Forward thinking organizations are recognizing the monetary value of their data and the opportunities that exist by turning that data into a revenue-generating product. Not only can that data benefit their company financially by becoming a profit center, but it can also disrupt their industry. A data product is an enterprise’s information assets wrapped in engaging analytics that drive significant value to its business network. IDC predicts big data-driven products and services will grow at a staggering 26.4% over the next few years, eventually reaching $41.5 billion. Six times the growth rate of the overall tech sector. While organizations understand the opportunity they have to unleash real value with their data and do something different from their competitors, they are overwhelmed by the data product deployment process and haven’t considered the resources they’ll need for an effective GTM strategy. Possessing the right data and wanting to build a data product is half the battle. Organizations need to determine if they are skilled enough in IT and have the resources they need to apply an effective design process to their data product, using all of the steps one would use in any product release, based on customer preferences, market need, and long term goals, that will inform engagement opportunities and utility of the data product. Bringing in an analytic partner with the right industry expertise is an effective strategy for those organizations that don’t know where to start in terms of deployment and determining the overall goals of their data product.
Before an organization can begin development on an effective data product, they need to determine the overall goals. Goals should be defined by the pain points that end users are facing. Customer empathy is the first thing that has to be realized before a vision for the product can be developed. The data product should be designed for a specific user and context, and by developing a user-led product design, organizations can identify target personas and their pain points. This information can be used to create a minimum viable product (MVP) to gather feedback from initial users and customize features to fit their needs.
The organization can then determine if they have the right data and if that data is of high enough quality to obtain their overarching goals. If an organization lacks the data needed, they may have to acquire outside sources of data, or partner with other organizations. Once the right data is aggregated, it’s important to conduct industry research and competitive analysis to determine a value proposition and point of view on what will benefit customers or disrupt their industry.
Once the product has been validated with end user feedback, the next step is determining the right pricing and packaging strategy. Some customers tax resources more than the others, so it’s effective to establish product tiers such as standard/premier/enterprise to segment the customer base appropriately. In order to define these different tiers, organizations need to identify the cost of specific product features as some will make the organization more money than others. These features range from predictive insights, benchmarking, Ad hoc analytics, custom metrics, and user uploaded data, and have varying costs.
Organizations don’t often predict the industry knowledge and guidance they’ll need after the data product is built to roll-out a successful GTM strategy.
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