Advertisers who run online advertising campaigns often utilize multiple publishers concurrently to deliver ads. In these campaigns advertisers predominantly compensate publishers based on effort (CPM) or performance (CPA) and a process known as Last-Touch attribution. Using an analytical model of an online campaign we show that CPA schemes cause moral-hazard while existence of a baseline conversion rate by consumers may create adverse selection.
Consumers are exposed to advertisers across a number of channels. As such, a conversion or a sale may be the result of a series of ads that were displayed to the consumer. This raises the key question of attribution: which ads get credit for a conversion and how much credit does each of these ads get? This is one of the most important questions facing the advertising industry today. Although the issue is well documented, current solutions are often simplistic; for e.g., attributing the sale to the most recent ad exposure.
We develop a descriptive method to estimate the impact of ad impressions on commercial actions dynamically without tracking cookies. We analyze 2,885 campaigns for 1,251 products from the Advertising.com ad network. We compare our method with A/B testing for 2 campaigns, and with a public synthetic dataset.
System and method for specification of a marketing mix econometric model using feedback from a digital attribution system
This disclosure provides a method including analyzing outputs from a digital attribution system, determining potential input factor attributes that are highly correlated to output dependent variables, and generating a specification for an econometric marketing mix model, the specification including the input factor attributes thus determined. The result is a marketing mix model specification that is more accurate for backcasting and forecasting than what prior approaches can produce.
Time-weighted attribution of revenue to multiple e-commerce marketing channels in the customer journey
In this paper we address statistical issues in attributing revenue to marketing channels. We describe the relevant data structures and introduce an example. We suggest an asymmetric bathtub shape as appropriate for time-weighted revenue attribution to the customer journey, provide an algorithm, and illustrate the method. We suggest a modification to this method when there is independent information available on the relative values of the channels. We compare the revenue attributions suggested by the methods in this paper with several common attribution methods.
Assigning credit to different marketing activities has long been an important but challenging goal for a marketer. With the advent of digital marketing, the marketer can now potentially record each interaction with a prospective customer. With this development it is possible to measure and assign credit for each marketing interaction. We propose an econometric model to estimate the true incremental number of purchases that can be attributed to a given marketing channel. We extend our model to attribute credit for revenue realization.
Multi-channel attribution is the process of understanding and assigning credit to digital and non-digital channels and tactics, which eventually lead to conversions and sales. This paper provides a comprehensive view into what multi-channel attribution is, including specific models. It will explore a real-life example of attribution modelling and explain how to link digital interactions to offline conversions. It will then outline how to improve cost per acquisition by focusing on the most valuable channels and tactics to the business.
Consumers of all types increasingly communicate with friends, family and companies via social networking sites. They are not shy in posting their opinions about experiences with companies, both good and bad. These comments hold rich insights that can drive business decisions in the areas of customer service, product development and marketing, but have yet to be utilised effectively by many companies.
Digital marketing attribution faces a number of challenges as the online landscape evolves. Flaws in last-click models are being exacerbated by the growing move to cross-device usage. With this trend unlikely to change, it is clear that accurate digital marketing measurement requires an understanding of the behaviour of people, rather than devices. This paper presents lift-testing as a methodology that allows for the building and validation of a robust cross-channel digital measurement framework.
The mobile phone is unique in that it is the bridge between the online world (ie the internet) and offline world (ie the real world). The device is used to interact with the real world through the internet to ride with Uber, interact with iBeacons or simply get directions to a store. As such, it has dramatically changed the consumer’s path to purchase. One of the biggest impacts is the consumer’s return to the phone call, given the ease of clickable phone numbers in search results and on mobile web pages and apps.