Product Managers know that your product should be future-proof. This means it must be agile enough to handle evolving customer expectations, growing data needs, and more complex processes to scale up or down on demand.

Embedded analytics is now a standard offering in customer-facing applications. We get our health stats from our fitness apps, and we analyze traffic insights for the fastest commute, consuming analytics effortlessly. Dashboards continue to be integral to many business apps, CRMs, and platforms, to help users engage with data to plan their business decisions. Customers demand more visibility into data, whether it’s their internal or external data, and the ability to slice and dice it in additional ways to extract insights for improved outcomes.

As a bright and customer-focused product manager, you know you have to embed analytics in your product to improve your product experience, analyze product adoption, stand out from the competition and create new sources of revenue. If the thought of taking on the project is overwhelming, know that it is manageable and that many organizations have been on that road before you and have wisdom to share.

This step-by-step guide will give you the knowledge you need to embark on your journey to embedding analytics, with use cases, expert tips, and insights from peers who have been there and done that.

Step 1: Define goals and set the roadmap

Planning, gathering requirements, and setting the scope of your analytics journey can be an intensive process. But by keeping the end goal and end user in mind, you can prioritize impact with embedded analytics.

Planning is the most crucial part of your embedded analytics project before you get started on implementation. Use this stage to define your goals, scope, success metrics, and deliverables clearly. This is where you can leverage your customer requirements to the fullest extent to really think deeply about which analytic capabilities will deliver the most value.

Ask yourself:

Where is the data coming from?
What visualizations will help your customers the most?
Where can you deliver the most value with embedded analytics?

Notes from the Trenches:

Before implementing its customized solution, Tessitura Network, a SaaS technology provider for 650+ global arts organizations, went the extra mile in gathering customer requirements to help define its embedded analytics goals and strategy. By setting up brainstorming workshops, focus groups, and interviews with customers and partners, Tessitura got deeper insights into the key metrics that mattered to each persona. The company also ensured that the BI goals were aligned with the corporate strategy of delivering a unified database and analytics experience to its customers.

Read the case study

Requirements Gathering Tip:

Set up a structured method to gather customer feedback or pain points and requirements.

Word on the Street:

“We used every bit of Sisense’s interfaces, including the REST APIs, dashboard, widget JavaScript API, the silent installer, and the console app. The best thing I can say about Sisense is that we never ran into a use case that we couldn’t do. We were able to implement everything we needed.”
— John Jakovich, VP of Business Intelligence, Tessitura Network

Product Manager Tool Kit:

Learn more about how you can plan this phase of development in the webinar, Using Customer Requirements to Drive Your Embedded Analytics Strategy, with John Jakovich, VP of Business Intelligence at Tessitura Network.

Watch the webinar

Step 2: Tackle the build vs. buy debate

This is the battle that every organization will face. Choose carefully here for a strategic win in the embedded analytics project.

Your analytics journey meets the fork at this critical juncture. Organizations are tempted to go the in-house route, thinking it offers them more control and better integration capabilities but often fail to consider the ground realities of such a code-intensive project. But before you make that all-important decision, review your requirements thoroughly — and if you have the resources to not just deliver an MVP (Minimum Viable Product) solution but also improve and nurture it over time.

Maintaining an in-house solution is a bottomless money pit — the costs and overheads of upgrading and maintaining a solution increase with the number of users and the data actions undertaken. So as you grow your user base, the costs of upgrading and maintaining that solution in-house will subsequently increase by leaps and bounds, and before you know it, your beloved solution has become a white elephant.

Also keep in mind that as data grows, more data sources are added increasing the complexity of data management, and that becomes more difficult to maintain and scale in-house. If you’re seeking cloud data storage, it’s better to partner with a cloud-agnostic analytics vendor so that you are not locked into a single cloud architecture for your BI platform.

Hear from Doug Henschen, Principal Analyst at Constellation Research, about why when it comes to putting analytics in your offering, buying and embedding is the way to go, instead of building from scratch.

Watch video

Ask yourself:

How many engineering and development resources will you need — not just to build but also to maintain embedded analytics?
Do you already have the experienced resources in-house, or will you need to hire?
What is your timeline for rolling out analytics?

Notes from the Trenches:

Energy management platform leader Vitality initially had a basic in-house analytics solution but was quick to adapt to changing customer demands for data, by white-labeling Sisense analytics into its product. With its customized embedded analytics solution, Vitality has since unlocked the tremendous growth potential by landing new enterprise clients and expanding into new markets.

Read the case study

Requirements Gathering Tip:

White-labeling is a great way to save time, increase adoption, and take the pressure off the product development process.

Word on the Street:

“Our core strength is algorithms. We hadn’t put resources and development behind data visualizations. I’m guessing we would have spent millions of dollars trying to build it ourselves.”
— Clayton Erekson, Vitality CEO

Product Manager Tool Kit:

Getting stakeholder approval for embedded analytics deployment can be a task in itself. Refer to the blog by Pat Bhatt, Director of Product Management at Sisense, for strategies on how to get your stakeholders on the same page.

