Your community contains loads of member and customer data. When you analyze it, you gain access to all types of valuable insight: which users are most engaged, what topics they’re most interested in, who is contributing to discussions and the list goes on. That data becomes even more powerful when combined with data from other systems, like an AMS/CRM, email marketing, marketing automation platform or Google Analytics.
This is the primary job of a data analytics tool: to display the full picture – from how your users interact with the community to which sessions they attended at a conference, and how engagement impacts retention rates and transactions. In short, a data analytics tool is designed to help you prove the value of your community and to provide a comprehensive view into your most important metrics.
Budget is a major consideration, of course, and you’ll want to plan accurately. Data analytics tools come in all shapes and sizes, so you need to select one that meets your organization’s specific needs. Ensure that your financial preparation is successful by accounting for the following:
1. Total cost of ownership
When it comes to data analytics platforms, there are two common approaches we see trending: a data lake and a data warehouse. These approaches relate to how the data is stored, and whichever path you select will set strong parameters around how the data can be used for visualization on the front-end.
How you choose to store your data will also majorly impact the total cost of ownership. Here are specific components to evaluate as you consider the platform and methodology:
- Infrastructure. Some solutions require you to purchase infrastructure while others don’t.
- Staff time. The level of staff involvement varies greatly in implementation, maintenance, and preparing and analyzing reports.
- Consultants. Some platforms require an outside consultant to manage various aspects of a project, from discovery to implementation to ongoing analysis. Others are self-sufficient.
- Training. Some platforms require training to use the platform; others are intuitive and easy to use out-of-the-box.
- Post-launch modifications. The level of effort to customize or modify a platform varies from low (inexpensive and quick) to high (expensive and time intensive).
- Subscription/license fees. Depending on how the platform is hosted, the price structure will either be a lower subscription fee or a high upfront licensing fee.
2. Predictable costs
To budget accurately, you should evaluate your current needs and account for how those will change as your organization grows. How a platform handles scalability is a telltale sign of how expensive it will be over time; you want to be sure customizations and growth potential are inexpensive and easy for your platform.
Being able to predict the actual financial commitment is also critical for maintaining accurate budgets. Some options have high startup costs and licensing fees, while others have a predictable subscription model, like SaaS.
Staff resourcing is a cost that isn’t always considered; some solutions require in-house expertise or consultants to maintain the product or even use it efficiently, while others require little to no training.
3. Rapid ROI
Being able to prove return on your investment and cash in on immediate insights is important, but there is a large variance in the time-to-value category. Some options take years, while other solutions can start showing value in weeks.
A project can lose momentum after years in the making, and any data analytics tool that doesn’t offer immediate value will be an uphill battle. A tool that provides value sooner will demonstrate ROI and provide avenues for further investigation, engagement, and adoption across your organization.
4. Risk avoidance
Both cost predictability and time-to-value lower the rate of unsuccessful projects. The longer a project takes to show value, the more risk increases.
Some data analytics platforms require significant capital investment up-front, but without seeing insight along the way, it’s difficult to shift course. Alternatively, a shorter implementation shows less cost variance and lets an organization pivot to adjust easily as needed. A simple change in business model can immediately outdate a platform, so it’s essential to choose one that can be easily modified to meet new business goals.
Account for More Than the Price Tag
Budgeting well means accounting for more than just the price tag; it includes the total cost, from implementation to staffing resources and long-term maintenance. Additional financial considerations include storing your data in a data lake or a data warehouse, and whether you should host it in the cloud, on-premise, or a combination of both.
As you make decisions about your data analytics requirements and develop a budget, you’ll be equipped to start investigating platforms and considering the features that will best fit your functional needs. Combining community data with other essential technologies will bolster your campaigns, increase your successes, and deliver insight that can drive true transformational growth.