Marketing data sources

1. Marketing data sources

In the last chapter, we introduced broad marketing concepts like channels and tactics. In this chapter, we will cover marketing data sources.

2. Marketing data needs

Marketing data comes from many sources, such as internal financial data, advertising tools, and website data. Even a single channel can use multiple advertisers with distinct data sources. Marketing Analysts utilize many data sources to analyze marketing engagement, cost, and revenue impact by different campaign attributes. Revenue data is usually stored separately from marketing engagement data, so analysts must join the data across sources to get a complete view of performance.

3. Campaign attributes

Campaign attributes are a type of metadata, or data that describes data. These attributes help Marketing Analysts compare data across channels and tactics. The most common examples of campaign attributes are campaign name and ID. We may also want to compare campaign types (like recurring holiday campaigns), or by campaigns targeting similar audiences.

4. Marketing performance by data source

Remember how we covered KPI themes in business questions to assess marketing performance? Partners inquire about overall business health or marketing health. Luckily, those groups map to data source categories of internal and advertising data. Business health metrics and dimensions about products, order quantity and revenue, and customer identifiers are all found in internal data. Advertising data contains marketing metrics and dimensions like campaign identifiers, marketing spend, and individual ad costs. Marketing Analysts need to assess the level of detail needed to answer a business question, and which source most likely contains relevant data.

5. Marketing channel granularity

Since marketing health data originates from advertisers, how would we approach comparing a TV campaign to a paid search campaign? Channel and tactic granularity is another complicating factor in analysis and reporting. Offline channels (like TV) do not tie ads to specific users, so they have the least granular data and aggregate at the campaign and tactic level. By comparison, online channels are very granular because they can be associated with devices and individual browsing behavior. Marketing Analysts have to navigate these granularity differences across channels in reporting.

6. Advertising data

Most campaign metadata comes from advertiser platforms. Advertiser platforms are systems and tools that facilitate the purchase and distribution of ads. Advertising platforms are used by marketers to execute media and monitor reports. When launching a campaign, marketers will log into the advertiser's tool to set spend limits, success metrics, and campaign metadata. To be able to advise on how to integrate advertiser data with internal data stores, a Marketing Analyst needs a strong understanding of advertiser data sources.

7. Audience targeting data

The final marketing data source that analysts should become familiar with is audience targeting tools. For example, marketers may have a target audience in mind, like "women who live in the US ages 35-49." Each advertising platform may have a different way of identifying these audiences, like "females" versus "women," or "US" versus "United States." Marketers use Demand Side Platforms, or DSPs, to more consistently set media spend by target audience, despite these differences between advertising platforms. Data management platforms, or DMPs, centralize audience and campaign engagement data across platforms. These tools work best when analysts use a DSP to understand target audience definitions and then a DMP to compare customer engagement data.

8. Audience targeting analysis example

Let's consider how DSPs, DMPs, and internal data can work together. First, use the DSP to look up target audience IDs and spend by different dimensions. Then look at DMP engagement data by the same target audience. Next, extract internal data to see all users (in target audience or not) to benchmark against. Finally, compare internal data to DSP spend and DMP engagement to evaluate performance.

9. Let's review!

Time to practice identifying the differences between marketing data sources!

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