Want to get a handle on marketing attribution? Here are 10 actionable insights to help you, taken from the latest AdRoll & Econsultancy attribution research findings in the UK and Germany.

Unify your data points in one platform to gain a holistic view
Forming a holistic view or ‘single customer view’ should be the goal of the majority of marketers aiming to optimise their media mix. To enable this, channel data must be cleansed and unified into a consistent format so that it can be plugged into a data management platform modelling system. With the study finding that a third of companies blame disparate tech platforms and data sources for lack of progress with attribution, unifying data and technology is a clear starting point for developing insightful attribution models.

Leverage data points effectively to invest in your most profitable channels 
One of the benefits of attribution is having a better understanding of the most effective channels, and with this understanding comes the ability to optimise budget allocation across channels (a benefit felt by 72% of survey respondents). This benefit hinges on actioning the insights provided by an attribution model. Even if you need to begin with small changes, by actioning the insights into the profitability of channels through adjusting budgets, the return on investment in attribution will begin to be realised.

Use data to learn about your customers and apply these learnings to your media strategy
Build rich personas by utilising the multichannel data that has been brought together into one platform and build on them by learning which channels are most successful for each persona through the results of your attribution model. Through this process, the understanding of customer behaviour is continuously improved, and the media mix optimised, leading to greater returns with time.

Cost/Benefit – invest resources and time into attribution to really learn about your customers and your real business goals
Attribution modelling will not bring returns without action, and this action requires business-wide commitment. Make investments before, during and after the actual modelling process (examples include data cleaning, attribution technology and analyst time, respectively) to enable the benefits to be realised. Investment in time and money is required. Setting objectives, implementing and optimising customer profiles and the subsequent media strategies is also required – all of which is driven by attribution insights.

Ask how data can help answer key business questions
Know your objectives for attribution from the start of the process and share these objectives through the business, with individual KPIs applied where appropriate. A clear set of goals from the outset will help you to decide the nature of the data included in the attribution model, and the specific model used. Models vary widely, and the one chosen needs to be supported by a business case with clear objectives. Once the model is chosen, think of the key stakeholders and other teams that need to contribute, and ensure the strategy is communicated to and supported by all. By concentrating on building internal skillsets and providing high-quality training, employees will feel empowered when it comes to handling data, which in turn increases the effectiveness of attribution.

Vendor research
Finding the right vendors takes time, but it is recommended to find the best fit for your business. Prepare yourself a list of questions that need answering from the vendors and get live demos to get a feel for whether the data points would be helpful. A lack of knowledge, time and technology limitations were all cited as barriers to successful attribution in this report. Ensure that these won’t be barriers for you by enlisting the vendor that will provide you will the right level of support for you. Think about training: how are you going to make sure your team are skilled and equipped with the knowledge needed to use the technology once you’ve invested in it? Ensure you know the route you’re likely to go down (in-house training, vendor training, employee-led training) before enlisting an attribution vendor.

Try different models that align to your business goals
Algorithmic models for attribution rely on rich, solid data sets and as such tend to be used by those further up on the data maturity scale, but there is no reason why companies at all levels can’t aim towards this. Try to remove biases through last click / first click models and see what channels really drive your business forward as a part of the whole marketing mix. Experimenting with different attribution models and methods allows you to determine what works best for your data and which processes will most effectively help to meet business needs.

Communicate cross-functionally
Teams working in a siloed fashion can create barriers to general digital transformation but also to the success of individual strategies like attribution modelling. It is key to understand the goals of each team and what success means to them in order to contribute to a proper attribution model that benefits all parties. The study showed that 40% of companies are feeling overwhelmed by the complexity of data, which can in part be down to this siloed working culture. Disparate tech platforms are causing similar problems, so invest time and effort into bringing these separate pots and separate teams together to create a full picture of the data and resource available.

Trial and error
Attribution has its fair share of challenges, and knowing where to begin is often the biggest. It’s important to remember that to drive innovation and deliver a strong customer experience, marketers must be able to understand and demonstrate the effectiveness of their campaigns. Consolidate your data first and understand which channels deliver results aligned with assigned budgets. Use this as your first step and then move into the modelling of the data, making small changes each time to try and move closer to your goal. Improve step by step and continue to learn with each one of those tests.

Flexibility
Allow some flexibility in your attribution platform. Attribution is not a problem to be solved and left alone; it requires work and development as with any element of the marketing mix. Your attribution model should be dynamic and allow for changes in rhythm when it comes to customer behaviour. It’s important to recognise that patterns of behaviour may change according to product, season or campaign and your attribution model should allow you to react to this. In addition to this, models and technologies are improving all the time to reflect the fact that attribution is not the perfect science – close to three-quarters of respondents believe a perfect attribution model is impossible to achieve. By allowing flexibility within your model these improvements can directly impact your marketing optimisation much sooner.

Want to know more? Check out the State of Marketing Attribution report here.

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