Food away from home manufacturers don’t have a data shortage problem – if anything, the challenge is the opposite: too many inputs, too many dashboards, and not enough focus on the decisions that actually drive growth.
In a recent episode of The Food Institute Podcast, Suzanne Cwik of Tibersoft and Eric Anderson of Conagra Foodservice unpacked why foodservice marketing can feel harder than retail – and what best-in-class teams are doing to translate complexity into clarity for sales.
Retail data often flows through more standardized systems and cleaner identifiers, but foodservice operates differently. The business is fundamentally B2B2B: manufacturers sell through distributors to operators, and multiple layers sit between the brand and the end location.
That structure creates a core challenge: data is generated for billing, logistics, and trade settlement, not for marketing and sales questions like where to focus next, which operators are most likely to adopt an innovation, or what “good” looks like at the location level.
Fast-moving signals may offer helpful context, but they aren’t always transaction-verified or precise enough to guide execution. Meanwhile, verified data provides truth — but often with lag. The gap between the two is where many teams get stuck.
What a Strong Data Foundation Really Means
A strong foundation isn’t about collecting more data. It’s about building something the organization can trust and reuse.
Cwik described three essentials: accuracy with shared definitions, connection across systems, and repeatability. Operator records need to be clean and de-duplicated, products mapped consistently, and teams aligned on definitions like targets and success metrics.
That said, multiple systems aren’t the enemy – silos are. CRM, ERP, TPM, and third-party sources can coexist if they connect to a consistent operator reality. Finally, repeatable workflows matter because they turn data from a one-off project into an operating muscle that can refresh quarter after quarter without starting from scratch.
When those pieces are in place, marketing and sales stop debating whose numbers are right and start aligning around what to do next.
Why Operator Location-Level Data Is a Game Changer
Operator location-level data is where marketing stops being directional and becomes actionable. It’s the difference between saying a segment looks promising and naming the specific locations in a territory that already buy similar products, along with a clear reason the solution fits their operation.
That level of precision supports targeting and measurement, but it also strengthens distributor conversations – especially around innovation. Foodservice innovation often runs into the classic chicken-and-egg problem: distributors want proof of operator pull before slotting a new item, but operators can’t buy what isn’t stocked.
Location-level detail provides credibility, showing which operators in a distributor’s book already buy comparable products and how consistently they purchase. Even when the view is directional rather than complete, it adds specificity that market trends alone can’t.
From the manufacturer perspective, Anderson explained that Conagra treats data as a set of inputs that answer different parts of the same question: where to focus, and why. Transaction data clarifies what’s happening at the operator level, while other sources provide context around segment dynamics, menu needs, and demand signals.
The important discipline is starting with the decision, not the data. Once the “job to be done” is defined – prioritizing targets, planning a launch, supporting a sales initiative – the right inputs come into focus and the noise drops.
Avoiding Analysis Paralysis
With more data available than ever, option paralysis is a real risk. Both guests emphasized that the most common mistake is pulling data first and trying to figure out the story later. The better approach is to define the decision up front, limit inputs to the sources that truly inform that decision, and build repeatable views so teams aren’t reinventing the analysis every time.
Consistency beats complexity, especially in foodservice. Perfect data rarely changes the recommendation, but it often slows down the work. The goal is directionally right insights that are clearly communicated and easy to execute.
What a Good Marketing Handoff Looks Like
A strong marketing-to-sales handoff removes guesswork. If sales has to interpret the insight, translate the strategy, and decide what to do next, friction has already been introduced.
The best handoffs are simple and practical: a prioritized target list delivered in the tools sales already uses, paired with a short narrative that can be taken directly into an operator conversation. Sales and brokers operate in fast-moving environments and don’t have time to log into multiple dashboards or digest long decks. When outputs reflect how the field actually works, insight becomes support instead of extra work.
Practical Starting Points for Teams with Limited Resources
The recommendations: start small, define what a good-fit operator looks like, and pilot with one sales leader or broker team before scaling. Small wins build credibility quickly, and real-world feedback from the field helps refine the approach.
The underlying theme remained consistent throughout the podcast episode: teams don’t win by having more data – they win by turning complexity into clarity that sales trusts and can execute.
