Track automated outbound across three tiers: activity (deliverability, bounce rate), engagement (reply rate, meetings booked), and pipeline (cost per opportunity, revenue attribution). Built for RevOps, VP Sales, and growth leaders running automated outbound. Signal-triggered plays produce 73% more replies than standard sequences, per Unify's Plays data, versus a 3.43% cold-email average industrywide.

Key Facts at a Glance

The numbers below are the ones cited throughout this article, pulled into one place so you don't have to hunt for them. Every figure names its source and the year it was published or last verified.

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Methodology and limitations. External benchmarks come from Instantly's Cold Email Benchmark Report 2026, covering platform data from January 1 to December 18, 2025, published January 12, 2026. Instantly discloses that its dataset spans billions of cold email sends but doesn't publish an exact sample size or a full sending-industry breakdown, so treat the 3.43% / 5.5% / 10.7% figures as directional industry context, not a guarantee for any specific list or vertical. Every Unify-specific number in this article is attributed to a single named customer story or a specific Unify product page, never blended into one platform-wide average. There is no single "Unify benchmark" dataset behind this article. What this framework doesn't cover: call and dial metrics, LinkedIn-specific engagement metrics, and paid-channel CPO comparisons. If you sell into regulated industries (financial services, healthcare) or the EU, treat the reply-rate and volume figures here as directional only. Consent and opt-in requirements change what a "normal" reply rate even means in those markets, which we cover in the edge cases section below.

What Is the Three-Tier Automated Outbound Metrics Framework?

The three-tier framework separates automated outbound measurement into activity metrics, engagement metrics, and pipeline metrics, each answering a different question for a different audience. Activity metrics (Tier 1) confirm the program is actually running: sends going out, inboxes staying healthy. Engagement metrics (Tier 2) confirm the messaging is landing: people are replying, and replying with interest. Pipeline metrics (Tier 3) confirm the program is worth the money: opportunities, cost per opportunity, and revenue.

Most teams get stuck reporting only Tier 1, because it's the easiest data to pull. Sends and opens are simple to log, but they don't tell a VP of Sales whether the program is generating revenue. The rest of this article works through each tier, then shows how to calculate true pipeline contribution and which dashboards to build so the three tiers reach the right audience.

Which Activity Metrics Should You Track for Automated Outbound?

Activity metrics confirm the program is technically healthy: mail is landing in inboxes, sequences are completing, and prospects aren't opting out in unusual numbers. They are necessary but not sufficient. A program can look perfectly healthy on Tier 1 and still generate zero pipeline.

Deliverability Rate

Deliverability rate is the share of sent emails that reach the recipient's inbox rather than bouncing or landing in spam. It's the first metric to check when reply rates drop, because a messaging problem and a deliverability problem look identical in a reply-rate chart but need completely different fixes. Unify's managed deliverability infrastructure, which handles mailbox warming, DNS configuration, and pre-send validation, keeps bounce rates 3 to 6 times lower than industry standard and supports 100,000-plus emails a month per customer, per Unify's Deliverability product page.

Bounce Rate

Bounce rate is the percentage of sent emails that fail to deliver, split into hard bounces (invalid address, permanent failure) and soft bounces (temporary, like a full inbox). Hard bounces are the one to watch closely, since a rising hard-bounce rate damages sender reputation for every future send from that domain, not just the current campaign. CandorIQ, an early-stage compensation software company that consolidated a fragmented outbound stack into Unify, cut its bounce rate from 15% down to under 2%, an 87% reduction, per CandorIQ's customer story.

Sequence Completion Rate

Sequence completion rate is the share of enrolled contacts who make it through every scheduled step without unsubscribing, bouncing, or getting manually pulled out. A completion rate that's trending down over several weeks, even if the absolute number still looks fine, usually means either the messaging is misfiring early in the sequence or the list quality has degraded. Watch the trend, not just the snapshot.

Opt-Out Rate

Opt-out rate is the share of contacts who unsubscribe or explicitly ask to stop. Unlike bounce rate, a spike here is a messaging or targeting problem, not a technical one. A sudden jump right after you change subject lines, sending cadence, or list source is the clearest signal that the change was the cause, and it's worth reverting before you keep sending to the rest of the list.

Which Engagement Metrics Prove Your Messaging Is Working?

