As a follow-up to our blog about solving ad attribution in the hospitality industry, this serves as a part 2. For Destination Marketing Organizations (DMOs), there’s a further step in the process of attribution that defines their success. This step connects their efforts to the bottom-line that drives their funding on multiple levels: economic impact.

If part 1 taught us anything about how difficult attribution is in a normal hospitality digital funnel, it’s that this exercise is a step closer to impossible.

Normal Attribution Funnels in Digital

In digital marketing and advertising there are a few normal digital funnels. They can be defined as “standard” categories from which most digital strategies are built. Pretty much any experienced digital strategist will tell you that each of these has about a 90%-built playbook with some fill-in-the-blank discovery exercises we all use to make sure unique business objectives and sales pipelines are accounted for.

1. Ecommerce: The Bread-and-Butter Digital Funnel

E-commerce is the simplest funnel (in most cases) for digital advertisers. The reason being, we can calculate the real top-line impact of our advertising dollars directly to revenue, using Return on Ad Spend (ROAS) as our operator. From there, many businesses will expand out to Cost per Acquisition (CAC), and/or a fully-fledged Return on Investment calculation, either of which will factor in various operational costs like staff, management, and cost of goods sold. But ultimately, that friendly top-line ROAS number which connects the “purchase” click up to the ad click is where we live, and how we thrive as digital strategists in this funnel.

2. Lead Generation: The Variable but Tested Funnel

In businesses that have longer sales cycles, direct outreach, or variable experiences on offer, the funnel from digital advertising typically ends with a form fill. Determining return form this can require a connection into CRMs (if available) or manual calculations based on close rates and other variables in the sales cycle. But ultimately as long as there are consistent variables to work with, a version of ROAS can be built and utilized as a performance benchmark.

3. Referral: The Tech-Stack Dependent Funnel

This is effectively where hospitality sits, aside from the biggest brands. The tracking methods we’re forced to use with 3rd-party booking engines place most of the industry in the bucket of needing to model attribution off referral techniques, which we discussed in detail in part 1.

4. Brick & Mortar: The Blurry, Reactive Digital Funnel

When we say “reactive” in this context, we mean that the data comes in over time, rather than being able to measure in real time. With brick-and-mortar digital marketing, we’re sending people to a physical location where decisions can be made completely outside the data-recording digital environment. We use things like promo codes, device offers, and pure “where’d you hear about us” customer service interactions to help with this. Ultimately we need weeks or months of data to see if there are performance ripples in revenue at a physical location before we can understand if our marketing efforts impacted the top (or bottom) line.

5. Economic Impact: The (Near) Impossible Digital Funnel

Here’s the challenge: Take the data gaps in funnels 2, 3, and 4, and apply each of them to a single business type. Then, solve for “return.” 

Why Economic Impact is So Hard to Calculate

Destination marketers face a dual challenge: first, bridging the long-standing data gap within the hospitality industry itself, and second, spanning an even wider divide to trace how those hospitality bookings directly influence on-the-ground consumer spending.

But, First: Why Does this Matter?

For DMOs, funding is a measure of economic impact. To oversimplify, If a government entity finds that a DMO is generating tax revenue within their region, funding is a simple matter of ask and receive. But if a DMO can’t prove its value with real data, those governing bodies may be less willing or able to fund marketing efforts, regional programming, and ultimately jobs within the organization. A well-informed data-driven DMO benefits not only from data-based strategic decisions, but also the increasing value in real numbers during budget discussions.

What are the Challenges to DMOs Calculating Economic Impact

1. Disconnected Data At the top of the funnel, as we mentioned earlier, there’s already some disconnection that would normally help us validate our marketing efforts against Hotel Occupancy tax data, which is a  major piece of the economic impact puzzle, and the starting point for DMO success metrics. Part 1 addresses how we do this. At scale when we look at a DMO, we multiply that effort out to however many properties within a region, which can vary from tens to hundreds of properties.

2. Brick & Mortar Spending Academics from all over the world have spent years refining and debating how we calculate economic impact regardless of the purpose. On top of that, we have the same issue we discussed in modeling for attribution in brick and mortar earlier. How do we track the dollars spent on the ground back up to our marketing and advertising spend in the digital environment?

3. Data Privacy Compliance Skipping forward a few steps, meaning this piece assumes we’ve already solved for the first and second challenge, we run into data privacy issues. Rightfully so, credit card companies, payment portals, and by extension the vendors attached to them, aren’t quick to give away data. Legality, morality, and risk are major blockers to understanding who spent which dollars on the ground in a DMO’s region. So then, beyond the attribution gap between spending on the ground and digital advertising, how can we prove that dollars spent were from the people the DMO brought in vs. regular residents?

It may not surprise you, if you’ve read part 1, but the answer to solving these problems is math.

So How Do DMOs Solve for Economic Impact Attribution?

There are 3 steps we take in solving for the challenges above, after we’ve already solved for attribution within the hospitality industry itself

1. Securing Data

Financial institutions and aggregators (like credit card networks or payment processors) cannot legally or ethically hand over raw transaction logs to a DMO or an agency. So the first step is removing the “individual” from the data. Organizations can run queries inside a secure, encrypted sandbox. By utilizing Privacy-Enhancing Technologies (PETs), transaction data can be aggregated, anonymized, and matched against digital audience segments without a single human ever seeing personally identifiable information (PII). While it was listed as the third challenge, addressing it first creates a lot of flexibility in how we can attribute spend on the ground within a DMO’s region.

2. Separating the Locals

We love our residents within DMO regions, but for the sake of our marketing efforts we need to exclude them. A lifetime resident may fluctuate their spend, but they aren’t likely to introduce new money to the region the way tourists will. It’s also worth noting, that if a visitor stays with a local, they spend 40-50% less per day than if someone stays at a hotel.

3. Building the Bridge

This is where payment processors, credit card networks, and financial aggregators come in. After passing the data through our security measures we apply a robust probabilistic model to the data, accounting for multiple factors:

• Seasonality & Baseline: the model is constantly learning from the last 12-18 months of data, establishing a baseline that allows us to see extra revenue beyond what would be expected from locals or regular visitors.
• Average Daily Rate (ADR) Fluctuations: Shifting room rates impacts the tax line and also can have a minor impact on spending behaviors. This must be accounted for in addition to the baseline.
• Booking-to-Stay Lag: DMO marketing campaigns begin planting the seed 8-12 weeks before a trip in many cases. Understanding what the lag in economic impact on the ground and booking data is helps factor in revenue spikes and dips against the baseline.

At the end of all that math, we land on a single, tactile output: A probabilistically modeled economic return range. We then use this range, an effective “revenue” number, to calculate return on investment all the way back up at the top where our ad spend and marketing dollars were used months prior to inspire people to stay, play, and spend.

The Brand Leader’s Ultimate DMO Payoff

By combining privacy-compliant financial data with probabilistic attribution models, the The Brand Leader transforms a DMO’s data into a value proposition, offering leverage in both strategic initiatives and budget discussions:

• After clarifying metrics, personalizing to the region, and building the full ad strategy, a DMO rep can walk into funding meetings and say “For every $1 of funding allocated to digital marketing, our model estimates that our region earned $x in on-the-ground merchant spending. Adding that to the impact on Hotel Occupancy Tax creates a full picture that can inform stakeholders on every level of DMO engagement.
• When return is calculable, it informs real-time strategic thinking around economic programming, events, and helps frame the landscape of what drives the highest value impacts on the regional economy.