Data Science For SaaS Companies

The data science plan has become an important part of businesses across all industries. An enormous amount of data is generated daily. By collecting and analyzing it, companies can receive helpful insights that enable them to make better business decisions and increase their ROIs.

However, when the field of data science first gained popularity, only a few niche players in the industry who had access to this technology were enjoying its benefits. This article will highlight the important aspects of data science for SaaS companies.

Today, data tools can be used by anyone and are not only limited to large enterprises willing to spend vast sums of money. Data science is so widespread today that over 59% of enterprises use analytics to a certain degree (Forbes). Companies benefit from the new insights extracted from this data in many ways, such as improving their advertisement campaigns and building their market strategy upon its knowledge.

Data Science For SaaS Plan Strategy | MindbowserThe importance of data in any industry is massive, particularly in the SaaS industry. SaaS is short for Software as a Service. Data science in SaaS companies provides cloud-based services to customers over the internet. These services include hosting and maintaining servers, databases, and application source codes.

The most significant advantage of this industry is that it allows customers to use software without concern about hardware and infrastructure costs. The main product of SaaS companies is software; therefore, storing and processing data is a crucial element of the SaaS industry.

If these companies use data science technologies to make the most of their existing data or collect more valuable data, they will be able to make better decisions and grow faster.

How To Implement Data Science In SaaS Companies?

The most crucial aspect contributing to a company’s success is tracking its metrics. A Data Science plan lets them recognize their strengths and weaknesses and modify their business strategies accordingly. The biggest mistake many companies make is assuming they already know which metrics to track.

When acquiring new customers, a company goes through five stages, acquisition, activation, retention, revenue, and referral. To obtain more customers, a company needs to understand which stage of the process users are getting stuck on and the weaknesses in the current customer acquisition strategy.

The best way to gain maximum utility from your data is to segment it. Data segmentation can help you better understand your customer’s journey and develop strategies to generate more leads for your organization. Therefore, you must give a lot of thought to the most efficient method to segment your data. Here are the three types of data segmentation:
Implement Data Science For SaaS Companies | Mindbowser

Customer Demographic

Information such as the user’s location and the devices they use will tell you where and how the user spends most of their time. It will help you narrow down the users interested in your services and follow a more targeted strategy to make your marketing campaign more effective.

Marketing Attribution

Whenever you acquire a new user, keep track of the marketing channel or campaign that brought the customer to you. This data will help you identify the most useful marketing strategy for your organization and the least effective.

After performing this analysis, you can decide to pour more resources into the campaign or channel that has generated the most leads. Thus, allowing you to maximize your profits and improve ROI.

User Behavior

Identify what features and functionalities of your product are the most used functions amongst your users. The analysis of this data can help you determine the best features of your product. Then, you can enhance those features to increase user engagement with your service.

Some of the KPIs that can help your company answer these questions and segment your customers are:

  • Number of people that have checked out your app or service
  • The average time spent by the user in each session
  • Many people  have signed up for the free trial

Meet Our Tech expert

Sandeep Natoo

Sandeep is a highly experienced Python Developer with 15+ years of work experience in developing heterogeneous systems in the IT sector. He is an expert in building integrated web applications using Java and Python. With a background in data analytics. Sandeep has a knack for translating complex datasets into meaningful insights, and his passion lies in interpreting the data and providing a valuable prediction with a good eye for detail.

Get Free Consultation

How To Select The Right Data Science Tools For Your Company?

After you start tracking your KPIs, your company is ready to begin the implementation of data science using analytics tools. But now, you are faced with the challenge of finding the right analytics data tool for your company. In reality, you will not be able to find one tool that can do everything.

You can pick a tool based on the task you want it to perform. Otherwise, you can create a stack of different tools for various functions. Here are some recommendations for data tools based on categories of tasks:

Select Data Science Tools | Mindbowser

Data Abstraction

Segment and mParticle can be used to simplify your data implementation requirements.

Strategic Development

Google Analytics, Appsflyer or Branch can be used to identify the best marketing campaigns to acquire more customers.

Usage Measurement

Heap and Amplitude can be used to understand what users are doing with your product.

Revenue Metrics

Recurly and Chargify can be used to track your SaaS revenue metrics.

Qualitative Data

Hotjar and Appsee can be used to track session recordings, surveys, and other qualitative data.

Guidelines To Create A Report

The purpose of a report is to visualize the metrics and KPIs you have tracked in the previous step. You can create the report however you want, but there are a few guidelines that you must follow.
Data Science Report Guidelines | Mindbowser


Keep in mind who will use this report and modify it according to their needs.


Your metrics do not mean much without any context. Always compare them to your metrics from the past or other benchmarks.


Your report must reflect your highest prioritized metrics and KPIs.


Grouping your users will make it easier to handle their data.

Once your basic reporting is set, follow the Assess, Execute and Improve (AEI) model to keep your reports and data growing.