Engagement metrics confirm the message is landing with real people, not just clearing a spam filter. This is the tier where signal-triggered and time-based approaches to automated outbound tend to separate from each other most visibly.

Reply Rate (Total)

Reply rate is the share of contacted prospects who send back any response, positive or negative. Industry-wide, the average cold email reply rate is 3.43%, with top-quartile campaigns at 5.5% or higher and the top 10% of campaigns exceeding 10.7%, per Instantly's Cold Email Benchmark Report 2026. Signal-triggered sends tend to clear that bar by a wide margin: Unify's own Plays data shows signal-triggered sends generating 73% more replies than standard, time-based sequences, per Unify's Plays product page. Named customers back this up individually. Perplexity's PQL play (targeting free and Pro users showing product usage signals) reached a 5% reply rate, and its MQL plays (targeting marketing-engaged leads) reached 20%, per Perplexity's customer story.

Positive Reply Rate

Positive reply rate is the share of replies that express real interest, as opposed to "not interested," an out-of-office auto-response, or a request to be removed. This is the metric that should actually drive sequence and messaging decisions, since total reply rate can look healthy while being mostly negative replies. See the edge cases section below for how to draw this line consistently.

Meeting Booked Rate

Meeting booked rate is the share of contacted prospects (or the share of positive replies) that convert into a scheduled meeting. Tracking it against positive reply rate specifically, rather than total contacts, tells you whether the friction is in your messaging or in your booking flow.

Meeting Held Rate (Show Rate)

Meeting held rate, or show rate, is the share of booked meetings the prospect actually attends. It matters because a program can book plenty of meetings that never happen, quietly inflating every metric upstream of it. Juicebox, an AI recruiting platform, booked 256 meetings directly from Unify-powered outbound with a 92% show rate in a single month, per Juicebox's customer story, which is a useful reference point for what a well-qualified, signal-matched invite looks like.

Open Rate (Use Carefully)

Open rate is the least reliable engagement metric available today, and it should be treated as a rough activity signal rather than a measure of real interest. Apple's Mail Privacy Protection routes Apple Mail messages through a proxy server that preloads images before the recipient ever looks at the message, which registers as an "open" whether or not a human actually reads it, according to Apple's own documentation on Mail Privacy Protection. Since a large share of professional inboxes run on Apple Mail or iCloud, this inflates open rate industry-wide and breaks any comparison to pre-2022 benchmarks. For context, Unify's own aggregate open rate across all customers sits at 48%, per Unify's Sequencing product page, but that number is offered as platform context, not as a target to optimize toward.

How Do You Measure Pipeline and Revenue Impact from Automated Outbound?

Pipeline metrics are the only ones that answer the question a CFO actually asks: is this program worth what we're spending on it. Everything in Tier 1 and Tier 2 exists to explain why the Tier 3 numbers look the way they do.

Pipeline Generated by Automated Outbound

Pipeline generated is the dollar value of opportunities created directly from the automated program in a given period. Named results vary by motion and company stage: Perplexity generated $1.7M in pipeline in three months without hiring a single BDR, per Unify's Perplexity case study; Pylon generated $300K in new pipeline within a few weeks of launching its first automated Plays, per Pylon's customer story; and CandorIQ, a single founding SDR working from one consolidated stack, attributed $1.8M in pipeline to Unify, per CandorIQ's customer story.

Cost Per Opportunity (CPO) by Channel

Cost per opportunity is total program cost, including tools, headcount, and data, divided by the number of qualified opportunities that program created over a given period. Calculate it separately for every outbound motion you run, using fully loaded costs on both sides, or the comparison is meaningless. A program with lower software spend can still lose on CPO once you add the rep hours it takes to run it manually. See the worked example below for the full calculation.

Pipeline Velocity

Pipeline velocity measures how fast opportunities move from creation to close, and it's calculated as (number of opportunities x average deal size x win rate) divided by average sales cycle length. Automated, signal-triggered outbound tends to help this number less through deal size and more through cycle length, since a prospect contacted right after a buying signal starts the conversation with more context already established.

Outbound-Attributed Revenue (Closed-Won)

Outbound-attributed revenue is the closed-won dollar figure that traces back to the automated program under whatever attribution model you've defined (see the edge cases section for why "sourced," "influenced," and "attributed" aren't the same thing). Across the full base of Unify customers, this reporting layer sits behind $277M in attributed closed-won revenue, per Unify's Analytics product page. At the individual customer level, CandorIQ attributes $121K in closed-won revenue directly to its Unify-powered program, per CandorIQ's customer story, which is the kind of specific, named number this metric should always be reported as, not folded into one aggregate.

