Thankfully, we can turn to our secret weapon, Python, to remove noise from our photos. While it’s not well documented, Python has some incredibly powerful image processing libraries. With a proper understanding of the algorithms, we can use Python to remove nearly all the noise from even the grainiest of photos. And to put our money where our mouth is, we’re going to put our Python script up against Adobe Lightroom to see which one can better remove noise from photos.

The Problem with Noise in Low Light Photos

No matter your skill level, whenever you head out to take photos in low light, you probably dream of coming home with a photo that looks like this.

Instead, you come home with a monstrosity like this.

Despite these two pictures being taken with the same camera less than 20 minutes from each other, why did the first one come out so much better than the first? Yes, post-processing does play a small role in it, but the main culprit is the camera settings and the photo composition. No amount of post-processing can bring back lost data in a photo that’s incorrectly composed. Because the second photo is not correctly composed or exposed, much of the data in the bottom half of the frame is lost.

Let’s compare the two photos.

Poor Composition Leads to Noise in Photos

From the photo metadata, we can easily conclude that the difference between the two shots is indeed the composition. More specifically, it’s the sun angle. When you take a picture looking into the sun, it will be higher contrast than a picture that’s taken with the sun behind you. When taken to extremes, you can actually have data loss at both the dark and light ends of the spectrum.

And that’s exactly the result when you take the photo a half hour vs 15 minutes after sunset. Because the second photo looks into the sunset, being further past sunset exacerbates the increase in contrast. As a result, you need to choose whether you want to properly expose the dark land or the colorful sky. You can’t have both. On the other hand, the first photo is able to capture the full spectrum of light that’s available, resulting in the spectacular dusk colors.

What Causes Noise: A Crash Course in ISO Levels

The ISO level sets how sensitive you camera is to light. Most cameras set the ISO levels automatically by default. The more sensitive your camera is to light, the brighter your photos will be. Lower ISO levels result in sharper images, while high ISO levels lead to grain in your photos.

Exactly how much grain appears in your photos depends on your camera’s sensor size. Professional cameras with large sensors often don’t have much of an issue with grain. On the other end of the spectrum, small or entry-level cameras are much more sensitive to grain because their sensors are so much smaller. Tiny sensors are why cell phone cameras struggle so much in low light. The technology has certainly gotten better over the past five years, but it’s still far from perfect.

On a bright, sunny day, use low ISO levels to keep photos sharp and avoid overexposing them. Alternatively, use high ISO levels for low light or night photography. Under normal conditions, your ISO levels should be between 200 and 1600. However, ISO levels on some very high end cameras can reach as high as 2 million.

Even Professional Image Processors Like Adobe Lightroom Can Only Do So Much to Remove Noise from Your Photos

As powerful as Adobe Lightroom is, it has its limits. You can’t just blindly take photos in low light and expect to turn them into masterpieces with some combination of Lightroom and Photoshop. As we mentioned earlier, no amount of post-processing can recover lost data in your photos. It’s up to you to properly compose your photos and use the correct camera settings.

However, even with proper composition, professional image processors like Adobe Lightroom can only get rid of so much noise. Adobe Lightroom does a spectacular job getting rid of much of the noise in your photos, but you’ll eventually find yourself in a situation where there’s just too much noise for it to handle.

However, where Adobe Lightroom leaves off, our secret weapon takes over.

Python Has Powerful Image Processing Capabilities

It’s not well advertised, but Python has incredibly powerful image processing libraries that as a photographer, you can use to boost both your productivity and income. However, I want to caution that you should use Python as a tool to compliment Adobe Lightroom, not replace it. Being able to write your own scripts, functions, and algorithms in Python to add to the functionalities of Lightroom is incredibly powerful and will set you apart from just about every other photographer.

Indeed, I do my post processing with Adobe Lightroom. Afterwards, I use Python to format, scale, and watermark pictures that I post both to this blog and to the Matt Gove Photo website. When I used to write blog posts that had lots of pictures (and before I had Adobe), it often took me upwards of an hour or more to manually scale and watermark each image. Then I had to make sure nothing sensitive was being put on the internet in the metadata. That all can now be accomplished in just a few seconds, regardless of how many pictures I have. Furthermore, my Python script will automatically remove sensitive metadata that I don’t want out on the internet as well.

