Close does not have a visibility problem, and the models are not rejecting it. They recommend it readily. The problem is an outdated identity. The models still describe Close as the SMB dialer CRM it used to be, and the AI teammate category it markets today barely appears in their answers.
That old picture lives in the models' training memory, not on the live web. The third-party pages they cite are mostly more modern than their own answers, yet 98% of answers still use the legacy label. And when the models search, competitors wrote nearly a third of the pages they cite, so rivals author the record. The cost lands in one place: autonomous AI selling. Against the autonomous AI-SDR startups (11x, Artisan, AiSDR), Close wins only 33% of forced choices. That weak spot sits exactly where demand is heading. Buyer search language has flipped, with AI sales terms now running even with CRM and dialer terms. Close's homepage already leads with the AI teammate claim, but the models have not absorbed it.
Because the perception sits in memory, it only moves at retraining, and site edits alone will not shift answers quickly. The durable fix is to out-write the competitor-authored record now, in Close's own words, so grounded answers improve steadily and the next retraining locks in the new identity.
AI-generated read of the lab's measurements, as are the explainers under each section's TL;DR; every number is measured on this page.
The index measures which brands the models (ChatGPT, Claude and Gemini) name unprompted; the lab puts one subject under
every other prompt condition a buyer creates: aided ("I'm
considering Close. Would you recommend them?"), forced choice
("Close or [competitor]: give a definitive answer"), and grounded
(web search on: which sources the models cite). Close is an enrolled lab subject, selected
by the operator; every prompt template is published in full below, name order rotates to
cancel position bias, and these answers never touch the Visibility Score.
Methodology →
The Stale Label
What the models say Close is
TL;DR The AI-powered sales CRM with an autonomous AI teammate story has not landed: 98% of the models' answers still call Close a SMB sales CRM with a built-in dialer.
The models overwhelmingly describe Close using its old identity, not the one it is marketing today. Every aided recommendation run also shows how the model characterizes Close, and we sort those descriptions into buckets. The result is lopsided: 118 of 120 answers reach for the legacy label of an SMB sales CRM with a built-in dialer. The category Close wants to own, an AI-powered CRM with an autonomous AI teammate, appears in just 1 of 120 answers. This gap matters because the label a model uses shapes its advice. A model that thinks of Close as a dialer-first SMB tool will recommend it to those buyers, and will leave it out when someone asks for AI-driven sales software.
The prompt, asked 120 times across four buyer personas and five needs:
“I'm [persona] and I need [attribute]. I'm considering Close. Would you recommend them? Give me pros and cons.”
a SMB sales CRM with a built-in dialer
98% (118)
an AI-powered sales CRM with an autonomous AI teammate
1% (1)
an all-in-one sales CRM (calling, email, SMS built in)
1% (1)
an a simpler, cheaper Salesforce alternative
0% (never)
While the models say sales-CRM, buyers type AI-sales: 1× the search volume
TL;DR The market's language flipped around Feb ’26: AI sales / AI SDR terms now out-search sales CRM / built-in dialer terms about 1 to 1 on Google.
sales CRM / built-in dialer terms · 85k searches over 48 monthsAI sales / AI SDR terms · 108k searches over 48 months
sales crm software -21% ai sdr -33% ai sales agent -27% inside sales crm breakout sales dialer software -22% ai cold calling software -73% ai sales crm breakout crm with built in calling breakout
US Google monthly search volume, Sep ’22–Aug ’26.
Baskets: sales CRM / built-in dialer terms = “sales crm software”, “inside sales crm”, “crm with built in calling”, “sales dialer software”; AI sales / AI SDR terms = “ai sales crm”, “ai sdr”, “ai sales agent”, “ai cold calling software”.
The models' dominant label for Close tracks the 1x-smaller vocabulary, not the one buyers are moving to.
The same gap, measured a third way: what Close's own homepage claims, next to where
the index actually ranks it.
Label lag
How Close positions itself vs how AI labels it
Close leads with AI-powered sales CRM with an autonomous AI teammate, but 98% of
the models' answers still call it SMB sales CRM with built-in calling. It ranks
#4 of 15 in CRM, so this is a labeling problem, not a visibility one.
