Signal-Based Prospecting: The Complete B2B Guide for 2026

Here's a truth about B2B outreach: most of it doesn't work because the timing is off.

You're emailing a VP of Sales who isn't thinking about your product yet. You're pitching a CTO who already locked their budget. You're reaching out to a company that doesn't care now. No matter how good your subject line is, timing is more important than the message.

That's the problem signal-based solves.

Of going through a list and hoping for the best you reach out when something just happened. Like a funding announcement or a new hire. These events tell you a company is moving.. Companies that are moving are companies you can sell to.

In 2026 B2B teams that consistently book meetings aren't the ones sending the emails. They're the ones who have a system that tells them who to call today and why. This guide is that system. The signals, workflow, tools and mistakes teams make when they start. Whether you're a sales person, a founder doing your sales or a RevOps lead trying to fix outbound sales lets get into it.

What Is Signal-Based Prospecting?

Signal-based prospecting is a way of doing outbound where you only reach out when a specific, meaningful event has happened at a target account, something that makes right now the right time to start a conversation.

Those events are called buying signals. A buying signal is any observable change that suggests a company might be in the market for what you sell: they raised money, posted a relevant job, switched a tech tool, or had someone land on your pricing page without filling out a form. These aren't gut feelings. They're actual data points you can track, automate, and act on.

The core mechanic is simple: instead of a static list you work through alphabetically, you have a dynamic queue. Accounts earn their way to the top of that queue by doing things that indicate they're ready to talk.

How It's Different From Traditional Prospecting

If you've done any B2B outbound the old way, you know the drill. Pull a list from ZoomInfo, load it into a sequence, send 500 emails, wait, follow up twice, and celebrate if 2% replied. The math technically "works" if you play the volume game hard enough but it's exhausting, it burns your domain reputation, and most reps hate it.

Signal-based prospecting b2b flips that model.

The shift isn't just tactical, it changes how reps think about their job. You're not a message-sending machine. You're someone paying attention to what's happening in the market and showing up when it actually matters.

Why the Timing Argument Is Backed by Data

There's a concept in B2B sales called the "buying window" the period when a company is actively evaluating solutions in a category. Research consistently shows that at any given moment, only 5–10% of your total addressable market is inside that window.

Think about what that means for a spray-and-pray outbound approach. You're reaching out to 100 accounts. Maybe 7 of them are in a buying window right now. The other 93 aren't thinking about you and sending them three more follow-up emails isn't going to change that.

Buying signals are how you find the 7. Funding just closed? The buying window just opened. New VP of Sales started? The buying window just opened. SDR job postings multiplying overnight? The buying window just opened.

That's the whole idea. Stop guessing and start watching.

Quick Definition: Signal-based prospecting is a B2B sales approach that uses real-world events funding, hiring, leadership changes, intent data to trigger outreach to accounts that are most likely in an active buying window right now.

The 7 Types of Buying Signals

Not every signal carries the same weight. Some tell you a deal could close in 30 days. Others are early-stage indicators worth noting, not worth a full-court press. Here are the seven signal types that matter most, and what to actually do with each.

1. Funding and Financial Events

What it is: A company closes a venture round, announces an acquisition, or secures a major credit facility.

Why it matters: Money means decisions get made. When a company raises a Series A or B, they're about to hire, they're evaluating new tools, and they have a mandate to grow fast. Vendors who show up in the first 30–60 days after a round get considered. Vendors who wait three months are competing against incumbents who already got in the door.

It's also worth noting that acquisition announcements work differently when the acquiring company suddenly has integration needs, process gaps, and a tech stack that may need consolidating. That's a real pain point you can speak to.

How to track it: Crunchbase alerts are the easiest place to start. Set up alerts for companies matching your ICP criteria. PitchBook is more comprehensive but expensive. The LinkedIn company page will surface some funding news organically, and Google Alerts on "[company name] funding" or "[industry] Series A" round it out.

What to do: Reach out within 48–72 hours of the announcement. Don't just say "congrats on the funding" and pivot to a pitch everyone does. Reference what the funding is likely for, and connect that to a specific outcome you drive. "You're expanding into enterprise that usually means the outbound motion needs to scale too. Here's how we've helped three similar companies make that transition."

2. Hiring and Headcount Signals

What it is: A company starts posting roles in a department relevant to your product, or shows significant headcount growth in a specific function.

