Driving revenue with data

ROI you can bank on

Have you ever heard your prospect or customer say something like this?

“What is the return on investment for working with your product and services? I need a business case to unlock the budget.”

Have you recently trained your marketing and sales organization on value selling or outcome selling? What if there was a way to actually quantify and deliver that ROI? What if your company already had all the critical assets it needed to equip your commercial teams with outcomes-oriented stories?

That’s what driving revenue with data (also called data monetization) is all about: the ability to transform your data and domain expertise into analytics products and services that deliver incremental value for your customers and create new revenue opportunities for your company.

We live in an ever-changing digital world of apps, data, and always-on insights. If your company isn’t looking for new ways to explicitly support revenue and margin growth for your customers and monetize your data, then you will not survive. However, all challenges carry inside of them an opportunity to create a better future. Data monetization via analytic apps is that opportunity.

Innovate to win

Innovation has long been the buzzword of large and small companies alike, especially in the digital age. However, just what that innovation looks like today can mean different things to different people. Turning your data into a new revenue stream, especially via an internal innovation center, is a powerful way for you to digitally transform your company and position it for future success. Failing to do so will cost you users, dollars, and ultimately your place in the market.

“Creating an innovation factory for your company results in faster innovation. You can do smaller experiments more cost-efficiently,” says Charles Holive, Sisense Managing Director of Data Monetization and Strategy Consulting. “This also helps you prioritize analytics projects that deliver the greatest value.”

More and more present-day enterprise apps, from Seismic and UiPath to Expedia, have made analytics a core part of their offerings. Users want to see and interact with their data; this functionality has gone from a fringe add-on to a must-have feature. Your offering must have some kind of data element as basic table stakes, but as an innovative differentiator, you’ll have a lot of options for in-app data. Whatever form your embedded insights takes, it will help increase stickiness.

In this ebook, we’ll delve into the ways in which the analytic app and data monetization world has evolved over the years, where it’s going, and how you can take advantage of it to position your company for maximum success in the new world. You’ll hear from product and data monetization experts and walk away with a solid list of must-haves and how-tos to help you take your offering to the next level.

Data monetization strategies

Because innovation looks different from company to company, you’ll need to define data monetization for yourself. There are many ways to become data-driven and start making your data work for your company.

The four main uses of data that companies turn to are selling data, driving internal efficiency improvements, adding data around existing products and services, and introducing new business models and revenue streams. Let’s dig into them each briefly to try to determine the best next steps for your organization.

Selling data

In the business world, the shortest distance between having a commodity and making money off of it has traditionally been to sell it: “Buy for a nickel, sell for a dime.”

If you think we are still living in that world, you can close this ebook right now.

There are so many better things you could be doing with your data than selling it! Selling data turns it into a dead commodity, one that you have extracted some value from, but which is inactive and contributes nothing to your business in a larger sense. While it’s perfectly acceptable to buy access to other datasets to complement your own and increase the range of applications for your own analytics efforts, selling your data is in essence selling your company short.

Driving internal efficiency improvements

Becoming a data-driven company is vital to surviving and thriving in the modern business world. Adding actionable insights at key decision-making points is a part of every company’s digital transformation (or inception, in the case of modern companies born on the cloud).

Your company collects immense amounts of data. If you’re not already using that data (and any other datasets you have access to) to make smarter, data-driven decisions, then your future replacement definitely will be. However, this is a jumping-off point and not an end goal.

Monetizing your data requires creativity and a will to build something that doesn’t exist yet. By all means, keep making data-driven decisions and look for more places to link actions and insights to help your colleagues do their best work, but don’t stop there.

“Your company can only be good at one subset of analytics of all the powerful analytics required to be fully data driven,” Charles says. “So companies turn to their trusted partners and buy advanced insights directly from them. This self-awareness creates the demand on the data monetization market. You should play to that demand with your domain expertise, selling unique insights only you can provide.”

