We’ve written and spoken so much about custom attribution that we often forget to answer the most basic questions everyone has for us: what is attribution for and, how is Croud custom attribution different?
So here it is - a brief introduction to the topic and some specifics around what a custom approach can do which a regular SaaS attribution platform cannot.
Multi-touch customer journey analysis is now a must-have for any company seeking to create a seamless omnichannel experience for their customers. From YouTube to Instagram to PPC, from the ad on the billboard to the display ad on Facebook - all these touchpoints contribute to the same journey and the final decision to buy. If you’re already familiar with attribution, then you can jump straight to the custom attribution section of this post!
What is attribution?
Marketing attribution, often referred to as Multi-Touch Attribution (MTA) is a granular digital marketing measurement approach that evaluates each customer touch-point online, for its role in driving sales (for those new to the topic we have posted helpful links at the bottom).
The flaws of positional models are well known: while last-click attribution models are undervaluing the upper funnel touch points, first-click models are not considering the lower funnel. You might think that the linear model is a fair compromise, awarding equal credit to each touchpoint, but this is not accurate and does not allow marketers to optimise marketing spending and increase the ROI by investing in touch points which really work.
Marketers use attribution to help evaluate which channels and campaigns work better than others and where in the process each channel plays a role. If the attribution model is accurate, then it can drive sustained growth and improved marketing return on investment will result.
Attribution models and 'data-driven' attribution (DDA)
Different attribution models have evolved over time, starting with 'positional' models - first touch, linear and so on. A key challenge is that, plainly, not all these attribution models are valid. Simple models help firms understand the bias in 'last-click' sales reporting, but cannot give definitive answers.
Data-driven models appear to promise more certainty, but on closer look give results that can vary widely between platforms and vendors. Online retailers should be cautious about black box 'data-driven' models which they do not fully understand and do not include indications of objective accuracy. For example, Google now offers a 'DDA' model in their platforms which, while an improvement on last click wins, has some significant limitations: it focuses heavily on last touchpoints, applying a 'one size fits all' modelling approach to only the last four interactions, using only the Google predefined channels, with no credit given to social and display impressions, and with no measure of model accuracy provided.
Sophisticated marketers want a more complete and independent view they can trust.
A more precise approach with custom attribution
Custom attribution is essential for businesses that want to accurately measure the impact of their marketing efforts and investments, helping them make decisions that drive the most value. With custom attribution, companies can ensure that their model is trained only on their own first-party data, and not on an industry standard or third-party data. This means that marketers can trust the accuracy of their attribution model and be confident that their decisions are based on data which is relevant and accurate.
The benefits of custom models include more accurate measurement, more actionable insights, leveraging your data with machine learning, better organised and clearer customer journey data and the ability to incorporate your own customer, product and even offline signals.
Many new marketing analysts with a background in data science are shocked to discover that even the most apparently advanced data-driven or 'true' attribution approaches, have absolutely no objective metrics or tracking of model accuracy. The vendors selling these models often cannot answer what accuracy even means for attribution, and have to refer such questions to specialist teams, who can only vouch for the general accuracy of their methods, not how accurate their attribution model is for your specific business.
How do you know if your attribution model is accurate? If you don't know the accuracy, how can you be sure that you won't use it to get sidetracked into wasting marketing budget on dud channels and tactics? Robust custom approaches, by contrast, should guarantee specific and trackable attribution metrics.
With a custom approach to attribution, brands can do so much more than just move beyond last-click attribution. They can gain a much deeper understanding of how different marketing activities contribute to their customer's journey. This allows marketers to be fully confident in identifying what channels are driving the highest value, and which ones are underperforming. Furthermore, custom attribution can be used to analyse social and display impressions, allowing marketers to better understand the role of these channels in the customer journey.
4 key benefits of custom attribution
Custom attribution models offer distinct advantages beyond basic measurement capabilities. These sophisticated models provide crucial insights that can transform marketing strategy and drive meaningful business outcomes. Let's explore the four key benefits that make custom attribution an essential tool.
