HOW TO EXPLAIN THE VALUE OF ALGORITHMIC ATTRIBUTION TO THE REST OF YOUR ORGANIZATION
Table of Contents Page 1 Introduction Page 2 What is Algorithmic Marketing Attribution? Page 3 What are Some Limitations of Rules-Based Models? Page 4 Critical Success Factors for a Speedy Adoption of Algorithmic Attribution Page 5 How to Overcome Common Challenges Page 6-7 Be Sure Your Solution Can Do This Page 8-9 Let the Data Pave the Way
Half the money I spend on advertising is wasted; the trouble is I don't know which half. John Wanamaker John was a department store merchant back in the early 1900 s when advertising was all print. Digital marketing brings the power of measurement but also introduces the challenge of omnichannel attribution. Today s marketing organizations depend on marketing analytics and decision support tools to optimize their investments. F or decades, various techniques have been used by marketing analysts to measure the effectiveness of advertising. Marketing mix models originated in the offline world as a way to answer Wanamaker s fundamental question. Some of the most popular methods used to model marketing effectiveness have been top down, bottom up and control/test. Modern marketers are beginning to use fractional attribution to better understand which half of their advertising investment is performing best at the customer journey level. Marketing mix models are often built at a much higher level of granularity and should include both offline and online marketing channels. Rules-based attribution models are used by most marketers but lack the learning intelligence of algorithmic modeling. More and more digital marketers are realizing that, in addition to rules-based models, there are great benefits to using algorithmic models to analyze the effects of your marketing. While more advertisers are acknowledging the importance of evidence-based algorithmic models, adopting one is no simple task. This article demonstrates the importance of algorithmic attribution and what to look for in a trustworthy solution. Page 1
What is Algorithmic Marketing Attribution? Algorithmic attribution is the process of assigning a portion of a credit for a conversion to each touchpoint based on effectiveness. The key differentiator of algorithmic attribution is its use of advanced statistical modeling and inferences to continually optimize and customize based on your results - put more simply, human-assisted machine learning. With algorithmic attribution you can: Statistically measure the effectiveness of each marketing event and its contribution to the conversion Statistically associate channel performance to channel weighting Account for external factors such as seasonality and market conditions in order to accurately measure contribution of paid media Forecast sales and conversions Optimize your marketing spend Provide decision support that reduces risks Page 2
What are Some Limitations of Rules-Based Models? Last Click is Limiting Optimizing marketing channels based on a last click model means that the last marketing event gets all the credit for the conversion. This model relies on recency alone and ignores other channels that may have contributed to the conversion. It also ignores channel frequency, relative channel effectiveness and external factors. Even Model is Inaccurate Optimizing marketing channels based on an even model means that the advertiser is rewarding frequency alone and not recency nor any external factors such as seasonal or macro-economic factors. The biggest problem with this model is that diminishing returns and relative channel effectiveness are not accounted for as all channels and path positions are credited equally. Path Position is Arbitrary Path position does not take into consideration customer behavior observed in the marketer s data. Time Decay Rewards Path Time decay models reward path position regardless of the relative effectiveness of each of the channels in the customer journey. Just like the even model, they ignore frequency and external factors. Page 3
Critical Success Factors for a Speedy Adoption of Algorithmic Attribution When it comes to spend in marketing, there typically aren t a lot of extra funds laying around. Algorithmic attribution is an investment that will make your dollars work much harder. Here are several things to consider that can impact the cost of implementation and become a barrier to a quick adoption: The project team must be clearly defined and include the necessary parties on both sides. Your solution provider should be able to identify the key players and estimate the time required of each deliverable. The attribution model should be well-documented and validated using best statistical modeling practices. You want a vendor who can explain how their model works to you, in turn helping you to explain to your stakeholders and get buy-in. Be wary of mysterious black boxes. There should be a comprehensive and flexible data model that accounts for the various data sources and the logistics of real-time integration. The reporting tool should be easy to use and understand to support fast decision-making. Once you ve tackled implementation, it s time to focus on stakeholder adoption. The ease of interpreting the algorithmic model results will greatly impact stakeholder adoption. For this reason, marketers need to be able to explain how the algorithmic attribution model works and how it is different from non-algorithmic models. At all times, the focus should be on how the algorithmic attribution model can be used to provide actionable insights such as shift dollars from one media to another, identify new target audiences based on behavioral insights, align creative to audience insights, and inform your forecasts (spend, traffic, revenue). The volume of data available to marketers is massive. According to Forbes, Data volumes are exploding. More data has been created in the past two years than in the entire previous history of the human race. This creates both an opportunity and a challenge. In order to benefit, marketers need to use sophisticated technology that can capture the data and provide actionable insights. Algorithmic attribution modeling is built to do just that; manage very large volumes of data and provide real-time decision support metrics. Page 4
