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How to Choose Your Embedded Analytics Strategy

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How to Choose Your Embedded Analytics Strategy

You know that your SaaS product needs analytics functionality, and you know you want to embed analytics in your product. But do you know how? Your engineers may be set on building it themselves, while your gut-level instincts are telling you to find an embedded analytics partner who can supplement the development and accelerate your time to market. Whether you’re looking to embed analytics in free dashboards for your customers, or create a tiered SaaS product strategy to turn your data into dollars, there are key considerations you’ll need to include in your decision process of whether to build or buy an embedded solution.

The Opportunity: If you’re looking to deliver analytics to your customers, there are embedded analytics solutions, that allow you to (1) increase customer retention, and (2) strengthen relationships with your customers and partners. With some, (3) you can even embed your analytical creations in software-as-a-service products that can actually turn your data into revenue.

The Challenge: It isn't enough to just have the analytics — you have to do something with them. You need to understand who will be using your data product, what problems they are trying to solve, and what functionality they require. You’ll need to not only build a unique data product that delights its users you’ll need to price it appropriately, deliver it effectively, and have the infrastructure and the staff to handle any issues that may arise.

With all that to investigate and plan and staff for, are you sure you have the resources (and time) to build your own solution from the ground up? Here are three key considerations people who are developing their embedded analytics strategy find valuable when in the midst of their decision process:

1. Know Your Value: Do you have what they want?

Whether your goal is to simply improve customer engagement and satisfaction or to accelerate your differentiation and create new upsell opportunities, you need to first understand what objectives and questions customers have, and focus your analytics strategy around that value. A bit of wisdom: “Solving your customers’ biggest problems or answering their most pressing questions with fewer pieces of data and fewer analytics is of greater value than giving them every piece of data you collect.” The key to getting this right involves a targeted and prioritized approach with early feedback and validation from a strategic subset of your customers.

“Look at the data and your analytic capabilities, and determine what questions you can currently answer that may be of interest to your customers.

CITO Research
Guide to Data Monetization

After you have a solid understanding of the insights that will provide the most value to your customers, you then need to consider their skill levels and how they will use the product. In most cases your end users will not have the skills of a traditional analyst, as a result you need to make sure that the experience you provide is intuitive, actionable and fosters consistent engagement. Clear headline KPIs, easily drillable dashboards, guided ad hoc analysis and mobile notifications are all key components to getting and keeping users engaged with your analytics.

2. Know How Much You Can Bench: ...and who should shoulder the weight.

When you start strength training, you gradually and steadily increase the amount of weight you are bench pressing to allow your body time to heal between workouts. In the case of adding analytics into your product, you’ll need to make sure that your infrastructure can handle the distribution and management of projects out to all of your customers, and seamlessly scale as your customer base grows.

This involves much more than the initial delivery of the analytics. Your team needs to keep in mind all of operational costs and manpower that going into the security, user provisioning and ongoing product updates that need to be tightly managed along the way in order to keep customers coming back.

When it comes to adding workload to your department’s agenda, be real about how much time they have to spend on developing an analytics product.

3. Learn the Best Path to Embedded Analytics Success

At this point you’ve identified the best way to provide a highly engaging set of analytics out to your customers, and you’ve even thought through how your team will manage the large scale distribution and ongoing management of those analytics, so the final piece--which is arguably the most important--is outlining the strategy to bring your analytics to market. Every organization will be slightly different but there are a number of key questions that your team should be sure to think through and align around in order to ensure the continued success of your new analytics offering:

  • Should you create a tiered model with additional analytic features to promote upsell?
  • If you decide to tier your analytics, what are the monetization triggers that you can package into value-based tiers?
  • What is your pricing sweet spot range?
  • Have you outlined the ROI model?
  • Are internal teams prepared to support an external launch of these new capabilities?

All of these questions are part of an important planning process to make sure that your team is aligning around the core goals for your analytics and ultimately creating a roadmap that will help accelerate the evolution of your offering, and quickly increase the value that you can provide to your end users. That level of visibility also gives your executive team a predictable timeline for how and when they can expect to see the financial impact of the investment that you’re making.

Want some help planning your path to success? That’s why we’ve created an interactive experience that walk you through monetizing your data with embedded analytics. Enhance your go-to-market knowledge, while learning how to roll out an analytics product in record time.

DataLand TW fixed

Written by GoodData Author  | 

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