Most advertisers want to answer one key question: Did my ads cause new-customer behavior, or would the people who saw my ads have converted anyway? Gauging the success of your marketing tactics is important no matter what the economic outlook. But when budgets are tight, as with the current economic situation, the prospect of proving impact and eliminating waste becomes even more attractive. So much so, that investment in measurement solutions are expected to accelerate over the next few years and exceed $2 billion by 2025, according to Forrester Research.To add to the complexity, given the fragmented marketplace where consumers are multi-tasking and making a myriad of decisions daily, it is unlikely that a consumer will only experience one channel or touchpoint along their path to purchase. Therefore, marketers need to have a clearer picture of the non-linear path to purchase, including the awareness, discovery, and action phases of the customer journey.Attribution modeling provides an accurate report of conversion totals (de-duplicated across all channels), as well as a consolidated view of digital performance. This allows advertisers the confidence to assess channel investment allocation, channel optimization and customer journey mapping. An attribution model can be a rule, a set of rules, or a data-driven algorithm that determines how credit for conversions is assigned to interactions along conversion paths.
How you think about your attribution modeling will greatly influence the way you interpret your performance data and ultimately the actions you take to optimize for better outcomes. Understanding the different models is key in determining which one is right for your business.Below are the most conventional models, but look out for variations and underlying methodology before you run with implementing any of these whether you partner, buy or build the capability:
A popular solution, Data driven attribution, is a form of multi-touch attribution (MTA), which is a marketing effectiveness measurement technique that takes all the touchpoints on the consumer journey into consideration and assigns fractional credit to each so that a marketer can see how much influence each channel has on a sale. MTA helps you understand how different channels work together and align their efforts with customer preferences and behaviors. Given many marketers typically use MTA to inform budget allocation and digital channel optimizations at the sub-channel level, it has become one of the more widely available attribution solutions.
Pros of MTA
- Holistic view of your sales cycle, with granular attribution across the customer digital journey
- Identifies source of quality leads
- Channel & campaign performance is more comparable
Cons of MTA
- Limited ability to track most offline touchpoints; limited ability to factor into attribution model
- Inability to track externalities (trends, weather, competition)
- Data regulations will likely impact trackability
For any organization that it is not ready to commit to this level of attribution, linear, time decay, and u-shaped MTAs are typically a better option than the first and last click single-touch attribution models, which have been known to be used as a default.
The right attribution strategy is critical to making progress on improving your business outcomes. To determine the appropriate attribution strategy for your business, here are some practical steps to take:
- Start with an attribution model that is suitable in terms of the maturity and the sophistication of your business organization and plot a path forward on improving your access to the right model if you have a more nascent current state
- Track and compare models side-by-side if possible to understand the interplay and implications for each
- Once you have assessed how each model delivers performance data, you can pair these insights with the purchase patterns of your typical customer and other nuances of the consumer journey to make a more informed decision on which attribution solution makes sense
- Make sure you have the right tools that can deliver attribution functionality, statistical modeling, and machine learning to holistically evaluate the performance of your marketing initiatives to confidently drive bottom-line impact
