Overcome These 4 Key Challenges in Customer-facing Analytics

Dodge the bullet by steering clear of these 4 challenges that B2B SaaS companies face when adopting customer-facing analytics

Dodge the bullet by steering clear of these 4 challenges that B2B SaaS companies face when adopting customer-facing analytics


In the world of sports, there is an intriguing concept known as the ‘Principle of Specificity’.

While most athletes undergo a combination of cardio and strength training, this principle implies that the bulk of the training should be specific to the sport. For example, cyclists will train best by cycling, swimmers by swimming, and so on. Though cyclists will strengthen their leg muscles by running. The primary training mode should be specific to the sport for maximum benefit.

The same is true for analytics – what do we mean by that?

As SaaS product owners, you already have a team of developers who have built your core product and are working on making it better every day. So when the time comes to foray into customer-facing analytics, you can simply use the same team to get it done, right?

Well, probably not. Customer-facing analytics entails specific nuances, components, features, and data experiences to be built that may not necessarily align with your core team’s strengths.

In this blog,  we share with you the 4 pitfalls that every B2B SaaS company encounters when adopting customer-facing analytics, and how you can avoid them.


#1 Deciding on Build vs Buy

Customer-facing analytics is all about giving your customers the right capabilities in a self-service, integrated, and holistic manner so that they can understand their business better and make smarter decisions.

Since it is built on top of your SaaS product, the temptation to get the components built internally by your software development team is understandable. However, in customer-facing analytics, data needs to be surfaced, pre-processed, and transformed much quicker – sometimes even in real time. Insights need to be highly consumable. They need to be accessed, generated, and understood by anyone with or without technical expertise. All of these would require the underlying infrastructure to be automated and the data experiences to be logically segmented based on the users’ needs.

Due to the above nuances, customer-facing analytics has carved out a niche for itself within the larger analytics space over the last few years. Traditional software development teams may not be equipped to provide the above capabilities at scale. Even if they are, engaging them in developing customer-facing analytics is likely to eat into their time, focus, and energy which can be better used in maintaining and enhancing your core SaaS product.

Now that we have established that internally building the components of customer-facing analytics may not be the best use of your engineering effort, this brings us to the next question: What should you look for in a customer-facing analytics vendor?

#2 Choosing the Right Vendor

The overwhelming importance of specificity in customer-facing analytics also means that randomly picking just any vendor will not do justice to your needs. A common mistake is to invest a large amount upfront only to find out later that there was a lack of sync between the vendor’s offerings and the customers’ needs.

Therefore, it is a good idea to scout vendors that fit the bill in its entirety, and not just in parts. Doing this right from the start can eliminate frustration, and wastage of resources later.

We at Verb, for example, started out with the mission of making customer-facing analytics work to perfection for SaaS product owners and their customers. For starters, customer-facing data experiences on Verb are no-code, fully integrated, self-service, and powered by cutting-edge embedded analytics. We deliver a hassle-free experience where data sourcing, pre-processing, integrating, transforming, analyzing, and visualizing all happen with just a few clicks.

Our libraries are richly stocked with many data visualization options that are appealing, intuitive, insight-driven, and customizable. Furthermore, our out-of-the-box user segmentation helps to meet the specific data and analytics needs of various user groups in the blink of an eye. This means that your customers can’t get enough of the incredible data experiences that you craft for them for better decision-making and drastically improved results.

#3 Unlocking Developer Productivity

Making your customers go ‘wow’ sounds great on paper. But if doing so siphons off your developers’ time and engineering effort, then we are back to square one. Developer productivity is a key concern of most B2B SaaS companies that adopt customer-facing analytics.

Verb’s tools to unlock your developer productivity are unmatched in the industry. For example, you can easily use our self-service data pipeline tools to bring your data together and make it accessible for better analysis. Furthermore, your development team can seamlessly integrate all data regardless of where the stack originates – be it database engines, REST APIs, existing data lakes/warehouses, or Pub/Sub message queues.

A key aspect of what makes Verb the preferred choice for B2B SaaS products is that you can build any data experience on top of the semantic layer without worrying about queries, performance, or segmentation. Verb orchestrates the entire process from sourcing raw data to data visualization, with managed materialization being a key component of the process. As your data grows and evolves, it continually optimizes its performance.

Lastly, non-technical teams can create dashboards, APIs, and more with Verb’s no-code builders that provide configuration tools to create rich design elements for a seamless white-labeled experience. These features and unique capabilities free up your developers’ time and enhance their productivity by redirecting it to your core SaaS product.

#4 Keeping an Eye on Modularity

Many times, SaaS companies have implemented customer-facing analytics only to realize that customers are finding it difficult to customize existing data experiences and build new data experiences on top of existing ones. Monolithic platforms are the reason behind this.

The key to this is modularity.

Modularity in Verb is a world in itself. When you hear the term, “modular data experiences”, the first thing that probably comes to your mind is the ability of customers to pick and choose from a rich pool of analytics and visualization components.

You are right in thinking so, but Verb extrapolates the implications of modularity well beyond that. It encompasses, for example, the ability of our clients to build upon existing data models and create new ones that better fit the decision-making needs of their customers. For example, if Predictive Modelling is a module that is not directly available in Verb, your development team can still use Verb’s SDKs and APIs and create a predictive analysis module in no time and with minimum engineering effort.

Modularity is how Verb helps you to future-proof your customer-facing analytics by leaving a wide margin for new and exciting opportunities to be capitalized on. Instead of a static approach, Verb’s modular approach gives you the upper hand in winning customers’ hearts and ensuring their loyalty to your product.

There you have it – these are the 4 common challenges that SaaS companies face when venturing into customer-facing analytics. Did you face a similar challenge yourself? How did you overcome it? Feel free to share your insights with us!