At ChartMogul, we are lucky to talk to many companies in the SaaS space and see the spectrum of challenges each faces at each phase of their growth.<\/p>\n\n\n\n
Depending on where these companies find themselves along that spectrum, we can find ourselves in conversations with teams that are comparing our Subscription Data Platform<\/a> against a tool they are planning to build internally.<\/p>\n\n\n\n Naturally, we want to get as many customers for ChartMogul as possible but are also very aware that for some teams, only an in-house tool can provide the level of insight they need.<\/p>\n\n\n\n Our experience has helped us develop a good understanding of what characteristics make a team a good fit for build vs buy.<\/p>\n\n\n\n In this post, we want to share some of that knowledge in order to help companies that are trying to decide whether they should build or buy their subscription analytics software.<\/p>\n\n\n\n Some additional interesting takes on the \u201cbuild vs. buy\u201d conundrum:<\/p> Many teams choose to \u201cbuy\u201d because they want to start using their data to their advantage as quickly as possible. Similarly, quite a few teams choose to \u201cbuild\u201d their solution in-house because they believe their use case is so unique that nothing in the market comes close to fulfilling their requirements.<\/p>\n\n\n\n No matter which approach you choose to go with at the end, it is important to understand you will always gain and lose something (speed vs. customization, etc.)<\/p>\n\n\n\n To make that decision for your company, it\u2019s not enough just to be aware of the advantages and challenges of each approach. You also have to develop a high level of understanding of both the business and technical requirements you have to fulfill to decide which approach would work best for your company.<\/p>\n\n\n\n Let\u2019s compare the two approaches and see how they stack against each other.<\/p>\n\n\n\n Building from scratch may appear to be a good option because it allows you to create a tool that is the perfect fit for your specific business model.<\/p>\n\n\n\n However, you also need to consider that it is improbable that you will get the resources to build this vision of the perfect<\/em> tool. One of the reasons is that investing in something that is not part of your core differentiator is probably not a great use of your resources in the first place.<\/p>\n\n\n\n Especially in the beginning \u2014 you\u2019re much more likely to start with something that looks a lot like a proof of concept<\/em> than the complete platform you envisioned during the planning stage.<\/p>\n\n\n\n Here are some of the other pros and cons building subscription analytics internally comes with:<\/p>\n\n\n\n Over time, the volume and complexity of the data your solution needs to account for compounds and your business and technical requirements change. So when planning for your solution, you need to anticipate that you may change billing systems later on and to be prepared to build support for that if and when the time comes.<\/p>\n\n\n\n Now, let\u2019s look at what this looks like when you decide to buy a pre-made subscription analytics product.<\/p>\n\n\n\n Two distinct motives usually drive teams that choose to buy an analytics platform.<\/p>\n\n\n\n The apparent reason is, of course, to save time, money, and engineering resources.<\/p>\n\n\n\n The equally-important-yet-less-prominent <\/em>motivation is that an external vendor \u2014 especially in the case where they are developing analytics for a specific niche \u2014 also brings deep<\/em> domain knowledge about the area you\u2019re interested in.<\/p>\n\n\n\n So, whether it\u2019s revenue, marketing, or product analytics, companies like Mixpanel, Heap, and others spend a lot of time thinking about the underlying discipline. I know we do<\/a>.<\/p>\n\n\n\n Besides, buying from an external vendor means you are (almost) guaranteed that the company will continue to develop the tool, so you\u2019re using something that is always following the latest trends in the industry.<\/p>\n\n\n\n Just as with building, buying an analytics platform requires a set of tradeoffs.<\/p>\n\n\n\n Now that you have a good understanding of the tradeoffs each approach involves, you should also consider the resources building internally would require.<\/p>\n\n\n\n If you\u2019re thinking about building a subscription analytics capability internally, it\u2019s important to consider the requirements and costs of such a project.