Read the blog

Step 3: Embed early, not later

The most successful products are never due to mere luck, but invariably due to revolutionary thinking and process management and a matter of following the data. Read on to learn why embedding analytics can deliver value at the earlier stages of product development.

There is usually pressure to build the product first, then add embedded analytics at the next version as a nice-to-have feature. The real innovators — tech unicorns — are the ones embedding analytics into their product sooner, rather than later, to achieve transformational, game-changing outcomes.

There are significant reasons to follow the innovators. Customer demand for data is high. Our in-house research indicates that 85% of our customers embed to meet customer demand for data, 73% embed to drive stickiness, and 57% embed to get a competitive advantage.

By embedding analytics early on, you get product differentiation, increased revenue, faster customer acquisition, and improved customer satisfaction from the get-go.

Ask yourself:

Do you want a good product or a best-of-breed?
Do you want to stand apart from the competition?
Do you want to launch at a higher price point?

Notes from the Trenches:

Productivity platform Hive wanted to embed analytics for a competitive advantage. Hive initially considered building an analytics solution, but upon deeper review, realized that like any other unicorn, they wanted to move quickly and that adding a self-service BI layer on top of the existing platform would propel them toward their goals. With Sisense, they were able to rapidly white-label their solution within 6-8 weeks and land large enterprise clients per their goal.

Read the case study

Requirements Gathering Tip:

If your aim is to go to market faster, evaluate the vendor’s ease of integration and level of support throughout the sales and onboarding process.

Word on the Street:

“Once people dig into the tool, they want to create things that work for them. We’re really happy we chose Sisense because it’s so easy to customize for each customer’s needs.”
— Pooja Hoffman, VP Marketing and Operations, Hive

Product Manager Tool Kit:

Learn more about the advantages of embedding early at every stage of the product development life cycle.

Get the white paper

Step 4: Build an Product that aims to delight

Embedded analytics delight users with effortless engagement and instant insights, translating to increased adoption and revenue growth.

This stage is an exciting and creative time in the product development cycle, and product managers can be tempted to boil the ocean by adding all the bells and whistles to make a sensational debut. But resist the urge to exceed your resources and unnecessarily expand the project outside of pre-set goals as that has the potential to ground your project before takeoff.

This stage also offers the opportunity to think deeply about the KPIs that really matter to the customer. Consider the security, compliance, and governance requirements of your customers and how those should be implemented in the product. You can test your customers’ appetite for data features and levels of analytics by offering them early access or beta versions of your product with new features.

Ask yourself:

How ready is your data — do you need to enhance it?
Where do your analytics fit in the user flow?
What are your customers’ security requirements?

Notes from the Trenches:

SaaS provider LogicBay used embedded analytics to present a highly configurable, flexible analytics platform to address its customers’ diverse needs; varying levels of access; and robust, hierarchical, and nested security requirements. It was also crucial to consider a hybrid analytics deployment model, due to the mashup of data sources and unique customer requirements. Sisense’s feature-first approach enabled LogicBay to deliver a highly customized data analytics experience to its customers, with individualized widgets and dashboards with role-based levels of access. Finally, LogicBay built a support process with multiple levels to drive adoption and customer success.

Read the case study

Requirements Gathering Tip:

By integrating or infusing analytics into user workflows, you can enrich the analytics experience and improve access, while producing superior business and customer service outcomes.

Word on the Street:

“We continue to hear from our customers that having access to quality data in real time is an essential requirement. Our ability to provide them with segmented information — by job role, geography, team, program type, etc. — is critical to helping them measure performance throughout their sales channel. Having a partner in Sisense ensures we are meeting these needs and increases overall customer satisfaction.”
— Dave Goulet, CTO, LogicBay

Product Manager Tool Kit:

Whether you have multiple role-based end users for your product or not, you’ll need to factor in data governance and security controls.You can govern data access securely with Sisense to safeguard and manage data.

Learn more

Step 5: Plan your rollout and adoption processes

Product Managers always have your customers’ back. Developing strong support systems for customers as well as your internal teams goes a long way in product adoption and success.

Some of you clever readers, especially those paying close attention to this book, may have already figured this one — the secret to a successful analytics deployment lies in your plan for the rollout, adoption, and support processes. Ensure that you can help your customers with all the support and training they need to use the product, not just occasionally, but indeed make it a part of their work routine.

Ask yourself:

Do you have a series of training videos?
What kind of support documentation will you need?
How can you use client onboarding opportunities to drive adoption?

Notes from the Trenches:

As you may recall from Step 1 and Step 4, both Tessitura and LogicBay made support planning a priority in their analytics project. Tessitura went the extra mile to build a community of analytics evangelists within the organization, backed by a knowledgeable support team to help with any analytics adoption issues, established pre-launch. Customers are supported with personalized onboarding and have access to training videos, webinars, and tutorials.

LogicBay developed a multi-tier support system that includes a support team and the larger development to drive adoption and listen in to the technical problems that customers face. LogicBay further works with trainers within larger customer organizations to ensure analytics adoption throughout.

Requirements Gathering Tip:

Leverage your customer communication opportunities to release important information about new educational initiatives like tutorials and webinars to drive viewership and adoption.

