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As VP of Analytics, Steven helps marketers understand the impact of a single campaign or marketing channel on sales through sound attribution practices and solutions. We recently sat down with him to learn more about attribution and how it helps marketers.
In today's always-on marketing environment, marketers use a variety of channels, strategies and incentives to drive customer behavior. But not all marketing efforts are created equal – some are more effective than others.
Attribution, also known as “matchback”, is the process of allocating or attributing sales or other responses (point/coupon redemption, product registration, social channel follows, etc.) to a single campaign, channel or combination of both. Attribution solutions typically assess the impact of direct-to-consumer channels, such as direct mail, email, display and social channels, on sales in order to understand return on advertising spend. All marketers want to optimize returns on these investments and the first step is to understand how different initiatives contribute to a brand’s overall business objectives.
Identifying which campaign(s) deserve credit for a given transaction has become increasingly difficult. Twenty years ago, attribution efforts generally had to solve for one or two direct mail touches and perhaps phone calls. Today, marketing programs include promotional and triggered emails, display ads, push notifications, SMS and social ads. Consumers also transact via search, without the influence of advertising. Calculating the impact of direct marketing on the bottom line requires the right tools and experience.
There are generally three types of attribution:
Last Touch attribution assumes the campaign immediately prior to the transaction is responsible for the transaction. For example, if a consumer receives two pieces of direct mail followed by an email prior to making a purchase, Last Touch attribution gives all credit to the email and no credit to the preceding direct mail initiatives. But in order to provide the most thorough view, this scenario would benefit from additional business rules to consider the influence of direct mail on purchase behavior.
Fractional attribution assumes multiple campaigns influence a transaction. The credit for the sale is attributed across campaigns through weighting. There are generally two types of weighting:
Equal weighting, which attributes the same level of influence to multiple campaigns
Unequal weighting, which attributes different levels of influence to each campaign. For example, through analysis we can determine that:
Media Mix attribution is the most sophisticated methodology of the three, as it is based on predictive equations which estimate the impact of each marketing initiative on sales or other types of response. As opposed to business rules used in Last Touch and Fractional attribution, Media Mix models and equations consider marketing from all channels, including non-direct ones such as search, affiliate programs, TV and radio. The equations are used to assess impact and assign the appropriate share of sales to each promotion. Media Mix does more than determine attribution; it serves as a tool to optimize marketing budgets based on changes in spend for each channel
The following table assumes a consumer spent $100 with brand X on February 3rd; the consumer received four marketing campaigns within one month of the purchase (on January 5th, 10th, 15th and February 2); and the consumer used search the week prior to the transaction (Jan 26 – Feb 2).
It depends on the complexity of your marketing environment, frequency of communications across channels, and the resources (time and cost) available to develop the right attribution model. When choosing an attribution solution, consider the following:
Partially. Good attribution gives marketers a solid indication of return on marketing spend. However, assessing incremental value generated by a specific campaign requires test design and the use of control groups. For more information on test design, check back to our Ask the Expert page for an in-depth post on the topic.
As Vice President in Yes Marketing’s Analytics & Insights organization, Steve has extensive experience providing consultative analytic support to marketers. He manages a team responsible for the development, testing and implementation of analytical solutions including revenue attribution, predictive modeling, customer segmentation and custom analysis applications.
Steve has a BA in Mathematics (Statistics) and Economics from St. Olaf College in Northfield, MN. He works out of Yes Marketing’s Minneapolis office.