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.
Step 1: Determining goals and developing a user-led product design
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.
Step 2: Establishing pricing and packaging
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.
Step 3: Rolling out a successful data product launch
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. A launch is just as important as development stages since the product needs to sell in order for the organization to make money. Marketing, sales and communication teams need to be on the same page in terms of product roadmap and working together towards a common goal. This is where bringing in an industry expert can be useful. They bring expertise to help guide conversations about building out the data product and help define target personas, their pain points, and motivations. An analytics partner can assist in developing an effective marketing and sales strategy to sell this new product just like they would for any other new product. An analytics partner can also help train end users on the product to ensure that the deployment runs smoothly.
Step 4: Maintaining a successful data product
Launching a successful data product is an ongoing process. A product may be in good enough shape in terms of functionality to start selling to customers, but must be continuously iterated with new features and functions. If an organization lacks the bandwidth to keep up with the constant cycle of upgrades, it’s beneficial to partner with a vendor who will take this iteration over and keep up with changing customer needs and circumstances. Constantly improving the data product will keep the organization relevant and ahead of the competition. Running the data product on a cloud based analytics platform makes updating and maintaining the product easier and more efficient. Organizations can better keep track of changing customer preferences and industry trends.
One Organization’s Data Product Story
One Retail ISV works to identify gaps in the retail technology market and enables grocers to elevate the shopping experience through its suite of digital solutions. The company partnered with an analytics platform partner to develop a 2.0 version of their existing solution and offer better insight into digital presence, shopper engagement and eCommerce. The first step towards building this data product was meeting with the analytics partner to define success criteria for the product and ways to measure this success (ie. customer feedback surveys, product usage, feature requests, etc). Then, primary and secondary personas were defined to create a user led design for the data product. This is important since executives, supply chain managers and retail site managers within the organization all needed access to different insights at different times. From there the ISV socialized these wireframes internally and with some customers to solicit feedback and began deployment on the product.
In order to build the most effective data product possible, organizations need to take a top down approach. Focusing solely on the development of the product isn’t enough, and they need to see the bigger picture of having an effective GTM strategy. This strategy should be built and focused on the needs of the end user. Data products need to be flexible enough to keep up with changing customer needs and circumstances, and partnering with an analytics expert who has planned and implemented for hundreds and thousands of customers should be considered to take advantage of the industry knowledge needed to quickly and efficiently launch a data product.
Sumeet Howe is Director of Data Products at GoodData, where she helps clients design, build, and bring to market their products. She has a background in product management, consulting services, and data analytics. She earned her MBA from Yale School of Management and her Bachelors in Computer Science from Thapar University.
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