The label they use98% still "SMB sales CRM with built-in calling"; only 1% the identity it claims
Why, and what closes it. The ranking is fine; the identity is stale. The old label lives in the
models' training memory, so it lags the live site. Getting the AI-powered sales CRM with an autonomous AI teammate story into the pages
the models cite is what moves it.
TL;DR 88% of the models' recommendations for Close come with conditions; 3% recommend against it outright.
Close rarely earns a clean yes, even when the buyer asks for it by name. In this test, a buyer names Close directly and asks the models for a verdict. Across 120 such runs, only 10 answers said yes outright and 4 recommended against it. Everything else was a yes wrapped in caveats, most often weak marketing automation or a smaller ecosystem than Salesforce or HubSpot. That wall of hedges matters because every caveat is an opening, and the models fill it by naming rivals such as Salesforce and HubSpot in the same answer. The pattern barely shifts with the audience: the qualified share moves by less than 18 points across all buyer needs and personas, so no buyer type gets a confident yes.
qualified 88%
unqualified yes 8% (best)qualified 88% (a hedged yes)recommends against 3% (worst)
attribute
unqualified yes
qualified
no
autonomous AI selling
0
20
4
built-in calling & email
1
23
0
fast setup, easy to use
5
19
0
pipeline & deal management
1
23
0
reporting
3
21
0
What the qualifications are about, in order of frequency:
Weak marketing automation capabilities · Limited marketing automation capabilities · Smaller ecosystem than Salesforce or HubSpot · Smaller ecosystem and fewer integrations. And when the models
hedge, they don't hedge into silence: the brands they name alongside or instead of
Close are Salesforce, HubSpot, Outreach, Pipedrive.
For the unaided version of this measurement, how answers portray Close when the
buyer never names it, see the sentiment stances on
the brand page.
The Coin-Flip Attribute
Forced to choose, how often the models pick Close
TL;DR Forced to pick between Close and a named competitor, the models choose Close 86% of the time; the weakest attribute by far is autonomous AI selling (78%).
Close wins almost every head-to-head, but one attribute drags the score down across the board. Each run puts a buyer who already knows both brands on the spot and demands a single answer. On most attributes the grid is lopsided in Close's favor, topping out at a perfect 100% for built-in calling and email. The crater is autonomous AI selling. Against the AI-SDR startups, meaning 11x, Artisan, and AiSDR, Close wins only 33% of those matchups. Yet even against traditional competitors Close wins 90% on this attribute, below its best marks, so the weakness sits with the attribute itself, not with one class of rival.
How to read the matrix: green cells favor Close, red favor the competitor; hover
any cell for the raw run counts. A single cell is only
6 runs, so treat differences under ~25
points as direction rather than precision; the row and column totals (42+ runs each)
are the reliable numbers. Brand-name order was rotated on every run and produced
identical win rates in both orders, so position bias is measured at zero.
Size
Use case
All
vs
startup needs
built-in calling & email
autonomous AI selling
fast setup, easy to use
pipeline & deal management
11x
100%
100%
33%
100%
100%
87%
Apollo
33%
100%
67%
100%
100%
80%
Artisan
100%
100%
33%
100%
100%
87%
Copper
100%
100%
100%
50%
100%
90%
Freshsales
67%
100%
100%
83%
100%
90%
HubSpot
67%
100%
67%
100%
67%
80%
Pipedrive
67%
100%
100%
17%
0%
57%
Salesforce
100%
100%
100%
100%
100%
100%
Salesloft
100%
100%
100%
100%
83%
97%
Zoho CRM
100%
100%
83%
100%
67%
90%
All competitors
83%
100%
78%
85%
82%
86%
Raw run counts per cell (for touch and keyboard readers)
vs
startup needs
built-in calling & email
autonomous AI selling
fast setup, easy to use
pipeline & deal management
11x
6–0
6–0
2–4
6–0
6–0
Apollo
2–4
6–0
4–2
6–0
6–0
Artisan
6–0
6–0
2–4
6–0
6–0
Copper
6–0
6–0
6–0
3–3
6–0
Freshsales
4–2
6–0
6–0
5–1
6–0
HubSpot
4–2
6–0
4–2
6–0
4–1–1t
Pipedrive
4–2
6–0
6–0
1–5
0–6
Salesforce
6–0
6–0
6–0
6–0
6–0
Salesloft
6–0
6–0
6–0
6–0
5–0–1t
Zoho CRM
6–0
6–0
5–1
6–0
4–1–1t
Each cell: Close wins–competitor wins–ties out of 6 runs.
