Our embedded analytics underperformed because we assumed users wanted flexibility instead of clear answers. As Founder and product lead at WhatAreTheBest.com, we shipped in-app performance signals and dashboards on category pages intended to help brands and partners understand how product rankings and scores behaved — for example on a live page like https://whatarethebest.com/shop-pet-supplies/shop-dog-supplies/shop-dog-training-supplies/ . While the data was accessible, engagement stayed low because users didn't know which metrics mattered or how to act on them. The assumption we got wrong was that more visibility equals more value. If rebuilding today, I would not ship configurable dashboards first. Instead, I would deliver concise, outcome-oriented insights that answer specific business questions. Albert Richer, Founder, WhatAreTheBest.com
Our embedded analytics underperformed because we optimised for "impressive" charts instead of helping users make one clear recurring decision. It was built into a multi-location services platform to show owners which locations, channels, and offers drove the best LTV and lowest CAC so they could shift budget and staffing. A public example that's close in concept and structure to what we shipped is here: https://www.zenoti.com/product/analytics-reporting. We assumed users wanted deep drill-downs and dozens of filters, but most just wanted 3-4 stable views they could trust and check weekly. I wouldn't start from the data model again; I'd start from 2-3 key questions the operator asks ("where should I move budget this week?") and design only for those, then layer complexity later. I'd also insist on an in-app onboarding tour tied to a simple workflow, instead of dumping people into a dashboard gallery and hoping they explore. Name: Josiah Roche, Fractional CMO, Silver Atlas, www.silveratlas.org.
The reason our embedded analytics underperformed was that we assumed customers wanted more data, when they actually wanted clearer decisions. In our product context at Opus Rentals, we shipped an internal-style dashboard into the customer portal that showed event timelines, inventory status, and order activity, intending to help planners self-manage rentals at scale (example: [https://www.opusrentals.com/our-process](https://www.opusrentals.com/our-process)). What failed was the assumption that users would explore charts the same way our operations team did; instead, planners were under time pressure and just wanted fast answers like "Is everything confirmed?" or "What's missing?". I saw this firsthand when a high-value client ignored the dashboard entirely and emailed screenshots with handwritten notes instead. If rebuilding it today, I would not start with dashboards at all—I'd start with embedded, context-specific answers and alerts tied directly to actions. Embedded analytics failed for us because we treated it as a visibility tool, not a decision-making tool, and that mismatch limited real adoption. **Nezhdeh Parsanj, Marketing Director, Opus Rentals** [https://www.opusrentals.com/](https://www.opusrentals.com/)
have to admit, embedded analytics didn't quite pan out when I assumed customers would be happy with a whole lot of depth, but not so much clarity. The dashboards were meant to give customers a live view into how their software was performing - right inside the platform they were using - but the usage just never took off. The users were getting bogged down in it all. What we thought we were giving them was a way to ditch manual reporting, and have all the data at their fingertips. But it turned out, we really just ended up giving them too many answers - to questions they probably didn't even need to ask. We thought that having a whole bunch of charts would equate to a whole lot of value, but boy, were we wrong - what users actually wanted were fewer questions answered, not more data to sift through. If I'm being honest, I'd not even bother trying to fix those analytics without first figuring out what specific decisions they're really going to help people make.
Our embedded analytics failed because we prioritized pretty, real-time charts over actually helping partners make decisions. The dashboard lived in our partner portal and was designed to be a simple view for local tour operators on how their booking funnel, cancellations, reviews, and payout were doing. A public page we made called "Partner Dashboard Overview" that catalogs what we shipped. Our biggest wrong assumption was that partners would want a deep drilldown; they actually wanted simple weekly benchmarks versus similar tours in their city and CSV exports that were easy for accountants to understand. I would not iframe BI embed a second time with that many complex fks and rls, which caused mobile to be slow and difficult to maintain. If starting from scratch now, I would anchor on three decisions (pricing, capacity, quality), show only the essential KPIs for those, and send insights by email or alerts so partners wouldn't have to hunt them down.
At Engrave Ink, we created an in-app customer dashboard which was intended to allow our clients to monitor their order status, read community story shares customized to them, and see soft engagement indicators such as campaign open rates of our memorial outreach. This feature was simply not performing well since in this sensitive area, the customers hardly used the feature repeatedly, as they favored one time updates through email when they experienced emotional processing. The assumption was that people will desire constant information regarding the trends in the community but the majority desired to have the information on how to close it. Rebuilding today, I would not coerce a complete dashboard to log-in again.. There is real essence in respecting the silent personal moments than pushing data on the screen, and these memories bring gentle lives in the hearts besides the screen..
