Data Science Plan For SaaS Companies

Data has become an important part of businesses across all industries. An enormous amount of data is generated every day, and 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.

Today, data analytics 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 are using analytics up to a certain degree (Forbes). Companies are benefiting from the new insights extracted from this data in many ways, such as improving their advertisement campaigns and building their market strategy upon the knowledge gained from this data.

Data Science Plan strategyThe importance of data in any industry is massive, particularly the SaaS industry. SaaS is short for Software as a Service. Any company in the SaaS industry provides cloud-based services to their 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 the 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 at a faster rate.

How To Implement Data Science In SaaS Companies?

The most crucial aspect that contributes to the success of a company is tracking their metrics. Data Science lets them recognize their strengths and weaknesses and modify their business strategies accordingly. The biggest mistake that many companies make is assuming that they already know which metrics to track. During the process of acquiring new customers, a company goes through five stages, acquisition, activation, retention, revenue, and referral. In order to obtain more customers, a company needs to understand which stage of the process users are getting stuck on and what are the weaknesses in the current customer acquisition strategy.

The best way to gain maximum utility from your data is to segment it. Segmentation of data 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 about the most efficient method to segment your data. Here are the three types of data segmentation:
Customer Demographic

Information such as the user’s location, the devices that they use, will tell you where and how the user spends most of their time. It will help you narrow down the users that are 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, always keep track of the marketing channel or campaign that brought the customer to you. This data will help you identify which marketing strategy is most useful for your organization, and which is the least effective. After performing this analysis, you can decide to pour in more resources into the campaign or channel that has generated 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

How To Select The Right 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 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 a variety of functions. Here are some recommendations for tools based on categories of tasks:

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 visualise the metrics and KPIs that 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.


Keep in mind who is going to 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 prioritised metrics and KPIs.


If you group your users, it will become easier to handle their data.

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

The AEI model is a process that will help your company to discover which KPIs and metrics you should track, implement the right 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:

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 experiments that 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 parameters such as engagement, onboarding, and conservation. Finally, add your baselines and targets to your key reports. It will simplify the process of tracking progress, and it will add context to your reports.

Why Mindbowser?

Our lean and agile team of full-stack data scientists, engineers, and application developers accelerate 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.

The implementation of data science in SaaS companies helps ensure that your business is moving in the right direction and to check the efficiency of your strategy. To make data science effective, the company must have a robust understanding of their business problem. Before implementing data science, the company must track specific KPIs and metrics using a tracking plan. Finally, after choosing the right tools and creating a report, they can start thinking about the more advanced methods to extract more value from their data.

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