Adding insights to your existing products and services

Now we’re getting somewhere! Whatever your company does (selling a product, offering a service, or featuring a subscription model that blends the two), there’s data associated with it. Your internal teams (customer success, growth, marketing, etc.) hopefully already use this data to keep customers engaged and happy, grow the business, and make smarter business decisions (see section above).

Adding data and analytics to your existing offerings takes the necessary next step: giving your customers insight into their own behaviors as they pertain to your product or service. We live in a world of data! It’s only natural that people will want to know how they’re using your product or service. Even a service as quotidian as Dollar Shave Club gives subscribers insights into how they’re using the service: How many blades are they going through? How often do they buy more shaving products? Even just giving them a list of past purchases and dates can remind them of the things they enjoyed and encourage them to buy more.

“Your analytics products and services must create enough incremental value (revenue and margin growth) for your customers to give you the opportunity to monetize that value,” Charles says.

Opening new revenue streams and new markets

This is the apex of your data monetization efforts. From a starting point of just adding some kind of data and insights to your product, you can take the next step by developing new data-based offerings.

There will always be a tier of customers for whom data and analytics in your offering are a must-have, but those customers don’t necessarily need to go too deep. Once you’ve satisfied them, you need to identify what kind of data and analytics will create value for other users, then build it (and charge them for it).

This is where creating an innovation center within your company really becomes vital. You take the data you have on hand and a few creative builders and let them start experimenting. These intrepid souls shouldn’t be tied to revenue goals immediately. Instead, they should have a mandate to create new things, try them out, and (if necessary) fail fast. The result of this kind of iterative process is that this team (and your company) spends a lot less time on projects and ideas that don’t work and more time on ones that do.

Once you start showing some successes with your data monetization efforts, you can double down on them and roll them out to more customers. As these new offerings gain more traction and start bringing in more revenue, the role of building and maintaining them will find an appropriate place within your organization.

More words of wisdom from Charles:

“Consider your new analytics products and services as a way to expand your addressable market either within your existing customer base, tackling new value propositions, and appealing to additional persona usually higher in the hierarchy,” he says. “They could also be your opportunity to really go after your vendors, partners, or even the government with a compelling analytics portal for them to get value from your data.”

Think long term for data monetization efforts

Where will your business be three years from now? Hopefully, by now you’re thinking about all the ways data, analytics, and data monetization will fundamentally change what you do and how you do it. Three years is a long enough runway for your eventual data monetization processes to have taken form and taken flight, so you can start thinking about the following goals:

Revenue: How much money could your data monetization efforts be bringing in? How will you market your data products to existing and new customers?
Attach rate: What percentage of existing customers can you count on to be part of your new revenue stream? Which new customers will join you specifically because of your new data monetization features/products?
Churn rate: No business retains 100% of its customer base as it grows and changes. What percentage of your current customers can you expect to lose over time? (And how might that number be greater without adding data and analytics to your product?)
Spread: Depending on the size of your company, getting your entire organization to even start using analytics and data to make smarter decisions can be difficult enough, but three years from now, every business unit in your company needs to have a functioning, revenue-creating data monetization app or service. This is where your innovation center can really create lasting impact by taking models that work for one division and experimenting with other divisions to help them accomplish similar outcomes with their unique data and needs.

What defines your business unit?

Before you start on your data monetization journey, it’s important to do some self-reflection and understand who you are as a business unit (BU). That will help determine how you can build the right data monetization play for you.

Type of BU/organization	Data monetization imperative	What should your goal be?
Software	Defense	Embedded analytics have become table stakes in software. Now is the time to invest and build a front-running competitive advantage with innovative insights
Hardware	Offense	Every organization today has to be a technology company or die. In addition to improving business processes, transforming with the goal of lowering costs, reducing cycle times, or increasing quality, technology can enable domain and business model transformation to capture new markets, expand existing customer bases, and extract unrealized value. Data monetization go-to-market plays with new analytic apps can help increase margins and drive new revenue streams otherwise left uncaptured.
Service	Scale	As a service organization, you can capture more value by moving away from low-return custom solutions and instead scaling out your solutions. Leveraging a third-party analytics software will enable you to focus on high-value tasks and drive innovation instead of wasting time and effort in reinventing the wheel. The right platform will allow you to extend your services and build automated, repeatable processes with cutting-edge technologies, easy extensions, and integrations into new ecosystems that allow you to provide the most value.

