Salesloft MCP Demo Server

# Customer Interview Transcript: Brian Garcia, CRO ## Salesloft + Clari Discovery Call **Date:** January 15, 2025 **Duration:** 32 minutes **Participants:** - **Sarah Chen** - Enterprise Sales Executive, Salesloft - **Marcus Rodriguez** - Solutions Consultant, Clari - **Brian Garcia** - Chief Revenue Officer, Heritage Foods Group **Company Context:** - Heritage Foods Group: $85M ARR, regional grocery operator - 340+ locations across 8 states - Category focus: Fresh & produce, cold chain logistics - Current challenges: Margin compression, operational complexity, technology fragmentation --- ## TRANSCRIPT **[0:00]** **Sarah:** Brian, thanks so much for making time today. We know you're juggling a lot right now with the Kroger-Albertsons situation and all the operational pressure it's putting on your teams. Before we dive into anything, I'm curious—what's top of mind for you right now from a revenue and profitability standpoint? **Brian:** Yeah, honestly, Sarah, it's the margins. Everyone talks about "razor-thin margins in grocery," and they're right, but it doesn't really hit until you're living it every single day. We're looking at 2.1% net margins—that's after everything. A one percent swing on shrink, spoilage, or logistics efficiency doesn't mean we optimize a process... it means we hit or miss our full-year targets. So when something lands on my desk, my first question is always: what's the ROI within 90 days? **Marcus:** That's completely fair. And I think where a lot of rev ops and forecasting tools fall short in your space is they're built for SaaS margins. They don't account for the physics of your problem—which is that a percentage-point improvement in shrink or fulfillment efficiency is actually worth millions for you. **Brian:** Exactly. We had a vendor pitch us a fancy CRM last year. Beautiful interface, AI features, the whole thing. But when we modeled it out, the implementation was 6 months, the training was 3 months after that, and the payback period was like 18 months. In grocery, 18 months is an eternity. We need to be making money on something within a quarter, or we're just not doing it. **Sarah:** So speed and tangible ROI are non-negotiable. Got it. Let's actually talk about where the complexity sits. You've got—correct me if I'm wrong—Banner-level decision-making, multiple chains under Heritage, each with different tech stacks? **Brian:** Yeah, we've got six banners. They were acquisitions over the last decade, so we inherited all their systems. One chain is on this old custom SAP implementation, another one's on a different system entirely. From a sales enablement and forecasting perspective, it's chaos. We have different CRMs, different data definitions, different sales methodologies across banners. Every single decision about a new system has to get blessed by... probably 12 to 15 different people before it actually happens. **Marcus:** Walk us through that approval process a bit. Because we see this all the time—companies with multiple divisions have multiple gatekeepers. Who are we actually talking to if we want to move something forward? **Brian:** So you'd need to start with me, but honestly, I can't greenlight anything without IT. Our CIO, David, is pretty rigid about integrations and data architecture—which I respect, but it slows things down. Then there's the VP of Sales Ops at each banner—they have veto power too. And if it touches inventory or supply chain data, you're talking to our COO, Linda. She's probably the toughest sell because she's thinking about cost, not revenue. **Sarah:** Is Linda more skeptical because of the implementation risk, or because she's just cost-focused? **Brian:** She's cost-focused, but she's not wrong. She's been burned. We tried to implement a forecasting tool five years ago that promised to optimize cold chain costs. The vendor oversold, implementation dragged, and we ended up writing off like $400K. So now Linda's like, "Show me the money before I believe it." And frankly, I don't blame her. **Marcus:** Have you guys thought about how you'd structure a proof of concept or a pilot to get her confidence? Like, what would Linda need to see? **Brian:** That's the thing—she'd want to see it work in one banner, with real shrink data, real spoilage costs, real logistics numbers. Not a model, not a projection. Actual results. The problem is, most vendors can't move that fast. They want to do a 90-day discovery before they even start a pilot. **Sarah:** But you need someone who can be operational within 30 days? **Brian:** If possible, yeah. Look, I'll be honest—we're also holding back because of the merger situation. Kroger's acquisition of Albertsons is in flux. If Albertsons gets acquired by someone else, or if the deal falls through, our entire market position changes. We could be facing direct competition from Albertsons within our footprint, or we could end up acquired by someone. So any tech investment right now is... cautious. **Marcus:** What does scenario planning look like for you right now? Are you preparing for three different futures? **Brian:** Four, actually. One: Kroger-Albertsons deal closes as-is, and we survive as an independent regional player. Two: deal breaks, Albertsons stays independent and we compete directly. Three: