Tech
This former PG&E engineer is building a ‘Google Maps for the underground’
Josh Mackanic left a 10-year engineering career at Pacific Gas and Electric because he kept thinking about what the utility company doesn’t know about what’s buried underground.
“I had a job that got shut down because a pipe was in the excavation [that] we didn’t know about. Fortunately, we saw it before we impacted it,” he told TechCrunch. “But then there was this three-day ordeal of running around [asking] like: ‘Whose pipe is this? Can we tap it? Can we not? What’s in it?’”
Mackanic eventually got the answer, but it took three days, a delay that cost $60,000.
Despite being one of the largest utility companies in the United States, PG&E (and companies like it) only has visibility into the many miles of electric and gas lines it owns and operates. Pipes carrying sewage or water belong to other companies, and so does the information about where exactly they are. This leads to around 200,000 so-called “utility strikes” every year.
So in 2020, Mackanic founded a startup called CivilGrid designed to solve this problem. The company gathers disparate data regarding utility assets, property ownership, and environmental regulations and bundles it into what Mackanic cheekily refers to as “Google Maps for what’s underground.” It then sells access to governments, civil engineering firms, and utilities — including his former employer.
Now, Mackanic has raised a $26 million Series A to grow CivilGrid into something bigger. The round was led by Spark Capital, with additional investment coming from early-stage funds Afore, A*, Ford Street Ventures, and SNR. CivilGrid also received investment from Energy Impact Partners, a fund with a number of utility companies serving as LPs, which Mackanic pointed to as another vote of confidence from the industry.
“California’s energy needs are growing rapidly, and our customers expect us to deliver the infrastructure they need safely, reliably, and affordably,” Christine Cowsert, senior vice president of enterprise business and technology modernization at PG&E, said in a statement. “That means planning smarter from the start with tools like CivilGrid, which help our teams identify risks earlier, build more efficiently, and avoid unnecessary costs while keeping safety front and center.”
A case study performed by PG&E has already identified $60 million in avoidable paving costs across 1,600 planned gas distribution projects by using CivilGrid. Mackanic said he wants to bring on more customers and, eventually, start tackling other red tape problems that make it so hard to build things in the U.S.
“Right now, we’re providing engineers the data to be able to make a decision” about where to place infrastructure, he told TechCrunch in an exclusive interview. But “once I’ve given them the constraints, it’s not too hard for me to say, ‘Well, I would recommend that you put the pipe down right here, and oh, by the way, here are the permits you need in order to move to construction on this pipe. Would you like me to file those permits? Okay, let me file those permits.’”
Mackanic said there’s a “lot more” CivilGrid could start to automate “now that we have built this dataset and kind of captured the core user who’s at the very early stages of making decisions about what’s going to be built.”
Creating better visibility into the country’s subterranean infrastructure is not a novel idea, Mackanic told TechCrunch. But he said nobody had found a way to solve the problem of collecting, sorting, and securing the data, and striking up relationships with the partners most likely to pay for that information.
“One of the reasons I left PG&E to start CivilGrid was because I felt like this was a problem that was going to be better solved from the outside in than the inside out, and not because there’s [a] lack of awareness of the problem or even interest in solving it,” he said. “Utilities, they run relatively lean, right? Nobody wants to pay more for their gas bill than they have to.”
Like many kids, Mackanic said he spent much of his childhood dreaming of sending rockets into space. But even though he studied mechanical engineering in college and graduate school, he said he never expected to work for or with utility companies, digging into his own planet.
“I was like, aren’t these places boring?” he remembered thinking.
But working at PG&E made him realize just how high-stakes utility work can be — all so people can turn on their lights or start a pot of coffee every day without worrying that those things will work.
“The reality is, unfortunately, we do ourselves a disservice in not championing the extent to which they work wonders every day,” he said.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
>
Fashion startup Atoire announced Thursday a $9.5 million seed round with investors including a16z Speedrun, Night Capital, and Lightspeed Ventures’ Jeremy Liew.
Shoppers can visit the Atoire website and buy handbags or even clothes made from the same material — and coming from the same factory — that manufacturers high-end goods. The items are reasonably priced, too, with an Italian leather handbag costing just a few hundred, compared to the thousands a brand like Prada or Louis Vuitton would sell it for.
The startup arrives at a time when dupe culture has become increasingly popular, while the luxury sector has faced backlash from consumers in the post-pandemic era due to swift price hikes.