You might have a mix of all three within your organization, but the principles remain the same.

Data Strategy Blueprint

“One of the biggest problems with data strategies,” says Charles, “is that everybody assumes that somebody else is taking care of the data and the result is, nobody does.”

If you’re reading this ebook, odds are you already know (or think you know) that you need a data strategy of some kind, but the specifics may escape you. That’s OK! There are as many data strategies as there are companies that employ them. However, there are some useful ways you can frame your strategy to get the greatest impact from it.

For starters, adopting an outcomes-focused mindset can go a long way toward helping you define your plan: Take a hard look at the data you already have at your disposal, then think of the kinds of business goals you want to accomplish.

“I recommend starting your data strategy with a right-to-left approach, focusing on the desired business outcomes first, instead of the data to support those outcomes,” Charles says.

These goals will necessarily fall in two broad categories: ones you can accomplish with the data you already have and ones you will need additional data to accomplish. This is where we return to your innovation center.

Your data strategy should include lots of experimentation by this innovation center using your existing data. These builders should also be dreaming big and thinking up possibilities that hinge on getting your hands on new data (whether you buy it from other sources, trade for it, create partnerships, etc.).

Charles also has another useful frame for thinking of your data strategy targets: “There are primarily three areas that industries across the world look to improve: the top line, the bottom line, and customer satisfaction.”

If you can improve any of these with the data you have (and take that to the next level with additional data down the road), odds are you’re on the right track.

Don’t try to get a handle on your company’s entire dataset at once. Dealing with that much data can make it hard to determine which data is most helpful and which might be helpful later but isn’t at present. This is another place where experimenting to win and building an innovation center are vital to the success of your data monetization efforts.

“Identifying the exact data you need to solve a singular problem results in a perfect candidate to go into your warehouse on the first cycle. This is because you know exactly what the business is going to do with that dataset,” Charles explains. “It’s powerful because it’s already informing or measuring a specific business outcome.”

When building a data strategy for monetization, have an end goal in mind, start building, and be bold in your experiments.

Data marts vs. data warehouses

It is undeniable that a core component to data governance is data warehousing, enabling the creation of an abstract layer from the data sources where you can run advanced analytics queries without compromising the production system.

Unfortunately, a common mistake is to consider data warehouses as a prerequisite to analytics, when the exact opposite is true: As you iterate on identifying what subset of data (data mart) is required to create the analytic app to support a specific business outcome, only then does the data mart become a candidate to populate your data warehouse.

This approach ensures that your efforts will lead to a guaranteed business outcome for your company and your customers. It will also enable you to filter out the less-impactful data requests and reserve the bandwidth of your teams for high impact work.

Product strategy — deciding what to build

Infusing analytics into an existing product

The time-tested and best way to build successful products is to listen to your customers and solve for their needs. Getting requirements from your customers is critical to making this happen.

It is important to go out and speak to customers, partners, and field-facing colleagues directly in order to identify their needs. Some ways of making this happen are to conduct:

● Brainstorming workshops and focus groups

● One-on-one interviews

While speaking to customers, it might also be useful to introduce the identified user personas and find out what specific questions and metrics matter to each persona.

Brainstorming new ideas across your organization and BUs

When your analytics opportunity is greenfield and your central team is looking to generate new ideas across your organization for data products, a simple way to get started is to hold “use case bootcamps” that engage all businesses and stakeholders. Creating a fund for new and innovative data products with an accelerator program is another way to supercharge your product-building efforts.

