Better visualizations enable better—and faster—decision-making. After spending hundreds of hours in client data, we’ve developed some charts to help us quickly understand what’s happening and what adjustments we should make. These are a few of them.
Find Data Visualizations Faster:
Traffic
- In-Market vs. Out-of-Market Traffic
- Paid vs. Non-Paid Traffic
- Traffic (Or Guests or Revenue) By Location Band
- Day/Time Heatmaps To Show When Your Site Is Busiest
- Conversion Rate Heatmap
Revenue
- Multi-Year Traffic or Revenue
- Share of Revenue By Source
- Revenue Or Guest Growth By Channel
- Guest Count or Revenue By Zip Code
- Revenue vs. Projections
- Timeline Charts For Tracking Revenue-Impacting Events
- Table Sortable By Gains and Losses
- Executive Summary for Multi-Location or Multi-Product Companies
- Revenue Change By Source and Category
Growth
Bookings
- Simple Daily Charts
- Unsold Capacity Value
- How Are Future Bookings Trending?
- Direct vs. Indirect Cost of Acquisition
Conversion Rate
Reviews
Advertising
In-Market vs. Out-of-Market Traffic | 100% Stacked Area Chart
What percent of website visitors are visiting while they’re inside our market vs. outside our market? Or what percent of revenue comes from people in those different areas? Understanding what percent of our visitors are shopping before or during their trip—and how that changes over time—can help us tailor our messaging on the website and in ads. It can also be a potential indicator of gain or loss of local search visibility.
How To Create It
Determine which regions are in-market for you. In Google Data Studio, in your Google Analytics data source, create a custom field that assigns a value of In Market or Out of Market based on the region the traffic came from. Use that field as the breakdown dimension in your 100% stacked area chart.
Bonus: Conversion Rate, $/user, $/transaction, and More
Once you’ve defined your market location dimension, you can use it for additional reports.
Multi-Year Traffic or Revenue | Line Charts With Breakdown Dimensions
Typical charts provide year-over-year comparisons, but what if last year isn’t a good baseline? For many of our traffic and revenue charts, we’ve switched to 4-year versions. It’s also possible to remove years that aren’t relevant, if you want to get rid of the noise.
How To Create It
When you create your line chart, choose a 4-year time span. Use month as the date dimension and year as the breakdown dimension. Turn off any date comparisons, since you’re creating your own comparison.
Unsold Capacity Value | Heatmap
We previously showed how to create a heatmap showing capacity utilization. While this is helpful, it can create some false positives or false negatives, if capacity was low for a given time slot. For example, if we opened just 10 seats in all of August for the Sunday 9 am time slots and filled 6 of them, it looks like we have a big problem on Sunday mornings; but we may have left 10x the revenue on the table during the 11 am Saturday slots, where we filled 80% of seats but had significantly more capacity.
For this reason, we also visualize unsold capacity value (or, unrecognized revenue).
How To Create It
See our tutorial on Visualizing Tour Capacity. Add another field to your underlying data that multiplies the unsold seats by the average seat price. In your existing pivot table or a new one, use that new field.
Paid vs. Non-Paid Traffic | 100% Stacked Area Chart
How dependent are you on paid traffic?
How To Create It
In Google Data Studio, in your Google Analytics data source, create a custom field that assigns a value of either Paid or Non-Paid based on the default channel grouping. Use this as the breakdown dimension in your 100% stacked area chart.
Traffic (Or Guests or Revenue) By Location Band | Stacked Area Chart
Geographic reports in Google Analytics can be noisy. For many tourism clients, Region is too broad, but City is too detailed. That’s where location banding is helpful. We can then look at this data in a stacked area chart as shown here, but we can also look at things like conversion rate by market location.
How To Create It
Create a custom field called Market Location in Looker Studio, based on city and region fields.
How Are Future Bookings Trending?
It’s possible to visualize the state of this year’s bookings vs. last year’s. In this example, we’re looking at guest count from bookings created in January – May, and we can see that not only was May a good month, but we’re also trending ahead for most of the upcoming months as well. You could look at revenue in addition to guest count, and if you offer multiple experiences, you could filter by experience.
