Being able to accurately measure and attribute success to marketing tactics was fairly easy when there were relatively few marketing channels, such as television, radio, print and direct mail in the 1960s. With the advent of digital marketing, attribution is no longer simple, and it is growing in importance. Fractional attribution rooted in proven math and statistical techniques is a critical tool to accurately improve/optimize the performance of an incredibly fragmented and complex system of channels and media, both online and offline.
With the rapid adoption of smartphones and tablets, the number of internet-enabled devices per internet user has increased over the last several years. As users employ multiple devices to complete their online objectives, the data used to understand online behavior are increasingly fragmented. Piecing together a complete picture of activity requires the ability to identify users on every device used. Without user identity, there is no way to connect events on one device to events on another.
Social media are becoming ubiquitous and need to be managed like all other forms of media that organizations employ to meet their goals. However, social media are fundamentally different from any traditional or other online media because of their social network structure and egalitarian nature. These differences require a distinct measurement approach as a prerequisite for proper analysis and subsequent management. To develop the right social media metrics and subsequently construct appropriate dashboards, we provide a tool kit consisting of three novel components.
We propose that attributions about an endorser truly liking, using, or desiring a promoted product mediate the relationship between source and message factors and persuasion via endorsement. In this paper, we integrate the persuasion literature into a framework for examining endorser effectiveness via focus factors (e.g., involvement, cognitive load) that determine whether a consumer thinks carefully or superficially about a message, and lead consumers to rely on different source and message elements (e.g., source attractiveness, argument strength).
Our main objective in this paper is to measure the value of customers acquired from Google search advertising accounting for two factors that have been overlooked in the conventional method widely adopted in the industry: (1) the spillover effect of search advertising on customer acquisition and sales in off-line channels and (2) the lifetime value of acquired customers. By merging Web traffic and sales data from a small-sized U.S.
The article explains tracking as a trending digital marketing attribution approach for franchises. Topics discussed include a 2012 Google study with eConsultancy revealed benefits of marketing attribution like better budget distribution and improved knowledge of digital channel function, and tips for tracking digital marketing activities like making duplicate landing pages, tracking links and tracking codes.
What Drives Managerial Use of Marketing and Financial Metrics and Does Metric Use Impact Performance of Marketing Mix Activities?
To increase marketing’s accountability, JM, MSI, and ISBM have advocated development of marketing metrics and linking marketing mix activities with financial metrics. While progress has been made, less attention has been paid to what drives managerial use of marketing and financial metrics and whether metric use is associated with marketing mix performance.
Driving Online and Offline Sales: The Cross-Channel Effects of Digital versus Traditional Advertising
Today's marketing environment is characterized by a surge in multichannel shopping and
ever more choice in advertising channels. This requires firms to understand how both digital and
traditional advertising drive sales within the same channel (e.g., digital advertising affecting
online sales) and across channels (e.g., digital advertising affecting offline sales). We develop a
Dynamic Linear Model (DLM) to measure these effects. The model addresses: (1) the
Attributing Conversions in a Multichannel Online Marketing Environment: An Empirical Model and a Field Experiment
Technology enables a firm to produce a granular record of every touchpoint consumers make in their online purchase journey before they convert at the firm's website. However, firms still depend on aggregate measures to guide their marketing investments in multiple online channels (e.g., display, paid search, referral, e-mail). This article introduces a methodology to attribute the incremental value of each marketing channel in an online environment using individual-level data of customers' touches.