<\/p>\n\n\n\n First, in terms of technology, you will need to provide solutions for the following three layers:<\/p>\n\n\n\n In terms of costs, it can be challenging to estimate the exact amount to build internally. Many of these tools cater to larger organizations and, as a result, don\u2019t have public pricing<\/a> available.<\/p>\n\n\n\n Based on some of the information we\u2019ve been able to unearth, building a best-in-class solution can easily require northward of $100,000 per year (excluding<\/em> headcount costs!) to build and operate:<\/p>\n\n\n\n Of course, you will also have to consider a full-time data engineer\u2019s salary that you will need to build the solution in the beginning and a data scientist that will be the primary person who will be using it. The average salary for both those is over $100,000 per year, according to Glassdoor<\/a>.<\/p>\n\n\n\n As you can see, the costs to build a \u201cbest in class\u201d solution can quickly escalate into six figures, so it is essential to keep that in mind when choosing which approach to take.<\/p>\n\n\n\n Some teams choose to build using cheaper and\/or open-source products, but that introduces a whole different set of challenges and usually, it ends up requiring even more data engineering effort, which makes the end result just as expensive.<\/p>\n\n\n\n With that, we do not want to imply that you should also choose to \u201cbuy\u201d over \u201cbuild\u201d. In fact, we believe there are circumstances in which either can be the better option.<\/p>\n\n\n\n We believe that your decision to build your own solution or buy from a third-party vendor should be driven by what end goals you want to achieve.<\/p>\n\n\n\n Suppose you\u2019re looking to implement analytics and start using the insight from them in well-defined areas such as marketing, product, or revenue<\/em>. In that case, it makes sense to buy from a vendor who already has built a dedicated product based on the expertise they have in the field.<\/p>\n\n\n\n However, suppose you are more interested in exploratory data analysis<\/a>, precisely in cases where you can combine different types<\/em> of data (e.g., specific types of in-product usage and<\/em> revenue\/subscription movements). In that case, it makes sense to invest in building your own stack and hiring a (team of) data scientist(s) to run this for you.<\/p>\n\n\n\n We also increasingly see a trend of what we call Reverse ETL<\/em>. Companies with a high level of maturity in their data operations connect their data warehouses to tools like ChartMogul to take advantage of pre-built integrations, metric computations, and other features. This preserves their control over data and gives them the ability to do any type of consolidation and analysis on their end.<\/p>\n\n\n\n Some ChartMogul customers also use our Metrics API<\/a> to pull their metrics and present them in their preferred reporting solution\/stack.<\/p>\n\n\n\n No matter which route you choose to go, we believe making a move is inherently better compared to pondering the decision too long.<\/p>\n\n\n\n A lot is at stake when it comes to your data and how you use it. There\u2019s a lot in it that can help you understand the potential of your business and grow it sustainably.<\/p>\n\n\n\n If you\u2019re currently facing some of these issues and would like to get a second opinion from an expert (it will be only slightly<\/em> biased, we promise), you can schedule a technical scoping session<\/a> with our team. We’ll help you define your business and technical requirements and avoid any serious pitfalls. Contact us at sales@chartmogul.com<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" Everything you need to consider before you\u2019re ready to make the Build vs Buy decision for your subscription analytics platform.<\/p>\n","protected":false},"author":69,"featured_media":17605,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12],"tags":[42,1985,1986,35],"class_list":["post-17604","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-gtm","tag-analytics","tag-build","tag-buy","tag-subscription"],"yoast_head":"\nFurther reading<\/h3>\n
Both Build<\/em> and Buy<\/em> involve tradeoffs<\/h2>\n\n\n\n
Building your own analytics tool<\/h3>\n\n\n\n
Positives:<\/h4>\n\n\n\n
Negatives:<\/h4>\n\n\n\n
Buying a third-party analytics product<\/h3>\n\n\n\n
Positives:<\/h4>\n\n\n\n
Negatives:<\/h4>\n\n\n\n
What does it take (and how much does it cost) to build your own analytics?<\/h2>\n\n\n\n
How to choose between Build<\/em> vs Buy<\/em><\/h2>\n\n\n\n
Pick a side, make a move<\/h2>\n\n\n\n