How To Create It
Your data source (a spreadsheet in our case) needs to include the date of the booking and the date of the experience. Limit the chart to show only bookings created within the selected date range, but use the experience date as the dimension of the chart.
Growth vs. Previous Year | Line Chart
A regular line chart can show growth or decline from the previous year (top right), but it can be helpful to also have a chart (bottom) that displays the growth rate alone.
How To Create It
We use a Google Sheets workbook with 2 sheets:
- Sheet 1: The detailed transactions or bookings report
- Sheet 2: Date, this year’s revenue, last year’s revenue—calculated from Sheet 1.
At that point, it’s easy to create a custom field in Looker Studio called Growth.
Share of Revenue By Source
Our clients often want to become less dependent on 3rd party bookings to improve their margin and not be subject to sudden changes in a platform (ever dropped far down on an important Tripadvisor page?). This split is simple to visualize.
How To Create It
Booking platforms each have their own way of displaying source. We typically create a calculated spreadsheet column called Source Group, to ‘roll up’ the sources into Direct or 3rd Party groups.
Pivot Table Heatmaps
Pivot tables allow you to see many combinations of 2 dimensions. This example shows conversion rate by location and device category (for example, our Florida location on desktop, tablet, and mobile). You could also look at revenue by tour and revenue source (3rd party vs. online vs. in-office) or a number of other possibilities.
How To Create It
The “pivot table with heatmap” in Looker Studio works well for this, but this is easily created in Excel or Google Sheets too, using a pivot table with conditional formatting.
Direct vs. Indirect Cost of Acquisition
Tracking your cost of acquisition by channel (particularly direct vs. affiliate) can keep things in perspective as you increase your marketing investment to drive direct bookings.
How To Create It
To generate this report, we merged Fareharbor booking data (utilizing some of the affiliate fields), digital ad spend, and marketing labor costs.
Revenue Or Guest Growth By Channel
Look beyond overall revenue to see which channels are growing or declining.
How To Create It
Sheet 1: A bookings list with a custom field that assigns a value of Direct – Online, Direct – Offline, or Affiliate, based on the booking platform’s Source field.
Sheet 2:
- Column A: Date
- Column B: Channel Group (create 1 row per channel group, per day. In our example, each day then has 3 rows)
- Column C: Revenue This Year. We use a SUMIF formula.
- Column D: Revenue Last Year. Another SUMIF formula. (Watch out for leap days; a workaround is required.)
Sheet 2 becomes the data source, and Growth is a custom field based on Revenue This Year and Revenue Last Year.
Review Velocity | Time Series Combo Chart
If you’re trying to get more reviews, you’ll want to track the percentage of guests that leave reviews.
How To Create It
Use a blend in Looker Studio to merge booking data (which we store in Google Sheets) with Google Business Profile data (which we get via a data connector). Divide review count by guest count.
Guest Count or Revenue By Zip Code | Bubble Map
If your booking data or waiver data contains a combination of zip codes and guest count, you can quickly create a map of that data. This can be useful in identifying areas where you may need additional marketing. This example uses a bubble map to show guest count by zip code, color-coded by business location for this multi-location company.
How To Create It
Use Excel’s 3D Maps feature. Try different options until you get the proper level of detail for your data set.
Revenue vs. Projections
If you have monthly revenue targets, it can be helpful to visualize your target and your progress. In this example, we also included a comparison to last year.
How To Create It
We use the same technique describe for other growth metrics shown in this post, but with the addition of projected revenue.
Sheet 1: Booking data (we export all bookings into Google Sheets monthly)
Sheet 2: Projection data by month
Sheet 3:
- Column A: Date
- Column B: Revenue This Year (using SUMIF)
- Column C: Revenue Last Year (using SUMIF)
- Column D: Projected Revenue (using SUMIF or XLOOKUP)
The comparison metrics can then be created as custom fields in Looker Studio.
Pro Tips:
- This can be accomplished using a data blend instead
- Watch out for leap days when calculating the previous year’s data compared to a leap day.