The AEI model is a process that will help your company to discover which KPIs and metrics you should track, implement the right data tools to collect accurate data and grow your reports and dashboards. However, this process should only be used after you get your basics right and have established a solid foundation.

Here are some critical aspects you must pay attention to before implementing the AEI model:

Data Science AEI Model | Mindbowser

Baselines And Historical Data

It is imperative to have a solid foundation before applying this model because it requires you to develop your baseline metrics and create realistic targets for the experiments you run. To create baselines with realistic goals, you must have your historical data in place.

Internal Roles

It is crucial to maintain your data over some time. To do so, you must establish internal roles in your organization dictating who will take ownership of data implementation. Generally, the ideal person for this role is a developer who works closely with product and marketing teams.

Testing And Target Reporting

Set up messages that will increase customer retention by driving engagement. Once you have enough data, run A/B tests to measure engagement, onboarding, and conservation parameters. Finally, add your baselines and targets to your key reports. It will simplify the process of tracking progress and add context to your reports.

Why Mindbowser?

Our lean and agile team of full-stack data scientists, engineers, and application developers accelerate the innovation and implementation of custom machine learning and AI products. We bring extensive cross-industry expertise backed by scientific rigor and deep knowledge of state-of-the-art techniques to design, build, and deploy bespoke AI solutions.

Mindbowser For Data Science



Data science is a beneficial technology for SaaS companies. But before applying it, a company needs to understand how to implement data science for their SaaS business.

In this blog, we discussed exactly that. We also explained how data science could help SaaS companies make better business decisions and increase their ROIs.

Next, the company should track specific KPIs and metrics using a tracking plan and choose the right tools. Then, in the future, they can use advanced methods to extract more value from their data.

Sandeep Natoo

Head Of Emerging Trend

Sandeep is a highly vigorous Machine learning expert with over 12+ work of experience with developing heterogeneous systems in the IT sector. He is an expert in building Java integrated web applications and Python data analysis stack. He has been known for translating complex datasets into meaningful insights, and his passion lies in interpreting the data and providing valuable prediction with a good eye for detail. He is highly optimistic and avid nature, for various challenges is his major strength.

The complete guide on "Data Science" is released - Get your copy and learn the trends of Data Science in 2022 :)

Download Free eBook Now!

Get in touch for a detailed discussion.

Hear From Our 100+ Customers

Mindbowser helped us build an awesome iOS app to bring balance to people’s lives.


We had very close go live timeline and MindBowser team got us live a month before.

Shaz Khan
CEO, BuyNow WorldWide

They were a very responsive team! Extremely easy to communicate and work with!

Kristen M.
Founder & CEO, TotTech

We’ve had very little-to-no hiccups at all—it’s been a really pleasurable experience.

Chacko Thomas
Co-Founder, TEAM8s

Mindbowser is one of the reasons that our app is successful. These guys have been a great team.

Dave Dubier
Founder & CEO, MangoMirror

Mindbowser was very helpful with explaining the development process and started quickly on the project.

Hieu Le
Executive Director of Product Development, Innovation Lab

The greatest benefit we got from Mindbowser is the expertise. Their team has developed apps in all different industries with all types of social proofs.

Alex Gobel
Co-Founder, Vesica

Mindbowser is professional, efficient and thorough. 

MacKenzie R
Consultant at XPRIZE

Very committed, they create beautiful apps and are very benevolent. They have brilliant Ideas.

Laurie Mastrogiani
Founder, S.T.A.R.S of Wellness

MindBowser was great; they listened to us a lot and helped us hone in on the actual idea of the app.” “They had put together fantastic wireframes for us.

Bennet Gillogly
Co-Founder, Flat Earth

They're very tech-savvy, yet humble.

Uma Nidmarty
CEO, GS Advisorate, Inc.

Ayush was responsive and paired me with the best team member possible, to complete my complex vision and project. Could not be happier.

Katie Taylor
Founder, Child Life On Call

As a founder of a budding start-up, it has been a great experience working with Mindbower Inc under Ayush's leadership for our online digital platform design and development activity.

Radhika Kotwal
Founder of Courtyardly

The team from Mindbowser stayed on task, asked the right questions, and completed the required tasks in a timely fashion! Strong work team!

Michael Wright
Chief Executive Officer, SDOH2Health LLC

They are focused, patient and; they are innovative. Please give them a shot if you are looking for someone to partner with, you can go along with Mindbowser.

David Cain
CEO, thirty2give

We are a small non-profit on a budget and they were able to deliver their work at our prescribed budgets. Their team always met their objectives and I'm very happy with the end result. Thank you, Mindbowser team!!

Bart Mendel
Founder, Mindworks

Mindbowser was easy to work with and hit the ground running, immediately feeling like part of our team.

George Hodulik
CEO, Stealth Startup, Ex-Google