Blog Project work
Who’s posting: Stephanie Burton
Which Company: Product Marketing Manager
Post was about: this post firstly sets out that companies that utilize business intelligence historically move from “reactive” to “proactive” use of analytics—first using data to instigate internal changes that impact company efficiencies before eventually launching customer-facing analytics offerings that create growth. It then sets out a recommended step-by-step approach to data monetization based on serving the needs of customers while creating additional revenue streams.
What did I get from the post: When looking to deliver analytics to customers, a company can embed their analytical creations in software-as-a-service (saas) products or create company-branded data portals. This provides an add-on service to the customer while also generating an additional revenue stream for the company.
6 Steps to Turn Data into Revenue
Stephanie Burton’s picture Stephanie Burton Twitter Google+
Product Marketing Manager
DECEMBER 17, 2014
The impact of analytics is always expanding. In just a few years, companies that once didn’t know what to do with the data they collected about their customers are now utilizing that data to build and deliver customer-facing advanced analytics offerings that have become new streams of revenue.
You could be sitting on treasure troves of data you may have not even noticed before. According to recent CITO Research, companies that utilize business intelligence historically move from “reactive” to “proactive” use of analytics—first using data to instigate internal changes that impact company efficiencies before eventually launching customer-facing analytics offerings that create growth.
The 4 Stages of Analytics
Reactive Analytics: In this stage, a company may collect data about how customers use its products, but doesn’t engage customers with this data beyond alerting people to problems.
Descriptive Analytics: By visualizing data, you’ll start to uncover patterns and trends. Through visibility into business operations, you’ll start to understand current situations, see where problems are occurring, and know where to focus your attention.
Diagnostic Analytics: When you start to recognize the value of your data, it’s natural to want to share this value with clients. Whether this happens as an epiphany or a request, this stage leads to ‘productization.’
Proactive Analytics: In this stage, companies evaluate their situation to see what new revenue can be generated by converting an analytics platform into a new product or embedding analytics into existing products.
A Roadmap to Data Monetization — Growing a Cash Crop with Your Data
“It can be difficult to determine which data sets offer the most value to customers,” cautions CITO Research. To help determine which will prove to be the most lucrative, here is a recommended step-by-step approach to data monetization based on serving the needs of customers while creating additional revenue streams.
Step 1: Perform business intelligence internally.
Look at the usage of your systems and products. Create dashboards for internal use so you can analyze who’s using the most of your products and services. Determine anomalies and how you should change things based on what you are learning.
Step 2: Share visibility with your clients and partners.
Embed dashboards and analytics to provide some basic visibility free of charge to customers. This provides visibility into their own usage and helps spark the beginning of data monetization.
Step 3: Add self-service or extensibility.
You may not realize how valuable your data is until you see others using it. Based on your free offerings, customers will begin to ask for new views, new angles, and may ask to white label your dashboards for their own users or stakeholders. Your answer is “Yes, for a fee.”
Step 4: Look at information you can aggregate.
You have benchmarking data that customers want—information on how they are performing compared with others. With no other objective way to gain this type of information, the aggregate data you can provide becomes extremely valuable to customers and partners.
Step 5: Find ways to personalize the data.
The more specific and personalized you can make the analytics you deliver, the better. Consider mixing in external data sources like geodata, address enhancement, machine, weather, demographic, or business data to enrich the data you already have.
Step 6: Keep listening to your customers.
As your customers request new types of analytics, such as churn analysis, internal activity reports, or longer views of historical information, you’ll get ideas for additional data products.
If you’re looking to deliver analytics to your customers, you can embed your analytical creations in software-as-a-service products or create company-branded data portals. The Ultimate Guide to Embedded Analytics provides a plan for taking the GoodData platform and building amazing data products that will solve mission-critical business problems for your users.