1. Model accuracy for multi-touch attribution
One of the main advantages of a custom built attribution model is accurate measurement. The model is based on first-party data, which is crucial in the current climate where third-party cookies are on their way out. If data-driven personalisation is also your marketing goal, then building the model based on first-party data allows you to understand your customer needs, rather than relying on generalised and biased third-party data.
2. Campaign measurement excellence
When it comes to the measurement of individual marketing campaigns, the data can be overwhelming and confusing. Increasingly, companies are using AI-powered data analytics tools for organising and clarifying the data, followed by interpretation of the results by the AI model itself and the team of data analysts. A custom attribution model allows you to review the tactical performance of campaigns and adjust them to focus on what is working best.
3. Customer-centric strategies
Companies working to find the right balance between acquisition and retention strategies can factor in the increased value of marketing when it brings in repeat sales. This can be done by applying Customer Lifetime Value to purchases by different customer groups each with their own purchase and repeat purchase behavior.
4. Enhanced insights through econometrics
A further benefit of custom attribution models is the ability to incorporate both online and offline marketing channels into your measurement strategy. By combining AI-powered data analytics with econometrics, you can factor in store sales into marketing analytics. Econometrics provides a scalable methodology for estimating the role and influence of external pressures like price and seasonality, on both online and offline sales, and helps to estimate incrementality and assist budget planning choices.
What data signals does custom attribution typically use?
Naturally the most important data points for attribution are the marketing touchpoints themselves. Each touchpoint has a huge amount of data and we try to use as much of this as possible for input into our predictive models. So for example, we can consider the position of the touch point in the path to sale, the frequency of interactions with that channel, the marketing channel itself, the time between the touch point and the time of conversion and also the device used to interact with the marketing.
Device use can be particularly challenging to track, as customers often switch between devices throughout their path to purchase. To address this, it’s necessary to introduce corrective techniques to manage the so-called cross device attribution bias, especially for larger drawn out purchases, where potential customers research and purchase on different occasions.
With attribution so much of the focus is on the value of marketing, it can be easy to forget that what we are primarily interested in are the actual and potential customers, and how the marketing affects them. Therefore our models will benefit enormously if we can include anonymous characteristics of the customers as they interact with marketing. If a business has particular customer or product segments for example, we would ideally include these in the custom model, which is one of the many reasons that every attribution model is entirely unique to each business.
With this in mind, it’s likely that an attribution model designed for a particular brand will include data points which are distinctly related to the way your business works. A travel company, for example, may need to take into account the time of year when a booking is made, or the availability of certain holiday inventory in peak periods when they evaluate their marketing. Therefore these signals can be incorporated into the custom attribution model and analytics.
Basic marketing strategy is to distinguish between new customer acquisition and building loyalty for repeat customers, often with different target ROI metrics such as a higher Cost per Acquisition target for new customers. Despite this central requirement for marketing strategy, the vast majority of attribution models, even so called data-driven models, don’t use any information about the repeat purchase status of the customer to help determine their likelihood to buy, and the contribution of marketing. In the custom models we produce for our clients, we always try to include new versus repeat customer data points in the marketing journey and sales data in order to understand how this affects marketing effectiveness and support brands who are targeting these groups in very different ways.
The Croud approach to building custom models
At Croud we use our own approach to developing accurate custom attribution models for each client brand, so if you're not using one of our models the following will not apply to you.
Our approach to attribution is designed to create conditions of objective accuracy combined with the ability to precisely measure the contribution of each touch point for sale, uniquely for every single customer journey.
We use a predictive approach that combines data from both customers who convert (buy) and customers who do not convert. These custom models actually predict whether or not a potential customer will buy based on all the data signals, mainly from marketing, but also other signals around the customer and unique characteristics of the business.
These are machine learning (AI) models and are trained on actual customer interaction history for the brand that we are trying to model. By excluding a representative 5 or 10% of data from the machine learning process, we can then evaluate any attribution model on this 'holdout' sample to test model accuracy i.e. we can precisely test how well a given model predicts each sale. To get to the optimal model, we use advanced optimisation techniques, supervising the training and evaluation of thousands of models including a genetic optimisation process. We pick the model with the best technical accuracy metrics, and we also play close attention to which data points are driving the prediction in order to make sure we’re not creating spurious predictions, or 'over fitting' our models.
