Ten years of CRM Analytics field reports.
Research drawn from our productized engagement portfolio across Wave Analytics, Einstein Analytics, Tableau CRM, CRM Analytics, and now Agentforce 360. Updated quarterly. Open access; no gating, no email-required to read.
28 articles indexed.
Agentforce + Anthropic era
What is Salesforce CRM Analytics? Wave, Einstein Analytics, Tableau CRM, and CRMA, explained once
Salesforce CRM Analytics (CRMA) is Salesforce's native analytics platform: datasets, data prep recipes, the SAQL query language, and dashboards that live inside your Salesforce org and respect its sec...
When a CRM Analytics Recipe Silently Drops Rows (and the Dashboard Renders Blank)
You open a dashboard, the widgets are blank, and the dataset preview shows far fewer rows than you expected. No error message. No red banner. The recipe ran and reported success. This is one of the mo...
CRM Analytics binding errors that break every widget on the page (and how to trace them)
You click a filter. Nothing moves. No red error banner, no console exception you can point to. The charts just sit there showing stale data, or worse, they go blank. You refresh the dashboard. Same re...
Cutting CRM Analytics Query Consumption Before You Hit the Limit
If you have landed here, something is probably already wrong. Maybe a dashboard started timing out. Maybe your Salesforce AE forwarded you a usage report and the query numbers look alarming. Maybe you...
Modernizing a Tableau CRM-era org: what actually breaks on the way to current CRM Analytics
Salesforce has shipped this product under four names: Wave Analytics, Einstein Analytics, Tableau CRM, and now CRM Analytics (CRMA). Each rename brought documentation reassurances that nothing meaning...
semi_join vs cogroup in SAQL: which one to reach for, and when it quietly costs you
If your CRM Analytics dashboard is running slow, returning wrong row counts, or both, there is a reasonable chance the SAQL has a `semi_join` where a `cogroup` belongs, or vice versa. The platform has...
SAQL Window Functions Without the Performance Cliff
If you searched something like "SAQL window function slow" or "running total kills my dashboard," you are in the right place. This is a real problem with a real cause. It is not a bug you need to file...
Why your CRM Analytics dashboard loads slowly, and how to cut page-load queries by a third
Most slow CRM Analytics dashboards are not slow because of data volume. They are slow because of how the dashboard JSON was built, and the fix is usually invisible to the people complaining about it. ...
Five CRMA pipeline failures we keep seeing in 2026 (and the field-guide rules that catch them)
In Q1-Q2 2026, our team audited 28 production Salesforce CRM Analytics (CRMA) orgs across financial services, healthcare, manufacturing, and CPG. Across industries, we found the same five pipeline fai...
MCP exposure patterns in production CRMA: what we have learned from 50+ Q1 2026 deployments
In Q1 2026, the first wave of Salesforce-Anthropic MCP (Model Context Protocol) deployments reached production. Organizations across financial services, healthcare, and manufacturing began integrating...
After the Salesforce-Anthropic partnership: the architectural readiness audit every CRMA implementation needs
In October 2025, Salesforce and Anthropic announced a deepened partnership that places Claude as the foundational LLM inside Agentforce 360 and within the Salesforce trust boundary. This integration a...
AI-augmented analytics
Dreamforce 2024: Agentforce and the future of CRMA dashboards
Dreamforce just wrapped, and we're still processing what we heard and saw over the last three days in San Francisco. Like a lot of years, there's a gap between the keynote energy and what actually mat...
Salesforce Data Cloud architecture in 2024: production patterns and agent-readiness gaps
By mid-2024, Salesforce Data Cloud - the rebranded Einstein Genie - had reached a critical inflection point. Organizations were moving from experimental deployments to full-fledged production usage. T...
LLM-augmented CRM Analytics in 2023: where ChatGPT and Einstein GPT actually deliver
In 2023, enterprise data leaders across financial services and healthcare were tasked with evaluating whether the wave of generative AI tools like ChatGPT and GPT-4 could meaningfully impact their Sal...
CRM Analytics rebrand: our third name in five years
If you've been around Salesforce's analytics platform long enough, you've developed a reflex. Someone in a discovery call says "we want to use Wave" and you pause, do a quick mental calculation of whe...
Tableau CRM + early AI era
Dreamforce 2022: Genie GA and what it means for our CRMA work
Dreamforce 2022 wrapped up in San Francisco on September 22nd, and we've spent the past several weeks digesting what was announced, pressure-testing the demos against what we actually see in productio...
Salesforce Genie and the customer-data-platform pivot: 2022's strategic shift for CRMA buyers
In September 2022, Salesforce introduced Genie, a real-time customer data platform designed to unify customer data across the enterprise. For CRM Analytics buyers, Genie fundamentally altered the role...
Recipes versus Dataflows in late 2021: why Salesforce's preferred path is sometimes wrong
In late 2021, Salesforce began aggressively promoting Recipes as the preferred method for data preparation in Tableau CRM. The company's documentation and marketing materials implied that Dataflows we...
Einstein Analytics + ML era
Tableau CRM rebrand: what we tell confused clients
It happened fast. One week we were deep in an Einstein Analytics implementation, updating our methodology decks and deliverable templates, and the next we were fielding calls from clients who had seen...
The 2020 data-quality shock: how WFH broke sales-data discipline
In Q3 2020, sales organizations across financial services, healthcare, and manufacturing began to feel the impact of a new kind of data degradation. The shift to work-from-home had been swift and larg...
When forecasting models break: lessons from the 2020 sales-volatility shock
Sales forecasting models built on historical data from 2017 through 2019 suddenly became obsolete in early 2020. The pandemic-induced demand shock caused forecast errors to spike from 8-12% to 35-50% ...
Einstein Discovery in production: feature engineering decisions that determine model quality
In 2019, RevOps leaders across financial services, healthcare, and CPG were evaluating Einstein Discovery as a production-ready AI solution for sales forecasting and opportunity scoring. The promise w...
Tableau acquisition: how we re-prioritized CRMA roadmaps for 2019-2020
Yesterday Salesforce announced it is acquiring Tableau for approximately $15.7 billion. That is, as of this writing, the largest acquisition in Salesforce's history. Our phones started ringing before ...
Wave Analytics era
Multi-org Wave Analytics: lessons from late-2018 enterprise migrations
In late 2018, enterprises with complex Salesforce footprints - often the result of acquisitions, global expansions, or multi-region deployments - faced a critical decision. Should they consolidate Wav...
Dreamforce 2018: Einstein Analytics becomes the default analytics layer
We're writing this from the Moscone Center floor on day one of Dreamforce 2018, and the short version is this: if you're a Sales Cloud Enterprise or Unlimited customer and you haven't thought seriousl...
Wave to Einstein Analytics: what 2018's rebrand means for production implementations
In early 2018, Salesforce rebranded Wave Analytics to Einstein Analytics. The move signaled a strategic pivot from pure analytics to AI-augmented analytics. Organizations that had built their data arc...
Salesforce Wave Analytics dataflow performance: a practitioner's guide for 2017
By mid-2017, organizations that adopted Salesforce Wave Analytics in 2014 or 2015 are encountering significant performance bottlenecks in their dataflow pipelines. These teams, who initially embraced ...
TrailheaDX 2017 Wave Analytics roadmap: what to plan for next 12 months
We're writing this coming out of TrailheaDX in early June, and the Wave Analytics track gave us enough to think about for the rest of the year. If you've been deep in Wave implementations, and we've h...
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