we get acquired by someone—Kroger, Restock, or a PE firm. Four: we end up consolidating with another regional to create scale. Each scenario has totally different IT priorities, inventory strategies, and sales org structures. **Sarah:** That's tough. So from your perspective, is there a category of tool or capability that helps you regardless of which scenario happens? **Brian:** Yeah. Anything that improves our baseline profitability and margins buys us optionality. If we're at 2.3% or 2.4% net margins instead of 2.1%, we're a more attractive acquisition target, we have more cash to survive competitive pressure, and we're more independent. So tools that directly address shrink, spoilage, logistics efficiency, or sales accuracy—those matter regardless of scenario. **Marcus:** Got it. Let's talk about the fresh and cold chain side specifically, because that's where a lot of your margin and operational complexity lives. Can you paint a picture of that operation? **Brian:** We move roughly 40% of our unit sales through fresh departments—produce, meat, deli, dairy, frozen. Cold chain is the most complex and most expensive part of our supply chain. A single degree variance in a truck or a case can mean the difference between fresh product and shrink. We've got 140 distribution points, each with its own cold chain configuration. Different equipment ages, different maintenance schedules, different spoilage rates. **Sarah:** Do you have visibility into shrink and spoilage by category and by location? **Brian:** We have data, but not good visibility. It's siloed. Our warehouse system reports inventory loss one way, store operations reports it another way, and produce managers are keeping spreadsheets. There's probably 15-20% variance in how we even define spoilage. Is a slightly softened apple shrink or a legitimate write-off? Depends who you ask. **Marcus:** So there's a data and definition problem first, before you even get to the operational problem. **Brian:** Right. We spent six months last year trying to build a unified shrink framework. Got it 80% of the way there, then realized we needed a data governance person just to maintain it. Never got funded. **Sarah:** This is actually where I think tools like Clari can add specific value for your operation. Not just in sales forecasting, but in helping you understand which products, which channels, which decisions have the biggest impact on your margin. Marcus, you've worked with other grocery and food distributors—what are we seeing on the cold chain side? **Marcus:** A few things. One, companies that have category expertise in cold chain ops usually win at shrink reduction faster than generalists. So you'd want to talk to vendors who understand the physics of your problem—not just software vendors who happen to have grocery clients. Two, most of the wins we're seeing are in the bundling: if you can tie together inventory visibility, sales forecasting, and promotional planning, you start to reduce shrink because you're not over-buying or under-buying by category. And three, temperature data integration—connecting your IoT sensors to your forecasting engine—is becoming table stakes. **Brian:** How deep do vendors typically go on that IoT side? **Marcus:** Varies wildly. Some are just pulling in historical data, not real-time. Real-time monitoring is rarer, and real-time alerting that actually connects to your supply chain decisions is even rarer. We've got a couple of implementations where vendors are flagging a cold chain deviation in a truck and automatically recommending a repricing decision at store level—sell it faster, reduce exposure to shrink. **Brian:** That's interesting. Do you have references in fresh grocery we could talk to? **Marcus:** We do. Not a lot—there are maybe 10-12 companies at scale doing this well. But yeah, we could facilitate introductions. The thing is, most of them are either PE-backed and growing fast, or they're larger publics who've built it in-house. Someone your size—regional, 8-state operation—you're actually in a sweet spot. You're big enough to have real problems, small enough to move fast on solutions. **Sarah:** Can I ask about your current sales tech and how it connects—or doesn't connect—to your operations? Because I'm hearing that you have shrink data silos, inventory silos, maybe sales forecasting silos too. **Brian:** Oh man. So we've got Salesforce for CRM at most banners—but not all. We've got Tableau for analytics. We've got SAP or variants of SAP for inventory and supply chain. We've got Slack and some folks are using Teams. There's a spreadsheet that lives on someone's hard drive that's basically the weekly revenue forecast. It's duct-taped together. **Marcus:** Who owns that revenue forecast spreadsheet? **Brian:** My VP of Revenue Operations, Amy. She's actually great—she keeps it accurate and updates it daily. But it's heroics. She's manually pulling data from three systems, reconciling it, and pushing it out. If Amy leaves, we lose the forecast for probably two weeks while someone else figures out how to rebuild it. **Sarah:** That's a pretty big operational risk. What would Amy do if she had time back? Like, what's she prevented from doing because she's buried in data plumbing? **Brian:** Good question. She'd love to