How can SaaS companies leverage applied analytics?

In my previous blog I shared that I have been working on a practicum project with a SaaS machine data analytics company. My last blog was a around how we evolved as stronger productive team. In this blog I would want to share some nuggets from my industry research. This blog would focus on how analytics can help SaaS companies. A SaaS company is a company that hosts an application and makes it available to customers over the internet.

Key focus areas to achieve success

Software as a Service (SaaS) business models have higher complexity when compared to traditional businesses. SaaS subscription based models are different as the value from their customer comes over time. The revenue is not realized at the time of customer acquisition but it is a function of the customer lifetime. This makes two things important for a SaaS company:

1. Customer Acquisition

2. Customer Retention to maximize the customer life time value

Additionally customer acquisition is way more expensive than customer retention and hence companies cannot afford to lose their existing customer base. This is exactly what my MSBA industry partner, which offers a SaaS product, focuses on.

Moving from intuition-based to data-based decision making

When the companies are focused on increasing the top line and bottom line they channelize their efforts more on increasing the market share and often rely on intuition based decisions. The leadership, sales strategy team and customer success team of my MSBA industry partner have realized how it is important to back their decisions with data driven insights.

“If you cannot measure it, you cannot improve it” — Lord Kelvin

The organization is on its journey to move towards data based decision and presently applies analytics by leveraging descriptive and diagnostic analytics. One of such practices is:

1. Defining Customer Health grades- This organization has applied analytics to determine metrics that define customer health based on the engagement, which help both sales and customer success team in account handling and relationship management.

There is some scope to refine the definitions and a vast scope to improve the accuracy. They have huge volumes of data but somehow with a constant focus on the future targets they did not prioritize dedicating resources to analyze the past trends. There is a huge potential to exploit and implement predictive and prescriptive analytics by using the historical data. This might take some time in data preparation because an end- to-end analysis will require merging the data coming from multiple sources (function/software). These sources might have fragmented data, incorrect data, nonexistent data and duplicate data! So initial phase would require documenting, standardizing formats, systematizing processes and data wrangling to have cleaner data schema for analysis.

Potential Applications of Analytics in SaaS companies

I intend to share in this blog how they can apply B2B analytics in multiple functions to make better business decisions for a SaaS company.

Some applications are:

1. Customer Segmentation using Clustering algorithms — This involves analyzing various attributes of customers ranging from geography, annual recurring revenue, sales executive, industry , contract length etc. to create cohorts. These cohorts can be given customized treatments to maximize customer lifetime value. They can be used by the sales and strategy team to tailor the go to market strategy.You can go through the video to understand the basics of customer segmentation.

2. Churn Prediction using regression and survival analysis — This includes predicting the probability of churn by analyzing their usage patterns, involvement, tenure etc. This in turn gives the retention rates. A time series analysis where we study the changes in the trends over time can give powerful insights about symptoms correlated to churn. These insights can be used by customer success team and account executives to invest more time and energy in a vulnerable customer.

3. Forecasting revenue by calculating customer lifetime — This entails utilizing the calculated retention rates for every cohort, margin and discount rate to compute the customer lifetime value. You can create models to find the revenue potential of an account given its behaviors and interaction history with the service. We can also model future scenarios to see projections.

Know the Challenges

Before you start preparing the data to do this analysis you would want to know that there are challenges in applying account based analytics.

1. The number of observations coming from CRM are smaller, they are account level information and not end user level observations like in a B2C company. You lose significance very fast when you do analysis on these thousands of accounts instead of millions of users.

2. The annual or multi-year subscriptions make the latency of your predictions long and you will take time to validate your model with real data.

Hope this blog was helpful for you to get an idea about how SaaS companies can make full use of the data they create to make better business decisions.