One Trait, Two Fates
Why Close wins, and why it loses
TL;DR In head-to-head answers Close wins on “Built-in calling, SMS, and email”; the models' most common objection is “Not designed for autonomous AI agents”.
Close wins on being a hands-on sales tool, and it loses when the question turns to automation. Every forced-choice answer explains itself, and we tag those reasons into countable labels, wins on the left and objections on the right. The top reasons to pick Close are practical: "Built-in calling, SMS, and email" appears in 26% of all runs, and "Purpose-built for sales teams" adds another 9%. Together they paint Close as a focused workspace where salespeople call, text, and email without leaving the app. The objections tell the flip side of the same story: "Not designed for autonomous AI agents" leads at 6%, followed by "Limited automation and workflow logic." The same perception drives both outcomes: a tool built around human sellers wins when that matters and loses when the buyer wants software that acts on its own.
Percentages are shares of all 300 runs, so a 21% differentiator is one the
models reach for in a fifth of every matchup they see.
Differentiators (named when Close is the pick)
26%Built-in calling, SMS, and email79/300
9%Purpose-built for sales teams26/300
6%Visual pipeline management and forecasting19/300
6%Automatic activity tracking and logging17/300
6%Intuitive UI with low training burden17/300
5%Native power dialer and predictive dialer16/300
4%Fast implementation and setup12/300
1%Transparent and predictable pricing4/300
Objections (named when a competitor is the pick)
6%Not designed for autonomous AI agents17/300
4%Limited automation and workflow logic13/300
4%Weak or missing CRM and pipeline management11/300
4%Higher cost and premium pricing11/300
3%Steeper learning curve and onboarding effort10/300
3%Requires Google Workspace; ecosystem limitations9/300
3%No built-in lead database or sourcing8/300
3%Lacks built-in calling and dialer features8/300
The Prompt-First Threat
The strongest case against Close, per competitor
TL;DR Pipedrive is the biggest real threat to Close, winning 43% of its head-to-head matchups.
Different rivals beat Close with different arguments, and overall win rates hide that. This section shows each competitor's share of head-to-head wins alongside the phrases the models repeat when choosing it. Pipedrive is the only competitor that wins on simplicity, taking 13 of its 30 runs with arguments about an intuitive interface and lightweight setup. Apollo and HubSpot win less often, at 20% and 17%, and their case rests on capability instead, such as a built-in prospect database and deeper automation. The newer AI-first tools win rarely, and only when the models value full autonomy over everything else. The real exposure for Close is a rival that feels easier, while feature-depth arguments carry runs only occasionally.
The chip on each card is that competitor's win rate against Close in this lab
(wins out of runs played); the biggest genuine threat reads first.
Pipedrive
wins 43% · 13/30
Extremely intuitive interface with instant understanding
Structured data fields provide AI qualification signals
HubSpot
wins 17% · 5/30
Superior API ecosystem and webhook reliability
Native workflow automation depth for autonomous logic
Extensive AI tool integration marketplace
11x
wins 13% · 4/30
Built ground-up for autonomous AI agents
Full-loop autonomy without human intervention
No workflow stitching required
Artisan
wins 13% · 4/30
Built specifically for AI-native autonomous outreach
End-to-end autonomous workflow native to product
AI SDR is first-class, not bolt-on feature
Copper
wins 10% · 3/30
Very fast setup with Google Workspace
Cleaner learning curve for reps
Natural Gmail and Google Calendar integration
Freshsales
wins 10% · 3/30
More intuitive for new users
Easier to configure quickly without admin work
Less intimidating standard CRM experience
Zoho CRM
wins 7% · 2/30
Deeper API/automation infrastructure for autonomous agents
Native AI layer (Zia) complements autonomous systems
Broader integration ecosystem and webhook coverage
Rivals Hold the Mic
Whose content grounds the models’ answers
TL;DR Competitors author 31% of what the models read about Close; Close itself authors just 1%.
When the models search the web, they mostly describe Close in pages its rivals wrote. Everything above measured what the models believe from training memory; this section turns web search on, asks the same battery again, and records which pages get cited across 136 grounded answers. Presence is not the problem, since Close appears in 64% of those answers. Authorship is. Nearly a third of the cited material was written by competitors, while Close's own site and docs barely register in the record. So when a buyer asks a grounded question, the models often narrate Close in its rivals' words, framing its strengths and tradeoffs on terms its competitors chose.