How to Overcome Common Challenges Here are some ways to overcome challenges: Challenge: Tips: Lack of full marketing data integration Try to integrate the most significant marketing channels in the study period. Work with an integration partner. Lack of visibility of some display banners on the visited page Partner with a vendor who tracks viewable impressions to provide better impressions input to the model. Cookie deletion issues Adopt a sampling method that minimizes the negative effect of cookie deletion to avoid negative impact on the model. Cross-device jumping Your vendor should have robust identity resolution capabilities to ensure accuracy in today s multi-device world. Difficulty due to identity mapping Implement an identity mapping method that helps achieve a higher matching rate. Difficulty related to competitive and personally-identifiable data unavailability Implement anonymous user identification by stripping personally-identifiable information and segmenting customers into homogeneous groups. Difficulty in integrating non-addressable offline channels and macro-economic factors in the user-level model Implement a modeling approach that can easily incorporate non-addressable and external macroeconomic factors. Page 5
Be Sure Your Solution Can Do This These key features must be present for an algorithmic attribution model to effectively aid decision-making: An integrated marketing tracking platform must collect and expose relevant marketing channels executed by the advertiser. The model should have the ability to implement an evidence-based attribution model that learns from the advertiser s own data and applies credit based on a statistical algorithm. The algorithm should be designed to measure the effectiveness of each channel in the customer journey. The attribution model must account for external factors such as promotional activities, seasonality, major holiday events, macroeconomic factors and competition. The attribution model must account for frequency, recency, differentiated channel effectiveness, diminishing returns and timing of advertising effects. The reality is that not all customers convert because they are exposed to an advertising channel. Some customers may go to the advertiser s site and convert because of the time of year or non-advertising events. Page 6
The attribution model should be able to generate intuitive performance metrics to help explain marketing events based on changes in inputs to the model. Algorithmic attribution model reporting should have the following: Trend of channel weights: provides insight on how channels are performing overtime and based on the existing optimization done by the marketer. Trend of each channel s execution side-by-side with their attributed conversions trend of spend weights: gives insights on relative channel executions overtime and how the marketer is allocating channel spend. Ideally, high-performing channels should get relatively higher spend than low-performing channels up to the point of saturation (diminishing returns). Period-over-period channel conversion comparison helps compare how channels are doing from one period to the next. Period-over-period channel spend comparisons helps compare changes on how the advertiser is allocating budget across channels. Incremental vs. non-incremental reporting: compare the credit from marketing contributions with the credit generated from non-marketing activities. The model used for attribution should be predictive at a reasonable level of statistical confidence so that the model can be used for decision-making. The model used to generate attribution data should be validated with in-sample as well as holdout sample (a sample of data not used in fitting a model). The holdout sample can be used to assess the performance of the models. The attribution model should produce outcomes that are consistent with economic theories of demand behavior and marketing science that is, an increase in advertising should result in an increase in demand with a diminishing return effect. It should also account for the effect of advertising s timing. Consideration should be given to how channel position in the path, channel recency and frequency can contribute to conversion. Timing of advertising does matter when we are approaching high seasonal days like the holiday season with peak days such as Black Friday and Cyber Monday. Page 7
Let the Data Pave the Way In today s digital world, consumers interact with as many as 4-5 devices in a day and are often exposed to many types of media before making a purchase. This customer journey shows just how complex the path to purchase can be: Imagine the value of an algorithmic attribution model. Imagine if you had a continually-learning system that can process every customer s path and tell you the next optimal decision. American statistician W. Edwards Deming said it best: Without data you are just another person with an opinion. Get ready to open your eyes and challenge assumptions. Start simple. Use gradual learning with the algorithmic attribution model. Initially use recommendations directionally, and then progressively adopt the recommendations based on evidence of improved effectiveness. Have a clear understanding of how the model works, namely what inputs are included in the model. An inability to explain the methodology can lead to doubt within the organization. Use the data to optimize channels and be able to demonstrate the impact of the data by comparing the benchmark to the modified investment and the resulting impact on revenue. Don t attempt to use non-algorithmic attribution model data to carry out channel optimization because algorithmic attribution model data measures effectiveness while the non-algorithmic attribution does not. Advertisers who have all their channels integrated in their tracking system will benefit most because algorithmic attribution attempts to incorporate all sources of demand generation -- the more complete the integration the better. The tracking data must be relatively clean with consistent dimension definitions and limited collection errors. As the saying goes, garbage in, garbage out. Page 8
The explosion of big data brings tremendous opportunity to marketers. With the right technology they can accurately track conversion paths and gain insights to more effectively reach their audience. If you can t measure it, you can t manage it. Attribution models, especially algorithmic models, provide a powerful advantage to marketing teams in this dynamic economy. Those who neglect to learn and implement these new technologies will be left behind. Want to learn more? Let's chat. Page 9