Pipeline Contribution Rate

Pipeline contribution rate is the share of total company pipeline that traces back to the automated outbound program, as opposed to inbound, paid, or manually-worked outbound. It's the metric that answers "how big a bet should we make on this channel next quarter," and it only means something once your attribution model (first-touch, multi-touch, or account-based) is documented and applied consistently across channels.

How Do You Calculate True Pipeline Contribution from Automated Outbound vs. Manual Outreach?

Calculating true pipeline contribution takes five steps, run in order, and skipping any one of them tends to bias the result toward whichever motion your team already prefers.

Step 1: Segment Your Account List by Outreach Method

Before you can compare automated and manual outbound, every account needs a clear tag for which motion is working it: automated only, manual only, or both. Accounts touched by both need a separate bucket, not a coin flip, because that overlap is exactly what step 5 accounts for.

Step 2: Track First-Touch Attribution at the Account Level

Credit the motion that made first contact with the account, not the motion that happened to be running when the deal closed. First-touch attribution is the fairest baseline for comparing two outbound methods specifically, since it isolates who actually opened the door.

Step 3: Calculate CPO and Pipeline Velocity for Each Group

Run the CPO and pipeline velocity formulas separately for the automated-only and manual-only groups from step 1, using the same time window and the same fully loaded cost definitions for both. This is where most comparisons quietly go wrong, by using list price for the automated tool but fully loaded compensation for the reps.

Step 4: Normalize for Outreach Volume and Timing

A channel that contacts ten times more accounts will generate more raw pipeline even if it converts worse per account. Normalize both groups to a per-account or per-1,000-contacts basis before comparing, and make sure both groups cover the same calendar period so you're not comparing a summer quarter to a fourth quarter.

Step 5: Account for Cannibalization

Automated and manual programs frequently target overlapping accounts, especially early on. Check whether pipeline credited to automated outbound would have closed anyway through the manual motion, and vice versa, using the "both" bucket from step 1. If overlap is high, the real incremental value of the automated program is smaller than the raw pipeline number suggests, and that's the number to report to leadership, not the gross figure.

What Dashboard Should You Build to Track These Metrics?

Build three separate dashboards, each for a different audience and cadence, rather than one blended report that tries to serve everyone and satisfies no one.

Operational Dashboard (Daily/Weekly for SDRs and Sales Ops)

Covers Tier 1 and Tier 2 metrics: deliverability, bounce rate, sequence completion, reply rate, and positive reply rate, broken out by sequence and by rep. This is the dashboard that catches a deliverability problem or a messaging miss within days, not months.

Revenue Review Dashboard (Weekly for VP of Sales and RevOps)

Covers pipeline generated and cost per opportunity by channel, with automated and manual outbound broken out separately per the five-step methodology above. This is where the "is this program worth it" conversation actually happens, on a weekly enough cadence to catch problems before a full quarter is lost.

Executive Dashboard (Monthly/Quarterly for Leadership)

Covers outbound-attributed revenue and pipeline contribution rate against total company pipeline, without the operational noise underneath it. Leadership needs the trend line and the dollar figure, not the sequence-by-sequence detail that belongs on the operational dashboard.

Unify's Reporting & Analytics ships six out-of-the-box dashboards along with a plain-English query interface, so a RevOps lead can ask a direct question about sequences, plays, or reps instead of building a new report from scratch every time leadership asks something new. For a deeper look at separating leading from lagging indicators across these dashboards, see Unify's guide on comparing Plays using leading vs. lagging metrics.

What Should You Look for in a Platform That Tracks These Metrics?

Whatever platform you use to run automated outbound, evaluate its measurement layer against a short, vendor-neutral list of criteria before you evaluate its price or its send volume.

• Attribution granularity: Does it attribute pipeline back to the specific play, signal, or sequence that created it, or only to a generic campaign name?
• Signal vs. time-based separation: Can it report signal-triggered and time-based sequences as separate populations, or does it blend them into one reply-rate number?
• Deliverability visibility: Does it surface bounce rate and domain health in real time, before a bad send damages sender reputation for weeks?
• Data portability: Can it push raw attribution data to your warehouse or CRM, or does the data live only inside the tool's own dashboard?
• Accessibility for non-technical stakeholders: Can a VP of Sales or RevOps lead get a straight answer to a plain-language question without waiting on an analyst to build a report?

