This is a short product study intended to show how I might approach a product such as Reportable. It is entirely based upon public information from https://site.reportablenews.com/ and contains many unverified assumptions.
Reportable streamlines the press release creation and distribution process. The web application is for any person or organization that wants to create and distribute press releases. Reportable takes a multi-step process requiring a variety of software and services, and condenses the process to save time and increase the efficacy of each release.
While Reportable could conceivably be used by any organization distributing news releases, it seems best suited to startups, small businesses, and other organizations that do not have a dedicated PR department and have not hired an outside firm.
The three basic personas I assumed were:
- News release creator.
- PR manager. I assumed this person to be ultimately responsible for the performance of the release. I’m sure in some cases they are also creating the release.
- Reporter. The recipient of the new releases.
It was clear to me that the product is well thought out and is already in use by the client, so I stretched quite a bit beyond the existing MVP to find areas to work on.
|As a news release creator, I need to quickly create and distribute a news release||Supported|
|As a news release creator, I need to include multimedia in the release.||Supported|
|As a PR manager, I need to track the performance of news releases.||Supported|
|As a reporter, I need images to include in articles.||Supported|
|As a reporter, I prefer to receive press releases via email.||Supported|
|As a reporter, I need to quickly determine if a release is newsworthy.||Supported|
|As a news release creator, I need to write content that results in release views and ultimately gets written up by the news organization. (Effective content)||Not supported|
|As a PR manager, I need to know which releases result in articles. (Story tracking)||Not supported.|
|As a reporter, I need the ability to schedule an interview to follow up on the release. (Interview scheduling)||Partially supported.|
|As a new release creator, I need to target my release to relevant reporters. (Targeted distribution)||Partially supported.|
This is a simplified prioritization matrix to get a sense of the assumed value of each feature. I use letter grades (A to F) to roughly assign values to each area. Like in school, A is good and F is not.
I make assumptions about the value of each need to the user. Assuming that we had come up with a similar list of needs in discovery, I would want to do further research, either customer interviews or a survey, to assign these values.
The ability for Reportable to assist organizations in creating effective content could be a boon for both the customer and Reportable. Considering the target audience may not be spending heavily on PR, there could be value in helping create the most effective content. I think implementation could be complex, depending on the chosen solution.
Story tracking seems a lower priority as most organizations are probably already monitoring stories about them using other tools. If someone reached out about a story, then the organization is already expecting publication.
Interview scheduling seems relatively straightforward, but I would want to see data on usage of the existing “Get in Touch” button before committing. Depending on the organization, it seems unlikely that they are so swamped by media contacts that converting email correspondence to a calendar invitation or phone call is a critical pain point.
I like the idea of targeted distribution, but my assumption is that news releases are a numbers game, so casting a wide net is preferable. Better targeting for releases would be dependent on better data about news organizations and reporters. Unless that data is available or for sale in a digestible format, building the data set could be expensive and time consuming.
Possible solutions to creating effective content for news releases with Reportable:
- Create training modules to help content creators. This could be delivered by an expert in the field, based on data, or a combination of both.
- A proofreading and feedback service built into the application. This could be an add-on item charged separately from the current pricing model.
- Using data on client content and the success of that content, automatically review content and provide tailored suggestions to improve the performance of that content. This could be like Textio, a product for augmented business writing, but focused on news releases.
Creating training modules could be effective, but dependent on user engagement. Research could ensure that money is not wasted on creating modules which will never be used. In addition, updating training can be quite expensive. Overall, there are several risks and a training focused solution seems to be outside the scope of the product.
A proofreading and feedback service would be more straightforward to design and implement, assuming we could demonstrate that clients would use it. This solution assumes a human would proofread content and provide feedback to improve effectiveness. Some concerns are turnaround time, scaling the service, and overhead costs. Multiple releases sent for proofreading simultaneously might overwhelm the service and create longer than expected turnaround times. Scaling the service with this model might be limited to increasing the number of people providing feedback, and increasing overhead.
Among the various solutions, I am most interested in the idea of providing feedback based on performance data from clients across the product. There are some challenges associated with this option:
- Extensive data analysis would be required to determine if there are content traits which correlate to a higher CTR, number of views, and published articles.
- Implementing this solution would require building an infrastructure for machine learning. Building and perfecting the model could also be time consuming.
- It would be necessary to make sure that existing customers are comfortable (or can be made comfortable) with the use of their data.
On the other hand, the value proposition for existing and potential clients could be immense. Reportable could be the platform for news releases which improve the effectiveness of each release with machine learning.
Reportable is helping organizations create and distribute effective news releases. An assumed pain point is the fact that some content performs better, and customers may not have the expertise and resources to create the most effective content. One possible solution is to use data from across the platform to understand the traits that make certain releases more effective than others at generating press coverage. These insights could be delivered to the news release creator during or after content creation, and would help them create more effective content with Reportable.