Why You Shouldn't Delay Customer-Facing Analytics

Your SaaS roadmap is long and winding with many feature requests. Customer-facing analytics is only one of them. So it’s tempting to deprioritize in favor of another squeakier wheel. But the further you push it back, the more it could hamper your growth.

Small businesses are 19 times more likely to succeed if they use data in decision-making. So the potential gains are enormous for your customers. And for your SaaS business too. Successful SaaS companies generate up to 20% of new revenue from analytics-based offerings.

Here’s why you need to accelerate analytics development.

The roadblocks to overcome

It’s no shame if your product doesn’t have analytics yet. Your product team might be holding off for various reasons:  

• The engineering team is spread too thin already.
• There are bigger fires to put out.
• It’s not a differentiated feature yet.
• You don’t know if your customers need it.
• The return on investment isn’t clear.
• Your team doesn’t have the expertise to pull it off.
• Your business requirements aren’t set in stone yet.

Firstly, these are all valid reasons for pumping the brakes. Being cautious of roadblocks is a good thing. You don’t want to rush into building something that’s not worth the time.

However, analytics supports a strategy or tactic that impacts your customer’s business growth. So it will keep reappearing on your to-do list until you bite the bullet and do it.

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Why it’s dangerous to delay customer-facing analytics

A great customer analytics experience can be a competitive advantage in a crowded SaaS market.

• Modern software users expect it.
• Users love insights. It helps them get more value out of your platform.
• Managers need metrics to prove the ROI of your software platform.
• Often, it also helps your customer manage strategy, tactics, and people
• Your competitors have analytics. So you may already be losing prospects and customers to them.

The opportunity cost of not building analytics is just as convincing. Because companies who don’t invest in customer-facing analytics lose out on:

• Higher client engagement.
• Reduced churn
• Monetization and upsell opportunities
• Useful product feedback
• More customer advocacy

The risk of long development cycles

Delaying customer-facing analytics is problematic. But taking too long to build it is risky too. Developing analytics in-house is a complex undertaking. It could take months of planning, development, and testing before your customer analytics are production-ready.

The time, cost, and maintenance of dashboards are resource-intensive. Those resources come at the expense of improving your core product. So unless it’s a core competency with a dedicated team, it often presents a Catch-22 situation. You are damned if you do and damned if you don’t.

This doesn’t mean you should throw the towel on the idea of customer-facing analytics. There are better and faster alternatives to create an amazing client-facing analytics experience.

Speed and delight with embedded analytics

SaaS companies outsource payment processing to Stripe, chatbots to Intercom, and CMS to WordPress. You would never waste time on building these components yourself. But here you are, considering letting your developers build a dashboard when there is an easier way forward.

Embedded analytics software is a low-code building block solution. It syncs to any data source, configures to any brand look and feel, and embeds with any front-end design stack. You’ll be ready for production in days, instead of months.

The faster you go to market, the faster you’ll see the ROI of embedded analytics.

Avoiding the pitfalls

To avoid delays, picking the right embedded analytics solution matters. Many established BI solutions were built for data scientists; not for SaaS builders. They are complex to set up and use, resulting in:

• delayed product sprints
• a drain on resources
• time lost on specialized training
• a poor user experience

Established BI tools may seem like a safe bet at first. But beware of the complexity. Ultimately, it will slow down your go-to-market.

Steps to take

Step 1: Define Clear Business Objectives

Start with the end in mind.
Before you begin collecting or organizing data, it’s essential to define what you hope to achieve with your customer-facing analytics. What questions do you need to answer? What decisions should the analytics support?

• Actionable Tactic: Gather input from both business leaders and customer service teams to map out key performance indicators (KPIs). This might include metrics like customer retention rates, usage patterns, or satisfaction scores.
• Why It Matters: Clear objectives set the stage for everything else. When you know your goals, you can design a data model that not only collects data but transforms it into insights that directly support your business strategy.

Step 2: Identify Key Stakeholders and Requirements

Involve the right people from the start.
Your data model should reflect the needs of everyone involved—whether that’s internal teams, external partners, or the customers themselves.

• Actionable Tactic: Host workshops or brainstorming sessions with stakeholders to capture their insights and pain points. Create a document that outlines the specific requirements for data access, security, and reporting.
• Why It Matters: Involving stakeholders early helps you avoid costly revisions later. It ensures that the final product not only meets technical specifications but also delivers real value to its end users.

Step 3: Audit Your Data Sources

Know where your data comes from.
A successful data model relies on having a thorough understanding of your available data sources. This includes internal databases, third-party services, and even external market data. Additionally, incorporating discovery phase services for product development can help identify and refine these data sources early in the process.

• Actionable Tactic: Conduct a data inventory. List all the sources of customer data, evaluate their quality, and note any potential gaps.
• Why It Matters: A complete audit prevents surprises later on and helps you design a model that integrates multiple sources seamlessly. This is critical for ensuring that customer-facing insights are both comprehensive and accurate.

Step 4: Ensure Data Quality and Consistency

Clean data is key to actionable insights.
Before you can build a data model, you need to invest time in cleaning and preparing your data. Data quality issues can lead to incorrect insights, which in turn can erode trust with your customers.

• Actionable Tactic: Implement data cleansing procedures. This might involve removing duplicates, correcting errors, and standardizing formats across different data sets.
• Why It Matters: Consistent, high-quality data is the foundation of a reliable analytics platform. When your customers see clean, accurate data, it reinforces their trust in your organization’s ability to deliver value.
  Utilizing makerspace software can streamline data standardization, ensuring consistency across different sources.

Step 5: Define Your Data Architecture

Blueprint for success.
Designing a data architecture means planning out how data will be stored, accessed, and transformed within your system. This is where you decide on the structure and relationships between various data elements.

• Actionable Tactic: Sketch out a high-level diagram that shows data flow from source to customer-facing interface. Identify key tables, fields, and the relationships between them.
• Why It Matters: A well-planned architecture reduces complexity and makes it easier to scale your analytics solution as your data grows. It also ensures that data retrieval is efficient and that insights are delivered in real time.

Step 6: Build Your Data Model

Turning structure into insight.
With a clear architecture in place, the next step is to build your data model. This involves creating a logical representation of your data that facilitates easy access and analysis, especially for complex applications like 3D product configurators.

• Actionable Tactic: Utilize data modeling tools to create a conceptual, logical, and physical model. Tools like ER diagrams or UML can help you visualize relationships and constraints.

Why It Matters: The data model is your blueprint for all future analytics. A well-designed model ensures that queries run efficiently and that your analytics layer can evolve as new data and requirements emerge.

The right time was yesterday

Creating an engaging analytics experience doesn’t need to be difficult. It requires a clear understanding of business objectives, a commitment to data quality management, and the strategic integration of technology and governance. The sooner you start, the faster you’ll reap the benefits like higher product adoption and new upselling opportunities. And with the right embedded analytics partner, you can get there in less than 30 days.

Luzmo focuses 100% on embedding and specializes in SaaS use cases. If you’re ready to bite the bullet, get in touch with our product experts for a consultation or a product tour.