Data Products

Getting Data Product Requirements Right

Often customer data products or applications go awry because of poor requirements.  While customers can describe a billing workflow or a mobile app feature, explaining how data should be used is less clear. Merely documenting a wish list of reports, fields and filters is a recipe for low adoption and canceled subscriptions.

To ensure that data requirements are relevant and the solution is useful to customers (profitable too) consider the expression Walking a Mile in their Shoes.   The original expression, which has evolved and used in different forms is before you judge a man walk a mile in his shoes.  Collecting good requirements is less about a laundry list of charts and metrics, but an understanding of how information can transform the business from how it exists today.

In 2017 I had the opportunity to work on an insurance industry project for the first time.  The challenge was to deliver the industry’s first insurance agency analytics solution.  The product team showed us their competitor’s dashboards and suggested we replicate them. The support team demanded more ad-hoc reporting functionality on top of the Crystal Reports report writer.   Customers wanted an embedded BI tool to make themselves more data-driven. Needless to say all parties were miffed when we accommodated none of their requests.

What we did was contrary to what everyone expected.  We didn’t talk about the data (at least not in the beginning) or ask them their report wish list, but strived to understand the drivers of success and behavior within an insurance agency.  To walk in their shoes, we scheduled agency office visits, had discovery meetings with executives, observed workflow and documented data usage.  In the discovery meetings we asked questions related to the end user’s data experience, how and when information was being used and what decisions were made using data.

Here’s a sample of our questions.

Data Consumers (Users)

  1. How well does the user understand the data?

  2. How much expertise do they have in the industry?

  3. What were some examples industry best practices?

  4. Are customers looking for data or insights?

  5. Does the end user trust the data?

Data Consumption

  1. What are some examples of business processes being influenced by data insights?

  2. What are the top 3 questions each audience wants to answer? 

  3. When is data being used and how, e.g. daily, weekly, monthly, in account reviews etc.

  4. How is information currently be displayed and disseminated?

Decision-Making

  1. What are the current metrics that measure business success?

  2. What are the key decisions made every day?

  3. What are the decisions not made or delayed because of missing data?

  4. What are the top data conversations had or that need to be improved?

  5. What are the metrics that drive top line revenue?

  6. What business processes will be impacted by this new information?

  7. What are some example actions that might be taken as a result of insights? 

Data

  1. What are the relevant time intervals that information will be updated, distributed and reviewed?

  2. What are the most relevant time comparisons, prior week, prior month, prior year? 

  3. Are these dashboard(s) meant to be exploratory or explanatory in nature?

  4. What offers the most relevant context to the end user?

Getting users to adopt 20 new reports or a single new dashboard can be challenging when habits are already in places. Your best bet for successful data product adoption is to improve existing workflow and/or meetings using the newly uncovered insights.  In the case of the insurance project customers already had access to 300 reports before we created their new analytics solution. 

As it relates to the insurance project our first phase developed three new data applications.

  1. Daily Cash Flow Application (printed) 

  2. Weekly Sales Meeting Dashboard (TV Monitor) 

  3. Monthly Carrier Marketing Meeting Presentation (2 Desktop Dashboards)

These solutions or apps solved specific problems and fit into their existing workflow.  In each case we developed a data application based on industry best practices.  

Just “knowing your audience” isn’t enough to get data requirements right.  Walking in their footsteps means understanding how their business works and how the right insights can impact it.  Some of the other benefits from this approach are:

  • Quantifiable Returns - It was easier to talk about the benefits of a data product when tied to a process where time or effort saved can be measured.

  • Increased Credibility - By taking the time to walk with customers we establish credibility.

  • Improved Stickiness - Tying new applications to existing processes not only aided in adoption, but made them harder to turn off over time with increase usage.

Much of what was discussed above can be found in the Juice design principles, resource page or in Data Fluency; however the quickest way to find our more is to schedule a case study review.  Click the button below, send us a message and indicate your industry.  We’ll walk you through a successful case study in a relevant industry and answer your questions.



Your Healthcare Analytics Solution: From Concept to Launch in 100 Days

Below is the video recording of a Juicebox healthcare analytics product webinar. The video is about 45 minutes and includes our tips for successful launches, a quick data product demo, and a Juicebox Q&A session at the end.