As a result, young consumers especially have sought cheaper, near-identical replications of these high-end goods; doing so has become almost a status symbol itself.
On Atoire, the items sold are mostly not dupes, says co-founder and serial entrepreneur Redouane Ramdani.
“It’s the same material, same craftsmanship,” he said. “It’s coming from the same factories.” He doesn’t consider Atoire fast fashion either. “It’s slow,” he clarified.
Before Atoire, Ramdani built the creator platform Snipfeed, which was acquired in 2024. Having grown up in France with a family that worked in luxury manufacturing, the founder always had an idea in the back of his mind that he would one day do something in the industry he grew up loving as a kid.
By the time he sold Snipfeed, however, the luxury manufacturing industry was quite different.
The biggest shift he noticed was that luxury factories were no longer just manufacturing goods. Traditionally, a brand like Ralph Lauren would bring its own designs and materials to a factory, which would then produce them. The problem was that brands had to commit to large minimum orders, which often pushed them to overproduce inventory. At the same time, a lot of these factories depend on working with a small number of large brand customers. If a brand pulled out at the last minute or not enough of that overproduced inventory sold, the factories faced financial and inventory risk.
“What’s changing is that the best factories increasingly have their own design and product-development capabilities,” Ramdani told TechCrunch. “Instead of simply manufacturing someone else’s designs, they can develop products themselves, adapt them quickly, and produce in smaller batches.”
AI also helps these factories by pulling data that helps them identify which products are likely to sell out before committing to large production runs.
“That reduces overproduction and allows factories to diversify beyond a handful of large customers,” he said.
These changes also gave Ramdani an idea, leading him to team up with Luis Angulo to launch the startup, which is now an AI-powered fashion brand that connects luxury manufacturers directly with consumers.
He compared his company’s approach to the retailer Quince, which is known for selling high-quality, low-priced items.
“We are talking to a different generation who is coming back for things that are trendy but well-made,” he continued, adding that consumers are getting tired of pure fast fashion, especially because of the harmful impact it’s having on the environment.
AI, of course, is used. Ramdani sees AI agents changing the entire shopping experience, where, in the future, people just instruct AI agents to buy things for them.
Ramdani said the company uses AI to analyze fashion trends and test what colors might look good on a product. It also uses AI agents to estimate consumer demand and trains the agents to predict when a material might start running short so factories can stock up in time.
On the consumer side, the Atoire website offers an AI agent that can build an outfit based on whatever — or whoever — inspires the customer. Over time, Atoire uses AI to learn shoppers’ habits to suggest what to buy next.
Ramdani said Atoire is already seeing an increase in sale referrals from platforms like ChatGPT and Claude.
The company said its goal is to become an alternative to Zara and offer something “very high quality for a price point that’s very affordable.” The company ended last year with around $5 million in sales, and is expected to reach an annualized run rate north of $55 million this year. “
We are growing super fast,” Ramdani said, adding that the team is working with over 40 factories across the world right now.
The fresh capital will fund logistics, build more AI tools, and support production. The company also plans to release its own in-house line, like Amazon Essentials, and will work with creators and influencers to help them launch their own clothing lines quickly.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
>
Tech
Hoomanely’s building a smart feeding bowl and an AI platform to help owners spot when their pup is sick
Dogs are amazing. But as many pet parents will tell you, they can be remarkably good at hiding when something is wrong. My Labrador-Great Dane mix, for example, contracted tick fever several times over his 14 long years, and it was always at least a couple of weeks before I suspected something was off. It nearly gave me anxiety ulcers a few times.
Hoomanely, a new startup out of Palo Alto, thinks it can help people like me spot health problems in their dogs (beyond those two struggling brain cells) much sooner with an AI platform that gathers data using a sensor-laden feeding station. Dubbed the EverBowl, the station measures dogs’ food and water consumption and eating speeds, and records chewing and swallowing sounds, facial thermals, oral motions, and a few other signals.
The company’s AI platform analyzes that data to establish a baseline and then builds a health record. Any subsequent and prolonged departures from that baseline are shown to the pet owner via an app and in long-term reports, which the company says can provide more useful data to veterinarians if and when they get involved. The app also accepts information from the user, so the baseline can be kept updated with information outside the sensors’ scope.
