-service and the Aqfer approach to multi-touch attribution and deduplicated reach.