In world of data for visitor attractions, language matters. Especially when it comes to the difference between a forecast, a plan, a goal and a target. Each of these terms carries a distinct meaning, a specific role in the strategy function, and a unique relationship with data.

At Dexibit, we believe that clarity in these definitions unlocks alignment, accountability, and ultimately, performance. Just as it’s important to have a best practice data model for the various domains and attributes your systems map to, it’s equally important to have the team all on the same page when it comes to metrics like these.

Let’s unpack the difference—and more importantly, how to build each of them right.

Forecast: What is likely to happen

A forecast is your best, data backed prediction of what the future holds. In visitor attractions, this means leveraging machine learning models like Dexibit’s unique models trained on historical performance, enriched with contextual variables like calendar events, school holidays, weather, marketing campaigns and informed by unified industry data only found in Dexibit. Forecasts should update regularly (ours retrain every night), using the latest available data, and are most powerful when they’re automated—taking the heavy lifting out of planning cycles, so you don’t need to remember when Easter was last year and whether it was raining.

Unlike other numbers, forecasts aren’t wishful thinking or budget lines—they’re about probability, not aspiration. They’re not what you want to happen, but what is most likely to happen.

Plan: what is assumed to happen

The plan is the backbone of the annual financial budget. It’s the operating assumption of performance that informs revenue expectations, staffing levels and resource allocations. Constructing a plan requires a balance of data and dialogue: a collaborative process between executives and operating teams, finance and marketing, visitor experience and programming. Being too bullish is exceptionally risky, being too conservative risks your chance of being exceptional.

A good plan isn’t just picking a number and backing it with justification. It involves understanding trends, integrating forecasts, reviewing event calendars (past and present), and stress testing assumptions. Plans are directional, not predictive.

Goal: What is hoped to happen

Goals serve a different purpose: inspiration. They’re the rallying cry for the team. Goals should stretch, but not snap. Set them too low and you lose momentum; too high and you risk demoralization. Finding the sweet spot of tension and reach is an art—and one that should evolve with the season, the audience, and the team’s maturity.

Goals also need to be visible. They’re about communication as much as they are about numbers—used to align and energize the people delivering the visitor experience.

Target: What is incentivized to happen

Targets bring skin in the game. These are the numbers tied to performance incentives and rewards. While they may resemble goals, targets need to be carefully constructed to avoid perverse incentives and maintain fairness.

They’re often set at the intersection of achievability and aspiration—high enough to reward outstanding performance, grounded enough to be realistically in reach. Get targets wrong, and you risk driving behavior in the wrong direction.

Constructing with care: the strategy behind the numbers

Each of these metrics — forecast, plan, goal, target — serves a different function. They should be constructed together, in concert:

• Forecasts and plans should blend data science with human judgment. Use machine learning to analyze patterns and detect subtle signals, but don’t go on autopilot—context still matters.
• Plans require collaboration. Bring together departments in a consultative process. Respect the wisdom on the floor as much as the numbers on the spreadsheet.
• Goals and targets demand a motivational lens. Design for psychological impact, not just mathematical logic. Balance challenge with confidence.

Comparisons must be fair: Don’t judge forecast accuracy without considering the prediction’s timing, input data (including context) and time horizon. Compare like for like.

Pro tip for analytics in action

Encourage your planners to lean on machine learning for the grunt work of planning processes. When you use forecasts to drive planning, you reduce the burden of manual analysis which means you can stay agile to change. Then, let machine learning be the voice of reason to call out when the gap between dreams and reality is widening. A plan that stays static as your year goes south is a recipe for disaster.

On a daily, weekly and monthly basis, compare actuals to forecasts and plans, so you can detect early if you’re tracking off course and need to reproject. To do this, layer actuals with forecasts, plans, goals and targets into performance dashboards, to give your team clarity and purpose. This is one of the simplest ways to make performance transparent, strategic and actionable.