do actual analytics—like, not just "what's the forecast," but "why did produce underperform this week, and what can we do about it?" She'd want to build dashboards for banner leaders so they can self-serve instead of asking her for reports. And she'd want to own sales strategy and effectiveness instead of just being a data janitor. **Marcus:** That's a common story. And usually, when a company can automate that kind of work, they find that their revenue operations people are suddenly valuable in strategic conversations instead of just firefighting. **Brian:** Amy would definitely be interested in talking to you guys. But here's the thing—Amy also reports to me, and she's trusted. If we're going to replace her tools or her process, she needs to be confident that the new tool is actually better, that it's not going to break when she takes a week of vacation, and that she's not going to spend her year managing a bad implementation. **Sarah:** So Amy needs to be part of the evaluation. She's a stakeholder with real veto power. **Brian:** Absolutely. I'd say Amy, the banner sales ops folks, David in IT, and Linda in operations—those are your four key stakeholders. You need all four to say yes. You probably lose Linda on cost, you probably gain Amy and the sales ops folks pretty quickly, David's going to want to know about data architecture and integrations, and I'm the one who has to broker peace between them. **Marcus:** Got it. Let's actually zoom out for a second. You're also competing with your own internal teams. Heritage has analysts, data folks, people who could potentially build some of this internally. How do you think about build versus buy in your environment? **Brian:** It's a huge tension. Our IT team definitely has "build it ourselves" bias. And look, they have points—they know our systems, they know our data model, they could theoretically build something optimized for us. But here's the reality: they're also trying to maintain legacy SAP implementations, keep the stores online, handle vendor integrations. They're not a software company. They're an IT operations team. If we ask them to build a real forecasting engine, we're pulling maybe one or two people off other priorities for probably 12-18 months. That's a lot of organizational opportunity cost. **Sarah:** Do you have a framework for how you think about that trade-off? **Brian:** Honestly, it's more gut-feel than framework. But basically: if it's core to our competitive advantage and it's proprietary to grocery, we build it. If it's a standard problem that lots of companies have solved, we buy it. Forecasting feels like a standard problem to me—lots of people do it. Shrink reduction specific to fresh produce and cold chain? That's maybe 60-40 buy versus build, because it's got some proprietary elements specific to our operations. **Marcus:** And TCO-wise, how do you evaluate a build versus buy scenario? **Brian:** I don't know that we do it super rigorously. We usually just add up salary costs for the people we'd need, plus infrastructure, plus opportunity cost. And if it's more than like two years of an external vendor license, we lean toward buying. But that's rough math. **Sarah:** What if a vendor could offer you a hybrid model? Like, best practices in their software, but customizable to your specific cold chain operations. Faster to implement than a full build, lower cost than traditional consulting-heavy software. **Brian:** That's interesting. That's kind of where I think the industry is going. Pure SaaS templates don't work for grocery because your operation is actually different. But pure custom builds are too expensive. So the vendors who win are probably the ones who have a platform with some configurability, who move fast on implementation, and who have reference customers in your space. **Marcus:** Do you have a sense of timeline? Like, when would this be something you'd actually want to evaluate? **Brian:** Couple things. One: the Kroger-Albertsons situation should clarify in the next four to six months. Two: we're closing the books on 2024 pretty soon, and we're building the 2025 plan. If this is something we're going to do, we'd probably want to scope it in Q1 and pilot it by Q2. So realistically, we need to be talking to people now, getting to pilots by April or May, and hopefully in production by Q4 2025. **Sarah:** That's actually a realistic timeline that works with how we move. Marcus, from a Clari perspective, what would a Q2 pilot look like for Heritage? **Marcus:** Honestly, I'd want to start narrow. Pick one banner, probably your strongest data environment, and focus on one revenue line first—maybe fresh produce or meat. Get real forecast accuracy, connect it to actual shrink reduction, measure it, and then expand. The whole thing in 12 weeks, with real results by week 8 that we can show Linda. That's how you build confidence. **Brian:** One banner is smart because it's not a company-wide change. Yeah, I could do that. Which banner would you recommend? **Marcus:** Honestly, I'd ask Amy. She knows which division has the cleanest data and the most motivated leadership. You pick the place where success is most likely, you invest there, and then you use that as a proof point for the other banners. **Brian:** Amy would probably say