PART TWO: The Nuts & Bolts
Choosing a consumer data partner is a multi-phase process; the ecosystem is crowded, and it is often difficult to tell which provider has the best solution to meet your needs. Part One of this three-part series covered the basic questions to ask when evaluating a potential partner.
In this second installment, Mobilewalla outlines the technical nuts and bolts that need to be addressed by a potential consumer intelligence provider.
Getting a sense of how the provider collects and analyzes their data is essential to parsing out the true differentiators of the partner versus other companies in the ecosystem. There are three key areas to focus on when evaluating a new data partner’s product offering.
1. Data Collection
How are the audiences captured – via raw data or curated data feeds? What kind of attribution are they offering? How does location factor into their data collection? How often is their data refreshed? How long is their data stored for? What is their location data based on: Lat/Long, IP, proximity size? Vertical location (buildings with multiple floors)? Is the data that is collected cookie-based or ma-based?
- Knowing whether the data is collected through raw data feeds or curated data will indicate how dynamic the partner’s data will be once you go to integrate it with your existing intelligence. Raw data will be easier to manipulate, but the findings will be somewhat less intuitive; similarly, curated data will have intuitive findings but will be less customizable over your existing data.
- Outdated or old data can reduce effectiveness. The more data they have over time, the greater the audience insights that can be provided, as well as an overall more stable view of the user.
- It is important to understand how granular the partner can be when it comes to targeted location-based marketing. Ask about their methodology, accuracy, and how often they update their points of interest locations. If l is not accurate and fresh, campaigns and efforts will suffer.
Does their collection, modeling, or products implement areas of machine learning or a? Can they create lookalike models off of first party customer data? What is their core methodology? Do they have the ability to match their devices against your current customer devices? Are they able to provide additional insights and attributes to your current CRM? What types of audiences do they offer in terms of categories, demographics and locations?
- The more advanced the methodologies used, the greater the revelations and insights you will have at your disposal to decision off of; you’ll have access to a wider breadth of data. Lookalike modeling is a helpful solution for prospecting, and so it’s critical to understand what this looks like for your partner.
- Matching devices will increase the value and scale of your current customer set, which stretches your marketing dollars much further. The audiences offered will help you decide if the partner is the most appropriate choice for your specific audience targeting and insights needs.
- Get an understanding of how much support you anticipate needing to see an integration through to its completion; this will largely depend on your use case and your existing internal resources.
- Understanding where a partner fits into your current stack and marketing efforts will save you time in the integration process. It may also reveal solutions for other areas of your organization to maximize the value of this relationship.
- A user interface can provide you with more visibility and transparency, allowing you to self-manage your data, if that’s important to you.
Asking the right questions is critical when selecting a new data partner. Learning more about the nuts and bolts of a potential partner’s offering puts you in a better position to ready yourself for integration and setup.
The next post in this series will help you understand the integration and functionality questions that are essential to navigating the data ecosystem.
- Navigating the Consumer Data Ecosystem - Part Three - Tying Up Loose Ends
- Navigating the Consumer Data Ecosystem - Part One - The Basics
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