Marketing’s transformation from a cost-centre to a revenue powerhouse — coupled with a boom in digital channels — means that marketers, now more than ever, require a granular account of their influence on pipeline and revenue.
Enter: Revenue Attribution.
B2B companies are prioritizing revenue attribution to measure their marketing performance and ROI, and track customer journeys. In fact, 76% of all marketers find that they currently have or will have in the next 12 months, the capability to employ a robust revenue attribution platform (Think with Google). Conceptually, the function of attribution is straightforward, but there are several mistakes that could easily skew your results and limit your progress when it comes to accurate, actionable revenue attribution analysis.
With that in mind, here are 8 common mistakes to avoid for your revenue attribution regime:
Despite the automation solutions that are embedded in most attribution tools today, it becomes easy to forget that your input plays a huge part in producing relevant results. Formulating a strategy is essential in being able to derive actionable insights from your attribution. At the end of the day, the relevance of tracking different channels and campaigns in a customer’s conversion journey is incumbent upon you.
Get organised! Start by cataloguing relevant channels to track as per your conversion goals. Label your channels and campaigns and assign budgets so that all your data across all your tools is coherent. Tracking irrelevant channels (or not tracking relevant ones) is a part of trial and error, but reliance on such incomplete data is a big red flag. One common example of this is: tracking only the performance of ad campaigns without testing its performance relative to other channels.
Communicating with the appropriate personnel and others involved in the strategy to gain better insight on what to track and what not to is a good start.
The tendency to rely on preliminary attribution models — single-touch models like first and last touch or the popular last-click model — may produce quick and simple results to measure your ROI. This, however, can be an expensive mistake. Don’t get it twisted, single-touch models have their use cases — attributing PPC and short sales cycles to name a couple. But relying solely on preliminary models for all your marketing decisions will likely do more harm than good. Single-touch models are linear in nature, which is not conducive to most customer behaviour. Attribution is more effective when you strive to get as close as possible to analysing a customer’s journey across several touch-points. And having one touchpoint attributed to a customer’s conversion gives a vague, and often inaccurate, image of their journey.
This mistake is likely to be a consequence of the previous point — excessive reliance on preliminary models. But why is it important to test other models? When it comes to rule-based attribution and multi-touch attribution models, the general reasoning behind adopting a model is the nature of the product, the number of marketing channels, the length of the sales cycle, etc. While there’s nothing explicitly wrong with this, we cannot only rely on those factors.
There are several omitted variables around the intent of your attribution — measuring the functionality of different campaigns in conjunction with other channels, the relative probability of channel interaction, opportunity cost of campaigns, or just simply mapping out the most influential channel and ROI. Even the type of campaigns and the medium through which the customer interaction occurs could affect your decision in choosing a model. Some models are more applicable than others in producing reliable results, and the only way we’ll identify this is by testing out what works and what doesn’t.
In practice, administering a combination of rule-based attribution and data-driven attribution is an effective way of producing reliable results. That being said, if you’re for the most part dependent on rule-based modelling, you’re unlikely to have transparent results. Rule-based modelling is limited, as the weights in the models would need to perfectly represent the influence of each channel in a customer’s conversion journey. This is highly unlikely as no two customers are the same. For example, a time decay attribution model will assign credits in ascending order regardless of the type of campaign or prospect’s actual behaviour. So, to help identify your most influential channels on average, data-driven attribution can be used to give credibility to different channels by assessing their KPI’s. This in turn will help you draft a custom model that makes sense to your attributing pattern.
In the pursuit of using your attribution data to aid your marketing decision making, sometimes you forget to categorize our data considering the customers involved or their lead quality. To make better sense out of your attribution data, we need to pair the interactions with customer IDs to avoid duplication of leads and accurate credit distribution across marketing channels.
Tracking our customers even helps assess the quality of their leads. What this means is some customers are likely to be more interactive and engaged with your brand than others. This even dictates if some of them become recurring customers or only ever interact with your business once. Tracking customer interactions helps you distinguish the quality of their leads. These values also contribute to calculating the LTV (Lifetime Value) of your customers.
These mistakes have to do with certain biases that might compromise your decision-making pertaining to attribution. The most common ones are:
When attributing credit to different channels along your customer journey, there is a possibility for certain interactions to conceive other interactions (or at least a level of other interactions). One could over/underestimate the influence of channels with other channels simply because of the natural conversion of targeted customers. A conscious consideration of correlation vs causation must always be kept in mind.
A confirmation bias is the proclivity to seek out information, and the interpretation of said information, to favour your results and personal beliefs. This type of bias is prevalent in attribution as it involves having to attribute your channels in accordance with the result that favour you. This would eliminate the organic element of attribution to favour your marketing ideals, ultimately leading to inaccurate findings and conclusions.
When you fail to recognize your channel’s intent, you fall short in gauging how much it facilitated a customer’s conversion. This could lead to poor decision making as a consequence. Some channels facilitate interactions with other channels more than they do sales — like a blog versus a targeted email campaign. Hence, it would be unfair to discredit the channels that did not directly contribute to sales — or other predominant goals — but probably contributed significantly to a customer’s decision to convert.
As convenient and resourceful as attribution is, they will never provide a holistic, extensive picture. While attribution is valuable in showcasing a blueprint of your campaigns, channels, and marketing performance. You still require other analytics tools — Funnel analysis, Anomaly detection, SEO optimization, CRM, and other web analytics tools that help assess channels using premeditated metrics. These tools will ultimately compliment your data-driven attribution for a far more comprehensive analysis of your campaign and channel performance. In order to do this effectively, you will have to use these tools cooperatively and in real-time.
Acknowledging these limitations and making a conscious effort to mitigate them will help equip and optimize your marketing attribution journey. Don’t let the idea that there is so much that could go wrong make you apprehensive about trying out marketing attribution to begin with. Undoubtedly, it’s a steep learning curve, but the rewards far outweigh the risk involved.
And there you have it! If you’re interested in understanding how some of the most popular single-touch and multi-touch attribution models work, you might enjoy this blog piece.
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