One reason our embedded analytics underperformed was that we assumed users wanted deep, customizable dashboards when they actually wanted quick, digestible insights. The product was designed to help small businesses track SEO metrics—traffic, keyword rankings, and conversion funnels—within their dashboard ([example here](https://seooptimizers.com/)). We overloaded the interface with filters and drill-down options, thinking flexibility equaled value. Instead, users felt overwhelmed and stopped using it. The assumption we got wrong was believing more data meant better decisions; in reality, most users just wanted a few key performance indicators visualized simply. If I could rebuild it today, I'd focus on clarity over complexity—showing fewer metrics that actually drive action rather than offering an ocean of unused options.
Nate Nead, CEO of DEV.co: Our embedded analytics initially failed because we shipped a feature-usage dashboard that answered "what happened" but not "so what." The dashboard was built for clients using custom SaaS platforms we developed and included charts for daily active users, API calls, feature clicks, session length, and error rates. The specific underperformance showed up in behavior: fewer than 15% of client admins revisited the dashboard after the first month, and almost none used it to drive product or ops decisions. Our flawed assumption was that giving clients visibility into raw metrics would naturally translate into insight. In reality, they didn't want to interpret charts—they wanted to know which feature adoption was lagging, where users were dropping off, and what to fix next. If rebuilding it today, I would not ship static dashboards; I'd ship opinionated analytics like "Feature X adoption is 32% below peer benchmarks" or "You're likely losing users at step 3—here's why," paired with clear recommendations.
I've managed over $300M in ad spend and built analytics dashboards for clients ranging from Microsoft to DTC brands--most of those dashboards failed because we gave marketers *attribution visibility* when what they actually needed was *budget allocation instructions*. We shipped a multi-touch attribution dashboard for a SaaS client that showed every touchpoint in the customer journey with beautiful funnel visualizations and channel overlap metrics. Usage dropped to nearly zero after week two because the VP of Marketing still had to manually decide how to redistribute budget across 12 channels every Monday morning. Our fatal assumption: that showing marketers "what happened" would help them decide "what to do next." Wrong. The dashboard had 40+ metrics, custom date ranges, and cohort breakdowns--but zero prescriptive guidance. In regulated financial services work, I saw the same pattern: compliance teams wanted red/yellow/green status indicators and required actions, not exploratory drill-downs into ad copy variations. When we rebuilt the system, we replaced the attribution dashboard with a weekly automated email that said "Move $4,200 from Facebook to Google Search this week because CAC dropped 18% and LTV models show 3-month payback." Adoption went to 100% and the client scaled from $40K to $180K monthly spend in 90 days. What I'd never do again: build a dashboard that requires a data analyst to interpret it. The portfolio example is live at berelvant.com/ai-marketing-strategies where we now surface recommendation engines, not visualization layers--because marketers get paid to execute, not to become statisticians.
The underperformance of our embedded analytics feature stemmed from insufficient user training and onboarding resources. Although the dashboard aimed to deliver actionable insights, customers struggled to interpret the data without proper guidance, resulting in low adoption rates. I underestimated the need for user education. If rebuilt, I would prioritize comprehensive onboarding materials, including tutorials and walkthroughs, to enhance user understanding.
The first embedded analytics feature we launched utterly flopped because we delivered a data exploration tool instead of a decision-making tool. We had visions of grandeur, and included a data exploration app to be built in a logistics platform to show clients their supply chain inefficiencies. Users resisted the power we gave them and ended up doing all the hard work themselves. Our #1 wrong assumption was thinking people would want to "analyze" the raw data we put in front of them, instead of wanting a straight answer to "what routes were the least profitable this week?" We've launched good dashboards since then (take a peek at our portfolio to see some examples: https://bimg.b-cdn.net/our-portfolio/portImage2.png), and I would never again go to market for the V1 with a ubiquitous filter-based ubiquitous interface, but instead; I'd start with a few clicky "insight cards" with clear, opinionated answers to the top 3 business questions we need to pay attention to; and force myself to get narrow enough to land somewhere useful with actionable intelligence from day one.