AI-sales-CRM framingA CRM platform with built-in AI (Chloe), calling, email, SMS, and automation capabilities across multiple pricing tiers designed for solo operators to scaling organizations.
all framingA sales CRM with built-in email, calling, SMS, and an AI agent (Chloe) that qualifies leads and books meetings, designed for small-to-mid sales teams doing high-volume phone and email outreach.
AI-sales-CRM framingClose is a sales-focused CRM with built-in calling, email, SMS, and an AI agent (Chloe) that autonomously calls, qualifies, and books meetings for small sales teams.
AI-sales-CRM framingClose is an action-oriented CRM built for small outbound sales teams with native phone, email, and SMS capabilities, plus an AI voice agent (Chloe) that autonomously calls and qualifies leads.
all framingA calling-first CRM that packages lead and opportunity management with built-in calling, SMS, email, workflows, and Chloe AI features, designed for outbound-led inside-sales teams.
legacy framingA phone-first CRM with built-in dialer (Power/Predictive) optimized for high-velocity call-based sales teams, fast implementation, and simplified UX without marketing automation or advanced reporting.
all framingClose is a sales CRM built specifically for inside sales teams that ships calling, email sequencing, and SMS natively in a single interface, with an AI agent (Chloe) on Scale that qualifies prospects and auto-books meetings.
legacy framingClose is an all-in-one sales engagement platform with a built-in power dialer, SMS, and email automation designed specifically for high-velocity outbound sales teams and SDRs.
Framing lines are AI-summarized from each domain's most-cited page about Close
(description audit, run with the same measurement pass).
The Paper Trail
Claim vs. record: where the site and the models disagree
TL;DR The models repeat uncontested objections about Close's limits, sourced from the record, while the site's own claims never reach it. Cheapest move first: claim the strengths the models already grant, like automatic activity logging, then out-write the cited objections.
Close's most expensive problem is silence. The objections the models repeat most, like limited workflow customization and a smaller integration ecosystem, go entirely unanswered on Close's pages. Those complaints also live in the third-party sources the models cite, so a fix means out-writing that record, not simply denying it. The cheapest win sits on the opposite side: the models already praise automatic activity logging on their own, so Close only needs to claim a belief that already exists. The homepage timeline shows how far behind the models' memory runs. Close now leads with an AI teammate message, yet 98% of the models' answers still describe it with the old SMB calling label, and the new positioning appears in just 1%.
2021 H1 to 2022 H1“Inside sales CRM with calling & emailing”
2022 H2“All-in-one CRM for growing sales teams”
2023 H1 to 2024 H1“The CRM built for growth”
2024 H2 to 2025 H2“Alternative to slow, cluttered CRMs”
2026 H1“CRM for small, scaling businesses”
Today“Sales CRM with AI teammate automation”
Homepage headlines from archived copies of close.com, one per half-year with a clean
capture. The claim left its original framing in 2022 H2;
98% of aided answers still file Close under it.
6 themesUnanswered objections
The models keep repeating these objections. The site never answers them, so reviews and rivals fill the silence.
Limited integration ecosystemModels cite smaller ecosystem than Salesforce/HubSpot 4+ negative timesSite does not rebutCited record: carries this too
Limited marketing automationModels assert limited/weak marketing automation 4+ negative timesSite does not address this gapCited record: carries this too
Limited workflow customizationModels flag limited customization for complex workflows 5+ negative timesSite silent on thisCited record: carries this too
Reporting gaps for complex RevOpsModels flag reporting limitations for attribution/RevOps 2+ negative timesSite does not rebutCited record: carries this too
Not designed for enterprise scaleModels assert not designed for enterprise scale 2+ negative timesSite claims enterprise alternativeCited record: silent on it
Limited custom objects and data architectureModels flag limited custom objects 2+ negative timesSite does not visibly address thisCited record: silent on it
6 themesClaims the models never echo
The site invests pages in these claims. The models' answers never repeat them, or repeat them as negatives.
