Customer insight fundamentals: Building a framework for access and activation

If you’re going to make an investment in machine learning and seek actionable insights from your customer data, you need to approach the effort strategically.

Customer insight fundamentals: Building a framework for access and activation

Our customer insight fundamentals blog series aims to unpack important components of effective customer data analysis, prediction, and activation strategies. This article was curated from Robbie Adler, Co-founder and Chief Strategy Officer at Faraday.


I feel like "turning insights into action” is the one of the most overused terms when it comes to marketing software data science services. Truth is, data and insights are relatively useless if your teams aren't prepared to act on them. So perhaps it's a bit ironic, but I want to focus on some critical learnings we've picked up around deploying customer insights and predictions at scale.

Set the right foundation and expectations

If you’re going to make an investment in machine learning and seek actionable insights from your customer data, you should not be looking for quick wins, done on the cheap. This doesn’t mean you need a team of data scientists or have to bite off a six or seven figure annual commitment, but it does mean you should approach the effort strategically.

At Faraday, we’ve spent a lot of time identifying what's needed at the foundation of an effective data science strategy for consumer brands (my colleague Tia covers data requirements in detail), but as a quick summary, we’d bucket them as:

  1. Identify your data sources.
  2. Identify the questions you are seeking to answer and why. Curiosity should not be the driver of these questions, but, rather, a clear path to action: "If I knew X, I would do Y."
  3. Identify the members of your team who will need to be involved in and supportive of the effort. In our work, these team members are most commonly involved in performance and field marketing, customer engagement, marketing strategy, and data science.

Build a solid data science framework

Once you have the right foundation in place, it’s critical you take the necessary steps to build on this. My colleague Bill chronicles how we approach predictive customer analytics for our clients, but to summarize:

  1. Survey your data. Avoid the “garbage in, garbage out” problem.
  2. Validate your models and insights prior to activation.
  3. Establish the integrations you need to support ongoing learnings and ease the path to activation. For us common integrations are data sources (e.g. data warehouses, customer data platforms (CDP), and CRM systems) and data destinations (e.g. ad platforms like Facebook and Google, marketing automation systems, and CDPs).
  4. Democratize access, so you’re not solely dependent on a unicorn, otherwise known as a “data scientist,” for activation. Good integrations inherently facilitate democratization. One small test of whether you’ve democratized access is whether access requires knowledge of SQL, R, or other programming languages. If so, you haven’t democratized access.

Audit team access and business impact

So, it’s finally time to activate. How can you tell if you’ve built the right structure?

  1. Are predictions and insights available in systems that your teams use in their day-to-day work? At Faraday, we’d want your performance marketer to see high-propensity audiences in Facebook, while we’d want your customer engagement marketer to see customer personas and product recommendations scores in their email marketing platform.
  2. Are your predictions and insights being clearly leveraged to guide strategy? At Faraday, this often manifests as our clients personalizing ad creative and copy, deploying promotions strategically vs. broadly, launching new products targeted at a specific subset of existing customers, and/or entering new markets based on predicted propensity of its inhabitants vs. qualitative bias or preference.

Learn more about customer insight discovery at Faraday and check out our integrations.