Word on the Street:

“One of our biggest wins with Sisense is the excitement in our community of customers when they realize what’s possible now.”
— Chris Wallingford, Director of Business Intelligence, Tessitura

Product Manager Tool Kit:

What are companies doing to counter stalled adoption rates and put actionable intelligence into the hands of users? Find out in the blog by Scott Castle, VP of Strategy, Sisense.

Read the blog

Step 6: Develop your GTM strategy around embedded analytics

Knowing your customers well is the key to your go-to-market strategy on your embedded analytics offering. Strategize your premium pricing around their appetite for advanced analytics.

While developing your go-to-market strategy, think about introducing a value-based and tiered pricing model in sales to appeal to a wider and more varied customer base. This factors in the value the end user gets with each tier of analytics capabilities. One strategy that companies typically use is to offer standard reporting at the lower price point and then charge a premium for advanced analytics capabilities that provide the customer with more valuable insights that directly impact business.

Ask yourself:

Do you have tiering in your users? E.g., basic/intermediate/advanced? Do they each have varying analytics needs?
Can you bundle a basic suite of analytics in a “free” tier to encourage adoption?
How can you refine these tiers to improve conversion rates?

Notes from the Trenches:

Erea Consulting, which offers its Sisense-powered data analytics platform EreaBI to retailers and suppliers, partnered with UniSuper, a retail giant with 100 supermarkets in the Latin American market, to address the digital transformation gap in the industry. Erea Consulting helped UniSuper monetize its data to improve revenue streams by $2 million annually. UniSuper white-labeled the EreaBI-Sisense analytics dashboard to not only improve its own operational efficiencies but also create a new revenue stream, presenting its suppliers with the insights they need to stay competitive.

Read the case study

Requirements Gathering Tip:

You can use tiered pricing models when you have a user base with diverse technical skills where you can leverage the level of advanced analytics that your product offers.

Word on the Street:

“Digital transformation is on everyone’s mind. Big companies are spending tens of millions of dollars on these programs, but we found that most of our retail clients didn’t have much to show for their current digital transformation initiatives. We wanted this solution to be an investment in digital transformation with immediate, concrete ROI.”
— Michael Corcuera, CEO, Erea Consulting

Product Manager Tool Kit

Learn more about how you can take advantage of the right go-to-market strategy to monetize your data and increase ROI in our on-demand webinar How to Craft Your Embedded Analytics Go-to-Market Strategy Using Best Practices

Watch the webinar

Step 7: Disrupt, then dominate

A successful product is always versioning, giving you opportunities to offer deeper value to customers while reducing the cost of development. Read on to learn how to use the insights from your embedded analytics to continue on the journey to product perfection.

Congratulations! You’ve successfully launched a best-in-class product that is set to disrupt the old way of doing business. To scale with market leadership, use the data and behavioral insights derived from your embedded analytics and evaluate hosting/deployment options (on-premises versus cloud or hybrid) to take your product to the next level.

Ask yourself:

Do you have a process to review insights from the embedded analytics?
Do these learnings go into your product roadmap?
How fast can you make this feedback loop?

Notes from the Trenches:

CTSI, the global leader in freight audit and payment solutions, was using the white-labeled Sisense embedded analytics solution successfully as a true product differentiator, resulting in a substantial increase in market share. In 2020, CTSI raised its game by pivoting to the cloud. The company realized gains within 6 months, with build times reduced by 20-50%, performance improvements across the board, and significant cost savings by partnering with Sisense Cloud.

Read the case study

Requirements Gathering Tip:

Build a partnership with your embedded analytics vendor to design and elevate your product to the highest level.

Word on the Street:

“The almost boundless agility of Sisense, combined with an efficient process framework, let us deliver and organically scale a market-differentiating product.”
— Todd Winton, Development Manager, CTSI Global

Product Manager Tool Kit:

Overcome the challenges of legacy on-premises solutions by taking advantage of Sisense’s cloud-agnostic platform to migrate to the cloud. Learn more in our webinar Maximize the Value of Your Data with Cloud Analytics.

Watch the webinar

Next Steps

Embedding analytics is key to improving the product experience for customers. It helps you stand out from the get-go in a crowded marketplace and gives you the leading edge over the competition. It further creates opportunities for new revenue streams and provides opportunities to upsell and set premium pricing. By partnering with the right BI analytics vendor, you can stay ahead of customer demands to create products and services that your customers will love.

More than 2,000 global companies rely on Sisense to innovate, disrupt markets, and drive meaningful change in the world. Ranked as the No. 1 business intelligence company in terms of customer success, Sisense has also been named one of the Forbes Cloud 100, the world’s best cloud companies, five years in a row.

Sisense is recognized as the leading cloud analytics platform by top industry experts

Forrester
A Leader for Augmented Business Intelligence Platforms

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G2 Crowd
Leader for ease of use and quality of support

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Dresner
Ranked overall Leader in Business Intelligence

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Gartner
Leading Visionary in the Magic Quadrant

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Discover the possibilities of embedded analytics and experiment with color palettes and white-labeling potential in our developer playground environment. Experience the power of Sisense, and infuse analytics everywhere.

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The Product Manager’s Guide to Embedding Analytics