The field team knows customers, their use cases, and the challenges they face. Providing the forum and support for these experts to surface ideas and bring them to life can create huge opportunities for your company to build new products and offerings that satisfy real customer needs.

While gathering new ideas, it will be helpful to appoint a data governance leader to manage the project as it scales. This person can also set up a data science council separately to help in a controlled fashion (as data scientists can be hard to come by). Don’t forget to surface both new revenue initiatives and marginal improvement initiatives and have a good balance between both.

Differentiating between simple and sophisticated embedded analytics

Once you have introduced the needs you’re trying to address and are in the process of selecting key performance indicators (KPIs) and building your product, you can start thinking about how to deliver actionable insights through analytic applications. This is where building boldly will set your company apart from others: You don’t want to just dump a bunch of traditional reports into your app and call it a day. A simple report will not add much value for your customer and can even reduce their willingness to pay for your solution. Instead, think about how you can help your customers make smarter, better data-driven decisions.

In order to make sure to do this, remember these four simple points when picking your KPIs:

Identify which KPIs are good or bad
Show your customer the cost of doing nothing; create some urgency
Provide a trend to show how they have done so far and how that will look in the future
Prescribe what to do with variables under their control

When you choose KPIs this way, you are empowering your customers with specific insights and actions they can take to better their outcomes. That is powerful.

Building a business case

Profit and loss builder (breakdown)

To be successful, analytics and AI should be measured as a standalone profit and loss (P&L), with its own three-to-five-year forecast, calculating:

DIRECT BENEFITS
- New revenue
INDIRECT BENEFITS
- Retention (% of revenue saved)
- Better win rate
- Cost saving (development headcount, other existing licenses)

Breaking down the investment:

Software licenses
Infrastructure
Go to Market (sales, marketing, etc.)
Dev (new headcount)

Executive/stakeholder buy-in

One of the common mistakes executive business leaders have made is to believe they can delegate their analytics strategy to a center of excellence. This is as far from the truth as you can get.

It’s highly recommended that you centralize some of the core components of your data monetization program, like:

Platform
Go-to-market and pricing framework
Continuous integration/continuous delivery
Data governance
Customer success

It is critical for each business unit or region to staff data and analytics leadership, to leverage that centralized set of resources and apply their domain expertise, customer proximity, and market expertise to build highly viable analytics and AI products and services.

It is a new muscle for your company that will lead to long-lasting innovation strength and competitive advantage. Every leader should master the competency of delivering better outcomes through data and analytics.

Key considerations

It is important to note that the steps from data to revenue and beyond rely on more than just the technology and platform. All three pieces — people, process, and technology — need to come together to make that journey.

Once you have identified the right data opportunities, built your P&L and use cases, gotten the right buy-in, and are ready to go, the next step is to turn these ideas to reality. If the solution is successful, then you need to ensure that the product scales and continues to be innovative as it grows.

Today’s end users are accustomed to seamless, customized experiences. They expect quick responses to their needs and great performance while receiving continuous added value.

When technology was less mature, it might have made sense for enterprises to build everything on their own. However, with the advent of mature platforms, unless analytics is your core business, you can get more value from focusing on your domain expertise, solving customer problems, and delivering improved outcomes versus reinventing the analytics wheel.

In order to build successful data monetization initiatives, you need to:

Innovate rapidly

Innovation rests on the ability to rapidly try, fail, iterate, make changes, and develop. Building data monetization products that create new revenue streams and new markets relies on a solid idea-to-product path. Even if you are embedding insights in existing products, a good product development strategy is agile, allowing for a rapid feedback loop from customers to the product.

This is only possible if the platform is easy to use and has the right architecture to support rapid prototyping so that you don’t waste time, money, or effort. You need a data layer that supports testing new subsets of data and makes it simple to bring together different datasets in an easy-to-use semantic layer (without first having to pipe it into a data warehouse). In addition, the ability to keep the calculations/aggregations at the visualization layer and not the data layer enables business analysts and nontechnical users to build on their ideas without constantly needing high-technical solutions. Lastly, capabilities that support citizen data scientists with point and click advanced (predictive and statistical analysis) and augmented (AI-driven automated insights) analytics are critical to be innovative.

