Software is what makes effective Marketing Attribution possible.
Historically, marketing budget allocation depended on regression analysis of aggregate data, past practices in the company, and the individual marketer’s hunch as to which channels and methods warranted receiving what part of the pie. The last decade’s explosion of digital social media has put paid to the effectiveness of such rough heuristics. The process of customer conversion has become complex to such a degree that data-driven Marketing Attribution analysis is fast becoming the default ‘correct’ method to strategize for marketing and to allocate budgets.
Attribution Software can run models as simple or complex as required in individual business situations. They can analyze large, complex datasets of customer behavior, throw colorful light on the effectiveness of particular marketing interventions, and thereby enable quick decision-making.
Marketing Attribution Software leverage advanced (data science) and machine learning to objectively determine the impact of each marketing touch(point) along a customer’s journey toward conversion. – “Top 22 Marketing Attribution Software”, Predictive Analytics Today
As the data analyzed for marketing attribution pertain to customer interaction touchpoints and customer behavior at and after those touchpoints, this data is typically captured by other process software, such as those used for POS, loyalty programs, marketing automation, sales analytics, etc. Ease of integration, therefore, is a key factor in zeroing in on a particular Marketing Attribution Software for purchase.
Classified by the type of modeling logic used, there are three broad categories of Marketing Attribution Software.
Single-touch and Multi-touch Attribution Software (ST- and MTA)
Multi-touch Attribution Software is used to identify and assign weights to marketing touchpoints across multiple channels to establish their sequence and influence on customer conversions. Multi-touch Attribution helps determine the value of each touchpoint in driving conversion.
Several attribution models, as described below, are commonly used in multi-touch attribution software. Two of the models (the first two below) attribute conversion to a single touchpoint as solely responsible, and software using these are called Single-touch Attribution Software.
First-Touch (or First Interaction) Attribution
Under this model, the first traceable interaction that leads to the customer finally converting is considered as responsible for the conversion. This is useful in cases where a series of marketing touchpoints is planned, in which the success of the first touchpoint is critical to the completion of further touchpoints and eventual conversion.
Last-Touch (or Last Interaction) Attribution
This model lays conversion success entirely at the door of the last touchpoint. This is the simplest attribution model, as it does not require tracking of touchpoints prior to the last one before conversion.
The linear model apportions credit equally to all the touchpoints that the customer goes through prior to conversion. The premise of this model is that every touchpoint has equal value in bringing about the desired outcome.
As the name suggests, this model considers the first touchpoint in the customer’s interaction journey and lead conversion as the most important, while also crediting the in-between touchpoints, albeit to a lower degree. Typically, the first touchpoint and lead conversion are attributed to generate 40% each of total conversion value, with the remaining 20% value brought in by the intermediate touchpoints.
This is similar to the U-shaped model in crediting the first touchpoint and lead conversion with higher importance, but differs in assigning equal value to opportunity creation. Thus, usually, 30% value each is assigned to the three touchpoints considered important, and the 10% that remains goes to the remaining touchpoints.
Time Decay Attribution
The Time Decay Model holds that touchpoints are as valuable as they are close to the conversion point. Thus, the last touchpoint is assigned the highest value, value decreases with distance of the touchpoint from the end. The first touchpoint receives the least, usually non-zero, weightage.
Full Path Attribution
This is a highly technical attribution model that is suitable for organizations that do marketing primarily to existing sales prospects. It is similar in treatment to the W-shaped model, but adds a fourth touchpoint to the list of high-value ones – the customer close. 90% of attribution value is assigned equally to these 4 touchpoints, and the leftover 10% is distributed among any remaining touchpoints.
Custom/ Algorithmic Attribution
In cases where the standard models do not accurately reflect the customer’s touchpoint path toward conversion, a custom attribution model may be built, incorporating an algorithm that reflects the real conversion process.
Attribution models aren’t perfect, of course. Multi-touch attribution models do use approximations in the assumptions they make, and so, may not exactly correspond to every customer’s actual personal experience. Certain restrictions imposed by large platforms like Google, Facebook, and Amazon, and some browsers, also make it difficult to access relevant data. Besides, offline experiences or touchpoints are off-limits for these models.
Marketing Mix Modeling (MMM)
Unlike Single- and Multi-touch attribution models, Marketing (or media) Mix Modeling does not track and analyze individual touchpoints of each customer’s conversion path. Instead, Marketing Mix Modeling uses multivariate regressions on aggregate data to calculate the impact of specific sales and marketing activities on customer behavior.
Data pertaining to environmental factors, such as seasonality, competition, macroeconomic conditions, influencing touchpoint outcomes and final conversion, which are not considered in multi-touch attribution models, find a place in Marketing Mix Modeling.
Multi-channel Attribution (MCA)
Multi-channel attribution uses individual level data, but its focus is on weighing attribution credit by channel and not per touchpoint. In some versions of MCA, data pertaining to online and offline channels are incorporated to map out the customer’s full path to conversion. Unified models (called Unified marketing Impact Analysis or Unified Marketing Measurement – UMM) that blend MTA and MMM seamlessly into a single set of rules are becoming increasingly popular.
The benefits of UMM over MTA and MMM are:
Online and Offline data:
UMM delves into data on offline aspects of campaigns alongside the online ones, thus providing insights into customer behavior across disparate online and offline channels in a normalized, integrated way that is more reflective of the real world.
Aggregate and Touchpoint-level data:
Trends and environmental factors are represented by aggregate data, which provide the context for touchpoint data, together presenting customer conversion paths in their real context.
Advanced UMM platforms can provide real-time insights and analysis, allowing marketing to pivot mid-campaign to optimize spend.