Why it matters: Job postings are an underrated goldmine. They tell you exactly what problems a company is trying to solve because that's what job descriptions are. A company posting five "Revenue Operations Manager" roles isn't just hiring; they're signaling that RevOps is a priority investment. A company suddenly posting SDR roles after two years of a small sales team is scaling outbound. These are precise, actionable intent signals prospecting teams can use.

The headcount angle also matters directionally. A company that doubled engineering in 6 months is in growth mode and probably needs tooling to match.

How to track it: LinkedIn Sales Navigator has job change and posting alerts built in. For more systematic scraping of job data, tools like PredictLeads or Coresignal are built specifically for this. Clearbit's Company API can surface headcount trends over time.

What to do: Use the specific job posting in your opener. Not "I saw you're hiring" that's lazy. "You're hiring six SDRs this quarter, which usually means you're about to 3x your outbound volume. The teams we work with at that stage typically hit a wall with [specific problem]." The more specific, the more it reads like you actually paid attention.

3. Leadership and Job Change Signals

What it is: A new executive joins a target account, or someone who's already familiar with your product moves to a new company.

Why it matters: New leaders are buyers. It's almost a rule in B2B. A new VP of Marketing comes in and re-evaluates the martech stack within 90 days. A new Head of Sales wants to put their fingerprints on the tools the team uses. A new CTO is going to audit infrastructure before they do anything else.

The second scenario of a champion from an existing customer moving to a new company is even better. They already know your product works. They're often pre-sold before you even reach out. This is one of the highest-ROI signals a sales team can track, and most don't bother.

How to track it: LinkedIn Sales Navigator's job change alerts are the standard here. Tools like UserGems and Champify are built specifically to track champion movement and alert your team automatically.

What to do: For new hires at target accounts, reach out in the first 2–3 weeks. After month two, they're swamped with internal stuff and harder to reach. For champion moves, reach out immediately; the window is short because they move fast when they land somewhere new.

4. Intent and Research Signals

What it is: Someone at a target company is actively researching your category, reading comparison content, browsing G2 reviews, and visiting competitor sites.

Why it matters: This is as close as you get to a prospect raising their hand. They're not filling out a form, but their research behavior says they're in evaluation mode. Third-party intent data surfaces this by aggregating anonymous browsing activity across thousands of B2B publisher sites. When a company shows a "surge" in a topic area, it means multiple people there have been consuming content about it recently.

It's not a perfect signal intent data has noise, and surge scores don't always mean an active deal. But combined with ICP fit, it's one of the strongest indicators you have.

How to track it: Bombora is the benchmark for third-party B2B intent data. G2 Buyer Intent is strong if your category has G2 traction. 6sense and Demandbase both fold intent data into broader account scoring models.

What to do: Don't treat an intent signal as permission to blast a generic sequence. It's a prompt to act fast and be specific. If you know they're researching "sales engagement tools," your message should reference the specific challenges that research is probably surfacing, not a generic intro to your product.

5. Technology and Stack Change Signals

What it is: A company adds, removes, or swaps out a technology particularly one that's adjacent to yours, competitive with yours, or a prerequisite for yours.

Why it matters: Tech stack changes are a window into a company's strategic priorities. If a company just adopted Salesforce, they're probably in the market for tools that integrate with it. If they just dropped a competitor's product, they're clearly unhappy and evaluating. If they just implemented a new data warehouse, they might need the analytics layer on top.

These are precise, trigger based prospecting opportunities because the signal is concrete and recent.

How to track it: BuiltWith and HG Insights are the main players here. They scrape public-facing tech signals and provide historical tracking so you can see what changed and when.

What to do: Lead with the specific change, not a generic pitch. "We noticed you recently moved to HubSpot. A lot of the teams we work with made that same transition and ran into [specific friction point] about 60 days in. Worth a conversation?" That's relevant. That earns a reply.

6. News and Company Events

What it is: A company announces a product launch, enters a new market, hits a major milestone, wins an award, or appears in the press.

Why it matters: Company news creates internal momentum. When a company launches a new product, the go-to-market team suddenly has new needs. When they announce expansion into a new region, they need localization support, compliance tooling, and new hiring. These milestones represent decisions being made and budgets being allocated which means vendors who show up at the right moment can get into those conversations.

