How Customer-Facing Analytics Is Different From Traditional BI

Traditional business intelligence (BI) is helpful in analyzing key data to make informed decisions, especially about company strategy and performance.

However, while traditional BI is undoubtedly useful, especially for business decision-makers, it's limited.

Traditional BI isn't optimized to be customer-facing.

Customer-facing analytics helps companies interact with their customers in real-time, which requires a different approach from traditional BI.

Let's take a look at how customer-facing analytics differs from traditional BI and how your business might benefit from using embedded analytics.


What is Traditional BI?

BI has historically centered on using structured data to:

  • Analyze past performance
  • Manage ongoing operations
  • Direct planning for the near future

When businesses want to improve their processes, collect data about what they're doing now, and hit current goals, they use BI solutions.

Examples of business intelligence tools vary from simple spreadsheets to complex analytics systems. Some are used for online analytical processing, company activity monitoring, data tracking and storage, and more.


How Customer-Facing Analytics Differs From Traditional BI

There are several differences between customer-facing analytics and traditional business intelligence. Perhaps the most important difference is that customer-facing analytics requires a different approach due to the need for speed and security.

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Customer-Facing Analytics Scope

Customer-facing analytics often only needs product data. Internal-facing analytics like traditional BI often draw from many more sources, looking at a company holistically, while most customer-facing data exposes product data to users. For that reason, the data scope is often much more focused than internal BI.

Customer-Facing Analytics Speed

Traditional BI can serve up a complete range of company data, but loading time is a relative non-issue and can take a while. Customer-facing personnel (like sales teams) need access to data at the moment — in real-time during interactions with their customers or their teams. An embedded analytics solution provides them with dashboards that instantly make contextually relevant data available.

Traditional BI has other speed disadvantages built into its UI, often requiring toggling between several apps to synthesize and analyze data. Customer-facing analytics can't be treated like internal BI and are a CX differentiator. Customer-facing analytics aggregates a narrower set of data and delivers it in a more easily-consumable way. This often does require the need to pre-process and transform data so it can be surfaced quicker.

Customer-Facing Analytics Security and Governance

Security also has higher stakes with customer-facing analytics than internal BI since a multi-tenant environment is more complex and is integrated into existing user applications.

The single-tenant security environment of internal use is less complex, so security has lower stakes than customer-facing analytics. Internal teams are a known quantity, so they simply don't represent the same level of security risk.

Sharing data with customers doesn't have to sacrifice your data security & governance protocols. With the right embedded analytics solution, you can establish separate security rules that are stricter than internal BI.

Your data governance policy should outline data cleaning and storage procedures, data modeling considerations, and data access credentials. Security rules should continue to evolve with your product.


Main Competitive Advantage of Customer-Facing Analytics

Both you and your customers can benefit from a customer-facing analytics solution by providing them with:

  • a seamless, user-friendly app experience with built-in real-time data access for your customers 
  • better security that reduces dependencies on your internal teams to build one-off security solutions to cover gaps left by traditional BI
  • improved customer service supported by strong user data; the longer people spend using the app, the more data it gathers, which over time becomes a valuable asset that can enhance the user experience
  • an interface explicitly designed for working with data that is being shared and accessed by end-users, so they're more intuitive and easier to use than traditional BI tools


Customer-Facing Analytics Grants Autonomy to Customers Through Self-Service

Customer-facing analytics frees up your IT staff because it's a self-service tool. Customers can immediately find solutions to their issues without assistance since embedded analytics provide users with easier, more seamless access to data with pre-configured dashboards and templates they can customize further.

White-label dashboard solutions have high customizability to the customer environment, where tools can be incorporated to leverage customer-facing analytics to their maximum potential. Customer-facing analytics dashboards aid UX & UI via familiarity and simplicity to grant insights that help leverage your product.

No-code, customizable dashboards increase the relevance and usability of data by serving crucial info without the need of a data scientist. Customer autonomy through self-service unloads your engineering team's bandwidth, both in terms of development and support, so they can concentrate on other things, like product development.


Customer-Facing Analytics Technical Requirements

Traditional BI has a high technical requirement burden on users, making it bandwidth-heavy to implement and maintain. Its development is often restricted to the engineers and data teams building it, blocking input from the business side during setup.

This is an acceptable outcome for internally-facing data access and analysis, where considerations like speed, security, and frictionless UI are lesser considerations when compared to scope.

Staff spending 15 minutes to pull a report is quick in the context of a board meeting — it's an eternity on a sales call and an obvious deal-breaker.

But designing a specific dashboard for each client is challenging because individual users have different needs and approaches to data. One-size-fits-all solutions aren't solutions, as your users won't get enough value if your dashboard is only 80% accurate to their needs. They need a 100% solution 100% of the time.


Conclusion

While a customer-facing analytics solution has a much lower technical burden on end-users, building one strains your developer bandwidth. There are other options.

Purchasing a white-label, secure, customizable embedded analytics solution from Explo has a lot of advantages.

These include customizability, so you and your customers get exactly what you need (not 80% of the way there). There's also less technical load on your engineering team. You can focus on your product and have users with high autonomy to analyze current, relevant data in real-time to predict outcomes.

Explo can help your developers save time by simply copying a few lines of code to embed our customizable interactive dashboards and reports directly into your web portal or application.

Want to know more?

Check out this brief demo to get a better idea of what we can do for you in solving your customer-facing analytics challenges.

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