Free trial and migration supportSite claims free trial and free migration service across 9+ pagesModels rarely echo thisCited record: silent on it
CRM data integration ecosystemSite claims CRM integration ecosystem across 3+ pagesModels do not positively echo thisCited record: silent on it
AI-powered data enrichmentSite claims AI-powered data enrichment and real-time web sourcingModels do not echo thisCited record: silent on it
Revenue attribution capabilitySite claims revenue attribution across 2 pagesModels do not positively surface this capabilityCited record: silent on it
Customer support includedSite claims customer support included in pricing across 2 pagesModels do not echo thisCited record: silent on it
AI-powered lead qualificationSite claims AI-powered lead qualification and autonomous decision-makingModels understate thisCited record: carries this too
2 themesFree equity
The models already believe these strengths. The site barely claims them, so they are the cheapest wins available.
Automatic activity loggingModels praise automatic activity tracking 5+ timesSite does not prominently claim thisCited record: carries this too
Enterprise security and complianceModels assert enterprise-grade security certifications 2+ timesSite does not visibly claim thisCited record: silent on it
10 themesClaims that landed
The site claims these and the models echo them back. This is what landed positioning looks like.
Built-in calling, SMS, emailSite claims built-in calling/email/SMSModels praise it universally across 12+ mentionsCited record: carries this too
Power and predictive dialerSite claims built-in callingModels praise power/predictive dialer in 9+ positive mentionsCited record: carries this too
Fast implementation and setupSite claims quick setup processModels echo faster implementation 6+ positive mentionsCited record: carries this too
Workflow automationSite claims workflow automation across 10 pagesModels echo activity logging and automation positivelyCited record: carries this too
Cost-effective pricing modelSite claims cost reduction and scale without overpayingModels praise transparent predictable pricingCited record: carries this too
Built for outbound sales teamsSite positions as unified sales platformModels praise outbound/high-velocity focus 4+ timesCited record: carries this too
Minimal admin overheadSite claims quick setupModels praise minimal admin overhead and no dedicated admin requiredCited record: carries this too
High rep adoption and usabilitySite claims rapid team adoptionModels praise intuitive UI and high rep adoption positivelyCited record: carries this too
AI sales agent (Chloe)Site claims AI sales agent and AI-powered lead qualificationModels and record confirm Chloe broadlyCited record: carries this too
Pipeline visibility and reportingSite claims revenue attribution capabilityModels praise strong pipeline visibility and reportingCited record: carries this too
Site claims come from a crawl of 80 of Close's commercial pages, summarized
per page; the 4,451 model assertions are harvested from the same
grounded answers scored in the grounding section. The triage is AI-classified, and every
theme keeps its receipts inline.
Words Worth Owning
The language already on the table for Close's messaging
TL;DR The models already credit Close with automatic logging, so the cheapest win is naming the problem it solves: the post-call black hole. Claim that phrase first, since reps reportedly lose 45 minutes after every call and no vendor has named it yet.
The clearest opening is automatic activity logging: the models credit Close with it unprompted, five times, yet Close barely claims it. Language the models already repeat is language they will keep repeating, because they draw on what exists in their record, so echoing their phrasing costs nothing and compounds. The strongest problem for Close to own is the post-call black hole, the models' picture of reps losing 45 minutes after each call on notes, field updates, and follow-up tasks. It is powerful because the belief behind it is already granted; Close only needs to name the problem and attach the automatic logging claim to it. The models also frame CRM-native dialers as the gold standard for logging and cite Close by name, which Close can quote almost verbatim. Taken together, the positioning writes itself: Close is the CRM-native platform where calls, texts, and outcomes log themselves, so small teams sell instead of typing.
What buyers type now
The rising search vocabulary in Close's market, from Google volume. Messaging that uses these words meets buyers where they already are.
ai sales crm450 searches in the last 12 months, breakout (barely existed the year before)
ai sales agent14,880 searches in the last 12 months, -27% vs the prior 12
ai sdr21,100 searches in the last 12 months, -33% vs the prior 12
ai cold calling software750 searches in the last 12 months, -73% vs the prior 12
sales dialer software (fading)1,300 searches in the last 12 months, -22% vs the prior 12
sales crm software (fading)23,960 searches in the last 12 months, -21% vs the prior 12
Phrases the models already use
The models repeat language that already exists in their record. Echoing their own positive phrasing is the cheapest way to reinforce it.
