How to improve your company’s profit with data analytics

Not all managers know what a data analyst does and how to utilize its skills to have a great impact in the company. Therefore, in this article I have described tasks that a data analyst can perform with data. These tasks can improve companies revenues and understand better their users behavior with the product.

Answering business questions and gain insights

Product managers, campaign managers, customer success departments and other Key holders in the organization will always want to know more things about the users and customers of the company. 

For example, a product manager would like to know how many users actually use the new feature she developed, a campaign manager would want to know if the users he brought in do not abandon after the initial use of the product and a customer success department manager would like to know how many customers she was able to revert from churn at the last month and if possible she want them breakdown by their countries.

A data analyst may answer all these questions with analysis she can perform, actually answering business questions from stakeholders is the main part of her role.

Building KPI reports

Every company has the Key performance indicators (KPIs) that are most important for its success (sometimes these indicators are also called North star metrics). KPI reports Make it possible to follow these indicators and gain insights in them easily.

In KPI reports the data analyst will present the KPIs and will break them into the important segment in the data,  create charts with their trend, and raise an alert when there is a deviation from their normal baseline. There are KPI reports produced on a daily, weekly and monthly level.

KPI reports are usually written in BI tools.

Acquisition funnel analysis

Before users purchase a product they have to go through several steps. These steps are called an “Acquisition funnel”. For example, on e-commerce sites, the common steps in the funnel are – adding product to the basket, entering credit card number and shipping details and usually the last step of the acquisition funnel will usually be confirming the purchase.

At each of these stages the users can abandon the process, and when analyzing the acquisition funnel the data analyst can analyze each of these steps and identify the failure points that cause many users to abandon the funnel without completing the acquisition. In such an analysis it is also common to break down the funnel by segments and find segments in which there are users that act differently.

Campaign analysis

Gaining new users to the product is done by marketing campaigns. These campaigns are expensive and not all users who we gained in the campaign are being converted into paying users. Campaign analysis examines the marketing channels and compares the conversion rate of them to identify the return of investment (ROI) of each campaign and help the marketing team to learn which campaign is worth to enhance and which campaign can decline. 

For example, there might be an expensive campaign that brings in a lot of users but their conversion rate is low. On the other hand, there might be some low budget campaigns that begin few new users but its conversion rate is very high. In that case, the data analyst will recommend increasing the budget for these campaigns.

Campaign analysis is usually done with tools such as google analytics. 

Customers LTV analysis 

LTV stands for LifeTime Value. In those kind of analysis the data analyst calculates the average income from the customers over a certain period of time (usually half a year or a year).

These calculations can be broken down by customer segments and identify the segments that have more profitable customers. LTV analysis can enhance the Campaign that was described at the previous section by adding more information about the Lifetime value from users that came for campaign or medium.

Click on the link to learn more about customers LTV.

Product Analysis

A product manager cannot know what customers are doing with the product without data. For proper data extraction, she needs to rely on a data analyst. The data analyst will define the events that the product manager wants to track (for example, clicks on buttons in the product), answer business questions, and create KPI reports for her.
Identifying Anomalies in Data and Creating Alerts.

Reality is dynamic and chaotic, things that work consistently can suddenly change without early planning. Identifying sudden anomalies in data can help us prepare for these phenomena. Using statistical methodologies, a data analyst can identify anomalies in data and create alerts when they occur.

A/B Testing

All product managers want to make changes to the product they are responsible for in order to improve it, but changes can also damage the proper functioning of the product. In order to ensure that the change they want to make will actually improve the product and not harm it, one can use the A/B testing methodology.
In this methodology, the data analyst plans a statistical experiment that examines whether the change that the managers want to make will not harm the existing state and will significantly improve the product.

Click on the link to learn more about conducting A/B testing.

User segmentation

There are two main types of user segmentation:

Business segmentation – segmentation of customers according to business rules. In this methodology, the data analyst will identify customers who meet the business rules defined by the managers and create segments from that can be broken down by important KPIs. For example – identifying segments of premium customers who make purchases.

Cluster analysis – in the methodology of cluster analysis, the segments will be determined from the data and not according to business rules. That is, the data analyst will use statistical algorithms to identify customers with similar characteristics and classify them into the same segment (cluster). This analysis is less common today.

Targeting the best customers

In these types of models, the data analyst will rate customers based on a number of metrics (for example, user usage time, purchase amount, frequency of use, etc.) and identify the best or worst customers. The beauty of this method is that it is possible to examine multiple aspects of customers and rate them according to one holistic score that determines the level of importance of each customer to the company.
With this method, it is also possible to rate segments – for example, rate the countries where the best customers are located.

Click on the link to learn more about targeting the best customers.

Customers Churn Analysis

In churn analysis, the data analyst identifies customers who were active but stopped using the product. After identifying the churned customers, the data analyst examines the churn rate for each segment to identify segments with high churn rates. For example, the data analyst can conclude that customers who signed up for the product on Fridays are more likely to churn because the psychology of customers who sign up on weekends to the product is different from the psychology of customers who sign up on weekdays.
These analyses can provide many insights on the reasons why customers churn and improve the product to prevent future churn.

Additionally, the list of churned customers can be handed over to customer success teams to try to bring them back to use the product.

Click on the link to learn more about Churn analysis.

Survey Analysis

Sometimes the best way to know what customers think about the product is to ask them. The answers received from surveys about customers are great material for analytical analyses full of insights that a data analyst can extract.