So far, it was standard for many advertisers to allocate a conversion success solely to the last marketing touchpoint. A customer journey, however, usually starts long before the last click, since a user is subjected to a large number of touchpoints from different marketing channels, all of which have more or less impact on the final conversion. It is therefore obvious to also include the advertising effect of these touchpoints. And that is what data-driven attribution does.
Data-driven attribution dynamically identifies the impact of each touchpoint within a customer journey, based on the collected data. The attribution method is based on advanced mathematical algorithms and statistical analysis, whose task it is to continuously calculate the value contribution of the individual contact points within the journey. To evaluate the touchpoints, it also considers many factors such as the position within the contact chain, time intervals between different touchpoints and conversion, the impact of the creative and many more.
Thus, with help of the gathered information, the model can determine its value contribution and/or its impact on the purchase decision of the user for each individual touchpoint. At the same time, the dynamic attribution model measures interactions and synergies between individual channels and devices and automatically considers changes such as customer behavior or market conditions.
Each of the marketing touchpoints of the customer journey is allocated a share of the conversion success. This percentage is based on the respective value contribution of that touchpoint, i.e. how much impact the marketing activities had on the purchase decision of the user. The model considers all journeys that successfully led to a conversion as well as those that did not lead to the desired outcome. Thus, the model quickly identifies a pattern to determine which constellation of touchpoints has a particularly positive effect. The technology is constantly evolving on the basis of these findings and learns with each new data set.
ACTIONABLE INSIFHTS – WHAT CAN MARKETERS LEARN FROM THESE INSIGTHS?
While for dogmatic attribution models, the result is determined by pre-set rules and weights, a data-driven model calculates the value contribution of a touchpoint dynamically based on customer journey data. That way, marketers draw completely new insights about the effectiveness of marketing campaigns that would not be possible with rule-based models. Even though rule-based models can provide initial answers about the campaign performance using customer journey data, deeper insights of the dynamic models, i.e. which campaigns contributed to the success in what way, are still missing. The key to any successful attribution system is to understand the backgrounds and interactions across different marketing channels and devices. This is only possible with data-driven attribution.
These insights not only help advertisers plan future marketing campaigns, but also optimize current ones. Advertisers can precisely invest the right amount of budget in every marketing channel, better understand the customer’s actions and distribute their budget to the most efficient campaigns with the highest ROI. Ultimately, advertisers save precious advertising budgets.
Further information on this topic can be found in our whitepaper.
3 CONDITIONS ANY MARKETER MUST FULFILL
The benefits of dynamic attributions are obvious. For a successful implementation, however, certain conditions need to be fulfilled for the model to reach its full potential.
Are There Enough Touchpoints For Meaningful Results?
Dynamic attribution is an essential part of data modeling, where complex algorithms and statistically relevant patterns are analyzed across a wide range of data. Not all organizations hold the necessary data maturity to benefit from an independent, data-driven attribution solution. Organizations that have little measurable marketing because they use very few channels, for example, often simply do not have enough available data to enable the algorithm to achieve meaningful results.
Does Your Company Have Measurable Digital Marketing Objectives?
Data-driven attribution requires unified success metrics and insights about the company's long-term objectives. Attribution is always based on a conversion event. The type of conversion depends on the objectives of the advertiser. For example, downloading an e-book or catalogue, registering for a newsletter or browsing a Facebook page can be counted as conversions just as much as a sale in an online shop - as long as the target is measurable for the attribution system. The objectives should be defined across the board and should be uniform across all channels. Non-standardized objectives such as deviating KPIs in different channels make it difficult to put findings from the data-driven model into practice.
Is The Media Volume Large Enough For an investment in Attribution?
Dynamic attribution is a great investment, provided a company’s media volume is large enough. The costs for investing in an attribution solution should therefore not exceed the gains from media optimization so that the introduction of an attribution tool pays off.
ATTRIBUTION ALONE IS NOT ENOUGH
Even the best data-driven attribution system doesn’t deliver well-founded results if the data base isn’t complete. For one thing, every step of a consumer needs to be traced, no matter which device the marketing touchpoint took place on. Users need to be identified across devices to depict a complete cross-device journey. If the user is allocated to only one device, the dynamic model draws false conclusions about the marketing efficiency of campaigns.
Also, companies still invest a large amount of money in traditional advertising, especially television. A purely digital attribution system doesn’t consider the impact these branding measures have on online sales. In fact, besides digital touchpoints, offline channels and external effects such as seasonality, need also be taken into account to be able to holistically explain the marketing efficiency of different measures.