Analytics

Introduction to Marketing Attribution & MTA Models

November 8, 2022
James Kinsley
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What is Marketing Attribution?

Marketing attribution uses a flexible form of data analytics that analyzes each step of a customer’s journey, up to the point of sale, in order to assign value to different touchpoints. The ultimate goal is to help marketers understand how different activities are contributing to sales. It helps with optimization of budget allocation as well as specific campaign activities, and measuring ROI.

Traditional data analysis tools only provide a one-dimensional view on conversion; looking at the last visit that the conversion happened in. Multi Touch Attribution reports record the complex way a customer can progress from initial interactions to conversion, acknowledging each of the many sources and activities that influence their journey.

The Importance of Marketing Attribution

Using three or more channels produces a 250% higher purchase rate, as Omnisend’s research shows, as well as a 90% increase in retention. It is vital to utilize a wide variety of channels to reach and nurture prospective leads.

Applying the same level of attention and resources to a larger number of channels can make marketers’ work much more challenging. Their attention is more divided, and additional unnecessary work can be created as some sources may yield a smaller impact. Giving credit to the correct interactions and exposures becomes more crucial than ever.

A marketing attribution report provides a clear picture of the success of each of the channels being used. This provides marketers with an overarching view of the ROI from different channels so they can decide where to allocate future budgets. For a follow-up campaign, more priority may be given to email marketing, or social media may be boosted, for example. This removes the need for guesswork when planning the next strategy, and future campaigns can be optimized.

Attribution can also be looked at in terms of content, not just the traffic sources. You can focus your attention on the parts of your website that impact a user’s decision and contribute to a conversion. As you investigate content in your individual web pages and blog, you can analyze and easily see which types of content perform better in your attribution report. You gain an understanding of which web pages are visited the most in converting sessions and should therefore be further optimized and promoted.  

Attribution trends can also be applied to the same categories that your business communication is filtered through, e.g., personas, groups, and stages of the customer lifecycle.

Marketing attribution determines how funds can be saved by reducing spending on less profitable channels. Investing in activities at the less competitive top of the funnel brings more customers that can be efficiently guided through effective touchpoints leading to more conversion. The more expensive channels in the lower part of the funnel can be used more judiciously. All of this combines to create higher conversion rates and greater ROI.

Choosing The Right Attribution Models

There are several types of marketing attribution models that track different combinations of channels and resources. These can be chosen and combined according to the different marketing channels you are operating in. Considering the type of sales cycle you use and its duration will enable you to make the right choice.

Different models focus on different parts of the sales funnel, so adapting a sales funnel to a new attribution model is a necessary consideration. You will also need to factor in whether your current goal for building your brand is based on leads or revenue when deciding on a model.

Attribution models fall under two categories: single-touch, and the more recent models known as multi-touch which incorporate more data about the user journey.

Single-touch models

These more simplistic models only display a single point of interaction and are rightly considered quite outdated. They leave a big gap in understanding when it comes to the different activities that may have led to a customer’s decision to convert.  

First touch 

This model only presents information on the first aspect of marketing that a customer experiences. It discounts the effectiveness of any interactions that followed which may have helped the customer complete their journey toward understanding and engagement with a product.

Last touch

The final piece of advertising before a conversion that a user engaged with is given the full weight of analysis in this model. It assumes that this was the most influential interaction and therefore the most responsible for a successful conversion.

It misses out on other key interactions in a customer’s journey over time that may have contributed to a customer’s confidence and willingness to complete a purchase. It is also the most commonly used model in recent times, and the default in Google Universal Analytics.

Last non-direct click 

Here the last non-direct interaction before a conversion is given all the credit. This is last click, but removing attribution to ‘direct’ channel which is either a direct visit (typing the URL into a browser, or clicking a browser bookmark link) or an untrackable visit. So, if the last click was direct, the attribution will move back one position and apply 100%.

Multi-touch models

These models all take into consideration the complete set of channels a user has engaged with on their journey. Interactions from two upwards are included.

 

There are several multi-touch attribution models that take advantage of different combinations of steps in a customer’s journey that can be grouped in an analysis. Various mathematical ratios then guide how much credit each touchpoint is given towards achieving a conversion.

Linear

The linear attribution model gives equal weight to every stage and platform of the user’s journey. This is a simplistic model which can quickly give an overview of performance, and highlight channels which have really underperformed others.

Time Decay

Progress along the journey to a successful conversion is charted with individualized and increasingly larger measurements for each touchpoint. More credit is given to each interaction as the user nears the final conversion moment.

This is based on the assumption that one channel leads to the next and that each channel is more effective than the previous one.

Position-based / U-Shaped

Regardless of the chosen channels, this model picks out both the first and last touchpoints and gives them equal, heavier weighting. The channels in between these two positions are seen as less influential in the user’s journey and are not given as much credit. This will often be a 40,40 split to the first and last, with 20% spread evenly to any channel's middle.

A slightly more advanced version of the position-based model is possible where the remaining interactions between the first and last are also given differing amounts of credit. It is based on the idea that a customer’s first and last interactions are the most valuable, and that after initial interest there is increasingly less engagement until engagement levels gradually rise again.  

W-Shaped 

Here, attribution is given to specific moments in a user journey. 30% is given to First touch,

30% to Lead Creation (when a customer goes from being a prospect to a viable lead), 30% to Opportunity Creation (the last touch before a lead becomes a customer) and 10% to all other touchpoints outside of these 3 attributed touches. This model is more regularly used for b2b companies.

Conclusion

Mass-market analytics only display a small part of the response to your marketing efforts and leave a myriad of questions unanswered. As competition in multi-channel marketing increases and customer journeys become more complex, measuring the ROI of your campaigns is more important than ever. A dollar wasted in a channel that doesn’t work is one not spent in a channel delivering return. To drive growth in today’s saturated digital environment, you need better information than your competitors – and attribution modeling can provide this.

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