Our tips include which project tasks are best done slowly (e.g. getting your data ready) and which tasks need to move fast (e.g putting a working data product prototype in front of customers). Knowing what to do fast and what to do more slowly is a big part of the tips we share on the video.

We also touch on Juicebox strengths and alignment to support fast and slow tasks, such as:

🐇 Design development agility

🐢 Enterprise-grade app development

🐇 Understandable data stories

🐢 Capture your customer needs

Enjoy the webinar. After watching you can schedule a personal demo where we can go into Juicebox or case studies. https://www.juiceanalytics.com/get-started

New Years Resolutions to be a Better Data Product Manager

It is the the New Year, my favorite time for New Year’s resolutions. Time to look inward to see how we can change ourselves to change your world.

If you’re responsible for a data product or analytical solution, you might consider a little self-reflection in pursuit of a better solution for your customers. Here are a few places to start:

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Empathy

the ability to understand and share the feelings of another.

When it comes to data products, you’ll want to foster empathy for the users of your data. More likely than not, they have concerns such as:

  • Your data may replace their power in the decision-making process.

  • They don’t have the data fluency skills to properly interpret the data and what it means for their decisions.

  • They are afraid of changes that will impact how they do their work.

Appreciating and acknowledging these fears is a first step in building trust with your users.

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Learn to flow

“I would love to live like a river flows, carried by the surprise of its own unfolding.” — John O’Donohue

We all a little guilty of wanting to make others bend to our view of how things should work. This year, you may resolve instead to “flow like water.”

Data products should enhance how people make decisions, giving them the right information at the right time. This is best accomplished when the data product can fit into the existing workflows so you are augmenting the user’s role rather than trying to change it.

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Patience

“Wise to resolve, and patient to perform.” — Homer

Patience is accepting that progress takes baby steps. This is a critical skill to help manage your data product ambitions. The possibilities for analytical features can seem limitless — there are so many questions that should be asked and answered.

Beware this temptation. You’ll want to find the most impactful data first to allow your users to learn what they can learn. Before you try to do it all, have the patience to gather feedback and plan your next release.

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Growth mindset

“People believe that their most basic abilities can be developed through dedication and hard work.” — Carol Dweck

Analytics is best served by a growth mindset, the belief that taking on a challenge (and sometimes failing) with expand one’s mind and open up new horizons. Useful analysis begets questions, which leads to more analysis and even better questions.

As a data product manager, you want to encourage this growth mindset in your customers, encouraging and enabling them to expand their understanding of their world.

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Inclusive

“We are less when we don't include everyone.” — Stuart Milk

Every year I tell myself I need to be better at meeting new people and keeping up with old friends. It’s a good ambition if you are leading efforts on a data products. It takes a diverse set of roles to get the support and commitment in your organization. Have you gotten legal on board? How about IT security? Does marketing and sales understand the value of your data product and who you are trying to target? You may need to change the way people think about making use of data to build company-wide support for your solution.

The Future Belongs to Purpose-Built Apps. We're Betting On It.

“Purpose-built apps”

“Low-code app development”

“hpaPaaS”

“Citizen Data Scientists”

“Data monetization”

Witness the cloud of new buzzwords floating in the air. Let me see if I can knit these concepts together to shed light on their meaning and implications for the future of analytics.

Collectively, these phrases are a reaction to the long-standing challenge of getting more data into more hands. “Democratization of data” can seem perpetually right around the corner (if you’re listening to vendor marketing) or a distant illusion (if you are in most organizations).

At Juice we have a picture that we call ‘The Downhill of Uselessness’. It shows how the usefulness of data seems to decline as you try to reach more users. On the far left, the most sophisticated data analysts and data scientists are happily extracting value from your data. But as you extend to the outer edges of your organization, data becomes distracting noise, TPS reports, and little-used business intelligence tools.

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Three barriers to democratizing data

The struggle of getting data to more people in more useful ways boils down to a few unsolved problems.

First, general purpose platforms and tools (data lakes, enterprise data warehouses, Tableau) can be a foundation, but they don’t deliver end-user solutions.