Scale without compromising agility and performance

As your solutions grow, it’s important to be able to handle not only increasing data and complexity but also users and use cases in an agile and automated manner.

There are three important considerations when talking about scaling and maintaining high availability:

Performance and availability: Your systems need to be able to maintain performance with larger sets of data and users with the power of the underlying data-caching mechanism and on-demand auto-scaling of resources
Automation: For true scale, processes like adding users, managing security settings, duplicating and managing data models, sharing dashboards, and sharing/moving assets from one environment to another should happen in a programmatic, API-driven fashion
Monitoring: No matter how well one plans, it is important to measure and detect system resources consumption and avoid outages to ensure operational continuity

What is important, however, is that the application does not lose agility when it powers up to provide performance and scale. Applications often have to choose between agility and scale. This is counterproductive to building successful data products. To deliver the best experience to your users, you need a combination of agility and scale.

Modular architecture with a unified semantic data layer that democratizes data along with the power to extend the platform with an API-first architecture and fundamental scalable cloud-native architecture are critical to providing this kind of agility.

Deliver invisible analytics and embedded insights

The best kind of analytics are invisible. Analytical dashboards are designed to get you answers to your pressing questions as fast as possible. But no matter how well they’re designed, they have a number of important drawbacks. First, they take time to interpret, meaning they only appeal to a subset of users and personas that have the mandate to uncover insights and make strategic decisions. Second, and more importantly, they require the individual to switch away from whatever they’re working on to use them, unless the analytics application itself is the core workflow.

Actionable insights should be delivered at the point of decision-making — whether that’s through their CRM application, an email, mobile notification, or on their screens wherever they are. Proactive insights and KPIs that tell end users what action they should take evolve this idea one step further.

Analytics should fit neatly into the end user’s workflow, providing suggestions on next steps or tying back into a workflow. For example, if the customer is running a marketing campaign via your application, they should be able to have KPIs in context showing them the performance. Based on the performance, the user should be able to stop or add more resources to the campaign all within the same context.

A robust set of capabilities including an actions framework, flexible and extensible embedding libraries, an open and extensive API framework, and easy-to-use customization and branding features will enable you go beyond the traditional white-labeled dashboard and truly blur lines between your application’s workflows and analytics.

Provide innovative solutions when incorporating advanced analytics

In today’s fast-paced and highly competitive environments, the pressure to innovate and deliver value with great experiences is real. Embedding standard dashboards and standard historic analysis is not innovative or new; it’s expected. In addition, it limits users from going deeper with their data.

Leveraging point-and-click predictive and statistical capabilities including forecasting and trends will let you set up these features easily while enabling your customers to get to deeper insights faster. At the same time, a workbench to build deeper SQL-, R-, and Python-based models can help you customize the experience for your customers as needed.

What can take this one step further is if the system can automatically generate advanced insights. Under the current embedded analytics paradigm, it’s incumbent upon the product team to think up every possible question a customer might want to know about and pre-build the answers into your product’s embedded analytics. If users need to go to a data engineer or data scientist for everything, they are losing precious time.

Augmented insights and AI/machine learning-driven automated capabilities like natural language queries or automated exploration paths help not only the builders but also the consumers by delivering deeper insights, more intuitively, with less heavy lifting from the development team.

Implementation best practices

The right analytics platform will allow you to white-label and customize the look and feel of your embedded analytics, making them look exactly the way you want to match your brand. This is the quickest way to get analytics to your customers.

If you already have an application, these tips can help you simply and successfully embed analytics in it.