How to track it: Google Alerts remain reliable and free for monitoring target account names. Mention and Brandwatch are stronger for scale. The Crunchbase news feed is good for growth-stage companies. LinkedIn will surface announcements organically.

What to do: The bar here is not to be the hundredth person who sends "congrats on the launch!" Be specific about what the event means commercially and where you come in. Fake enthusiasm is easy to spot and instantly ignored.

7. Engagement and Behavioral Signals

What it is: Someone from a target account interacts with your brand visits your pricing page, downloads a resource, watches a webinar, or engages with your LinkedIn posts.

Why it matters: Honestly, this is the most valuable signal category, and it's yours alone nobody else can see it. Someone spending four minutes on your pricing page and then clicking through to your case studies isn't just browsing. They're in evaluation mode. Signal based outbound at this stage isn't cold at all, it's a warm follow-up that the prospect just doesn't know they've set up.

How to track it: Clearbit Reveal identifies companies visiting your site without filling out a form and can route that data to your CRM automatically. RB2B goes a step further and identifies individual visitors, matching them to LinkedIn profiles. Koala is purpose-built for this for PLG teams.

What to do: Speed matters here more than any other signal type. A same-day outreach from a rep who references the specific page visited converts better than anything else in outbound. Done right, it feels like you're paying attention because you are.

How Signal-Based Prospecting Works (Step-by-Step Workflow)

Understanding signal types is the theory. This is the practice. Here's the actual workflow, step by step.

Step 1: Define Your ICP With Signal Layers

This is where most teams rush and pay for it later. Your ICP needs two things in a signal-based system:

Firmographic fit: The basics industry, company size, revenue range, geography, tech stack. This hasn't changed.

Signal fit: Which signals are actually predictive for your product? A sales engagement tool should probably weigh "new VP of Sales" and "SDR hiring surge" highest. A DevOps platform should weight rapid engineering headcount growth. A compliance tool should weight expansion into regulated markets.

If you skip this step, you end up chasing signals indiscriminately and drowning your reps in noise. Spend time upfront mapping your 2–3 highest-converting signal types.

Step 2: Build Your Signal Stack

Now set up the infrastructure to capture signals on autopilot. You don't need everything at once. A realistic starting stack looks like:

• Funding signals: Crunchbase Pro alerts
• Hiring signals: LinkedIn Sales Navigator + one job scraper
• Engagement signals: Clearbit Reveal or RB2B on your website
• News signals: Google Alerts for your top 50 target accounts

As you grow, you layer in intent data (Bombora or 6sense) and tech stack monitoring (BuiltWith or HG Insights). But the lean version above is functional and affordable.

Step 3: Aggregate and Score Signals

When a signal goes off in six tools all at once it can be really exciting or really overwhelming. This depends on whether you have a system in place to handle it. The main goal of this step is to take these signals and turn them into a list that is ranked and easy to act on.

Some teams do this by hand. They use a spreadsheet or a simple view in their customer relationship management system sorted by the date the signal happened and the score of the customer profile. This works okay when you do not have a lot of signals to deal with.

Larger teams use platforms like 6sense, Clari or Common Room to do this automatically. These platforms take in signals from different sources, score the accounts and show you a list of priorities every day.

At the end of this step you want something like a daily or weekly list of accounts to get in touch with and a clear reason why you should contact them today. You want a list of accounts to contact and each account on the list should have a reason why you should contact them now. This list of accounts to contact is what you are working towards in this step.

Step 4: Research and Personalize

This step is where the actual work happens and where most reps cut corners.

For each account in the queue, you need to do three things: identify the right person to contact (the one most affected by the signal), understand their current context (what's happening at their company beyond the signal), and connect the signal to a pain point you specifically solve.

This shouldn't take more than 5–10 minutes per account. The goal isn't a 500-word deep-dive. It's one specific, relevant hook that makes the message feel like it was written for this person today not copied from a template.

Step 5: Write and Send Signal-Triggered Outreach

Your message structure is: hook → relevance → ask. Three parts, no fluff.

The hook is the signal, stated specifically. Not "I noticed you've been growing" that's vague and says nothing. "Noticed you posted 6 SDR roles this week" is specific and shows you were paying attention.

The relevance bridge connects the signal to a real problem. Why does this event create a need you can address? Be direct about it.