"Vendors and often analysts express the idea that you can master big data through one approach. They claim if you just use Hadoop or Splunk or SAP HANA or Pervasive Rush Analyzer, you can “solve” your big data problem. This is not the case.”

— Dan Woods, Why Purpose Built Applications Are the Key to Big Data Success

Second, reporting and dashboards deliver information, but often lack impact. In our experience, most data delivery mechanisms lack: 1) a point of view as to what is important; 2) an ability to link data insights to actions in a users’ workflow.

Third, the people who truly understand the problems that need to be solved don't have the technical capacity to craft re-usable solutions. We all have that elaborate spreadsheet that is indispensable to running your business and, frighteningly, only understood by a single person.

A better path forward

Finally, there is a realization that these problems aren’t going away. There needs to be better approach. It will come in two parts:

  1. Focus on creating targeted solutions (applications) that solve specific problems. Apps can integrate into how people work and the systems where actions occur. They attempt to let people solve a problem rather than simply highlighting a problem. And applications are better than general purpose tools because they can bake in complex business rules, context, and data structures that are unique to a given domain.

  2. Give greater impact and influence to the people best know the problems. It has always been unfair to ask technologists to create solutions for domains that they don’t deeply understand.

This direction aligns with Thomas Davenport’s view of Analytics 3.0 (from way back in 2013). He postulated that the next generation of analytics would be driven by purposeful data products designed by the teams who understand customers and business problems. (No offense, Tom, but we were griping about ivory tower analytics back in 2007.)

And so emerges a new model and new collection of buzzwords...

Purpose-Built Applications

Solutions that start with the problem and craft an impactful answer. Their success is measured by fixing a problem rather than in terabytes of data stored.

…built using a high-productivity Application Platform as a Service (hpaPaaS)

Cloud-based development environments requiring little coding ability (‘low-code’) — but requiring knowledge about the domain and the problem to be solved.

…to be used by Citizen Data Scientists (CDS).

the people who know the problems most intimately.

At Juice, we may have backed into this trend or cleverly anticipated it. Either way, now I can say that Juicebox is a low-code hpaPaaS designed for CDS to create purpose-built apps. Better yet, we are now fully buzzword compliant.

Is It Time to Jump-Start Your Data Offense?

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Legendary Alabama coach Bear Bryant believed in defense:

“Offense sells tickets, but defense wins championships.”

Legendary boxer Jack Dempsey saw virtue in offense:

"The best defense is a good offense.”

Legendary analytics guru Thomas Davenport takes a more neutral stance in his Harvard Business Review article What’s your Data Strategy?

"The key is to balance offense and defense.”

Davenport goes on to say:

“Data defense is about minimizing downside risk, including ensuring compliance with regulations, using analytics to detect and limit fraud, …and ensuring the integrity of data flowing through a company’s internal systems.

...Data offense focuses on supporting business objectives such as increasing revenue, profitability, and customer satisfaction.

…The challenge for CDOs and the rest of the C-suite is to establish the appropriate trade-offs between defense and offense and to ensure the best balance in support of the company’s overall strategy.”

Balance is fine. But at Juice, we’re all about building data products. That’s an offensive data strategy (we’re with you Jack Dempsey, June Jones, Mike Leach, and Mike D’Antoni).

In practice, most organizations start from a defensive crouch. The relevant question is: when is it important that you shift to a more offensive data strategy?

Davenport shares a few indicators that suggest more data offense is warranted. For example, offensive strategies are often employed at organizations that operating in largely unregulated industry where customer analytics can differentiate. He also sees opportunity for offensive data strategies at that those organizations with decentralized IT environments and where “Multiple Versions of the Truth” are encouraged.

His HBR article even provides an evaluation tool to determine whether your organization has shifted its balance toward offense or defense, giving you a snapshot of where you’ve (organically) evolved. It doesn’t tell you where you should be.

When we think about the dozens of companies we’ve worked with who are launching data products, some common patterns emerge in terms of the characteristics of those organizations. Here are four categories where an offensive data strategy provides like a good fit:

Government, non-profit or public-service organizations

These organizations aren’t necessarily in the “competitive” markets that Davenport describes. Nevertheless, they are sitting on tons of valuable data that can shape conversations and influence the decisions of their constituents. We’ve worked with Chambers of Commerce, Universities, and State Departments of Education that are taking on offensive data strategies.