Planning phase

Put together the right team: Earmark a developer resource along with the champion

A common mistake is not setting aside the right resources for the project. A good team for embedded analytics should involve a developer who can handle the embedding and integrating the BI implementation. If it’s one person, then that person needs to have both skills. It’s also important that all the stakeholders are involved in the project during the planning phase to keep all aspects of the project in mind while it’s still being laid out. Trying to add this person too late in the process, when a lot of decisions have been made that could adversely affect the embedding, will likely result in a lot of lengthy rewrites and extra time added to the whole process.

Embedding shouldn’t be an afterthought

As with all projects, it’s important to plan for embedding early on. Below are few points to consider while planning:

Scope the functionality that needs to be embedded along with the BI requirements (like KPIs and visualization) before starting the implementation: Plan in phases — start small and expand.
Plan the interaction between the hosting application and analytics: For example, will filters be created in the hosting application or within the BI tool? How will the user access the analytics embedded in the host application?
Design the architecture to handle the expected scale and scope of usage: It’s imperative to estimate usage metrics like the number of users (concurrent and total) and data size, as well as the scope and complexity of the dashboards. You also need to define uptime requirements to ensure that the architecture is designed and built to support it.
Plan the user login, access rights, and security flow: It’s important to decide which user or group of users gets to see what early on. Once created, can the user be added to groups to define access and security, or should data-level security be set up directly per user instead?
Think of the entire offering you will provide to the end user and its commercial value (where applicable): What insights will users get out-of-the-box through the embedded analytics? How do they relate to each other, and where will they be placed? What should be the priority for their release? Will your customers get to design them?

Implementation phase

Start the embedding development in parallel with the BI development; don’t wait

While some forms of embedding are faster than others, it is important to not wait till the very end (near the go-live date) to start the embedding process. A good process is to start both developments in parallel. Once the final widgets/dashboards have been built, the two tracks can be merged and tested.

Plan big, start small; iterate and expand

It’s easy to fail by trying to do everything at the same time. Start with the core functionality — your minimum viable product. Roll it out to a test group and then build on it by adding more features.

Focus on a seamless user experience

Here are a few elements of modern application design that will deliver the kind of experience users are accustomed to in today’s apps.

Single sign-on integration: SSO will ensure that the end user can seamlessly access the dashboard and widgets once inside the parent application.
Keep a consistent branding and look and feel between the parent application and the embedded analytics: White-labeling, color palette, formatting, and font changes help make the embedded analytics consistent with the parent application.
Provide documentation and self-help tools for your end user: You can build an excellent analytics tool, but the user won’t gain any value from it if they don’t know how to use it. One method of helping them learn is to design a homepage that holds introductions, how-tos, links to documentation, or links to simple videos showing how to navigate your new embedded analytics.

Marketing and sales enablement

Make sure to create buzz around the data products that your organization is building by working with marketing and product marketing during your implementation phase. Webinars, announcements, campaigns, landing pages, keynotes in user conferences, etc. can play a huge role in getting the word out and building momentum.

In addition, make sure to get champions within your organization and field-facing teams. Building expertise and knowledge across your organization will ultimately help sell the product. The best is when the field handles evangelism and presentations to prospects and customers.

Post-launch strategy for adoption

No product launch should happen without the support systems in place to ensure its adoption and success. A good strategy will set up processes and services to support customers.

Support

Before launch, make sure to train the product support teams to be the front-line team for all questions and issues related to your analytics service. This will help ensure that embedded analytics will be supported using existing procedures, rather than building out a wholly separate support structure.

Client onboarding

Once a client is on board, a great idea is to deliver a bespoke analytics orientation where you review the offering and show the client how to personalize or make full use of analytics.

Additional services

If the client wants more services and does not have the bandwidth or resources to get the most out of your embedded offering, you can provide consulting services at a cost to help clients customize their analytics. Ideally, you want to build and scale that to all your customers to remove one-off custom requests.

Documentation

The importance of good documentation cannot be understated. A simple way to get started would be to provide white-labeled vendor’s documentation to your clients to ensure that clients are well equipped for success.

