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Analytics – The Glue That Makes Customers Sticky

Analytics – The Glue That Makes Customers Sticky

embedded analytics

How I Used Data to Get Thin

A couple of years ago, I went on a health kick losing about 100 lbs (45 kgs) in the process. Apart from exercise, giving up alcohol, and adhering to a strict protein diet, I found that data collection and continuous analysis became key weapons in my fight to get fit. Data was gathered by MyFitnessPal, Renpho, and my Apple Watch (a device I thought idiotic when I was obese).

Other apps in the mix included Apple Fitness, 10K Runner, AllTrails, and Surge for fitness training which my PT instructor used to enter the results of my grunting and groaning sessions in the gym. Each morning, I would analyze the previous day’s results and either celebrate with a glass of sparkling water or commiserate, launching a deep dive into the data to see where I had gone wrong in the previous 24 hours.

Engagement Equals Loyalty

This experience illustrates how data and analytics have become a part of our everyday lives. It shows how customers are engaged by data which increases satisfaction and retention, aka stickiness. It’s no surprise that SaaS app vendors are embedding analytics into their digital products.

As stupid as it sounds, though now completely redundant, I haven’t canceled my subscription to 10K Runner because this app played a vital role in my journey to fitness. Paying a subscription for a product you don’t use, how’s that for brand loyalty?

With the advent of data analytics over the last decade, businesses already have valuable insights to enhance their customer engagement strategies. Marketing uses data to understand customer behavior, personalize the message, predict outcomes to determine the next-best action, segment customers, and engage them in real time with the right offer.

But I’m not talking about that old hat stuff. A more direct way to engage customers is to make analytics-driven insight part of the app they deliver to their customers and give them complete personalization. Wouldn’t it have been great for me on my fitness journey to see trends of what’s working well, what isn’t, where unforeseen changes in habit had a positive impact, and so on? Maybe even have entire fitness programs and meal planners designed automatically? Or see immediate corrective actions to take?

Embedded Analytics in Apps: A Game-Changer for Customer Experience

Such analytics embedded in apps is becoming more commonplace. Embedded analytics refers to the integration of data analytics capabilities within existing software applications or platforms, allowing users to access and analyze data directly within their workflow or application environment. It exists in both b2c and b2b apps. Diverse examples include the wealthtech Stripe, the CRM behemoth Salesforce, and machine learning in FACTSET. Today, embedded analytics is a high priority for any SaaS application vendor. Our partner, Panintelligence, can tell you all about it.

Data plays a vital role in understanding customer engagement, identifying trends, and making informed decisions, but it also plays a vital role in the digital product itself, an entirely new “feature” that delights customers increasing their stickiness and depending on the application, a new way to monetize data.

Backend Databases Fall Short

So, this is very nice for customer engagement, but it creates a headache for the Developers building the apps. Their apps were not built for deep analytics and certainly not on the scale most apps support. This is an issue of concurrency, performance, and workload management as query complexity vastly outstrips capabilities of an OLTP database.

MongoDB is the defacto backend database for many of these apps. While MongoDB has many benefits for certain use cases, it may not be the best choice for analytics – something it was never designed for.

MongoDB does not support the complex queries that traditional SQL databases offer. This can make it challenging to perform advanced analytics on large datasets. MongoDB does not support SQL joins, making combining data from multiple sources difficult for analysis. And so on (just ask ChatGPT!).

Customer Retention is a Top Priority SaaS Vendors

Achieving stickiness for any SaaS vendor is a top-level priority. Revenue retention is a KPI used by Financial Analysts to determine the value of any SaaS company. Analytics has become an embedded part of the digital product, but technology designed for transactional data management cannot deliver on the scale and complexity of analytics to truly engage their users.

Oh yes, and did I tell you I lost 100 lbs?

Make Your App More Engaging and Stickier with Analytics Features

Umair Waheed, our Head of Product Marketing, discusses enabling rich analytics to deliver more personalized and efficient services to your customers in this blog: Users Crave Insights, Not Just Features

Build Must-have Analytics and BI Into Your SaaS Solution

Join Yellowbrick, Panintelligence, and industry experts for a discussion on embedding analytics into your SaaS solution at a fraction of the cloud computing cost.

  • Hear real-world insights from Matthias Baumhof, CTO of LexisNexis ThreatMetrix on strategies for achieving cost-effective in-app analytics experiences.
  • Christian Garcia, Product Manager at Apptricity, will share practical tips and techniques to unlock unique customer insights without requiring extensive developer resources.
  • Jim Curtis, Senior Research Analyst at 451 Research, will provide industry validation and insights based on years of experience.

Register here: Learn How to Rapidly Embed Must-Have Analytics into Your SaaS Solution at a Fraction of the Cloud Computing Cost

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