“Practically, the bowl is the best capture environment in a pet’s life: same place, same posture, same routine, twice a day, for years,” Sai Supriya Sharath, co-founder and CEO of Hoomanely (pictured above, in the middle), told TechCrunch. She added that appetite, hydration, and oral comfort are among the first things to be disrupted by pain, dental disease, tummy issues, endocrine changes, and illness.
“Dogs are evolved to mask lethargy and limping. They are far less able to mask how they eat and drink … reduced or altered intake and changed drinking are presenting signs across a wide range of conditions.”
Hoomanely says its platform was built after an 18-month beta testing phase during which the company gathered about 5 million data points across more than 80 dogs.
During testing, the platform detected changes in one dog’s eating patterns, which were eventually tied to a chipped tooth that was starting to become infected. In another case, the company’s app showed a sustained change from the dog’s regular eating and temperature baselines, which prompted the owner to take their dog to the vet and get a diagnosis for tick fever.
While this sounds useful, there are some caveats. The cohort for this beta was quite small, and the company seems to have shipped only about 50 devices so far. It currently offers the EverBowl and the app reports through $29-per-month subscription service.
Sharath also said the company does not yet have independent sensitivity, specificity, or false-positive rates for clinical events, as that would require a study comparing the system’s alerts against actual diagnoses. Hoomanely admits that its platform is not a replacement for a veterinary diagnosis, but is instead meant to help owners and vets to identify potential issues faster.
The company is using its data for formal veterinary studies to find out whether its system works across a larger group of dogs. “The veterinary studies being designed will measure sensitivity, specificity, positive predictive value and false positives for each alert category, and we intend to report results by use case rather than as one headline number.” Sharath said.
Along with those studies, Hoomanely plans to build more devices to gather data. EverSense is planned to be a wearable that will measure movement and rest information, and EverHub will be able to accept inputs from third-party devices such as smart collars, feeders, or home devices to record environmental data.
Sharath said the long-term plan for Hoomanely is to eventually become an animal health data company by using the data gathered from its devices to support research and serve nutrition and insurance companies. Because the data capture system isn’t specifically designed for dogs, she said it may eventually expand to other animals, such as cats, livestock, or horses.
The startup has raised $1.8 million in pre-seed funding so far and says it’s starting conversations for a seed round.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
>
Sales teams rarely have a shortage of data. The harder problem is turning CRM records, pipeline activity, forecasts, buyer engagement, and rep performance into information revenue leaders can actually use.
The right sales analytics software can help RevOps and sales leaders identify pipeline bottlenecks, compare performance across teams and segments, monitor conversion, assess forecast risk, and determine where sellers should focus. The best choice depends on what needs analyzing: CRM performance, account and buyer signals, forecasts, customer conversations, or data spread across multiple business systems.
My picks for the top sales analytics tools include ZoomInfo for account intelligence and buyer signals, Salesforce for CRM-native analytics, Clari for forecasting and pipeline analysis, Gong for conversation-driven insights, and HubSpot CRM for growing teams.
ZoomInfo combines company and contact intelligence with enrichment and buyer signals to give revenue teams more context for account prioritization and sales analysis.
| ZoomInfo | ||
| Salesforce Analytics | ||
| Clari | ||
| Gong | ||
| HubSpot CRM | ||
| Salesloft |
Best sales analytics software at a glance
Why you can trust TechRepublic
|
To ensure we provide readers with the best answers, the TechRepublic editorial process adheres to strict standards, including rigorous research, assessment, and provider scoring. In this review, I evaluated the most important aspects of sales analytics software for revenue teams, including pipeline and forecasting capabilities, account and buyer intelligence, conversation analytics, integrations, ease of use, and pricing. My methodology also considered how well each platform supports distinct revenue use cases, along with product usability and feedback from real users. Furthermore, I leverage the following work experiences when carrying out software reviews:
|
Bianca CaballeroMethodology: How I evaluated the best sales analytics tools
I compared these sales analytics tools based on the capabilities that matter most to sales leaders and RevOps teams, with an emphasis on how effectively each platform turns sales and revenue data into actionable insights.
- Pipeline and conversion analytics: How well the platform tracks pipeline value, movement, coverage, stage conversion, win rates, sales cycles, and potential bottlenecks.
- Sales performance analytics: The depth of reporting across reps, teams, territories, products, accounts, and customer segments.
- Forecasting and revenue visibility: The ability to monitor expected revenue, forecast changes, pipeline risk, deal progression, and performance trends.
