Embedded Analytics: The Pitfalls of Pricing

One of the obvious questions, when you’re in the market for a data analytics solution, is that of pricing. Regardless of whether you plan on embedding analytics into a software product, or rolling out analytics across a ten-thousand-strong enterprise as well as to your business partners, finding an analytics solution with a pricing model to suit your needs is paramount. But it’s a little more complex than; which one’s the cheapest or the best value for money.

First, set out your needs

To understand which platform is the right one, you need to assess what you want out of it. If you’re a small company looking for an analytics tool for internal collaboration, your needs will likely differ from that of a software company developing a new application with embedded analytics accessible to end users. Understanding your needs and how they translate into costs is a crucial first step.

Predictable pricing or perpetual headache

While per-user or per-query pricing may seem like the best plan to start off, their perceived benefits might not add up in the long run. Despite the majority of analytical platforms charging for their services in one or other of these ways, they make predicting your future costs a difficult task.

Paying per user

Organizations come in all shapes and sizes and that includes your customers. If you’re in the b2b market, it’s impossible to predict how many users each of your future customers will have, making paying per user a matter of guesstimates, unless you plan to charge per every user of every customer. Consequently, your margin calculations will become increasingly difficult to calculate, leaving your Finance team with a perpetual headache.

Paying per query

If calculating long-term margin based on per-user pricing sounds hard, then paying per query takes it even further. Each time an end user displays an analytics insight or explores information in a self-service analytical tool, they will hit a pay-as-you-go database (something required by every query). The result? More unpredictable costs, finance team headaches, and lost margin.

GoodData: predictable scalability

Conversely, with a fixed price per workspace, you can encourage as many end users as possible to use your analytics solution without your customers, or you, worrying about the costs. Pricing can be easily calculated by the number of workspaces, the need for additional data storage, and specific feature add-ons, as required. This allows you to easily forecast revenue as well as profit margins, in both the short and longer term. Importantly, bringing on a new customer (organization) in a b2b scenario, won’t have a negative impact on your profit margin.

Example

With GoodData, which employs the pay-per-workspace model, if your product is being used by 10 different customers and you need 25 GB of additional data (on top of the standard space) to distribute among them, your yearly costs will be: $20 per workspace X 10 customers + 25 X $3.5/1GB = $287,5 X 12 = $3,450 / year. Learn more at www.gooddata.com/pricing.

Keen to find out more about GoodData analytics and its optimized pricing model? Why not sign up for our free trial and start building insights for your teams or your customers today?