The task is small. A question, a resource, a 15-minute call. The goal of this message is not to close a deal, it's to start a conversation.

A real example for a hiring signal:

"Noticed [Company] just posted six SDR roles this week scaling outbound fast. We work with teams at exactly this stage to make sure new hires don't spend their first 60 days figuring out who to call. Worth a quick chat?"

Not fancy. But specific, relevant, and human.

Step 6: Follow Up With Context, Not Apologies

Signal-triggered outreach rarely converts on the first message. You need a short sequence of 3 to 5 touches over 10–14 days but each follow-up should add something, not just remind them you exist.

Bring in a new angle. Share a relevant case study. Reference a second signal that's fired since your first message. Ask a pointed question. "Just following up" is a waste of both your time and theirs.

Step 7: Measure Which Signals Are Actually Working

This step gets ignored constantly, and it's the one that turns a decent signal system into a great one.

Tag every opportunity in your CRM with the signal that triggered the outreach. After 30–60 days, look at the data. Which signal types produced the most replies? The most meetings? The fastest closes? You'll almost always find that one or two signal types dramatically outperform the others for your specific product.

Double down on those. Deprioritize the rest. Your signal system should get sharper over time, not just bigger.

The Signal-Based Prospecting Tool Stack

There's no one-size-fits-all stack here. What you need depends on your stage, your ICP, and your budget. But here's a practical breakdown of the categories and the tools worth knowing.

Intent and Account Intelligence

These platforms track third-party research behavior to tell you which accounts are actively investigating your category even if they've never visited your site.

Bombora is the established standard. Their data cooperative covers thousands of B2B publishers and surfaces "surge" scores when a company's research activity around a topic spikes above baseline. It's not perfect, but it's the most widely used intent source in B2B sales for a reason.

6sense goes further; it's a full revenue AI platform that layers intent data, predictive scoring, and channel activation together. It's expensive, but for mid-market and enterprise teams running ABM programs, the signal aggregation is genuinely useful.

Demandbase is the other enterprise option, particularly strong for teams where advertising and sales intelligence need to live in the same system.

Sales Intelligence and Trigger Monitoring

These tools surface real-time company-level events, the funding rounds, hiring trends, leadership changes, and news that make up your signal queue.

Apollo.io is the most accessible starting point for most teams. Contact data, trigger alerts, and sequencing in one platform. It's not the deepest in any single dimension, but the breadth is hard to beat for the price. It's a natural starting point for building a prospecting automation guide workflow.

LinkedIn Sales Navigator is non-negotiable if personnel signals matter to your ICP (and they usually do). Job change alerts, new hire notifications, account news are all more reliable here than anywhere else.

Crunchbase Pro is the go-to for funding and M&A signals. Set up saved searches for your ICP criteria and let the alerts do the work.

PredictLeads is more developer-friendly and API-first, but strong for teams that want raw job posting and tech stack change data piped into their own systems.

Website Visitor Identification

Most of your best prospects will visit your website without filling out a form. These tools tell you who they are.

Clearbit Reveal identifies anonymous website visitors at the company level and can push that data directly into Salesforce or HubSpot. The basic version is free.

RB2B is the newer, more aggressive version that identifies individual visitors and delivers LinkedIn profile links in near real time. Genuinely impressive when it works, and it's changed how a lot of signal-based teams think about warm outreach.

Koala is purpose-built for PLG companies and teams doing product-led selling. It combines website activity, product usage data, and intent signals in a single view, which is powerful if your funnel includes a free tier or trial.

Sequencing and Execution

You need somewhere to run the actual outreach once signals are identified.

Outreach and Salesloft are the enterprise workhorses. Both have added AI-assisted features for signal detection and message personalization. Good if you're at 20+ reps and need workflow governance.

Instantly and Smartlead are popular with leaner teams doing high-volume email outreach. Deliverability tooling is strong, and they're considerably cheaper. Both work well for signal based outbound at scale when your signals are firing frequently.

The AI and Automation Layer

This is the part of the stack that has changed the most in the last 18 months.

Tools and workflows now exist that can monitor signals across all your sources automatically, score and prioritize accounts without a human touching anything, and draft a personalized first-touch message using the signal as context all before a rep opens their laptop in the morning.