Heritage-branded stores in Georgia. That's where we have the newest POS system and the most data-centric store manager team. Could work. **Sarah:** So from a process perspective, here's what I'm hearing: we'd need to do a discovery call with Amy, David, and Linda. We'd scope a Georgia pilot around produce and fresh meat. We'd commit to real data and real ROI within 12 weeks. And we'd be positioned as a partnership to improve margins, not as a tech implementation. **Brian:** Yeah, that's how I'd frame it internally. But here's where I need to be careful. If I bring you in, and you pitch IT integration and data architecture, David's eyes are going to glaze over. Linda's going to ask why we're not using our internal team. And Amy's going to want to know if you're going to replace her or make her job easier. **Marcus:** All fair. So positioning matters. We should frame this as: we're bringing proven practices in shrink reduction and fresh category forecasting, we're connecting it to your existing systems so David doesn't have to rebuild the world, and we're making Amy's life easier by automating the data plumbing so she can do strategy. **Brian:** That's better. Actually, that's much better. The thing is, our industry moves slower than tech. We've watched vendors come in with promises and disappear, or overstay their welcome without delivering. So when you're talking to Linda and David, they're going to be skeptical. You need to earn trust by being realistic about timelines, honest about what you can and can't do, and focused on results. **Sarah:** What would build trust with Linda specifically? Like, beyond just pilots and ROI numbers—what does she need to believe you're different? **Brian:** She needs to meet your best customer. Not a reference customer, but someone she can call and ask hard questions to. She'd want to know what went wrong, how you fixed it, and whether that vendor would recommend you if they were starting over. Linda doesn't do references for validation—she does them for patterns. She's looking for whether you failed and hid it, or failed and fixed it. **Marcus:** We can do that. We've got food service operators and specialty grocers who've done this. Not a ton, but a couple who'd be good reference calls. We can absolutely facilitate those. **Brian:** Good. Here's another thing Linda would ask: what happens if we grow faster than you expected? Like, what if a pilot works so well that we want to roll it out to all six banners in six months? Can you scale with us, or do we hit some kind of ceiling? **Sarah:** That's a great question. Marcus, what's your honest answer on that? **Marcus:** Honest answer: we can scale pretty fast with your data and your use cases. We've done bigger rollouts. But there's a difference between pushing data through the system and getting adoption across six banner teams with different cultures. That's where you'd want to invest in change management and training. So we could technically handle six banners in six months, but I'd recommend doing it more like three banners in three months, then the next three in the subsequent three months. Gives your teams time to learn and adapt. **Brian:** That's refreshing because you're not promising something unrealistic. Most vendors would say, "Oh yeah, we'll have you running on all six banners in six months, no problem." And then you're halfway through and it's a disaster. **Sarah:** So it sounds like there's appetite here, the timing roughly works, and the key is getting the right conversations set up. What does next look like from your perspective? **Brian:** I think next is: you send me something simple—not a 50-page proposal, but like a one-pager that explains what you do, why it works for fresh grocery, and what a pilot actually looks like. I'll loop in Amy. The two of us will decide if this is worth showing to David and Linda, and if it is, we'll schedule a meeting. **Sarah:** Timeline on that? **Brian:** I can turn it around in a week. Getting everyone in a room with David and Linda—probably two weeks out, just because of calendars. **Marcus:** Perfect. And one last thing—is there anything else we should know about Heritage or your operation that we haven't talked about? **Brian:** I think the biggest thing is just understanding that we're not that different from other regionals, but we're also not Albertsons. We don't have unlimited capital to throw at problems, we don't have massive internal tech teams, and we're dealing with the same margin pressure that everybody's dealing with. So a solution that's built for scale-up problems isn't going to work for us. You need to understand that we're playing a different game. **Sarah:** Understood. And honestly, that's where we think we can win—because our platform is designed for companies that need accuracy and reliability, not necessarily infinite scale. We're built for people who can't afford to make a mistake. **Brian:** Well, that's us. We can't afford to make a mistake. **Marcus:** One last tactical thing: should we send you some warm intros to those reference customers so you can talk to them before we formally kick off? **Brian:** Yeah, that would be helpful. Let Amy filter them—she'll know what questions to ask. **Sarah:** We'll get those to you by end of week. Brian, I really appreciate