- Account and buyer intelligence: The depth of company, contact, intent, engagement, and other buyer signals available for account analysis and prioritization.
- Data integration and enrichment: How easily the platform connects with CRM and other revenue systems, synchronizes relevant data, and enriches existing records where applicable.
- AI and automation: How effectively the platform uses AI and automation to surface patterns, risks, signals, summaries, or recommendations that reduce manual analysis.
- Customization and usability: How easily RevOps and sales leaders can configure reports, dashboards, metrics, and workflows, investigate performance changes, and get useful answers without excessive technical support.
I also evaluated each platform according to its strongest use case rather than assuming all six solve the same problem equally well. This approach helps buyers identify the platform that best addresses their revenue team’s priorities instead of looking for a single product that does everything equally well.
What makes ZoomInfo the best for account intelligence and sales prioritization?
Pros
- Strong B2B company and contact intelligence
- Useful intent and engagement signals for account prioritization
- CRM enrichment can improve segmentation and downstream reporting
Cons
- Pricing is not publicly listed
- Not a dedicated sales forecast-management platform
- May be more platform than smaller or lower-volume sales teams need
Why I chose ZoomInfo
ZoomInfo is strongest when the analytics problem starts with the quality of the underlying account and buyer data. Its combination of company and contact intelligence, CRM enrichment, intent signals, account segmentation, and conversation intelligence provides RevOps with more context to decide which accounts deserve attention and to analyze performance across meaningful customer segments.
The main trade-off is forecasting: ZoomInfo is not primarily designed as a forecast-management platform. For teams focused on account quality, buyer activity, segmentation, and prioritization, though, its intelligence layer can strengthen the data feeding CRM, analytics, prospecting, and sales workflows.
Also read: Revenue Intelligence Platforms: Key Features to Know
Key features
- Company and contact intelligence: ZoomInfo provides B2B company and professional data that helps teams research accounts, identify decision-makers, and add organizational context to CRM records.
- CRM data enrichment: Enrichment can fill missing account and contact fields and refresh existing records, improving the data used for routing, segmentation, and reporting.
- Buyer intent signals: Intent data helps surface accounts with increased research activity on relevant topics, giving sellers another signal to prioritize outreach.
- Account segmentation and prioritization: Firmographic, contact, and behavioral data can help RevOps organize accounts by ICP fit, territory, company attributes, or buying activity.
- Conversation intelligence: ZoomInfo’s broader platform includes conversation-intelligence capabilities that can provide additional context from sales calls and buyer interactions.
ZoomInfo pricing
ZoomInfo does not publish standard monthly or annual list prices. Contact ZoomInfo for a customized quote based on the included users, data allowances, add-on costs, and any usage-based fees.
What makes Salesforce Analytics the best for CRM-native sales analytics?
Pros
- Deep analytics tied directly to Salesforce CRM data
- Highly customizable dashboards and analytical models
- Strong forecasting and broader analytics ecosystem
Cons
- Advanced analytics can require additional licenses
- Configuration and administration can become complex
- Best value depends on an existing Salesforce ecosystem
Why I chose Salesforce Analytics
Salesforce Analytics makes the most sense for organizations that already treat Salesforce as the operational center of the sales process. Pipeline analysis, forecasting, configurable dashboards, CRM Analytics, and the broader Salesforce analytics ecosystem give mature teams substantial flexibility while keeping sales insights close to the underlying CRM records.
Complexity is the tradeoff. Advanced deployments can involve additional products, licensing, and administration. For organizations already deeply invested in Salesforce, however, keeping opportunity data, workflows, forecasting, and analytics within the same ecosystem can outweigh that overhead.
Key features
- CRM-native analytics: CRM Analytics works directly with Salesforce data, helping teams analyze sales performance without having to routinely export data to a separate system.
- Pipeline and opportunity analysis: Revenue teams can examine opportunity progression, pipeline changes, sales performance, and other CRM-based measures.
- Sales forecasting: Salesforce supports forecast management and expected-revenue analysis, with additional purpose-built revenue capabilities available through Revenue Intelligence.
- Custom dashboards and metrics: RevOps can create tailored dashboards, filters, models, and analytics applications around its sales processes and reporting requirements.
- AI-powered analytics: Higher-tier offerings add capabilities such as Einstein Discovery for predictions, automated data discovery, explanations, and scoring.
