As a dedicated Google Local Guide, I’ve devoted considerable time over the past two years to documenting mainly my city of London through photo contributions on Google Maps. The journey so far has been fascinating, from achieving early guide milestones to learning the ever-shifting nuances of the maps platform’s algorithms.
Throughout this experience, I’ve strived to capture locations from a variety of unique perspectives and angles to best showcase what makes each place noteworthy. Capturing the essence of a destination in a single, impactful photo is a constant challenge yet greatly rewarding when an upload resonates and finds an engaged audience.
Of course, as contributions increase exponentially, it’s natural to reflect on tactics and wonder how strategies could evolve. To gain fresh insights, I decided to conduct an evaluation of my highest performing photos. Specifically, I wanted to understand patterns affecting long term visibility and placements over time on Maps.
With over 35,000 photos now uploaded showcasing diverse neighborhoods, attractions, cuisine and culture, the dataset provided a decent opportunity for comprehensive self-analysis.
The objective was to determine several key performance indicators for photos amassing over 1 million views, including: current ranked placement, total views accrued to date, and date of initial contribution.
As there are no easy tools, I exported a list directly from Google Maps containing all photos uploaded by my Local Guide profile that had accrued over 1 million total views. Basically I sorted my photos by views, scrolled all the way photos above 1m views, copied all the text into a file and used ChatGPT to sort into nicer file…
From there, I developed a simple tracking spreadsheet to capture key fields for analysis: venue name, address, total views to date, current placement rank, and some place for comment. This allowed sorting and filtering data in useful ways.
I was surprised to see I got now 331 such photos - more than expected.
So something of this style:
Name | Address | Views | #1 |
---|---|---|---|
Burrell St Sexual Health Clinic | 4-5 Burrell St, London SE1 0UN | 10,363,363 | Yes |
Beijing Dumpling | 23 Lisle St, London WC2H 7BA | 8,044,209 | No |
Zafferano | 16-18 Lowndes St, London SW1X 9EY | 7,233,110 | Yes |
Dozo Soho | 32 Old Compton St, London W1D 4TP | 7,181,068 | Yes |
Imperial China | 25a White Bear Yard, Lisle St, London WC2H 7BA | 7,013,534 | No |
Patty&Bun - Liverpool Street | 22-24 Liverpool St, London EC2M 7PD | 6,612,689 | Yes |
G-A-Y Bar | 30 Old Compton St, London W1D 4UR | 6,416,979 | No |
Soletrader | 264 Oxford St, London W1C 1DP | 6,206,725 | Yes |
New China | 48 Gerrard St, London W1D 5QL | 5,867,946 | No |
The auditing portion proved quite time intensive, requiring manually verifying placement details for each photo directly within Maps.
For every photo, I would zoom on the location and “Photos” to note its current ranked placement. If no longer in the top spot, I recorded which guide’s photo or media type had replaced it.
Photos do change position naturally all the time as views are added (to some of them) so finishing this check needs to be quick or you will need to keep searching for photo on the sheet.
After about 200, I realized the conclusions were quite clear and I could stop the check and focus on this sample.
Some insights:
Almost all the photos are external and not inside location.
While I typically photo mostly as I walk around locations, I also have plenty of photos contributions from visiting inside location and reviews including food and products. Still, none of these was in the top featured photos.
I found that 51% remained in the top ranked #1 media slot for their respective venues.
As gaining over 1m viewed takes time (most of these been there over a year), seems that knocking off a successful top featured photo does not happen quickly or easily - once the algorithm picks a favorite, it tends to stick by it for a while.
While I’m happy to get the photo views, I wonder what it means - places do change (parks with seasons, shop window displays with seasons and fashion) etc.
However, checking similar exercise I did a year ago, it was notable that some photos had achieved the top spot back after being replaced temporarily by others’ higher rated content. This suggested placement is dynamic based on continual uploads.
Among photos no longer ranked #1, approximately 25% fell into recurring patterns:
- I had uploaded a better performing photo myself, nudging the original from primacy
- An immersive view or video uploaded had surpassed photos (not another photo)
In some of these cases, my photo was still there just not placed in #1 place.
This revealed the fluid, competitive environment for gaining exposure on popular locations.Placement evolves as all contributors strive to engage users.
Perhaps most interesting was the recurring trend of immersive views and videos displacing static photographs over time.
This aligns with platform shifts favoring interactive and long formats, impacting long-term photo visibility regardless of view counts accumulated.
Going forward, successfully innovating content types will be key to maintaining relevance and engagement on Maps. While photos remain valuable, diversifying format is now essential strategy for guided visibility.
Constant re-evaluation of performance metrics provides opportunity to evolve our practices in step with changing algorithms and consumer preferences.