How Unify Covers This

Unify's Reporting & Analytics attributes pipeline and opportunities back to the specific plays, signals, and sequences that created them, not a generic campaign label, across six out-of-the-box dashboards, and lets teams ask plain-English questions about sequences, plays, and reps instead of waiting on a custom report. Because every send in a Play is tagged as signal-triggered or not, reporting can separate the two instead of blending them, which is how we know signal-triggered sends generate 73% more replies than standard sequences. On the activity side, Unify's managed deliverability keeps bounce rates 3 to 6 times lower than industry standard and supports 100,000-plus sends a month per customer, which is the infrastructure behind CandorIQ's drop from a 15% bounce rate to under 2%. None of this requires stitching together a spreadsheet from four different tools, which is the stack CandorIQ was running before it consolidated into Unify, per CandorIQ's customer story.

Sign up for Unify to see your own plays, signals, and sequences attributed to pipeline automatically, instead of rebuilding the same dashboard by hand every week.

Which Metric Should You Prioritize First? A Decision Framework

Different teams should weight these three tiers differently depending on motion, size, and regulatory environment. Use whichever line below matches your situation to decide where to start.

• If you're PLG with fewer than 50 AEs on a self-serve motion, prioritize signal-to-reply speed over raw send volume; pipeline tends to hide in fast follow-up on product usage signals, not in list size.
• If you're sales-led with more than 50 AEs on Salesforce, prioritize attribution governance (a documented first-touch or multi-touch model) before you chase any specific reply-rate target.
• If you're an early-stage team with a single founding SDR, start with three metrics only, reply rate, meetings booked, and pipeline generated, before building a full three-tier dashboard you don't yet have headcount to maintain.
• If you sell into regulated industries or the EU, treat the reply-rate and volume benchmarks in this article as directional only, since consent requirements change what counts as a normal baseline.
• If your current stack already reports three different reply-rate numbers for the same sequences across three different tools, prioritize consolidating attribution before you add another campaign.
• If leadership only ever asks about opens and clicks, use the Tier 3 cost-per-opportunity comparison to redirect the conversation toward pipeline, since that's the number that actually justifies the program's budget.

What Does This Look Like in Practice? Two Worked Examples

Worked Example 1: Signal to Pipeline, Perplexity's PQL and MQL Plays

Perplexity needed to build an enterprise outbound motion from a standing start, without hiring a BDR team, off the back of a large base of free and Pro product users. It built a PQL Play targeting free and Pro users who showed specific product usage signals, and a set of MQL Plays targeting leads already engaged with marketing campaigns. The PQL Play reached a 5% reply rate; the MQL Plays reached 20%, both well above the 3.43% industry average for generic cold email. Over three months, the combined motion produced 75-plus outbound opportunities, 26-plus enterprise meetings booked, and $1.7M in pipeline, entirely without a dedicated BDR function, per Unify's Perplexity case study.

Worked Example 2: A Cost-Per-Opportunity Calculation (Illustrative, Not a Reported Customer Result)

This example is an illustrative walkthrough of the CPO formula, not a specific customer's disclosed figures. A 30-person B2B SaaS sales org runs two outbound motions side by side for one quarter. A 4-person manual SDR team costs roughly $320,000 for the quarter once salary, commission, and tools are fully loaded, and produces 65 qualified opportunities, for a CPO near $4,900. A signal-triggered automated program costs $45,000 for the same quarter, covering the platform and a fraction of one RevOps person's time, and produces 60 qualified opportunities, for a CPO near $750. Applying the five-step methodology above: both motions ran the same 13-week quarter (step 4), and a review of the account overlap (step 5) found the two motions were mostly working separate account lists, so cannibalization was minimal. The resulting comparison, roughly 6.5 times lower CPO for the automated motion in this scenario, is what the formula produces once the two motions are normalized and compared honestly. Your own numbers will differ; the point is the method, not this specific ratio.

Do These Metrics Change by Role or Team Structure?

