What s New in Attribution

Transcription

1 What s New in Attribution April 2017 VIEWPOINTS

2 What s New in Attribution Jason Chung Yuanying Huang Maya Kramer 1

3 Contents 3 Introduction 3 Cross-Device Measurement 4 Social Media 5 Integration of Top-Down and Bottom-Up Attribution Analysis 6 Moving Forward 2

4 Introduction Marketers are gaining access to unprecedented amount of data today. Advertising technology companies are offering new capabilities to impress and win consumers. Brands want to understand the optimal marketing investment, and using a data-driven approach helps to yield stronger results. Attribution identifies and measures the influence that consumer actions, marketing channels, strategies and tactics have on business outcomes, which can offer extremely valuable insights, inform your future marketing activities and help you meet the goals. It is at the heart of putting all marketing tactics in the same playing field to get a true view of performance and offer opportunities to improve advertising ROI. In this Viewpoint, we will focus on three key trends in attribution: cross-device, social media and integration of top-down and bottom-up attribution analysis. Cross-Device Measurement Cross-device measurement became a focus for marketers after smartphones and tablet devices entered the scene. Since then, consumer attention has become more fragmented. It is now the norm for people to have several different devices at their fingertips. For example, a consumer may first be exposed to a brand while browsing the internet on her laptop at work, then Google it on her mobile device during lunch break, and finally make the purchase while at home on her tablet. Tracking the sequence of customer journey touchpoints allows marketers to identify the media tactics that are and are not working. Although cookies have historically been the main tracking vehicles for users online, they become ineffective for measuring users who are navigating between their devices. Why cookies are ineffective Cookies have tracking limitations on desktop due to different browsers, cookie expiration windows and cookie deletion, which all result in cookie loss. Cookies become even less reliable on mobile because they get lost when browsers close and users navigate from mobile web to apps and between apps, which makes them worthless when tracking across devices. As a result, one user may be tied to several cookies simultaneously. Cross-device measurement essential is for identifying unique users. So how do we do it? Cross-device measurement methodologies and partners In general, there are two ways to identify unique users across devices: probabilistic and deterministic models. The deterministic approach is more straightforward as it relies on a single unifying ID, which is usually a user login. For example, Google recently came out with a crossdevice measurement product that uses Google sign-ins in order to recognize users on Google 3

5 apps and properties regardless of the device they are on. Facebook is also a major player and coined the term people-based measurement with its purchase of Atlas, Facebook s measurement product. The advantage of deterministic matching is its accuracy (usually ~90%), while the disadvantage is lack of scale since it is limited to the size of the registered user base. The probabilistic approach is more complex. It inputs a collection of data points (e.g. IP address, browsing behavior) into probability algorithms to infer the likelihood that several devices are linked to a single user. While these algorithms generally allow for broader reach than the deterministic approach, there are trades-off between accuracy and match rates. The more matches, the less accuracy and vice versa. When evaluating probabilistic cross-device vendors, we recommend evaluating both. Drawbridge and Tapad are two of the most prominent vendors in the space. Social Media As advertisers continue to increase their social media spending, there is a vital need to quantify social media s impact as part of an omni-channel approach. emarketer expects that Facebook will make up 39.1% ($16.33 billion) of the US display market in In addition, the increased time spent on social media, a medium intrinsically linked with mobile devices, has driven growth in cross-device measurement. With the seamless integration of social into the mobile sphere, we expect spend will continue to increase. With these points in mind, algorithmic, multi-touch attribution must take social media data into account, both from a strategic and tactical standpoint. Social media is widely recognized as an effective top and middle-funnel marketing channel, especially effective in a symbiotic fashion with other channels. However, as social formats have developed and evolved, they have sometimes become superior middle to bottom-funnel tactics for nurturing and retargeting. Industry standard last-touch attribution can struggle to analyze the full impact of social when combined with a mix of media channels. Using an effective multi-touch attribution model can help marketers understand social s true impact on the customer journey, as well as enabling further ideation on how to best use social to impact bottom-of-funnel activity. One challenge of working with social media is the lack of measurement opportunities that exist for many platforms. Many of the social platforms operate as walled gardens, which makes measurement in the traditional cookie and click-based sense a challenge. Many attribution vendors are in the midst of developing partnerships with the various social platforms. For example, Neustar, Analytic Partners and Marketing Evolution have announced integrations with Facebook, using a people-based approach to augment traditional measurement solutions. Visual 1 Market/

6 IQ and Neustar have integrated with Facebook s Atlas as the measurement product running on their social graphs. Social media vendors are on track to work more cohesively with measurement partners to help determine success and how much more growth these social platforms have left. While social has and will continue to provide internal metrics to define its own success, most of these metrics are meaningless unless they are normalized against each other, especially since so many campaigns are comprised of wide-ranging channel mixes. Increasingly, consumers have multiple touchpoints on the path to a conversion event, so marketers must be aware of how consumers are progressing through that journey. Marketers must challenge social media platforms to increase transparency and continue to build upon partnerships with unbiased measurement platforms. Integration of Top-Down and Bottom-Up Attribution Analysis Two distinct advanced attribution approaches have helped brands understand advertising impact: top-down and bottom-up. Top-down attribution typically provides a macro-level view on marketing performance as a part of business performance evaluation. It measures the incremental impact of each marketing channel and provides guidance on strategic channel planning. On the other hand, a bottom-up attribution approach often uses individual data to quantify the impact at a much more granular level. MMM (Marketing mix modeling) is often used in a top-down, data-driven attribution approach. Availability of multi-year, time-series data enabled the use of these models, and they have been in place for decades. One advantage of MMM is that it not only captures marketing touchpoints, but also incorporates non-marketing drivers that impact sales, which allows for a more holistic view on business drivers. It typically provides macro-level channel impact measurement, but lacks more real-time feedback, very often due to data availability and the efforts required for establishing robust statistical models. Rapid updates and more availability of granular data have enabled faster turnaround and more detailed bottom-up digital attribution analysis. In addition to a channel-level view, digital attribution analysis can often provide details such as site, placement, creative, keyword, messages, etc., with shorter duration of historical data and daily model updates. While the two approaches can operate independently to serve different purposes, recent efforts are integrating the two. Brands need to evolve as consumers do. Initially, the integration is as simple as scaling the channel contribution from one system so it matches the contribution from another system. This essentially allows the two approaches to operate independently and merge at the end. Another integration incorporates macro-level variables in the digital attribution models 5

7 so the blending happens during the modeling phase. This method attempts to include all variables at once, but requires more consideration in setting up the models and update frequency and granularity. So far, there is no single standard for top-down and bottom-up integration, but it is clear that advertisers want to plan better and act faster in the multi-channel, multi-device environment. We are getting much more sophisticated in attribution and measurement, but more is to come. Moving Forward As attribution vendors race to keep up with the latest trends, cross-device measurement, social tracking and top-down/bottom-up integration have continued to be at the forefront. Although no one has cracked the code, certain vendors have made more progress than others through partnerships and evolving statistical approaches. When evaluating potential partners, it is important to understand how they plan to tackle these measurement gaps and how far along they are in the process. In this competitive space, these pieces will be the key points of differentiation. 6

8 What s New in Attribution Written by Jason Chung, Yuanying Huang, Maya Kramer Published by Neo@Ogilvy For the latest industry news, trends and happenings, follow us on Twitter For more information, please contact: Rachel Serton rachel.serton@ogilvy.com