You may recall that in some of our previous Python tutorials, we have used the Python Imaging Library, or Pillow, to process photos. Today, we’ll be using the OpenCV library to remove noise from our photos.

How Python Algorithms Remove Noise From Photos

Whenever you write Python code, you should try to understand what built-in functions are doing. This will not only give you a better understanding of what your script is doing, but you will also write code that is faster and more efficient. That’s especially critical when processing large images that require lots of computing power.

Example: Removing Noise from COVID-19 Data

Before diving into our photos, let’s look at a very simple example of removing noise from a dataset. Head over to our COVID-19 dashboard and look at the time series plots of either new daily cases or new daily deaths. Without any noise removal, the plots of the raw data are messy to say the least.

To smooth the data curves and remove noise, we’ll use a moving average. For every data point on the curve, we’ll calculate the average number of new daily cases over the previous seven days. You can actually average as many days as you want, but the industry standard for COVID-19 data is seven days. We’ll plot that 7-Day Moving Average instead of the raw data. The resulting curves are much cleaner and presentable.

People use moving averages more much more than just COVID-19 data. It’s often used to smooth time series in the Stock Market, scientific research, professional sports, and much more. And we’ll use that exact same concept to remove noise in our photos.

How to Average Values with Python to Remove Noise in Photos

There are several ways to go about doing this. The easiest way is if you take several versions of the same shot, lay them on top of each other, and average the corresponding pixels in each shot. The more shots you take, the more noise will be removed. To ensure that your scene does not shift in the frame as you take the shots, use a tripod.

Mathematically, noise is random, so averaging noise pixels will effectively remove the noise. The scene that is actually in your shot does not change, so the non-noise pixels should far outnumber the noise pixels when you calculate the average. As a result, calculating the average removes the noise.

Consider the following equations. For the sake of this argument, let’s say you’re looking at just a single pixel. The actual value of the pixel in the scene is 10. However, in four of your shots, noise is introduced, and the camera records values of 4, 15, 9, and 18. Remember that the average is the sum of the values divided by the number of values.

In your first attempt, you take 10 shots of the scene. How would you do in noise removal?

average = ((6*10) + 4 + 15 + 9 + 18) / 10 = 106 / 10 = 10.6

Not bad, seeing as the actual value of the pixel should be 10. But we can do better. Instead of taking 10 shots of the scene, let’s take 100 instead.

average = ((96*10) + 4 + 15 + 9 + 18) / 100 = 10.06

That’s much better. It may not seem like much, but even just a small change in value can make a big difference for removing noise.

What Does This Method of Removing Noise From Photos Look Like in Python

Believe it or not, we can write the “stacking average” algorithm to remove noise from photos in just 12 lines of Python. We’ll use numpy for the calculations because it can natively store, calculate, and manipulate grids or matrices of data with just a line or two of code. As a result, all of the photo data will remain in the grid or matrix of pixels we’re familiar with. We don’t need to break it down into rows, columns, or anything else.

First let’s make sure you have installed numpy and OpenCV. If you haven’t, you can easily install them with pip. Open a Terminal or Command Prompt and execute the following commands.

pip3 install numpy
pip3 install opencv-python

Next, it’s time to write our Python script. Let’s start by importing everything we need. The cv2 library we’re importing is part of OpenCV.

import os
import numpy as np
import cv2

Second, tell Python which folder the image you’ll be averaging are stored in. Then list their filenames, skipping any hidden files that are in the image directory.

folder = "noise-imgs"
image_files = [f for f in os.listdir(folder) if not f.startswith('.')]

Third, open and read the first image into the average variable using OpenCV. Store pixel data as a numpy floating point number. You’ll use this variable to store image data and calculate the average of the image pixels.

path = "{}/{}".format(folder, files[0])
average = cv2.imread(path).astype(np.float)

Fourth, add all of the remaining images in the directory to you average variable. Don’t forget to skip the first image because we’ve already added it in the previous step.

for f in files[1:]:
    path = "{}/{}".format(folder, f)
    image = cv2.imread(path)
    average += image

Fifth, divide your average variable by the number of images to calculate your average value.

average /= len(image_files)

Finally, normalize the averaged image and output it to a jpeg file. The cv2.normalize() function boosts the quality, sharpens the image and ensures the colors are not dark, faded, or washed out.

output = cv2.normalize(average, None, 0, 255, cv2.NORM_MINMAX)
cv2.imwrite("output.jpg", output)

That’s it. There are only 14 lines of code. It’s one of the easiest scripts you’ll ever write.