Salesforce Analytics pricing
- CRM Analytics Growth: $140/user/month
- CRM Analytics Plus: $165/user/month
- Revenue Intelligence: $220/user/month
- Industry Cloud Intelligence: $220/user/month
- Service Intelligence: $220/user/month
Free trial: 30 days
What makes Clari the best for forecasting and revenue predictability?
Pros
- Strong forecasting and pipeline-inspection capabilities
- Purpose-built for CRO and RevOps workflows
- Helps connect deal risk with forecast and pipeline reviews
Cons
- Pricing is not publicly listed
- May be excessive for teams with simple forecasting needs
- More specialized than general CRM reporting
Why I chose Clari
Clari excels when forecasting and pipeline inspection are formal operating disciplines rather than occasional reporting exercises. Its revenue forecasting, pipeline analytics, deal-risk capabilities, and trend visibility map closely to the questions leaders ask during forecast calls and pipeline reviews.
That specialization can be more than smaller teams need. For larger revenue organizations with structured forecasting processes, however, the narrow focus becomes an advantage because Clari is built around revenue predictability rather than generic dashboard creation.
Key features
- Revenue forecasting: Clari supports structured forecasting workflows that help sales leaders monitor expected results and forecast movement.
- Pipeline inspection: Teams can analyze pipeline coverage, changes, opportunity progression, and other signals that may affect future revenue.
- Deal risk analysis: Deal-level signals help managers identify opportunities that require closer inspection or intervention.
- Revenue trend reporting: RevOps can monitor patterns across forecast periods, pipeline movement, and historical performance.
- Revenue data integration: Clari brings information from CRM and other revenue systems into its forecasting and pipeline workflows.
Clari pricing
Clari uses custom pricing and does not publish standard monthly or annual per-user rates. Contact Clari to request a custom quote based on user count, modules, integrations, and deployment scope.
What makes Gong the best for conversation and deal analytics?
Pros
- Strong conversation intelligence capabilities
- Connects buyer engagement with deal and pipeline context
- Supports forecasting alongside conversation and deal analytics
Cons
- Pricing is quote-based
- Broader platform may be unnecessary for dashboard-only needs
- Value depends on consistently capturing buyer interactions
Why I chose Gong
Gong adds a layer of evidence that conventional CRM reporting often misses: what buyers and sellers are actually discussing and doing within an opportunity. Conversation intelligence, buyer engagement, deal analytics, forecasting, and coaching insights can help managers interpret whether a deal is genuinely progressing rather than relying only on manually maintained CRM fields.
The platform’s breadth can be unnecessary for teams that need only standard dashboards. When calls, meetings, stakeholder engagement, and rep behavior materially influence deal outcomes, though, Gong’s interaction data provides context that traditional sales reporting may not capture.
Also read: What Is Sales Intelligence? Tools, Benefits, and Use Cases
Key features
- Conversation intelligence: Gong captures and analyzes calls, meetings, emails, and other buyer interactions to surface patterns and important moments.
- Buyer engagement insights: Teams can examine stakeholder participation and engagement to understand whether opportunities have meaningful buyer involvement.
- Deal analytics: Gong combines CRM data and interaction signals to help managers evaluate opportunity health and progression.
- Sales forecasting: Forecast functionality supports revenue projections, pipeline review, and manager forecasting workflows.
- Coaching analytics: Conversation and activity data can help managers identify seller behaviors and coaching opportunities.
Gong pricing
Gong does not publish fixed monthly or annual list prices. Contact Gong to obtain a custom quote based on user licensing, included products, and implementation expenses.
What makes HubSpot CRM the best for growing revenue teams?
Pros
- CRM, sales execution, and reporting are closely integrated
- Lower starting price than many enterprise-focused platforms
- Monthly and annual billing are available on key tiers
Cons
- More advanced analytics require higher tiers
- Professional and Enterprise carry onboarding fees
- Less flexible than dedicated BI software for complex modeling
Why I chose HubSpot CRM
HubSpot CRM strikes a useful balance between capability and accessibility. Pipeline reporting, activity analytics, forecasting, conversation intelligence, and CRM workflows give growing teams a path beyond spreadsheets without immediately requiring a separate analytics stack.
The limitation is that the strongest reporting and analytics features become increasingly tied to higher subscription tiers. Even so, the integrated CRM experience and comparatively lighter administration can make that tradeoff worthwhile for teams prioritizing adoption and speed.
