How Customer Analytics Tools Democratize Data

More and more companies are looking for customer analytics tools that will enable their entire organization to apply advanced analytics, uncover meaningful customer insights, and quickly act on those insights to drive business growth.

It’s all part of the larger trend towards first-party customer data democratization.

Companies now realize they need to adapt their customer analytics practices to both apply predictive machine learning models and analytics to unified, first-party customer data and enable all parts of the business to immediately act on the output of those efforts.

The companies that can do both quickly and at scale are the ones that win today.

How modern customer analytics tools democratize analytics and data science

Soon-to-be-gone are the days where only a select few within a company utilize customer analytics tools that are completely detached from the other parts of the organization’s technology stack that are responsible for customer interaction.

Now, companies of all kinds are looking for more advanced solutions that can help them fundamentally change the way they operate for the better.

More specifically, they’re looking for tech that will bridge the gap between what their data science and analytics teams produce and the other parts of the business that are responsible for driving growth (e.g., marketing, commerce, product, customer experience).

The aspiration for analytics and data science democratization holds true for both global enterprises with centralized analytics teams as well as smaller companies with a handful of analytics or business intelligence (BI) resources for data analysis.

In both cases, these companies want customer analytics tools that can achieve two things:

  1. Reduce the distance between the output of analytics and data science efforts and the business users responsible for activating on those insights.
  2. Create a closed loop to bring data back from activation end points and into the hands of analytics and data science professionals and their tools.

In short, companies want customer analytics platforms that actually improve operational efficiencies internally as a pre-requisite to improved business decisions and outcomes.

Assessing the operational impact of potential customer analytics tools

When evaluating new customer analytics tools, it’s important to assess to what degree they will enable data democratization across the organization while simultaneously creating operational efficiencies.

Here are some key questions to consider at the start of your technology evaluation process:

  • In what existing systems do you currently build your customer segments?
  • What type of customer data do you have available for segmentation today?
  • What does the end-to-end process of building and activating segments look like in terms of who is involved, how long it takes, and the number of steps in the process?
  • In what ways does the current process put limits on what you can do — whether it’s in terms of granularity, cost to pull to data, and/or speed to build and activate segments?
  • What customer scores do you use? How often can the scores and models be updated?
  • What is the time between customer score calculation and when that data becomes available to business technology users for activation purposes?

Once you’ve fully documented answers to the above questions, then you can evaluate prospective customer analytics tools through this operational lens and ask, “How would these answers change if I had a new analytics solution in place?”

Why you should consider a CDP with customer analytics capabilities

The broader value of a customer data platform (CDP) is to unify your first-party data at an individual level, and (equally important) deliver that unified profile data back to marketing, analytics, commerce, and other growth teams.

But that data must be in a format their tools can use to improve how they engage with customers, conduct modeling and analytics, and build multi-dimensional segments.

A CDP fills the unification-activation gap in your tech stack. It serves as the lynchpin between the breadth of the data and the breadth of the activation channels, all in one place.

By leveraging a pure-play CDP that makes multi-dimensional segmentation and predictive modeling capabilities readily available, your organization can:

  • Eliminate external costs, steps, and/or redundant technologies in the current process to make the end-to-end process more efficient
  • Enrich the data in your web analytics, journey analytics, and business intelligence tools
  • Enable business users to act on the output of customer analytics tools in a way that allows them to iterate more quickly and test-and-learn in a non-cost-prohibitive way
  • Empower business technology users without robust analytics or data science skills to utilize these capabilities in a scalable, streamlined manner
  • Utilize segmentation as a form of insight by coupling multiple attributes such as age, previous purchase, interests, and location with what customers are doing right now

Simply put, by investing in a CDP as your customer analytics tool — and the go-to tech your growth teams can use to better understand and engage customers — you set your company on a path to true business transformation.

Originally published here.

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Author: Michele Szabocsik

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