As big data continues to gain volume, validity and velocity, the imperative for marketers—particularly those in the traditionally linear television space—is to acquire the knowledge and skills to take the initiative, articulate and respond to clients’ needs.
The TV ecosystem will incrementally slip into the data space, catalyzed by new distribution platforms, new content consumption behaviors, new targeting capabilities, new analytics-driven intelligence, and maturing automated buying. Despite the foreignness of the language, and the near-overwhelming plethora of acronyms, we do not all have to instantly metamorphosize into data scientists.
Conversely, this is an opportunity for us to better grasp how data can have real-world applications (and implications), understand the vernacular and varied methodologies of data-science-based research, and find new, yet creative, ways to deploy data adroitly to effectively accomplish marketing goals.
Data is merely a new asset within our playbook, albeit a powerful one. That said, the first step in using data is understanding what it means. If you can’t talk the talk, you certainly won’t be able to walk the walk. To get started, we can start littering our conversations with the following commonly used nomenclature; some may be more familiar than others.
A broad, catch-all term for vast amounts of data that cannot be processed using decrepit technology. This data might be structured, or unstructured. Challenges include data capture, storage, analysis, curation, search, sharing, transfer, visualization, querying, updating, and, most critically, information privacy. As the saying goes, it is not the size of data that matters, but what you do with it.
This is the act of connecting entities (e.g., a marketer’s customers) in one data set to their corresponding IDs in another data set. For instance, you could match cookie data culled from your website to CRM data about your customers and make sure Joe Customer gets treated the way Joe Customer deserves whenever he’s on your site or in a brick-and-mortar store. To comply with privacy regulations and best practices, offline data needs to be anonymized before it can be matched to online identifiers.
Data onboarding is the process of matching data collected offline to data collected online. A marketer’s offline customer data may include first-party information, for example, purchase/ownership data (collected in-store) or loyalty-card data, as well as third-party information like demographics and buyer interests and propensities (sourced from Experian, for example). This offline data can be matched to online identifiers (e.g., Devices and Digital IDs) in a privacy-compliant way by companies like Experian, LiveRamp or Neustar, modeled to scale, then delivered (anonymized) to the ad platforms, DMPs, and social channels, where advertisers can run their targeted campaigns. This process powers more strategic Cross-Channel, and Omnichannel, campaigns.
Before any data onboarding, or matching, can occur, the handling of Personally Identifiable Information (PII) is critical. PII is a highly sensitive issue in information security, as well as privacy laws. PII is information that can be used to identify, distinguish, or locate a single person, or to pinpoint an individual in context (both behavioral or to differentiate one person from another). PIIs can take the form of first/last name and postal address, email address, or (unhashed) customer ID.
Data matching: deterministic/probabilistic
Data matching can be either deterministic or probabilistic. Deterministic matching performs comparisons based on given factors and weighing calculations on two data records to determine a precise match. It then generates a score for whether the data records match. In contrast, Probabilistic matching takes into account the relative closeness of the data and the context of the data records, and then assigns each of the identifiers a weighted score for the likelihood that the data records match. This is considered a “fuzzier” methodology.
The Match rate is the percentage of corresponding/overlapping IDs when two disparate data files (offline IDs to cookies, pixels, or tags, for example) are combined. This is a key factor in validating how well a marketer can execute cross-device identity mapping. The higher the match rate, the greater the viability—or adroitness—of any insights gained from the matched data set. For example, a 70% match rate means that 70% of the two files used for matching corresponded to each other, while the remaining 30% did not have a common identifier/characteristic, which precluded it from the match. Unmatched IDs (depersonalized) are typically discarded in a match: The percentage of unmatched IDs is called the attrition rate. As data quality and identity unification (across media) become increasingly vigorous, the attrition rate will (we hope) become correspondingly lower.
As the scale of marketing efforts grow, and granular targeting capabilities of both offline (TV, in particular) and online channels/audiences evolve, it is apparent that marketing needs to become more custom. One-to-one marketing is about delivering experiences that have been created to cater to the likely preferences of a given customer rather than broad segments.
Single source is the gold standard of attribution measurement for TV and other media/marketing exposure and purchase behavior over time for the same individual or household. This measurement is gauged through the collection of data components supplied by one or more parties overlapped through a single, integrated system of data collection. In TV advertising measurement, single-source data are used to explore an individual’s loyalty and buying behavior in relation to advertising exposure within varying windows of time (e.g., year, quarter, month, or week). In this sense, single-source data is a compilation of home-scanned sales records and/or loyalty card purchases from retail or grocery stores and other commerce operations; TV tune-in data from cable set-top boxes, or people meters (push-button, or preferably, passive) or household tuning meters; and household demographic information. The value of single-source data lies in the fact that it is highly disaggregate across individuals and within time. Single-source data reveals differences among households’ exposure to a brand’s ads and their purchases of those brands within advertising fluctuations. (Credit for this descriptor goes to Bill Harvey, the progenitor of single source.)
If someone saw a banner, a tweet, a TV ad, and a magazine ad, how can you tell which led to his or her decision to buy? The basic aim of attribution modeling is to figure out which marketing actions or channels contributed most to a certain customer action. More specifically, it is about using analytics to give credit where credit is due, and knowing how much credit is due. This gives you the data you need to optimize everything, from budget allocations to messaging to campaign strategies.
The confusion experienced by most TV marketers when they encounter the fragmented nightmare that is their data layer. Or after reading this article.
Boon Yap serves Standard Media Index (SMI) as its first-ever vice president of product and partnerships, responsible for creating strategic relationships with a focus on market research companies, and developing attribution products that help brands better direct their advertising expenditure. Yap has nearly 20 years of experience working within all sectors of media including data, digital, mobile, social, and TV. Prior to SMI, Yap served as director of partner success at TiVo Research, where he helped create and scale some of the larger data partnerships with companies like Oracle, LiveRamp, Cardlytics, The Trade Desk, and Quantcast. In his MPG and IPG agency days, Yap spearheaded ground-breaking, and award-winning, investment strategies for companies like Volkswagen, American Legacy, Orbitz, IAC brands, Fidelity Investments, Royal Caribbean, Olympus, Mass Mutual, and Wachovia Bank. He has also implemented SEO, digital, social, and web-architecture strategies for various entities.