Like any attribution model, we have to be able to evaluate the 'credit' given by each touchpoint (credit is the share of contribution to sale). This is done on a case-by-case basis for every conversion journey wherein a calculation is made to identify the contribution of that touchpoint to the likelihood of sale. If a marketing touchpoint greatly increases the likelihood of sale according to the model, then it will take a higher share of credit.
Very often a marketing touchpoint will increase the likelihood of sale for one consumer but will not play much of a role in the case of another. For example, a search which leads to a new customer discovering your brand and purchasing for the first time, is contributing far more than the same search done by a repeat customer who is just browsing around. Because the attribution model includes all of these data points it will take account of this and adjust the credit on a case-by-case basis. This is done for every single journey to sale for every single conversion with unique credit applied in each case.
The total of the touchpoint credits is added up to produce an attributed sales value ensuring that all sales and all marketing is fully accounted for. In some cases our clients are looking for a measure of incrementality, which is fully measurable using this approach, and in these custom projects we use an alternative logic to sum up only incremental touchpoint contributions.
In summary then, Croud custom models work by predicting your sales as well as we possibly can, based on an objective measure of accuracy, and then the contribution of each marketing touchpoint is equal to how much it increases the likelihood of a potential customer making a purchase. All of this is done from the raw journey to sale data, including from people who do not buy, to properly measure whether the marketing really 'moved the needle' for you, or not.
Custom attribution models in action
If you are a client of Croud and you have access to the Croud platform, then there are several ways you can get to better understand your customer attribution model. If you’re not sure how to find your way around the platform at first, look for the in-context help guides, or if necessary ask for a training or insight session with the Croud support team.
A good place to start is in the attribution 'Overview' reports which all include a comparison between your attribution model and another simple attribution model, such as 'last click' applied to your custom channel definitions. A marketing channel which scores higher on attributed sales than on last click sales, is one which is driving value earlier in the customer journey with some upper funnel value that is missed by the last click wins assumption.
You can use the attribution overview reports to look at total attributed sales, attribution revenue and also percentage and indexed views allowing you to compare to last click attribution, with cuts for customer groups, devices, product lines and so on. These can be seen channel by channel, and then in the KPI reports, also at campaign level, or publisher or keyword level.
If you’re looking for more insight into how your marketing drives value throughout the user journey and how that is reflected in the attribution model, then you should look at the marketing funnel touchpoint reports. These show you where in the customer journey each marketing channel typically occurs giving you a measure of whether it is an upper funnel or a lower funnel sales driver. You can also look at the time to convert using these reports to find out how long it takes someone to purchase after they have interacted with your marketing, up to a maximum of 90 days before sale.
To see exactly how attributed sales are shared out by the model, use the 'touch type mix' report. This allows you to see attributed sales according to whether each marketing channel was upper funnel, mid-funnel or lower funnel. It also breaks out attributed sales caused by a single touch before sale, which can be especially important in the case of activity like brand search. Again, all of these analytics can be broken out by date, customer type, device, product and your custom filter variables.
If all this does not quite answer all your questions, or if you’re a data scientist or analyst who wants to get right inside the technical intricacies of your particular model, then you can request a review of your custom attribution model by getting in touch, so that a Croud Data Science Specialist will take you through it.
In summary, custom attribution modelling is all about knowing your customer. By knowing your customer needs and wants businesses will be able to scale and personalise their marketing strategies to achieve high marketing ROI. The advantages of high accuracy, innovative measurement techniques and the ability to predict and plan, are helping marketing teams to automate highly optimised marketing and focus on strategic growth. In an online environment that is more competitive than ever, only the very best in class attribution solution, custom built for your business, will give you the edge you need to break through your marketing KPIs.
It's your model, and it’s very important that you get it right, so we want it to be as transparent as possible for our clients to understand how exactly it evaluates your precious marketing budget.