Example: Dusk in the Oregon Pines

We’ll throw our Python algorithm a tough one to start. Let’s use a photo of a stand of pine trees in Oregon taken at dusk on a cloudy winter night. Here’s the original.

The photo is definitely grainy and really lacks that really crisp sharpness and detail. I don’t know about you, but I’d be tempted to just throw it away at first glance. However, what happens if we let our Python algorithm have a crack at removing the noise from the photo?

That looks much better! I’ll admit, the first time I ran the algorithm, I was absolutely floored at how well it worked. The detail was amazingly sharp and crisp. I unfortunately had to shrink the final image above to optimize it for the web, so the detail doesn’t appear quite as well as it does in the original. For the ultimate test, we’ll put our Python algorithm up against Adobe Lightroom’s noise removal shortly.

A Travel Photography Problem: What Happens If You Only Have a Single Shot and It’s Impossible to Recreate the Scene to Get Multiple Shots?

Good question. This is a common problem with travel photography, and is why I always encourage you to take multiple shots of things while you’re traveling. You never know when you might need them. Unfortunately, the above method really doesn’t work very well in this case. However, there are other ways to remove the noise.

We’ll use the same strategy to remove the noise as we did for the COVID-19 data. But instead of averaging over the previous seven days, we’ll average each pixel or cluster of pixels with the pixels that surround it. However, there’s a catch, here. The more layers of pixels you include in your average, the less sharp your image will be. You’ll need to play around to see what the exact balance is for your specific photo, but the OpenCV documentation recommends 21 pixels.

Thankfully, the OpenCV library has this algorithm built into it, so we don’t need to write it.

cv2.fastNlMeansDenoisingColored(source, destination, templateWindowSize, searchWindowSize, h, hColor)

• source The original image
• destination The output image. Must be same dimensions as source image.
• templateWindowSize Size in pixels of the template patch that is used to compute weights. Should be odd. Defaults to and is recommended to be 7 pixels.
• searchWindowSize Size in pixels of the window that is used to compute weighted average for given pixel. It’s value should be odd. Defaults to is recommended to be 21 pixels
• h Luminance component filter component. Bigger h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
• hColor Color component filter component. 10 should be enough to remove colored noise and do not distort colors in most images.

When we run the image through the OpenCV algorithm, it outputs the following.

The noise is certainly removed, but the image is still very dark and you can see the fuzziness around the edges. To sharpen the image back up, go back into Lightroom and find the original image. Remove as much of the noise from the original image as possible in Lightroom, and then export it. Next, average the OpenCV image and the Lightroom image using the stacking method from the previous section. That will both sharpen the image and brighten the colors.

That looks much better than the original photo. Other than a little touch-up post processing in Lightroom, that’s about as much as we can help this photo.

How Does Our Python Script Hold Up Against Adobe Lightroom’s Noise Reduction?

All right, it’s time for the ultimate test. It’s time to see how our Python algorithm does against Lightroom’s noise reduction capabilities. If you read the title of this post, you can probably guess how this went. Turns out it wasn’t even close. Our Python script blows Lightroom out of the water.

Despite the results, I want to caution you that this comparison is a bit like comparing apples to oranges. Sticking with the pine tree reference, it would be like cutting a tree down with a chainsaw vs. a hand saw. Because Lightroom only has access to the single photo, it must use the algorithm that takes a cluster of pixels and averages the pixels surrounding it to remove the noise. Do you notice how much sharper the Python image is compared to the Lightroom image? It’s because our Python algorithm has far more resources available to it to remove the noise from the photo. 63 times the resources to be exact. That’s why it’s not exactly a fair comparison.

Lightroom vs. Python Comparison on a Level Playing Field

To level the playing field, forget about averaging over multiple photos to remove the noise. Let’s say we only took one photo of the pine forest in Oregon. As a result, we can use only the single original image. We’ll process it using the same method we did for the sunset at Arches National Park in the above section. When we put it up against Lightroom this time, it’s a much closer contest. However, I still give the edge to the Python algorithm because the final image is noticeably sharper.

Want to Try the Python Script Yourself?

If you want to try out any of the Python algorithms we covered in this post, please download the scripts for from out Bitbucket repository.