This is the domain of GTM engineering, the discipline of building custom revenue infrastructure using APIs, Clay workflows, and AI writing layers. For a deeper look at how these pipelines are architected, our guide on what GTM engineering covers the specifics.

If you're a founder doing this without a dedicated ops person, the good news is the no-code tooling has gotten genuinely usable. Our piece on signal-based prospecting for founders at B2B startups walks through a lean version of the stack.

What the Numbers Actually Look Like

The results from signal-based systems are compelling, but it's worth being honest about the variance. Here's what practitioners and published data consistently show.

Conversion Rates Go Up Across the Board

When teams shift from volume-based outbound to signal-triggered outreach, the numbers tend to move in the same direction:

• Reply rates climb from the 1–3% range up to 8–15% or more
• Meeting-to-opportunity conversion improves by 20–40%
• Deal cycles shorten usually by 15–25% because the rep enters the conversation when the prospect is already mid-journey

The reason isn't magic. When your outreach references something that actually just happened at a prospect's company, it doesn't read like spam. It reads like a useful signal. People reply to things that feel relevant.

The Cost-Per-Opportunity Math Works Out

Here's a counterintuitive thing about signal-based prospecting: even though the tools are more expensive than a basic Apollo subscription, the cost per opportunity often goes down.

Why? Because reps aren't burning sequences on accounts that will never respond. Because you need fewer touches to get a reply. Because meetings actually show because the prospect agreed with context and genuine interest rather than just clicking "accept" out of curiosity.

A rep working a signal-based queue of 50 accounts per week books more meetings than a rep cold-blasting 500. That math holds up at scale, and it's one reason why more revenue leaders are making this shift even when it requires new tooling investment.

What Enterprise Teams Have Proven

Companies like Gong, HubSpot, and the former Drift (now part of Salesloft) have each published data showing that accounts displaying intent signals convert at 3–5x the rate of their non-signaling counterparts. That's not a marginal improvement. That's a fundamentally different pipeline quality.

For teams also wrestling with the AI SDR vs human SDR question, signal-based systems keep coming up as the common denominator. The AI SDR setups performing best aren't just blasting sequences; they're running signal logic on top of an ICP filter and using AI to handle the personalization at scale. The signal layer is what makes the automation actually work.

The Mistakes That Kill Signal-Based Results

You can do all of this and still get mediocre results. Here's where teams go wrong.

Mistake 1: Using Signals as a Fancier Targeting List

The most common failure mode: a team buys intent data, filters for accounts showing surge scores, and loads all of them into a generic sequence. Same message, just a "better" list.

That's not signal-based prospecting. That's list-based prospecting with a more expensive data source.

The signal has to change the message. Every outreach should be tied to the specific event that triggered it, not a generic intro to your product. If the message could have been sent to anyone, you wasted the signal.

Mistake 2: Sitting on Signals Too Long

Signals decay. A funding announcement you saw two weeks ago has already been acted on by a dozen other SDRs. A hiring surge that peaked in January is less relevant in March. The window for a leadership change signal is roughly the first 30 days.

Build a system where signals surface within 24–48 hours and reps have a clear SLA to act on them. The teams winning with this methodology are the ones who move fast not the ones who batch signals into a weekly review.

Mistake 3: Mentioning the Signal Without Doing Anything With It

"I saw you raised a Series B congrats! I'd love to tell you about [product]."

That's not a signal-based message. That's a name-drop with a pitch stapled to it. Prospects see through it instantly.

The signal has to connect to something real. Why does that event create a need you can solve? What does that hiring push tell you about the problem they're trying to fix? Answer that question in your message, and you'll be in the top 5% of outreach that prospects actually engage with.

Mistake 4: Tracking Only One Signal Type

Teams that rely exclusively on intent data miss hiring signals. Teams that only watch LinkedIn miss funding. Single-signal systems have obvious blind spots and they mean you're only ever seeing part of the picture.

You don't need to track everything. But stacking 3–4 complementary signal types gives you meaningful coverage without creating noise that overwhelms reps.

Mistake 5: No Feedback Loop to Improve the System

A lot of teams build a signal workflow, let it run, and never look at which signals are actually producing pipeline. That's leaving real performance improvement on the table.

Tag every opportunity in your CRM with the triggering signal. Review that data monthly. Kill the signal types that aren't converting. Put more resources into the ones that are. Your system should compound over time, not stay flat.

