Our recent research found most marketers (69%) agree that accurately attributing value across channels is vitally important to their organisation, yet the majority (82%) say they are using a single-touch point method, such as first-click and last-click attribution, to evaluate the success of marketing. And only 3% strongly disagree with their organisations approach.
Using a basic approach to attribution often results in a distorted view of the impact of campaigns, and where you should focus spend. More importantly, it means you are missing out on vital information that helps to understand your customers so that you can improve their journey.
The three biggest attribution challenges
- Measuring in isolation. One of the main challenges with attribution is that departments and specialist agencies measure channels in isolation. By measuring the impact of one channel at a time (e.g. PPC), you aren’t looking at the holistic customer journey. As you know, when a customer engages with your brand, they are likely to do so across multiple touchpoints. For example, they may see a TV ad, search for it on the internet and see a display ad, before purchasing a product. This combined effort drives the sale, yet marketers still aren’t recognising attribution across all channels.
- Subjectivity. People decide which attribution model to use, which channels work best, what weightings to apply to engagements and so on. These opinions drive the output, giving a skewed and inaccurate view of the impact of campaigns.
- Using the wrong method. Different attribution methods yield pros and cons and need to be suited to your business structure – rather than what is the simplest approach. For example, if a business decides to use a last-click model, marketing channels that are efficient ‘closers’ in the customer journey will be highly valued, and channels that drive awareness or consideration will be undervalued. This will result in a marketing strategy that is efficient in the short term but does not build for the future.
Weighing up the methods
Here’s a quick overview of the pros and cons of some of the most commonly used attribution methods, starting with the most basic:
Single touch (first/last click)
This method assigns 100% of the revenue credit to a single touch point – the customer’s first or last interaction.
- Helps to decide where marketers should invest top-of-funnel spend – hence why it is suitable to companies with short sales cycles
- Relatively low-level complexity so easy to implement and requires low level investment
- Results in high weighting on brand awareness
- Ignores the efforts that processed a lead through the marketing funnel
- This makes it difficult for marketers to make informed decisions on strategy in the middle and bottom of funnel
- Gives insight into the touch points that are converting the most leads
- Relatively easy and cost-effective to implement
- Tends to result in marketers focussing spend on one piece of activity – the last touch point before a sale (under-numerating what works throughout the rest of the journey)
- Ignores the efforts that first engaged and continued to engage a lead through the marketing funnel
This method considers all the touch points that a customer interacts with throughout their purchase journey. This would be best suited to a short buying cycle with minimal research. For example, buying flowers as a gift.
- Provides more information on the full customer journey than single touch-point attribution
- Evenly distributes credit – less likely to focus too little or too much spend on one channel
- Still a relatively simple model - assumes all interactions are weighted the same (e.g. email interaction and an event attendance which could result in investment in less effective campaigns)
- Taking part’ gains credit so increasing any marketing activity (e.g. serving more display ads) will increase value attributed to that channel, which will result in more investment in that channel
- More complex than single-touch so can be more challenging/costly to implement
This model gives credit to each touchpoint in the customer journey, but rather than giving equal credits to all, it assumes a different importance for the first and last events and then shares the rest of the credit equally across the remaining touchpoints. Consider marketing across a customer journey from introducing (first event) to closing (last event) with all middle points as promotors, the U-shaped model can be flexed based on business understanding.
For example, for a long buying cycle with lots of research (e.g. a holiday) during ‘consideration’, promoting touchpoints will be important. In this case, the model may be designed to divide a large proportion of the value between middle touchpoints. In contrast, for a product with a short buying cycle (e.g. a washing machine) then it is likely that the introducer will get most credit followed by the closer with any promotor touchpoints being assigned minimal value.
- More sophisticated model that tracks every single touchpoint
- Gives credit to multiple touchpoints throughout the customer journey
- Relies on subjective opinion
- Length of buying cycle and number of touchpoints impacts the value given to promotors regardless of genuine impact
- More complex requires assumed understanding of customer journey to choose weightings
For this model, channels are given different weightings/rules. These models are more sophisticated because they consider both position in the customer journey (first, last, middle) and the natural (assumed) strength of the channel. They are also more complex, adding an extra layer of subjective rules. This model is still rules-based but is sometimes wrongly named data-driven.
- More sophisticated model - tracking every single touchpoint
- Applies different weighting to touchpoints
- More complex to design and build
- Relies on subjective opinion
- Doesn’t take into account interactions across the customer’s lifetime
All the rule-based attribution models described above have the same issues. The two most fundamental of these are:
The danger of the self-fulfilling prophecy – the value attributed by these methods will drive future investment, so what you choose to value will receive more investment over time.
They are all subjective – this underpins the inherent flaw in rule-based models. It is a human decision to choose the shape and is therefore biased.
The only way to eliminate subjectivity and gain an impartial measure of marketing effectiveness is with a true data-driven model.