KPI Gauges
Once you’ve decided on your KPIs, gauges can be a great way to keep an eye on your progress.
How To Create It
The gauge we used here is available as a Community Visualization in Looker Studio. Showing standard metrics is straightforward, but showing growth rates—such as the web revenue growth rate shown here—requires some extra work as mentioned previously in this article. In this case, none of the data requires manual exports from a booking platform, so the data auto-updates every day.
Simple Daily Charts
For many of our data visualizations, we’re looking at weekly or monthly intervals, because daily data is quite volatile and noisy. However, it does have its uses. This example shows a part of our page of daily GA4 data, which we use often, just to see how things are trending or whether there are any issues—such as after we’ve made a change to the website, booking engine, or measurement.
How To Create It
Simple column chart in Looker Studio, Google Sheets, or Excel. We use Looker Studio for this, with the standard Google Analytics connector.
Ad Spend and ROAS History By Week and Year
Ad spend should be considered on a weekly basis, not just a monthly basis. This is especially true around holidays and shoulder season. Plan your upcoming weekly ad spend and monitor performance vs. previous years by using a pivot table showing spend and ROAS by ISO Week and Year.
Tracking Revenue-Impacting Events | Timeline Charts
A timeline chart can be helpful for tracking what occurred over a given period, such as promotions and closures. Lining up the timeline chart with your revenue or guest count can sometimes help explain and interpret some of your data.
How To Create It
The timeline chart below is a fairly new chart type in Looker Studio. We used a Google Sheet as the data source and color-coded the events by category. Each item can have a tooltip with more detail as well, which can also come from the spreadsheet.
Day/Time Heatmaps To Show When Your Site Is Busiest
When should you have staff available to answer questions, particularly if you encourage customers to contact you via chat or phone? A heatmap such as this one will show you when visitors are on your website. You could even filter this report by page so that you can see when people tend to be on pages targeting groups vs. individuals and families. This example shows that visitors are on the site on weekends more than on weekdays and that many visitors are browsing until 8-9 pm.
Percent of Visitors Reached
How do you assess whether your marketing efforts are working when the visitor count in your area varies so much? If your local tourism authority publishes visitor numbers, calculate the percentage of visitors that book with you, and aim to grow that number over time.
Table Sortable By Gains and Losses
These basic-looking tables have a couple of tricks up their sleeves. First, they are displaying affiliates that wouldn’t normally be showing up in this month’s report because they ‘fell off’ the report this month by having no revenue. Secondly, we can sort by gains and losses. How are these useful? In this example, notice that Affiliate Z created over $18k in revenue for us last year, but none this year. That’s normally quite difficult to spot. And Affiliate M brought in almost $21k this month, but even more notable is the fact that it brought in more than twice that much a year ago. These are actionable insights for the sales team.
Cart Abandonment By Item
If your booking platform sends properly formatted e-commerce events, such as add_to_cart events, to Google Analytics, you can create a report that shows which items are more or less likely to be abandoned. The report below isn’t a true abandonment report, but it’s close enough to provide useful data, and it’s fairly easy to create. This can help you take targeted action as needed rather than simply looking at your cart abandonment rate in aggregate.
Executive Summary for Multi-Location or Multi-Product Companies
A high-level summary of location performance can help busy multi-location managers identify how each company is performing on key metrics.
Conversion Rate | Heatmap
A pivot table showing traffic channels and landing pages, utilizing conditional formatting, can show particularly strong or weak performance, and help inform decisions about where to increase or decrease paid traffic (for example). To cut through the noise, we highlight high or low conversion rates only when the session count reaches a specified threshold.
Revenue Change By Source and Category
Where did you see the biggest wins and losses? Combining booking source and item category (if your items list gets too noisy or item names have changed over time) provides an easy way to see one level deeper than your total booking revenue.
Funnel
Funnel visualizations are common in marketing for a variety of things such as the ecommerce process. In this example, we’re using data from Meta Ads to show how easily people progress from one step (such as clicking an ad) to another. Look for friction points identified by the funnel, implement improvements, and revisit the funnel to see whether the modifications had a positive impact.
