In our data-driven world, digital marketers are leaning hard on insights from customer information to shape decisions, strategies, and engagement. But simply having data isn’t enough. Businesses need to collaborate and share data to truly unlock its potential. This allows them to gain new perspectives, improve products and services, and drive sustainable growth.
This guide delves into the best practices for data collaboration, offering a roadmap for forging powerful partnerships and preserving connectivity in a world without third-party cookies.
Data collaboration is when two organizations or partners join forces to combine and analyze their data assets, surfacing valuable insights that would be missed if they worked in silos.
According to the IAB, data collaboration can be defined as the use of technology to combine and analyze data sets within an organization or with partners to enable a wide range of use cases, from uncovering new consumer insights and enabling accurate cross-screen measurement to expanding reach and creating brand-building media networks.
At its core, it’s about different stakeholders — whether within the same company or across different companies — coming together to permission data with the goal of extracting insights to fuel marketing strategies. You see this playing out across industries, as companies use collaboration to break down data silos, fuel business intelligence, and spark overall innovation.
With data privacy laws tightening and third-party cookies going away, data collaboration is essential for digital marketers to make the most oftheir data, all while navigating a new landscape.
The rise of data collaboration has been propelled by the demand for scaled, diverse data sets, the push to make data-driven decisions, and the challenges created by organizational silos. Collaborating allows companies to combine their collective insights to better steer strategy and understand customers.
First-party data refers to information obtained directly from your audience or customers. This includes data collected from sources such as:
The direct connection between the collector (business) and the source (customer or audience) ensures a higher level of accuracy and relevance, making first-party data extremely valuable.
Finding ways to increase the value of your first-party data is becoming more and more important as third-party cookies disappear. By uncovering direct insights into customer behavior, preferences, and feedback, businesses can tailor their products, services, and marketing strategies to meet the precise needs of their target audience. This customization enhances customer experience, boosts loyalty, and drives conversion rates. Best of all, no third-party cookies are required.
External data collaboration represents a paradigm shift in how organizations approach second-party data and analysis. Through collaboration, companies can unlock new insights, enhance customer experiences, and drive innovation. However, embarking on a data collaboration initiative requires careful planning and execution.
Here’s how organizations can set realistic expectations and ensure the success of their data collaboration efforts.
Before initiating a data collaboration project, it’s crucial to define clear and measurable objectives. Whether it’s enhancing customer insights, developing new products, or improving marketing strategies, having a clear understanding of what you aim to achieve helps guide the selection of partners, data, and technologies.
Ensure that your data collaboration efforts align with your broader business strategy. This alignment helps to secure buy-in from stakeholders across the organization and ensures that the collaboration drives value towards achieving overarching business goals.
Timelines are essential for managing expectations and ensuring the smooth execution of data collaboration projects. Establish realistic timelines for the exchange of data, analysis, and the implementation of insights. Factor in potential delays and build in time for thorough analysis before utilizing insights for campaigns or strategy sessions.
Selecting an appropriate data collaboration platform is a critical decision that requires careful consideration of various factors to ensure the success of your data initiatives. Here are several factors you should consider:
The clean room landscape showcases various types of data collaboration platforms, each catering to different needs:
Once you’ve decided what data collaboration platform or technology you will use, it’s time to choose the partner you will collaborate with.
Use the following questions to help you determine the right partner for your collaboration.
Now that you have chosen the appropriate partner to collaborate with, it’s time to get into the nitty-gritty. First, you should share with your partner how the data will be collected. Provide clear descriptions of how the data is sourced, highlighting what makes the data unique. The Lotame Data Exchange’s newest taxonomy focuses on data sources, like contextual consumption, location, transactional, and survey-based, to easily analyze what people read, where they spend their time, and what they spend their money on, as examples.
Next, it’s time to categorize the data. Group data into meaningful categories (demographics, interests, buyer propensity) for easier analysis and targeting. High-level categories are helpful for broad and quick analysis, but typically more meaningful data is found in the lower-level interests within taxonomies and these should be formatted in a way to easily extract insights.
Organize data in multiple levels to easily generate insightful charts and reports (i.e., using pivot tables) for partners. Data analysis charts are often organized by taxonomy source (ex. contextual or keywords), customer types (ex. loyalists or high spenders), or personas. These can all inform a more well-rounded recommendation from a media placement, creative strategy, or audience target perspective.
Identify and exclude information not relevant for collaboration. Data specific to other brands or use cases may create noise for some partners.
Data collaboration platforms are an emerging technology within the marketing technology ecosystem. A subset of data collaboration is clean room point solutions. They have been making headlines as a way for companies to work together in a highly controlled manner to join and analyze data in a privacy-compliant way. However, clean rooms are just one option for data collaboration. They may not be appropriate or accessible for all use cases, so you may want to explore all available options for collaboration before making a decision.
Below are some common collaboration scenarios to help inform partnerships and campaign planning with built-in data clean room technology:
Lotame’s end-to-end data collaboration platform, Spherical empowers marketers and media owners to unite, analyze, and activate first-party data in smarter, faster and easier ways. With our technology, you can connect the dots between different pools of audience data with both internal and external partners to generate actionable insights, data-driven audiences, and identity-powered activation. Gain everything you need to know about your consumers to power your company’s innovation and help hit revenue goals. Contact us today to learn more.
This post was written by Lotame’s VP of Consumer Intelligence & Analytics, Kristen Whitmore.