This article is written by Snowflake and originally appeared on the Snowflake Blog here: https://www.snowflake.com/blog/how-marketers-can-drive-roi-from-customer-data-platforms/
As first-party customer data continues to explode, companies have struggled to make it actionable for personalization, advanced analytics, and other business purposes. As a result, customer data platforms that consolidate and activate known customer information have emerged to help companies generate ROI from their data. At present, nearly 80% of marketing organizations already have a customer data platform or are developing one.
Most customer data platforms offer similar core functionality. First, they ingest first-party customer data from dozens or even hundreds of sources in real time, including identifiers such as emails or device IDs as well as demographic or psychographic information. Second, they consolidate profiles on an individual basis, tying attributes to identities. Finally, they enable marketers to segment and share these profiles with marketing systems to personalize the content of email campaigns, digital ads, and other channels.
To successfully launch and operate a customer data platform, start with the four tips below. For more detail and recommendations download our ebook, 5 Tips for Implementing a Customer Data Platform.
IDENTIFY YOUR COMPANY’S PRIMARY GOAL
Customer data platforms run the gamut in terms of functionality and features but typically fall into four benefit categories: making ad spend more efficient, increasing marketing team productivity, reducing engineering costs, and surfacing insights. Start your search for the best-fit system with a strong understanding of your organization’s goals and make sure your wider team is aligned on the business objectives a customer data platform can help achieve.
CONSIDER THE ADVANTAGES AND DISADVANTAGES OF BUILDING VS. BUYING
Buying an off-the-shelf solution has the considerable advantage of making it easy to start gaining insights into straightforward areas (for example, email marketing) right away. This often makes buying ideal for smaller businesses whose needs are relatively simple. Conversely, building a custom customer data platform solution requires more lead time and significant engineering resources, but the higher upfront cost may have a bigger payoff.
The complexity of a company’s customer data can also make an out-of-the-box customer data platform solution untenable. In a recent Snowflake survey of marketers, 20% of respondents report having over 50 sources of customer data; it would be unrealistic to expect a purchased solution to be configured to ingest data from all of them. But if a customer data platform solution were built on top of a company’s data warehouse, all of the data sources would be present there.
PUSH DATA TO MARKETING CHANNELS FOR PERSONALIZATION AT SCALE
By pushing data out to marketing channels, customer data platforms enable marketers to send personalized content, offers, and experiences to granular audiences. To make that happen seamlessly, you need to find a way to connect customer data to your email platform, Facebook, and other customer touchpoints.
If you opt to buy an out-of-the-box customer data platform solution, you can leverage one with prebuilt connectors to platforms such as Google, Facebook, and popular email systems. Conversely, if you’re building your own solution, you need to design a system for syncing data from your data warehouse to your various marketing channels. This comes with great flexibility and power but a higher integration cost.
UNDERSTAND HOW MACHINE LEARNING CAN SCALE YOUR EFFORTS
Machine learning (ML) technology can advance your personalization initiative in several important ways, informing decisions about media spend, channel mix, merchandising, and customer experience.
Many out-of-the-box customer data platforms leverage built-in ML models, which are powered by structured data sets that virtually every marketer has, such as email touchpoints. But if companies want to uncover insights in areas that are more unique to their business, an investment in proprietary ML models becomes necessary.