• Sales (AE/BDR): Watch positive reply rate and meetings held day to day, not raw sends. A rep hitting activity targets with a falling positive reply rate has a messaging or targeting problem, not an effort problem.
• Growth/Marketing: Own the Tier 1 and Tier 2 operational dashboard, and structure plays the way Perplexity did, by segment (PQL, MQL) rather than one blended campaign, so attribution stays clean from day one.
• RevOps: Own pipeline contribution rate and the CPO-by-channel comparison. This is the team that should be running the five-step true-contribution methodology every quarter, not just at renewal time.
• BDR/SDR leadership: Treat sequence completion rate and opt-out rate as leading indicators, reviewed weekly, since both move before reply rate does when a list or message is going stale.

Common Confusions to Watch For

• Reply rate vs. positive reply rate: a reply is not automatically a lead. "Not interested," an out-of-office auto-reply, and "please remove me" all count as replies but should never count as positive replies.
• Pipeline sourced vs. influenced vs. attributed: "sourced" should mean outbound created the opportunity from nothing, "influenced" means outbound touched a deal that originated elsewhere, and "attributed" should point to whatever specific model (first-touch, multi-touch) you've documented, not whichever story favors the channel you already like.
• Signal-triggered vs. time-based sequences in a shared inbox: the two look identical in a raw reply-rate chart unless every send is tagged at the play or sequence level with which approach triggered it.
• Open rate before and after Mail Privacy Protection: comparing a 2026 open-rate figure to a benchmark from before Apple rolled out MPP overstates or understates real engagement change, since the underlying measurement changed, not just the behavior.
• US opt-out cold outbound vs. EU/GDPR opt-in requirements: the same "reply rate" metric describes a different population in each region, since an EU list built on consent will naturally look different from a US cold list built on legitimate interest.

When Should You Stop or Adapt a Sequence Based on These Metrics?

What Are the Most Common Mistakes Teams Make When Measuring Automated Outbound?

• Reporting open rate as a primary success metric despite Apple's Mail Privacy Protection making most opens unreliable.
• Blending signal-triggered and time-based sequence results into one reply-rate number, which hides which approach is actually working.
• Tracking activity metrics (sends, accounts contacted) without ever connecting them to pipeline or revenue.
• Calculating CPO only for the automated program while ignoring the fully loaded cost of the manual outbound it's being compared against.
• Rebuilding the same attribution report from scratch in a spreadsheet every week instead of automating it at the source.

For more on the specific traps teams fall into when scaling this kind of program, see Unify's guide on automated outbound mistakes, and for a broader look at separating true program cost from list price, see the GTM stack cost calculator.

Frequently Asked Questions

What metrics should you track to measure automated outbound success?

Track three tiers: activity metrics (deliverability rate, bounce rate, sequence completion, opt-out rate), engagement metrics (reply rate, positive reply rate, meetings booked, meetings held), and pipeline metrics (pipeline generated, cost per opportunity, pipeline velocity, attributed revenue). Activity metrics tell you the program is running. Engagement metrics tell you the messaging works. Pipeline metrics are the only ones that answer whether the program is worth the spend.

What is a good reply rate for automated outbound?

Industry-wide, the average cold email reply rate is 3.43%, with top-quartile campaigns at 5.5% or higher and the top 10% exceeding 10.7%, per Instantly's Cold Email Benchmark Report 2026. Signal-triggered outbound tends to outperform generic time-based sequences: Unify's own Plays data shows signal-triggered sends generating 73% more replies than standard sequences, and named customers like Perplexity have seen reply rates as high as 20% on signal-matched plays.

What is cost per opportunity (CPO) and how do you calculate it?

Cost per opportunity is total program cost (tools, headcount, data) divided by the number of qualified opportunities that program created in a given period. To compare automated versus manual outbound fairly, calculate CPO separately for each motion over the same time window, using fully loaded costs, then compare. A program that looks cheaper on tool spend alone can still lose on CPO once rep time is included.

How do you calculate true pipeline contribution from automated outbound versus manual outreach?

Segment your account list by outreach method first, then track first-touch attribution at the account level so credit goes to whichever motion made contact first. Calculate CPO and pipeline velocity separately for each group, normalize for outreach volume and timing so a larger list doesn't look artificially more effective, and finally account for cannibalization, since automated and manual programs often compete for the same accounts.

Why is open rate unreliable for measuring automated outbound performance?