Training videos and webinars

As a part of delivering a high-quality experience, give your clients access to frequently published webinars, tutorials, and videos about your analytics product and how to leverage it to their advantage.

Continuous learning
Collecting feedback from the end users will go a long way in continuously improving the embedded offering. A flexible solution will allow for these change requests to be implemented easily and pushed to production quickly, thereby continuing the BI implementation life cycle.

Success stories

Data monetization can take many forms. To get your own imagination going, we’ve collected a handful of success stories from Sisense clients that have monetized data in creative ways. Whatever data you’ve got, the right data and analytics platform can help you turn it into a new revenue stream.

Case Study: Erea Consulting

Optimizing supermarket supply chains

Erea Consulting specializes in helping supermarkets in Latin America and Europe be more profitable. Its EreaBI Analytics Platform for Retailers makes commercial insights available to the entire retail supply chain in one place. Erea knew that timely insights would empower suppliers to be more strategic with their resources and improve retailers’ bottom lines as well. However, the path to this win-win situation was figuring out a way for retailers to monetize supplier data and present it in a form that suppliers would pay for.

The result was the EreaBI Analytics Platform, which empowers a variety of customers. Supplier teams can improve inventory tracking and planning at an SKU-level per store by leveraging advanced forecasting models enabling agile inventory allocation never seen before in the industry. Supermarkets can optimize inventory levels at stores based on real-time changes in demand. Customers are happy, suppliers are happy, retailers are happy, and Erea monetizes data in a way that becomes integral to its users’ workflows.

The takeaway: Make your data monetization deployment a must-use part of your clients’ core job.

Case Study: BillingSavi

Partnering with healthcare billers

The healthcare industry is ripe for disruption. Think of every time you have an interaction with a healthcare provider: They bill you and/or your insurance company, starting a massive payments and processing machine that creates and collects huge amounts of data.

One piece of data that healthcare billing companies were missing: how much they could collect based on the different billing codes for the same procedure. Enter BillingSavi and its white-labeled Sisense deployment, SaviSense. With SaviSense, billing companies can effectively measure how much they’re making from the various procedure codes they’re using and optimize their coding to maximize income from insurance companies without increasing their own costs.

BillingSavi’s approach to revenue sharing is truly inspiring and creates a deeper bond between it and its users: profit-sharing. BillingSavi takes a percentage of each transaction, effectively making it a partner with the practices that use it.

The takeaway: Revenue can come from subscriptions, per-use charges, or even profit-sharing like BillingSavi. The trick is to find the form that works for your industry and your application. Get creative!

Case Study: Profectus

Spotting billing errors, saving money, making money

Profectus is an international technology and services company that provides leading technologies for rebate and deal management, contract compliance, and accounts payable audits. For Profectus, data is everything: Accounts payable data comes in as direct feeds from enterprise resource planning finance systems as well as hundreds of thousands of invoices, along with any agreement data that customers have with their suppliers.

The data monetization opportunity for Profectus came in ensuring that every transaction was aligned with a particular deal and that billing compliance was absolute. Before implementing Sisense analytics, the error rate was around 3.5 to 4 transactions per 10,000 transactions. It’s not a huge number, but when you’re dealing with millions or even billions of transactions, it adds up. With the right analytics, Profectus was able to save its clients millions of dollars and prove its worth.

Bring your idea to life

The world needs you and the world needs what you can bring to it, just like your customers need the data and analytics and insights you’re going to embed into your product. Armed with the information in this ebook and the ideas in your head (plus your skilled, dedicated team), you have all you need to scope, spec, design, and build the right data monetization elements for your product or service.

Whatever you do, you can do it better and charge a premium with the right insights inside your core offering. Your users will be happier, your product or service will be stickier, and you’ll have helped take us all one step closer to a world where data is truly democratized and insights are everywhere. Build boldy!

Sisense Makes It Simple

Your Complete Guide to Data Monetization With Data and Analytic Apps