Taking smarter risks to monetize your data

BrandPost By Charles Holive, Managing Director, Strategy Consulting Business at Sisense
Feb 02, 2020
IT Leadership

Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. In this article, we uncover some BI Best Practices to help demystify the analytics world so you can make smarter decisions when it comes to monetizing your data.

Building value on data For modern organizations, data is the ultimate building block. High-profile companies are using data to build profitable new products, new lines of business, even entirely new industries. It’s the starting point and the finish line for every new business creation. In an age where every company is making moves to be more data-driven, those that figure out how to efficiently monetize their data insights will be the biggest winners.

The process of translating data into new revenue is not easy. To do it well, companies need to embrace data analytics as a building block, a means to build new products and new lines of business that can open the door to entirely new revenue.

Use data to create new value

At the core of business is an exchange of value. Companies create a product or service that adds new value for potential customers and then trades them that value for revenue. In order for a company to grow with data, it needs to continue to create new, transferable value from data that is desirable to customers. Often that new value comes in the form of removing a common pain point.

The right place for a company to start creating new value with their data is to understand their customer base and identify a problem that can be solved or a new benefit that can be created with the data they have. To be clear, when you build with data, you’re not selling data to the customers. You’re also not selling the new products you’re creating with that data. You’re selling outcomes. Your customers aren’t buying your features, they’re buying problems coming off their plate. They’re buying a shortcut to making difficult decisions. They’re buying opportunities for a return on their investment.

If you can picture the tactical, hands-on ways that your data will improve the lives of your customers, you’re on the right track to turning data into revenue.

Innovation as the path to value Once you’ve identified the monetizable value your data insights will create, you need to actually build a sellable good that delivers that value. This is the process of innovation. From my experience, there are three different paths to successfully innovating:

  1. Improve an existing product to achieve the produce value faster cheaper and better
  2. Evolve an existing product to provide new value
  3. Transform an industry to create a new market

All three are legitimate paths to innovation, but the cost and the reward of each approach is different. Improving an existing product probably won’t cost you much, but your gains might be marginal. Evolving a product will take significantly more effort, but could open up entirely new types of customers. Transforming the market is extremely rare, but also incredibly lucrative.

The companies that are winning with data monetization are those that create scalable paths to each of the three types of innovation. No matter how high a business aims, there’s one extremely important step to take to pave the way to successful innovations: lower the cost to try.

Innovation is not always successful. Unfortunately, it’s very easy to fail when you try to build new things. The more ambitious the innovation, the harder it is to do it right. If it’s easy to fail fast and try another new innovation soon after, you’ll end up building your winning ideas a lot faster. The more things you can try in a given time period, the better chance you have to hit it big with one of them. If you’re collecting feedback well on your new creations, you’ll improve a little with every new iteration too.

Six steps to monetizing data-driven innovations To help builders of all kinds with their data monetization strategies, I’ve broken the process down into six steps, each using data to achieve an outcome that is critical to business success.

1. Build a strong value proposition

Start by identifying the persona you will create value for and articulate the ROI that they will get from your product. Understand whether you’re changing their top or bottom line. Find a way to quantify that impact for your audience using the competitive landscape and set a timeline for delivering that return.

2. Consider value-based pricing

Once you know the buyer and you’ve quantified the return they’ll get from your product, it’s time to start thinking about the actual monetization part of your innovation — how much people will pay you for it. Look at your top- and bottom-line effects and consider a price of around 10-20% of that. For example, if you’ll provide $1M of new value, charge them between $100k–$200k.

3. Build an effective business case

It’s time to start treating your analytics innovations like new business units at your company. What does adoption look like? What is the forecast for orders, revenue, cost, etc.? A productive business case will have specific data for anticipated profit and loss and build toward an expected breakeven point. The data-based constraints of these targets are extremely important to keeping your innovation on track.

4. Prototype your product

The product that you eventually build and monetize has to reflect the value proposition that you aimed to build. It has to meet the requirements that you lined up, move through beta tests with users that look like your intended audience, and make a deployment deadline that fits your intended timeline.

5. Execute effective sales channel activation

It’s very important that your sales team is enabled to sell not just products and features like they’re used to, but to sell outcomes. The new value they’re selling is a different animal. Selling your data product well is going to take a significant amount of onboarding, training, and education upfront and then ongoing support throughout the lifecycle of your product.

6. Secure executive buy-in

This is a step that needs to happen all the way through the data monetization process, not just at the end, but it affects each of the other steps. It’s not easy to ask executives to invest in new products that are likely to commercially behave very differently from the core business. You need strong internal sponsorship to have the freedom to try new things, otherwise you’re doomed to fail. This mentality of data monetization via the creation of new products has to start at the top.

Don’t wait to innovate Your company has all the data you need to profitably build new things today. You can build new things that are faster, better, and cheaper or answer new questions. To transform that data into new revenue, you need to build an ecosystem that will reduce the cost of trying new things and then pave the way to use data at every step of your building process.

The business landscape is in constant motion. Someone somewhere is always building the next big thing. Analytics offers the opportunity to build it right, but only if you have a sense of urgency about your creations. If your data reveals an opportunity, start building today! If you don’t build the next big innovation, someone else will. The cost of using data to experiment with new things is very small compared to the cost of doing nothing while your competition out-innovates you.

About Sisense

Sisense offers the only independent analytics platform for builders to simplify complex data, and build and embed analytic apps that deliver insights to everyone inside and outside their organizations. Sisense lets builders collaborate on a single platform, delivered in a hybrid, cloud-native environment with the industry’s lowest cost of ownership, to create true democratization of data and analytics. More than 2,000 customers across the globe rely on Sisense, including industry leaders like Tinder, Philips, Nasdaq, and the Salvation Army.

Learn more at www.sisense.com.