Mistake 6: Chasing Signals at the Expense of ICP Fit

A strong signal from a company that's wrong for your product is still a waste of time. When teams first discover signal data, there's a temptation to act on every alert that fires which leads to chasing accounts that would never convert regardless of timing.

Signal strength and ICP fit are two separate axes. Run both before you move anything into your active queue. Strong signal plus strong fit is your priority. Strong signal plus weak fit goes in the deprioritized bucket, full stop.

Frequently Asked Questions

1. What is signal-based prospecting?

Signal-based prospecting is a B2B sales approach where outreach is triggered by specific real-world events called buying signals rather than working through a static list. The idea is to reach out when something has actually happened at a target company that makes right now a relevant moment to start a conversation: a funding round, a leadership change, a spike in research activity, a job posting. You're not guessing at timing. You're responding to evidence.

2. What are buying signals in B2B sales?

Buying signals are observable events or behaviors that suggest a company may be in the market for a solution. They fall into several categories: financial events (funding, acquisitions), hiring activity, leadership changes, third-party intent data (research behavior), technology stack shifts, company news, and first-party engagement signals like website visits. Some signals indicate immediate buying intent; others are early-stage indicators that warrant lighter outreach or a nurture sequence.

3. How is signal-based prospecting different from intent-based prospecting?

Intent-based prospecting is specifically about third-party behavioral data tools like Bombora that track anonymous research activity and surface companies showing elevated interest in a topic. Signal-based prospecting is broader. Intent data is one input among many, alongside hiring signals, leadership changes, funding events, tech stack signals, and first-party engagement data. If intent-based prospecting is a single instrument, signal-based prospecting is the full orchestra.

4. What tools do I need to get started?

You don't need an expensive stack to start. At minimum: LinkedIn Sales Navigator for personnel and company signals, Google Alerts or Crunchbase for funding and news, and whatever CRM or sequencing tool you're already using to execute outreach. That's a functional starting point. Add intent data (Bombora or 6sense) and website visitor identification (Clearbit or RB2B) once you've validated the basic model is working.

5. How many signal types should I track?

Start with two or three that are most relevant to how your best customers typically come to market. More signals sound appealing, but they create noise fast and confused reps default to ignoring the queue entirely. Validate that your initial signals are producing a real pipeline before you expand the system.

6. Does this work for small or solo sales teams?

It actually works especially well for small teams. When you're a team of two SDRs, you can't afford to burn time on accounts that aren't ready. A focused signal queue of 30–40 accounts per week all with a real reason to reach out today will consistently outperform a 500-contact blast for a team your size. You just need to be disciplined about working the queue instead of reverting to list-building.

7. How do I measure whether it's working?

Track four things: reply rate, meeting booked rate, signal-to-opportunity conversion rate, and average deal cycle length. The important thing is to segment all of these by signal type. That's how you figure out which signals are actually predictive for your product versus which ones are just noise you've been acting on.

8. How does AI fit into signal-based prospecting?

In 2026, AI will become the execution layer for signal-based systems at scale. The best setups use AI to monitor signals across sources, score accounts automatically, and draft personalized first-touch messages based on signal context so reps start their day with a queue of ready-to-send outreach rather than spending the morning doing research. For context on when to automate versus keep humans in the loop, our AI SDR vs human SDR breakdown covers the tradeoffs honestly.

Conclusion

There's nothing revolutionary about the underlying idea here. Reach out when something relevant just happened. Don't reach out when nothing has. Sales reps have always known this instinctively. The problem was they had no way to know when the relevant thing happened at 500 accounts simultaneously.

Signal-based prospecting solves the infrastructure problem. It gives you a systematic way to monitor the market, identify which accounts are in motion right now, and reach out with a message that reflects what you actually know about what they're going through.

The teams doing this well in 2026 aren't special. They just built a better system than their competitors. They know which signals matter for their ICP. They act on those signals within 48 hours. They measure what converts and keep refining.

You don't have to rebuild everything at once. Pick one signal type funding, or job postings, or website visits and spend two weeks being disciplined about acting on it with personalized, contextual outreach. Look at what happens to your reply rates. That data will tell you everything you need to know about whether to go deeper.

The signal-based model is less about tools and more about a different relationship with timing. Once that clicks, it's hard to go back to blasting lists and hoping for the best.

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