Incorporating Feedback Into CSR Dashboard Design

Align design decisions with business goals and user needs. What does this mean? Aligning business goals with user needs is not a one-time effort–it's an ongoing process that requires regularly involving stakeholders, some of whom will be more engaged than others. To keep everyone aligned, create a regular cadence for reviewing design decisions, using user metrics and feedback as key inputs. Since stakeholders aren't closely tracking user behavior, design decisions should focus on making the feedback easy to understand and use, helping simplify their decision-making process. Here’s a dashboard redesign example that explains each point: → Purpose of design Explain what problem the design solves and why it's important for the business. Example: Redesign the dashboard to help clients better manage their supply chain data, making it easier to spot inefficiencies. → Impact on business Describe how the design will help achieve business goals. Example:  We want to reduce customer support requests by 15% and increase retention among enterprise clients by improving the dashboard → User metrics Identify the data points to measure the design's success. We use Helio. Example: Track client sentiment, engagement, and usability of the new dashboard features and how quickly they resolve supply chain issues. → User feedback Share what users said or did that influenced the design. Example: Clients reported that the current dashboard is too complex, so we're simplifying the interface to make data easier to access. → Risks Point out any potential problems and how they will be managed. Example: We’ll add an option to toggle between simplified and detailed modes to prevent advanced users from missing in-depth data views. → Timeline Provide an explicit schedule for when the design will be completed and tested. Example: The redesigned dashboard will be ready for beta testing in three weeks, with a full rollout planned for the end of the quarter. #productdesign #productdiscovery #userresearch #uxresearch

Make mistakes with lower stakes. When I'm working with clients, I always advocate for trying out design ideas early and often with the people who will eventually use the dashboard. Why? ☑️ A sketch or a quick Figma mock up takes far less time to put together than a fully baked dashboard with a complete data model. Get feedback on low fidelity versions to create quicker feedback loops between designer and users. ☑️ Input early helps people build a sense of ownership over what's being created. That can translate to greater adoption later, since the dashboard reflects their real needs. ☑️ What people say they want, even in the best discovery interviews, can shift when they see those ideas translated into visuals. Don't fall in the 'black box' development trap where you disappear for a week or month and come back with a final product that you think is perfect (without much billable time left on the contract). In Dashboards that Deliver, we talk about this approach through a 'double diamond' approach adapted from the design world, focusing on exploring lots of needs and then narrowing to priorities first (discovery), then brainstorming lots of visual ideas and then refining to a final mockup. How do you use prototyping in your #dataviz work? Have you experienced push back from clients that insist on working 'in the real tool' from the start? Andy Cotgreave Steve Wexler Jeffrey Shaffer

I talked with 100+ product over the last months They all had the same set of problems Here's the solution (5 steps) Every product leader told me at least one of the following: "Our feedback is all over the place" "PMs have no single source of truth for feedback" "We'd like to back our prioritization with customer feedback" Here's a step-by-step guide to fix this 1/ Where is your most qualitative feedback coming from? What sources do you need to consolidate? - Make an exhaustive list of your feedback sources - Rank them by quality & importance - Find a way to access that data (API, Zapier, Make, scraping, csv exports, ...) 2/ Route all that feedback to a "database-like" tool, a table of records Multiple options here: Airtable, Notion, Google sheets and of course Cycle App -Tag feedback with their related properties: source, product area customer id or email, etc - Match customer properties to the feedback based on customer unique id or email 3/ Calibrate an AI model Teach the AI the following: - What do you want to extract from your raw feedback? - What type of feedback is the AI looking at and how should it process it? (an NPS survey should be treated differently than a user interview) - What features can be mapped to the relevant quotes inside the raw feedback Typically, this won't work out of the box. You need to give your model enough human-verified examples (calibrate it), so it can actually become accurate in finding the right features/discoveries to map. This part is tricky, but without this you'll never be able to process large volumes of feedback and unstructured data. 4/ Plug a BI tool like Google data studio or other on your feedback database - Start by listing your business questions and build charts answering them - Include customer attributes as filters in the dashboard so you can filter on specific customer segments. Every feedback is not equal. - Make sure these dashboards are shared/accessible to the entire product team 5/ Plug your product delivery on top of this At this point, you have a big database full of customer insights and a customer voice dashboard. But it's not actionable. - You want to convert discoveries into actual Jira epics or Linear projects & issues. - You need to have some notion of "status" sync, otherwise your feedback database won't clean itself and you won't be able to close feedback loops The diagram below gives you a clear overview of how to build your own system. Build or buy? Your choice