Key features
- Pipeline reporting: HubSpot CRM provides pipeline and deal reporting that helps teams monitor opportunity stages and sales performance.
- Sales activity analytics: Managers can analyze emails, calls, meetings, tasks, and other activity alongside deal results.
- Forecasting: Professional and Enterprise support more advanced forecasting and team-level revenue visibility.
- Conversation intelligence: Enterprise includes conversation intelligence capabilities for recording, transcription, and coaching insights.
- Sales automation and reporting: Higher tiers add customizable workflows and deeper reporting for more complex sales processes.
HubSpot CRM pricing
What makes Salesloft best for sales engagement and revenue execution analytics?
Pros
- Connects analytics directly with seller workflows
- Combines engagement, conversation, and deal insights
- Includes reporting and AI-powered workflow capabilities
Cons
- Pricing is not publicly listed
- Broad platform can be excessive for teams with simple reporting needs
- Less suited to standalone BI and highly customized analytics
Why I chose Salesloft
Salesloft is most compelling when analytics need to drive direct seller action. Its engagement workflows, conversation intelligence, deal management, analytics, and CRM synchronization make it particularly useful for teams that want reps and managers to act on insights inside their day-to-day sales process.
The downside is breadth: organizations that only need lightweight reporting may not need a full sales execution platform. For teams already investing heavily in structured sales engagement, however, bringing analytics and execution together can reduce the need to move between disconnected tools.
Key features
- Conversation intelligence: Salesloft can capture and analyze customer conversations to give reps and managers more context around interactions and deals.
- Deal management: Deal-focused workflows help teams inspect opportunities, identify risks, and determine appropriate next actions.
- Reporting and analytics: Salesloft includes reporting capabilities for monitoring seller activity, engagement, and performance across sales workflows.
- AI-powered workflows: AI can help guide seller actions and reduce manual work by using signals and workflow context.
- CRM synchronization: Bi-directional CRM sync helps keep engagement and sales data connected with the organization’s system of record. Salesloft lists reporting and analytics, AI-powered workflows, coaching, and bi-directional CRM sync among its platform capabilities.
Salesloft pricing
Salesloft does not publish monthly or annual list prices. Contact Salesloft to request a custom quote based on included platform capabilities, number of user licenses, implementation services, and add-ons.
Sales analytics software vs revenue intelligence software
Sales analytics software primarily helps teams measure and analyze sales performance. Revenue intelligence platforms typically go further by combining pipeline information with buyer activity, forecasting, and risk or opportunity signals.
| Primary purpose | Measure sales performance | Improve revenue execution |
| Typical outputs | KPIs, dashboards, trends, conversion analysis | Forecasts, deal risk, buyer signals, recommended actions |
| Common inputs | CRM, pipeline, sales activity | CRM plus calls, emails, meetings, and engagement data |
| Core question | What happened and where? | What is likely to happen and what needs attention? |
| Typical users | RevOps, sales leaders, analysts | RevOps, CROs, managers, sellers |
The categories increasingly overlap. Salesforce provides analytics and revenue intelligence capabilities, while Clari and Gong combine analytics with forecasting and execution workflows. Buyers should focus on the capabilities their teams need rather than the category label alone.
What features matter most in sales analytics tools?
The strongest sales analytics tools do more than create dashboards. They let revenue teams move from a high-level performance change to the accounts, opportunities, activities, or segments behind it.
CRM and data integration
Determine which systems the platform connects to and what data actually moves between them.
Verify supported CRM objects, custom fields, historical data, sync frequency, and external data sources. A long integration list has limited value if the fields required for your reports cannot be synchronized.
Pipeline and conversion analytics
Look for the ability to analyze pipeline coverage, stage conversion, win/loss performance, sales cycle length, deal slippage, and pipeline trends.
Managers should be able to move from a company-wide change to the team, segment, rep, or individual opportunities responsible for it.
Forecasting and trend analysis
If forecasting matters, determine whether the software merely displays current pipeline or helps explain changes in expected revenue.
Look for historical comparisons, pipeline movement, forecast categories, risk indicators, and drill-down capabilities appropriate to your sales process.
Custom dashboards and metrics
Your reporting model should match your sales motion.
Check whether RevOps can define custom metrics, filters, segments, and dashboards without repeatedly depending on engineering or vendor support.
Segmentation
Useful sales analysis should allow teams to compare performance by dimensions such as rep, team, territory, product, customer segment, source, and deal size.