Data science startups

There are hundreds of start-ups who are building their businesses on offensive data strategies. These companies have mechanisms for collecting data across an industry and are adding value through predictive algorithms, identifying patterns, and ultimately helping their customers make smarter decisions. We’ve working with a couple healthcare start-ups who have proprietary methods for predicting performance of healthcare providers. This is deeply valuable information for health systems and employers, and a purely offensive strategy.

Consultants

We’ve seen a couple different offensive data strategies by consulting firms. First, if they are delivering a project with an analytical deliverable, why not make the deliverable a recurring data solution? Another approach by the most innovative consultants is to view data collection and data products as an opportunity to proactively identify problems for clients. An annual survey of customer brand awareness can be turned into an incisive discussion starter, spurring clients to pursue the next project.

Companies with dominant market shares

If you are a market leader, you may be collecting enough data from your customers to be able to provide benchmarking solutions. In some cases, this offensive strategy is core to the original purpose of the business (e.g. US News & World Report’s surveying of colleges). In other cases, the opportunity to create new data products can be a result of “data exhaust”.

If you find yourself wondering how your data might be turning into a revenue-generating or customer-differentiating solution, you should download our ebook Data Is the Bacon of Business: Lessons on Launching Data Products.

Is Your Data Product Ready for Launch?

Looking to transform your data into a valuable, customer-facing data product?

From concept to design and launch, we've worked with dozens of companies to create successful data products. Our checklist provides seven evaluation criteria to see if your data product has what it takes to succeed.

Does your data product...

  1. Solve a distinct problem?

  2. Meet users where they work?

  3. Guide users to insights and actions?

  4. Make users feel safe and in control?

  5. Bring credibility to your data?

  6. Have the ability to operationalize the solution?

  7. Support customers for success?

Download the PDF here.

Data Monetization Workshop 2018: Key Themes & Takeaways

“Data Monetization is a hot topic because it has two words that everyone loves. We all love data, and who doesn’t want to monetize something?”

These were the words that kicked off the 2018 Data Monetization Workshop to a roomful of attendees and industry experts who had gathered to discuss the question that followed this observation: what does Data Monetization actually mean?

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This question was discussed at length over the course of the half-day event and was the impetus for speaker topics related to using data for social good, how to account for data on a balance sheet, how AI will affect the future of Data Monetization, and more. Here are some of the most important themes and takeaways from the discussions of the day.

What Is Data Monetization?

  • Data monetization is about data value, not data dollars. It’s not about selling customer lists, but about deriving value.

  • Data Monetization encompasses business intelligence and takes a much broader perspective on what can be done with data. Analyzing what options exist outside the enterprise, what products and services can be created using data, and trying to get data into the hands of decision-makers are all components of Data Monetization.

Data for Good

  • Most organizations aren’t trying to sell your personal data; they’re focused on using information to improve city performance, prevent mass shootings, and rescue people from sex trafficking.

  • Nobody owns data. Companies and organizations have rights to data, but in order for progress to be made data must be shared and communicated.

The Dark Side of Data

  • While data offers many beneficial opportunities, there also exists a dark side of data. What complications does something like what happened with Cambridge Analytica have on future opportunities for Data Monetization?

  • Using certain data is not always a question of “Is this legal?” but rather “Is this ethical?” Sometimes data is available but not right to use, which can feel like a restraint at times but leads to being an organization being perceived as trustworthy. It is important to have a solid core philosophy on what data you do and don’t use before it becomes necessary to bring in lawyers and PR teams.

Education, Train, Explain - Data Literacy

  • Poor data literacy is seen across the board. If you don’t read the fine print, you can sign your data rights away. Many problems with the use of personal data are often due to mismatched expectations.

  • People don’t always understand how valuable data is and what an asset they hold. You have to teach people to think in technicolor. Some companies try to exclude information, but more information changes the landscape and provides more context.

  • Creating data products with different derivations is one way to communicate data to different roles (e.g., an analyst versus a CEO). You have to meet people where they are.

  • Being transparent with a product roadmap is a great way to demonstrate to people that data products will look different as time goes on. Users can know what features they can expect and when.

