One-click purchases are a rarity, rather than a reality for most businesses, and yet the standard attribution models imply that this is how purchases happen online. But companies, who are crediting only the last touchpoint in analytics for overall marketing success, are missing out on 20%-40% of potential ROI. Knowing more and guessing less about your customers’ journeys can help you recuperate that money and even multiply your marketing gains. That’s what data-driven attribution modelling is designed to accomplish.
As mentioned in our previous post, attribution models that follow pre-defined rules (e.g. assign credit to first/last click) do not provide full visibility into your marketing campaigns. They are decent “patch” solutions to track certain activities, but they fall short when you want to dig into the complex customer journeys of today.
Google offers a Data-Driven Attribution (DDA) model which accounts for the importance of every touchpoint a prospect goes through before converting and determines which marketing actions played a role in the process. However, to start using this model, your business must first meet the general eligibility requirements. To meet these, you must have:
Unfortunately, this isn’t an option for many businesses, but there is another way.
Yes, you can develop your own model instead, based on GA data. We have built these for several of our customers and they are highly effective. And, unlike the Google model, with ours, you only need a minimum of 100 sessions per day, including traffic from all channels, which makes it much more accessible for many companies.
These models comb through different conversion paths and identify the number of touchpoints in different sequences, the order of exposure, creative assets used and several other factors to give you a complete and actionable view. The model uses the conversion path data from MultiChannel Funnels, as well as path data from customers who don’t convert.
We can then set up predictive analytics modelling that will supply your business with insights and valid predictions in near real-time. This way, you can identify winning campaigns and sequences, and make better decisions on-the-fly.
Custom data-driven attribution enables you to create a personalised model that will depict the actual state of affairs for your business. You can add, track and optimise as many channels and touchpoints as you need to fully document the different types of customer journeys.
The best part is that all insights are connected, which provides a detailed look both within and across channels. Specifically, you can unlock the following benefits:
Ultimately, data-driven attribution allows you to give proper credit to previously hidden actions, such as conversions that came from non-branded keywords or from mobile devices (after a visit from a desktop), as well as exercise more precise control over individual campaigns/channels. Additionally, you gain access to predictive analytics insights, showing you the scope of change you will achieve when trying strategy A, B or C.
Several brands in different industries are already seeing great results after switching to DDA:
As mentioned already, you don’t need to be a Google Analytics 360 customer if you want to benefit from data attribution modelling with our help. But there are still a few technical requirements you need to meet:
Implementing data-driven attribution at a lower daily traffic threshold is still possible, but you won’t be getting complete value out of it at that point.
Your analytics will only be as good as your data is. Businesses that are able to gather large, consistent and high-quality data sets across the channel mix will benefit most from data-driven attribution. Specifically, you’ll want to ensure that your data is:
Source: Google Attribution 360 White Paper
Remember: all the big data stored in your systems will have to be operationalised and prepared for further analysis. In fact, that’s what we tend to focus on during the first two months of working with a customer.
Data-driven attribution works best when your KPIs and goals are compatible across channels and departments. For example, if your PPC campaign is geared towards increasing the webinar leads, your Facebook ad campaign goal should be the same.
But there are different ways to measure those goals, right? What’s great about data-driven attribution is that it allows you to look beyond the vanity metrics such as clicks or shares on social media and focus on post click insights (e.g. visits) instead.
Not every social media campaign you run may be aligned with a specific outcome, such as sales. An Instagram campaign you run might be tailored to create brand awareness in a new market. But measuring that “buzz” can be problematic. With a DDA model, however, you can effectively capture social media traffic that never generated a conversion, and analyse how it had helped the performance of other channels.
Depending on the company size and how much value you see in data, a data-driven attribution model can be the tool you need to help you answer important marketing and business questions, such as:
Our Data Science team can help you collect the right data and start answering your marketing questions. We will also help you ensure that the data you collect is as accurate as it can be. A great data model can only be as good as the data you have. To find out more, call us on 02392 830281 or submit your details here and we’ll call you.
George is an SEO Specialist and Data Scientist in the Portsmouth office. He has worked in E-Commerce and Digital Marketing across many industries, for and with companies all over Europe. Before joining Vertical Leap, George worked as Marketing Director for his own company, for which he managed to expand the company’s activities to 5 European countries. George has been creating websites for more than 10 years and he has in depth experience in designing and bringing optimised E-Commerce websites to market.
Digital marketing under-performing and not sure why? We'll do a free SWOT analysis and reveal priority focus areas and quick wins
Categories: Data & Analytics
Categories: Data Science