your time and your transparency. This has been incredibly helpful. **Brian:** Yeah, it was good. Thanks for actually listening instead of pitching. That's rare. **[31:45]** --- ## KEY TAKEAWAYS ### Organizational & Decision-Making - **Four key stakeholders:** Brian (CRO/Champion), Amy (Revenue Ops/Day-to-day user), David (CIO/Technical gatekeeper), Linda (COO/Cost validator) - **Decision process:** Needs all four to move forward; Linda requires proof over promises due to prior bad experience - **Organizational structure:** 6 banners under Heritage, each with legacy systems; any solution needs to work across heterogeneous tech environments - **Timeline:** 2-week calendar window to get stakeholders aligned after initial validation ### Margin & ROI Drivers - **Razor-thin margins:** 2.1% net margin; 1% improvement = millions of dollars impact - **ROI expectation:** 90-day payback or business case fails - **Primary pain:** Shrink/spoilage in cold chain (40% of unit sales in fresh); currently seeing 15-20% variance in how shrink is even defined - **Secondary pain:** Manual data consolidation (Amy's weekly forecast spreadsheet is heroics, not sustainable) ### Strategic Context & Risk - **Merger uncertainty:** Kroger-Albertsons deal in flux; preparing for four different scenarios (deal closes, deal breaks, Heritage acquired, consolidation) - **Build vs. Buy framework:** ~2 years of internal cost vs. vendor license; forecasting is standard problem (lean buy), cold chain shrink optimization is 60-40 buy vs. build - **Data silos:** Salesforce (not all banners), Tableau, SAP variants, spreadsheets; no integrated revenue ops layer ### Cold Chain & Fresh Category Specifics - **Operational complexity:** 140 distribution points, different equipment ages, 1-degree variance = shrink risk - **Data governance gap:** No unified shrink framework; spent 6 months on governance effort, never got funded - **Category expertise requirement:** Vendors who understand fresh grocery cold chain physics win faster than generalists - **Real-time integration:** IoT temperature data connecting to supply chain decisions is becoming table stakes ### Positioning & Messaging - **Avoid "tech implementation" framing** → Frame as "margin improvement partnership" - **Make Amy's life easier** (not replace her) → Automation of data plumbing so she can do strategy - **Be realistic on timelines** → Vendors who overpromise on speed lose credibility - **Reference selling matters** → Linda validates through patterns, not just credentials; needs to hear about failures and fixes ### Next Steps & Timeline - **Deliverable:** One-pager (not proposal) on what we do, why it works for fresh grocery, what a pilot looks like - **Turnaround:** Brian + Amy review within 1 week - **Stakeholder meeting:** 2 weeks to calendar David + Linda - **Pilot scope:** One banner (Georgia Heritage-branded), one category (produce/fresh meat), 12-week engagement, results by week 8 - **Expansion logic:** Prove Georgia, then roll 3 banners in 3 months + next 3 banners in subsequent 3 months - **Warm intros:** Amy to filter reference customer introductions before formal kickoff ### Risk Mitigations & Objection Handlers - **Skepticism risk:** David has "build it" bias; Linda was burned 5 years ago ($400K write-off) - **Change management risk:** Six different banner cultures; need phased rollout with training, not big-bang implementation - **Integration risk:** Legacy systems across banners; need to abstract away complexity, not expose it - **Scaling risk:** Can technically handle six banners in six months, but should do 3+3 to allow adoption --- ## CALL SENTIMENT & MOMENTUM **Opening:** Warm, but guarded. Brian immediately sets expectations (ROI within 90 days, faster implementation timeline). **Middle:** Engagement increases as we address specific operational challenges (shrink data silos, cold chain complexity). Brian volunteers information about stakeholders and decision process, suggesting trust-building. **End:** Positive momentum. Brian acknowledges "Thanks for actually listening instead of pitching—that's rare." This suggests: - Vendor noise is real problem for Heritage - Active listening and transparency are differentiators - Brian is open to moving forward if positioning is right **Confidence Level:** Medium-high. Brian is genuinely interested, timeline works, and he's giving us access to stakeholders. But Linda (the COO cost-validator) is the real blocker, and we haven't won her yet. Success hinges on reference calls, realistic positioning, and pilot execution. --- ## FOLLOW-UP ACTIONS FOR SALESLOFT/CLARI TEAM 1. **Within 48 hours:** Coordinate Marcus to deliver one-pager to Sarah; Sarah sends to Brian 2. **By end of week:** Facilitate warm intros to 2-3 reference customers (food service or specialty grocery); ensure references can speak to failures + fixes 3. **Week 2:** Calendar async calls for reference validation (Amy to filter) 4. **Week 3:** Prepare for formal stakeholder meeting; develop David + Linda-specific deck (CIO data architecture + COO ROI focus) 5. **Week 4:** Georgia pilot scope document; map fresh produce + meat forecasting + shrink reduction to Heritage's POS/SAP integration points 6. **Ongoing:** Track Kroger-Albertsons deal news; adjust positioning if scenario shifts