Doing Things Differently and Looking to the Future

  • There are emerging technologies that can help make processes easier. Right now you just have to ask yourself, “How can I do things a little bit differently today?”

Doug Laney Is One Cool Dude

  • Doug Laney was kind enough to join us remotely from his vacation to answer audience questions about his book Infonomics -- of which every audience member got a free copy!

Special thanks to all of the speakers, to MapR for sponsoring the post-workshop networking reception, and to everyone that attended! If you have questions or comments about the Data Monetization Workshop, feel free to reach out to info@juiceanalytics.com.

Related Reading:

4 Steps to getting started with data products

Over the years, we’ve had the pleasure to work with many great individuals and companies and through our work have gained the ability to sympathize with their experiences of what we like to call “going from 0 to 100."

No, we’re not endorsing excessive speeding in your car. We’re talking about going from having nothing but hopes and dreams about delivering engaging analytics (0) to having an interactive data story that your users don’t want to put down (100).

Because we’ve focused our efforts on taking clients from 0 to 100, commonalities or trends for best practices in the data and design experience (read: everything between 1 and 99) have become increasingly clear. Use these four tips to make your introduction to data products a better, more frictionless experience.

1. Know your audience

  • What do the end users you have in mind for the product look like? What questions will users ask of the data? What actions will they take with the answers to these questions? These are all things you should know before beginning to work on data products.

  • Be specific about for whom you are creating a data product. If you try to provide insights for too many types of business roles you run the risk of making it too broad for any role to gather insights from the data.

2. Gather the right data

When putting together the data to be used in your product, it’s important to discern the difference between “more data” and “more records."

  • More data: It’s not always in your best interest to gather the most “data” possible. By doing this, you run the risk of gathering data that you may not use and wasting money in the process.

  • More Records: Gathering “more records” (read: rows of data) is a better strategy as you prepare for your data product. Doing so can alleviate the effects of outliers and unearth trends in the data.

3. If you’re new to the data, begin with an MVP (Minimum Viable Product) and let your users determine what features should be included

Building out all the bells and whistles you think you might need at the beginning the data product’s life can be expensive. Starting with an MVP that is put in the hands of actual end users will help determine what data is actually needed and what design aspects are best for your purposes.

  • Helps with data: Starting with an MVP helps determine the shape and caveats that exist within your data, and allows your users to make decisions about what data is most important to them.

  • Helps with design: By starting with an MVP, all of the questions that you and your users have for the data are answered by the design. Additional features can then be added from that point on in a more cost-effective manner.

4. Be open-minded about visualizations

  • We won’t get into data visualization principles in this section because that warrants a totally separate article, but a simple point here: just because you saw similar data in a pie chart once doesn’t mean that is the only (or best) way to visualize your data.

  • Because your users are the ultimate consumers of the data, let them be the judges of what visualizations will be most effective for them.

Easy peasy, right? We think so, but maybe that’s only because we’ve helped so many customers get from 0 to 100. If you're still not sure what your next steps should be, we’re here to help. Learn more about our 0 to 100 process by checking out the document below.

6 Cool Companies Who Are Rethinking How We Work

It can be a challenging climb to reshape how people think about solving problems. We encounter this challenge daily as we work to build the best solution for communicating data the world has ever seen. We operate in an arena where good-enough solutions — Excel, PowerPoint, and other visual analytics tools — have left people with deeply-rooted habits and a blasé acceptance of the status quo. That’s not good enough for us, and it isn’t good enough for these six companies that are rethinking how business tools should work:

Slack

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Slack is the current king-of-the-hill for shaking up the status quo. Sure, we had email, file sharing, and messaging apps before Slack, but we didn’t have single, elegant tool for team collaboration.

What’s cool about it?
Slack made integrations easy from the start. We use everything from ChatOps with our development team to HeyTaco for everyday appreciation of our colleagues. Slack's approach to 'channels' found the right balance for open communication by topic.

Flourish

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I only recently stumbled across the excellent visualizations available through Flourish. There are many, many tools for putting data visualizations on a screen; few vendors are so obviously passionate about their craft. 

