One of the key challenges of value attribution models is ensuring the quality and availability of data. Data quality refers to the accuracy, completeness, consistency, and relevance of the data used to track and measure customer interactions and conversions. Data availability refers to the accessibility and integration of data from different sources and platforms. Poor data quality and availability can lead to inaccurate or incomplete attribution results, biased or misleading insights, and missed opportunities. To improve data quality and availability, you need to establish clear data governance rules, use consistent data definitions and standards, validate and clean your data regularly, and leverage data integration tools and platforms.
Another challenge of value attribution models is choosing the appropriate attribution method and assumptions. Attribution methods are the rules or algorithms that determine how much credit each touchpoint receives for influencing a conversion. There are different types of attribution methods, such as last-click, first-click, linear, time-decay, position-based, and data-driven. Each method has its own advantages and disadvantages, and none of them can capture the full complexity and dynamics of customer journeys. Moreover, each method relies on certain assumptions, such as the length of the attribution window, the definition of a conversion, and the weighting of different touchpoints. These assumptions can affect the validity and reliability of the attribution results, and they may not reflect the actual behavior and preferences of customers. To choose the best attribution method and assumptions, you need to understand your business goals, customer journey stages, marketing channels and strategies, and data limitations.
Crucially, one must standardize their attribution and conversion windows (i.e. not leave them open or dynamic) in order to fairly compare between periods. Otherwise, recent periods will always short-change the value or efficacy of one/some/all touch types vs. the matured effects of past periods, while biasing velocity, since that can only be computed from converted touch cohorts. And naturally, no standardized window will cover 100% of long-tail effects, but the point is to get enough of a read within recent periods to determine whether, when all else is equal, we've improved between periods and channel/campaign mixes.
A third challenge of value attribution models is dealing with cross-device and cross-channel attribution. Cross-device attribution refers to the ability to track and measure customer interactions and conversions across different devices, such as desktops, laptops, tablets, and smartphones. Cross-channel attribution refers to the ability to track and measure customer interactions and conversions across different channels, such as email, social media, search, display, video, and offline. Both cross-device and cross-channel attribution are challenging because customers often use multiple devices and channels to research, compare, and purchase products or services, and they may switch between them at different points of the customer journey. This makes it difficult to identify and link the same customer across different devices and channels, and to attribute the value of each device and channel accurately. To overcome this challenge, you need to use advanced tracking and measurement tools, such as cookies, pixels, device IDs, user IDs, and unified customer profiles.
A fourth challenge of value attribution models is performing attribution analysis and optimization. Attribution analysis is the process of interpreting and evaluating the attribution results to gain insights and make decisions. Attribution optimization is the process of using the attribution results to improve the performance and efficiency of marketing campaigns and channels. Both attribution analysis and optimization are challenging because they require a lot of skills, knowledge, and experience. You need to be able to analyze complex and large data sets, identify patterns and trends, test hypotheses and assumptions, measure and compare ROI, and communicate and visualize your findings. You also need to be able to apply your insights and recommendations to optimize your marketing mix, budget allocation, channel selection, content creation, and targeting strategies. To perform attribution analysis and optimization effectively, you need to use analytical tools and platforms, such as dashboards, reports, charts, graphs, and models.
A fifth challenge of value attribution models is creating an attribution culture and alignment. Attribution culture refers to the mindset and behavior of the organization towards value attribution. Attribution alignment refers to the coordination and collaboration of different stakeholders involved in value attribution. Both attribution culture and alignment are challenging because they require a lot of change management, leadership, and communication. You need to create a culture that values data-driven decision making, supports continuous learning and improvement, and embraces experimentation and innovation. You also need to align your goals, expectations, incentives, and responsibilities with your team members, managers, executives, partners, and vendors. To create an attribution culture and alignment, you need to use change management tools and frameworks, such as vision, strategy, action plan, feedback, and reinforcement.