Apple's Mail Privacy Protection routes Apple Mail messages through a proxy server that preloads images and hides the recipient's IP address, which registers as an "open" whether or not a human actually reads the email, according to Apple's own support documentation. Since a large share of business inboxes run on Apple Mail or iCloud, open rate now overstates engagement and should be treated as a weak activity signal rather than an engagement metric.

How does signal-triggered outbound compare to time-based sequences?

Signal-triggered outbound contacts a prospect because of a specific, timely behavior (a website visit, a product usage spike, a new hire), while time-based sequences fire on a fixed schedule regardless of buyer behavior. Per Unify's Plays product data, signal-triggered sends generate 73% more replies than standard sequences. The tradeoff is coverage: signal-triggered programs only reach accounts that trip a signal, so most teams run both in parallel rather than replacing one with the other.

What dashboards should you build to track automated outbound metrics?

Build three: an operational dashboard reviewed daily or weekly by SDRs and sales ops covering activity and engagement metrics, a revenue review dashboard reviewed weekly by the VP of Sales and RevOps covering pipeline generated and CPO by channel, and an executive dashboard reviewed monthly or quarterly by leadership covering attributed revenue and pipeline contribution rate. Each answers a different question for a different audience, which is why one blended report usually satisfies no one.

How does Unify help you track all three tiers of automated outbound metrics?

Unify's Reporting & Analytics attributes pipeline and opportunities back to the specific plays, signals, and sequences that created them rather than a generic campaign label, using six out-of-the-box dashboards, per Unify's Analytics product page. Because Plays tag every send as signal-triggered or not, teams can separate the two in reporting instead of blending the numbers together, and managed deliverability keeps bounce rates 3 to 6 times lower than industry standard, per Unify's Deliverability product page.

Glossary

• Activity metrics: Tier 1 measures (deliverability rate, bounce rate, sequence completion, opt-out rate) that confirm an outbound program is technically running.
• Engagement metrics: Tier 2 measures (reply rate, positive reply rate, meetings booked, meetings held) that confirm outbound messaging is landing with real people.
• Pipeline metrics: Tier 3 measures (pipeline generated, cost per opportunity, pipeline velocity, attributed revenue) that confirm an outbound program is generating a return.
• Signal-triggered outbound: Outreach initiated because a prospect showed a specific, timely behavior, such as a website visit or a product usage spike, rather than firing on a fixed schedule.
• Cost per opportunity (CPO): Total fully loaded program cost divided by the number of qualified opportunities that program created in a given period.
• Pipeline velocity: The rate at which opportunities move to close, calculated as (opportunities x average deal size x win rate) divided by average sales cycle length.
• Positive reply rate: The share of replies that express genuine interest, excluding "not interested," out-of-office auto-replies, and unsubscribe requests.
• Pipeline attribution: The model used to assign credit for an opportunity to a specific channel or touchpoint, typically first-touch, multi-touch, or account-based.
• Mail Privacy Protection (MPP): An Apple Mail feature that preloads message images through a proxy server and hides the recipient's IP address, making open-rate tracking unreliable.
• Sequence completion rate: The share of enrolled contacts who make it through every scheduled step of a sequence without unsubscribing, bouncing, or being manually removed.

Sources

• Instantly, Cold Email Benchmark Report 2026
• Apple Support, Use Mail Privacy Protection on iCloud.com
• Unify, Reporting & Analytics product page
• Unify, Plays product page
• Unify, Deliverability product page
• Unify, Sequencing product page
• Unify, Perplexity customer story and companion blog post
• Unify, Pylon customer story
• Unify, Justworks customer story
• Unify, Juicebox customer story
• Unify, CandorIQ customer story

Related reading on Unify's Explore hub: Pipeline as a Science: Metrics That Matter in Modern Outbound, Outbound Pipeline Attribution Across Plays and Signals, and Manual vs. Automated Outbound: Which Method Works Best.

About the author: Austin Hughes is Co-Founder and CEO of Unify, outbound AI for sellers where AI agents and reps work side by side, from finding the buyers already in market to reaching them with the right message. Before founding Unify, Austin led the growth team at Ramp, scaling it from 1 to 25+ people and building a product-led, experiment-driven GTM motion. Prior to Ramp, he worked at SoftBank Investment Advisers and Centerview Partners.