This is also where account data quality matters. Incomplete company and contact records can limit the usefulness of otherwise sophisticated reporting.
AI-assisted insights
AI should reduce analytical work rather than simply add a summary box to an existing dashboard.
Ask vendors to demonstrate how AI identifies anomalies, surfaces risk, explains changes, prioritizes accounts, or recommends what a manager should investigate.
Governance and metric consistency
Analytics become less useful when different teams create competing definitions of the same KPI.
Look for permissions, standardized metrics, controlled dashboard access, and sufficient transparency so users can understand where reported numbers originate.
How to choose the best sales analytics software
The best sales analytics software is the product that answers your team’s most important revenue questions without creating unnecessary data or administrative complexity.
- List the questions your current reporting cannot answer.
Document the decisions that are difficult to make today before evaluating software.
Example: If managers can see total pipeline but cannot identify why stage conversion is falling, prioritize tools with conversion and pipeline diagnostics instead of simply adding more dashboard templates.
- Decide whether you need CRM-native or cross-system analytics.
CRM-native analytics may be simpler when most relevant sales data already lives in one system. Broader analytics platforms become more valuable when RevOps needs to combine CRM data with finance, marketing, product, or other sources.
Example: A Salesforce-centric organization may be able to answer most operational questions with native analytics, while a RevOps team combining CRM, billing, profitability, and product usage data may need a broader BI layer.
- Test the platform with your own sales data.
Vendor demo environments are designed to look clean. Your production data may not.
Example: Ask each vendor to reproduce your current win rate, pipeline, sales cycle, and forecast reports using representative records. Compare the results with your existing numbers and investigate discrepancies.
- Check metric flexibility.
Determine whether RevOps can adapt calculations to your organization’s definitions.
Example: If your company calculates pipeline coverage differently for enterprise and SMB teams, verify whether the platform supports both definitions without exporting data to a spreadsheet.
- Evaluate drill-down capabilities.
A dashboard should help explain changes, not merely report them.
Example: When win rate falls, test whether a manager can move from the company-wide KPI to segment, territory, rep, stage, and individual opportunity data without building a new report.
- Measure administrative overhead.
Consider what happens after implementation.
Example: Ask who needs to modify a dashboard when the sales organization adds a segment or changes opportunity stages. If every adjustment requires technical resources, include that dependency in the buying decision.
- Compare the total analytics stack.
Do not evaluate a platform in isolation.
Example: Map the analytics products you already pay for against the proposed platform. Identify which functions it replaces, which it complements, and where reporting would remain duplicated.
Common sales analytics software buying mistakes
Revenue teams can add sophisticated analytics without improving decision-making if they choose software before defining how it will be used.
- Buying dashboards instead of answers: More visualizations do not necessarily help teams diagnose performance or make better decisions.
- Ignoring CRM data quality: Analytics cannot fully compensate for inconsistent stages, missing fields, duplicate accounts, or stale close dates.
- Choosing based on vendor demos: Test products against real sales questions and representative company data.
- Overlooking adoption: Managers and reps need to understand and trust the metrics if analytics are going to influence behavior.
- Creating duplicate reporting layers: Another analytics platform can worsen metric inconsistency when data ownership and KPI definitions are unclear.
- Ignoring administration costs: Licensing is only part of the investment. Factor in implementation, integrations, data modeling, governance, training, and dashboard maintenance.
>
Watch ‘Outlander: Blood Of My Blood’ Season 2 Trailer
United Rugby Championship: Ospreys sign former Australia wing Marika Koroibete
Fashion startup Atoire raises $9.5M to bring consumers luxury goods without the markup
-
movies3 months agoSearch For Canadian TV Actor Stewart McLean Now Homicide Investigation
-
Fashion9 years agoThese ’90s fashion trends are making a comeback in 2017
-
Fashion9 years ago
According to Dior Couture, this taboo fashion accessory is back
-
Fashion9 years ago
Model Jocelyn Chew’s Instagram is the best vacation you’ve ever had
-
Fashion9 years ago
Emily Ratajkowski channels back-to-school style
-
Fashion9 years ago
9 Celebrities who have spoken out about being photoshopped
-
Fashion9 years ago
Your comprehensive guide to this fall’s biggest trends
-
Anime3 months agoRurouni Kenshin: Hokkaido Arc Manga Takes 1-Issue Break – News