Every owner of a subscription business aspires to operate a profitable store, thus it should come as no surprise that mastering your subscription analytics is one of the most crucial elements of success. It’s critical to understand what is and isn’t working effectively for your Shopify brand in order to assess the health of your subscription service.
When we discuss subscription analytics, we mean the process of monitoring and presenting any information pertaining to your subscription service. This can involve tracking your attrition rate, average order value (AOV), subscriber count, and a host of other metrics.
Why Is Subscription Analytics Important?
Why it matters to analyse subscriber data: These indicators are essential to efficiently optimizing and expanding your subscription business.
You have the ability to forecast your company’s future. Although it is impossible to anticipate the future for sure, one advantage of the subscription model is the ability to use excellent analytics to produce insightful forecasts. The beauty of subscriptions—and the reason they’re considered recession-proof—is that, by tracking your monthly recurring revenue (MRR) and calculating your customer lifetime value, you can accurately estimate sales.
Your AOV can be raised. You can deliberately product bundle, cross-sell, and up-sell to your subscribers to increase your average order value (AOV) by utilizing data about your best-sellers and often purchased products together.
You are able to become intimate. Customization is essential for keeping and attracting new subscribers. According to studies, 75% of US consumers say they are devoted to businesses that cater to their specific demands. What’s the key to being able to get intimate? Information. Apart from gathering zero-party data via surveys and quizzes, analytics can assist you in delighting subscribers by providing personalized product recommendations for each individual subscriber. We will also discuss about data model best practices in this post.
You can keep an eye on and lower attrition rates. You can monitor your churn rate closely with the aid of subscription analytics, which can help you develop important client retention measures. For instance, you might have a great chance to provide members a discount to entice them to continue if you observe a steady decline after the third month. Another useful strategy for figuring out why members are canceling and making adjustments to improve your business is to enable cancellation reasons.
Since proper data modeling requires time, one of the main concerns may be why it’s crucial and worthwhile to put in the work. Let’s examine a few of data modeling’s direct and indirect advantages.
Best Practices for Data Modeling
Ensure Your Data Model Is Well Documented
It is useless to have the greatest data model in the world if no one knows how to use it. It will be easier to use the data to produce insightful analyses and for others to understand what information is accessible in the system if there is thorough documentation in place. Because your documentation serves as an up-to-date single source of truth for the data model’s present state, properly documented data models can also speed up the onboarding of new team members and make future maintenance or modifications easier.
Vary in Depth of Abstraction for Various Individuals
Three categories comprise the typical data modeling process: conceptual, logical, and physical data modeling. The various degrees of abstraction seen in each of these tiers might be useful in helping even nontechnical users comprehend the data model. Additionally, they enable the separation of technological decisions from conceptual data models at a higher level, where such complexity is superfluous. Here is some information about the three categories of data modeling:
Conceptual: Things are described in terms of the business and how it operates, as well as any applicable limitations and company policies.
Logical: The objects that were decided upon during the conceptual stage of data modeling now have more details. The precise data types and attributes that every object should have are typically included in logical data models.
Physical: This model outlines the exact technical aspects of data storage, such as database implementation specifics. The physical model would contain the SQL to build tables and specify data types for each column in the tables that represent the objects described during the conceptual design stage, using a relational database as an example.
Select the Appropriate Data Modeling Method
There are numerous approaches to data modeling. The kind of data you are dealing with and the nature of your task should determine which one you select. The most straightforward illustration would be if you are developing a data model for data analysts who would mostly do analytics queries or for an end-user application.
In the first scenario, relational data modeling is a popular option that you may wish to consider. Using dimensional modeling might make more sense for the second. Every selection has trade-offs; the trick is to determine which data model offers the greatest advantages while mitigating the drawbacks according to the specifics of your use case.
Steer Clear of Premature Optimization
The objective here is not to overthink things and slow down your development process, even though it may seem to contradict a lot of what has been written previously. It is vital to make some significant judgments well and then iterate since diminishing returns are a real possibility. Remember not to over-engineer your system as another important consideration. Try to keep things as straightforward as you can, and just worry about scaling your system when necessary.