What’s cool about it?
Flourish is more than another charting library — they are making world-class visualizations accessible. I was particularly impressed by the clever use of animation in those visualizations. At Juice, we appreciate that new users won’t always be able to read a visualization without some guidance. Animation can help draw a user’s attention to the most important information right off the bat.

Kialo

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Kialo is “a debate platform powered by reason.” It cuts through the noise of social and online media by removing the worst parts of debating online (trolls, fake statistics, unrelated cat gifs) while strengthening the best.

What’s cool about it?
Kialo creates a structured dialogue with visualization, voting, and commenting. Whether discussing politics or the merits of a new project, Kialo has focused on an overlooked need: a place other than the comments section to examine arguments and consider new viewpoints. 

Typeform

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Typeform is "the versatile data collection tool for professionals." It's a thoroughly modern survey-building solution that I’ve enjoyed using for over a year.

What’s cool about it?
Typeform's survey-authoring interface is remarkably intuitive. Adding questions, structuring logical flows, and navigating your survey is silky smooth. Similarly, the end-user experience is beautifully designed with selectors and animations that make it (almost) fun to fill out a survey.

Beautiful AI

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From the founder of SlideRocket, Beautiful AI is a next-generation solution for creating web-based presentations. They say all you have to do is "think of an idea, choose a template, and get to work."

What’s cool about it?
Beautiful AI has taken a giant leap past a tool like Google Slides. It comes with a collection of smart slide layout templates. Better yet, these slide layouts automatically update as you add more content. The tool also comes with an easy-to-use integration with third-party image libraries so you can incorporate pictures into your presentation.

Toucan Toco

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A data storytelling solution to build data apps for your business. We may be a bit biased, but that sounds awesome.

What’s cool about it?
While I don’t have hands-on experience with this solution, I love their message. Like Juice, they see the need to:

  • Communicate data to non-analysts with guided narratives ("Address the remaining 99% of your employees”)

  • Create targeted applications that solve specific business problems (“A business need = an app”)

  • Include simple, clear data visualizations ("The comfort of using consumer apps, finally in a business setting”)

Honorable mentions

  • Quid: Quid puts the world’s information at your fingertips, drawing connections between big ideas.

  • Skuid: Accelerate deployment of personalized applications that let your business people drive innovation, without the wait.

  • Trifacta: Trifacta enables anyone to more efficiently explore and prepare the diverse data.

  • Datawrapper: Datawrapper makes it easy to create beautiful charts.

Thinking about changing the way you work? Check out our app trial process. Download the info sheet below to learn more.

You Don't Need a Slide Factory

You might be surprised to learn that one of our most popular blog posts of all time is Automated PowerPoint Generation, or Making a “Slide Factory.” Even though this post was published almost nine years ago, month after month we continue to see it rise to the top of our most visited pages. 

Whenever someone reaches out to us asking if we have a ‘Slide Factory’ solution, we tell them two things:

  1. Sorry, we do not.

  2. You don’t actually need a slide factory.

In fact, the need for automated presentation delivery is the genesis of our data storytelling solution, Juicebox. We are intimately familiar with the need to deliver data to customers, co-workers, stakeholders, etc., in a consistent, structured manner that communicates a message while providing each person with the data that is most relevant to him or her. Instead of attaching a 50-slide PowerPoint deck, Juicebox does that same job with an interactive reporting application. Users benefit from a guided analytical story, ability to capture insights, and features to collaborate with others.

Not only do your users benefit, but you no longer have to deal with report production and ad hoc headaches! Juicebox was designed with the data consumer in mind, meaning that the need to spoon feed your audience data and information through long-drawn-out PowerPoint slide decks is no more. 

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In recent months, we have made it more affordable and simpler than ever to get started with Juicebox. Through our Guided Design Process, customers are seeing what their data looks like in a Juicebox application within days not months. We give you four weeks and ten user accounts to test Juicebox with your data, you have plenty of time to get user feedback and build a business case for using Juicebox. Pricing starts at only $6 per user (with a 50 user minimum). With tiered discounts for more than 500 users, Juicebox is a competitive option for any budget!

If you would like to test drive your data in Juicebox, fill out our Get Started form and we will be in touch ASAP.

Check out some of our Juicebox apps in action: