Meet Dana Lopez: Analytics Lead at Rollin’ Eats Food Trucks

We sat down with Dana Lopez, who’s been in marketing analytics for over 7 years, mostly in the restaurant and food-truck scene. Having helped food-truck fleets grow from a handful of trucks to over 300 nationwide, Dana brings a practical, hands-on approach to cross-channel analytics—especially when things get complex at scale.

What’s the biggest challenge for an entry-level marketer handling cross-channel analytics in a large, restaurant-focused company?

Dana: The sheer volume and variety of data sources can be overwhelming. When you’re marketing for a single food truck, tracking sales and social media is fairly straightforward. But with 500 to 5,000 employees and dozens or hundreds of trucks, you’re suddenly juggling data from websites, multiple social platforms, in-app orders, loyalty programs, customer surveys, and even offline events—like pop-ups or festivals.

One gotcha I see often: data silos. Different teams use their own tools—maybe one team reports on Instagram, another on SMS campaigns, and another on point-of-sale (POS) systems. When you scale, if these aren’t connected, your reports become inconsistent or incomplete.

How should a beginner approach organizing these multiple data streams?

Dana: First, get a clear map of where your data lives. Don’t assume you know or that it’s all in one place. Talk to your colleagues—marketing, ops, sales, even finance. For a food-truck business, you might find data coming from:

• POS systems like Square or Toast
• Social media ad managers (Facebook Ads, Instagram Insights)
• Google Analytics for your website or online ordering
• Survey tools like Zigpoll, SurveyMonkey, or Typeform for customer feedback
• Email marketing platforms like Mailchimp or Constant Contact

Once you list these, start categorizing by channel (paid social, organic social, email, offline events), then by format (numbers, text feedback, clicks, sales).

A practical tip: Use a simple spreadsheet to track source, owner, update frequency, and format. It sounds basic, but it saves headaches later.

When scaling to hundreds of trucks, what breaks first in cross-channel analytics?

Dana: Automation—or the lack of it—becomes the weak spot. When you’re a small team, manually pulling reports might be doable. But with 300 trucks, that’s hundreds or thousands of rows of data to merge and analyze every week.

A common pain point is the slow manual consolidation of reports. Someone downloads CSVs from each system, tries to clean it up in Excel, and it’s error-prone. One missed update, and suddenly your whole analysis is off.

Another issue is inconsistent tagging. If campaign tags or UTM parameters aren’t standardized, your traffic sources get mixed. For example, one truck’s Facebook campaign might have utm_source=fb, another utm_source=facebook, and a third fb_ads. When you scale, this inconsistency skews attribution.

Can you walk us through a process or tool setup for scaling automation?

Dana: Absolutely. Start simple:

1. Standardize tracking: Before data hits your systems, agree on naming conventions. This means consistent UTM parameters for every campaign across trucks.

2. Centralize data collection: Use a tool that pulls all your channels into one place. For food trucks, platforms like Google Data Studio or Looker Studio can connect to Facebook Ads, Google Analytics, and even POS exports with some setup.

3. Automate regular reports: Set up dashboards that update automatically—daily or weekly—so you don’t have to manually export and combine data.

4. Schedule routine data audits: Weekly or biweekly, check for missing values or anomalies—like a sudden drop in orders from a big event. Early discovery prevents bigger errors down the line.

A note on limitations: Some POS systems don’t have easy integrations, so you might need manual CSV uploads or use middleware tools like Zapier or Integromat. These can automate uploads but watch out for rate limits or delays.

How do you keep things simple when the team and data complexity grow?

Dana: I recommend a divide-and-conquer approach. As your team grows, assign channel ownership—someone for paid social, one for email marketing, another for offline event tracking.

Make sure every owner understands both the data flow and the marketing goals for their channel. For example, the social media lead should know which metrics matter most for trucks at specific locations—like click-to-direction or order conversions.

Also, encourage brief daily or weekly syncs. It keeps everyone aligned and surfaces data issues fast.

What’s a common misconception new marketers have about cross-channel attribution in restaurants, especially food trucks?

Dana: That it’s all about last-click attribution. If you’ve ever seen how customers find your food truck, there’s a journey—maybe they first saw an Instagram story, then a sponsored Facebook ad, and eventually clicked a Google Map link.

Relying only on last-click can undervalue the impact of earlier touchpoints—like brand awareness campaigns or email newsletters reminding customers where you’ll be that day.

The downside is that more advanced attribution models, like data-driven or time-decay, require more sophisticated tools and often more data than you have at entry level.

Can you share an example where a food-truck marketing team improved their cross-channel analytics and what that meant for growth?

Dana: Sure. One brand started with just two trucks and expanded to 100 in three years. Early on, their marketing team tracked social media and online orders separately. When they standardized UTM parameters and set up a Google Data Studio dashboard pulling data from Facebook Ads, Google Analytics, and POS, their conversion insights improved dramatically.

They discovered that their SMS campaigns outperformed email by almost 5x in prompting repeat orders. By focusing budget and messaging on SMS, they increased repeat customer conversions from 2% to 11% over six months.

The catch? They had to build trust with store managers to get timely sales data and train non-marketing staff on tagging campaigns properly.

What role can customer feedback tools play in scaling analytics?

Dana: Feedback is gold when scaling. Tools like Zigpoll, Typeform, or even social media polls can help you understand why customers respond to your campaigns.

For example, you can run quick surveys asking where they heard about today’s location, what menu items they crave, or what promotions they prefer. This data, combined with your sales and ad metrics, gives richer insights.

But be mindful of survey fatigue. Keep surveys short, no more than 2-3 questions, and rotate them across your customer base to avoid drop-offs.

When expanding teams, what should new marketers watch out for in maintaining data quality?

Dana: Training and documentation. When people join, they need to know:

• How campaign URLs must be tagged
• Where to find data dashboards and reports
• How to flag data issues

Without this, you risk inconsistent reports and wasted effort.

Also, avoid too many tools. It’s tempting to jump on every new shiny platform, but too many systems multiply confusion and errors. Pick a handful and get really good at them.

Is there a difference in approach for brick-and-mortar restaurants versus food trucks when it comes to scaling analytics?

Dana: Yes. Food trucks move—location matters. So attribution needs to include geographic data and time stamps to match marketing efforts with truck locations.

Brick-and-mortar might focus more on in-store promotions or local SEO, while food trucks often rely on real-time social updates, SMS, and geo-targeted ads to drive nearby foot traffic.

Scaling analytics for food trucks means integrating location data into your dashboards. For example, tying Facebook Ads clicks to truck GPS data for that day gives you precise ROI per truck.

If you could give one practical tip for an entry-level marketing professional stepping into cross-channel analytics at scale, what would it be?

Dana: Start by mastering one channel’s data deeply before trying to connect all channels. Build trust in your data by checking numbers, testing assumptions, and asking questions.

Once you’re confident in one area—say, paid social—you’ll be better equipped to spot inconsistencies when you start combining data sources. That foundation makes scaling less intimidating.

Remember, numbers tell a story but only if you know where they come from.

Quick Comparison Table: Tools for Cross-Channel Analytics in Food-Truck Marketing

Scaling cross-channel analytics in food trucks or large restaurant enterprises isn’t easy—but with attention to data flow, automation, and team structure, you can build a foundation that grows with your business. Start small, keep things consistent, and gradually build up your tools and skills.