“Built-in calling, SMS, and email”Lead line on homepage hero and product overview, the single most repeated model description of Close. (12 assertions)
“Built-in power dialer and predictive dialer”Feature headline for the calling/dialer product page. (9 assertions)
“Built specifically for small sales teams”Positioning statement in ICP messaging and comparison pages against enterprise CRMs. (7 assertions)
“Faster implementation and setup”Onboarding and trial messaging, especially in comparisons to Salesforce/HubSpot. (6 assertions)
“Automatic activity logging”Should be promoted as a headline claim, not left implicit, since models already credit it unprompted. (5 assertions)
“Less admin overhead than Salesforce”Direct comparison copy for Salesforce alternative and switch pages. (4 assertions)
Problems ready to be named
Buyer pain the models and the market articulate that no vendor has put a name on. Naming a problem first is how categories get claimed.
Post-call black holeModels describe reps losing 45 minutes after each call updating CRM fields, writing notes, and creating follow-up tasks (framing-2/claude).Close can claim it eliminates the post-call black hole through automatic logging, turning an already-granted belief into a named, ownable problem.
Toggle taxModels state switching between tools can consume up to 70 percent of a rep's day (framing-0/gemini).Position Close's all-in-one calling, SMS, and email as the fix for the toggle tax, a term already circulating in model language.
Fragmented stack bottleneckModels say fragmented tools have become a significant bottleneck slowing down SDRs (framing-1/claude).Frame Close as the antidote to stack fragmentation for teams currently stitching together dialer, CRM, and sequencer tools.
Complex workflow ceilingNegative theme: limited customization for complex workflows and sales processes (5 and 3 mentions).Reframe this as a feature not a flaw: Close is built for speed and simplicity, not the complexity ceiling that slows down bloated CRMs.
Integration sprawl fatigueNegative theme: smaller integration ecosystem than competitors (4 and 3 mentions).Counter this by messaging Close's native, pre-built stack as removing the need for sprawling third-party integrations in the first place.
Framings in play
How the models frame the buy decision when no vendor is named. Messaging can lean into a framing that favors Close or answer one that does not.
Modern sales tech is framed as four required layers: Intelligence, Sequencing, Dialing, and CRM, all needing to integrate cleanly.Show Close as the rare platform that natively covers Sequencing, Dialing, and CRM in one, skipping the integration layer entirely. (framing-1/claude)
Models frame CRM-native dialers as the gold standard for automatic logging, explicitly naming Close CRM as a built-in example.Quote this framing directly: being CRM-native, not bolted-on, is why calls and outcomes log themselves. (framing-0/gemini)
The fix is framed as moving from a manual entry mindset to a signals and automation workflow.Use this language shift in messaging: Close moves teams off manual entry and into automatic capture, not just faster typing. (framing-1/gemini)
Solving manual dialing, logging, and follow-up chasing is framed as three buckets solved together by the best modern platforms.Structure product messaging around these same three buckets, positioning Close as the platform that solves all three natively instead of piecemeal. (framing-0/claude)
Sources: Google search volumes (12-month sums vs the prior 12); the models' phrasing and
problem language, distilled from the grounded assertions and the no-vendor-named probe
answers collected for this lab. AI-distilled; each item keeps its receipt.
What the next measure watches
These are the three numbers that would move first if Close's repositioning is landing.
The lab re-runs monthly from the same battery, so each is directly comparable measure to measure.
The label. 98% of answers currently say a SMB sales CRM with a built-in dialer; the AI-powered sales CRM with an autonomous AI teammate share (1%) is the KPI for the perception shift, because the label decides which buyers hear about Close at all.
The weak attribute. autonomous AI selling at 78% is the one place the models already steer buyers elsewhere; it carries the largest weighted opportunity in the measurement.
The authorship share. Competitors write 31% of what grounds the models' answers today; movement here shows up in grounded answers within weeks, while the label itself only moves at model retraining.
Measured September 27, 2026, alongside the September 2026 snapshot. Models: ChatGPT (gpt-5.4), Claude (claude-sonnet-4-6), Gemini (gemini-3-flash-preview).
Grounded runs are a separate measurement surface (web search on) from the sections above, which measure what the models know from training alone.
A forced choice is a different measurement than open visibility: a brand can dominate this
lab and still be invisible when buyers don't name it. Read the lab and the index together.
Copper 
Freshsales 
HubSpot 
Pipedrive 
Salesforce 
Salesloft 
Zoho CRM