Conclusion

Removing noise from photos is an endless frustration for photographers at all skill levels. To add insult to injury, high-end image processors such as Adobe Lightroom can only do so much to remove noise. However, with mathematical knowledge of how noise works, we can write an algorithm that does even better than Lightroom. And best of all, it’s only 14 lines of Python code. You can actually apply these algorithms to videos as well, but that’s a discussion for another day.

However, even though we put our algorithm up against Lightroom, we mustn’t forget that as photographers and image processors, Python must be used as a tool to complement Lightroom, not replace it. Because when we pit the two against each other, it’s only us that suffer from reduced productivity and a less effective workflow. If you’d like to boost your productivity by adding Python to your photography workflow, but don’t know where to start, please reach out to me directly or schedule a free info session today. I can’t wait to see what the power of Python can do for you.

The post How to Remove Noise from Photos with 14 Lines of Python…and Blow Lightroom Out of the Water appeared first on Matthew Gove Blog.

When used correctly, a hero image is a great way to make positive first impressions that instantly builds credibility and trust for your brand. Given the popularity of hero images, it’s no surprise that many businesses and organizations that use them often feel like they could be getting more from them. Unfortunately, when you’re dealing with graphics and images, all it takes is one minuscule misstep to send your audience running for the exits.

1. Your Hero Image is not Telling Your Story

They say a picture tells a thousand words. That’s especially true with hero images. In fact, the less text that accompanies them, the better. However, keep in mind that reducing the amount of text shifts even more of the burden to your hero image. As a result, it puts even more pressure on you to ensure everything is perfect.

Your hero image should tell your story. Without even reading the text, your audience should have a pretty good idea of as many of the following as possible.

• What you are selling or showcasing
• Personality of your brand
• Your brand’s mission and/or values

You can find some spectacularly terrible examples of web design from just a quick Google Image search. In this screenshot, can you figure out what this company does without reading any of the text?

Put aside the font and color choices for a sec. A grainy image of a couple puffy clouds tells us nothing about the company! At first glance, you’d have no idea the website was about horses unless you read the text. What makes it even worse is that after reading the first two lines of text, you still have no idea what they do. It’s not until you get to the third line that they reveal that they sell horses.

So how do they make it better? First and foremost, the hero image should have an image of one of their horses. Then add a little personality. If they’re selling show horses, put a picture of one of their horses at a show. Selling to a summer camp? How about a picture of a kid on a horse actively engaging with an instructor? Anything is better than the clouds.

2. There’s No Clear Call to Action

I’ll be the first to admit, I have been guilty of this in the past. Without a call to action, your audience has reached the end of the road. And it’s often a dead end road. With no clear indication of where to go, a small fraction of your audience will poke around your navigation menu. A few more will turn around and back up. But the vast majority of visitors will simple hit the red “X” in the corner and leave. The lack of a clear call to action is the leading cause of prospects exiting your sales funnel.

A Minor Detail Makes a Huge Difference

We can actually use on of my own websites to demonstrate the effect of the lack of a call to action. The Matt Gove Photo site uses a large hero image on the home page with links to my most recent adventures. Because the site is focused on travel and outdoor adventure, the heading and subheading reference the specific adventure and the state or country in which it’s located. Underneath, you’ll find the call to action: links where you can view photos, blog posts, videos, and more. Now, how would you react if you landed on the site and those calls to action had suddenly disappeared?

When you look at that hero image, you really want to see the rest of the photos. But without a call to action, you have nowhere to begin. You’d have to go searching through the whole photo gallery to find them. I don’t know about you, but I’m far too lazy for that. I’d have a quick scroll through the home page and then probably leave.

Thankfully, that example is purely hypothetical. If you visit the site, rest assured that the calls to action are all still there.

Amazing how such a small detail can make such a big difference, isn’t it?

Can’t Think of a Good Call to Action? Here’s What to Do.

So what should you do if you can’t think of a good call to action? Or maybe there actually is no logical path to your next step? When in doubt, give these a try. Your goal here is to keep your audience engaged, not make a sale.

• Signup form for your email list, with a free giveaway to encourage people to sign up
• An exclusive offer you won’t find anywhere else on the site
• List of references where they can learn more about you or the website’s topic
• A link to book an appointment, meeting, or phone call with you
• Links to follow you on social media

3. There are Too Many Calls to Action

On the flip side, it’s easy to get caught up and include too many calls to action. In an ideal world, the clearest call to action you can make is to only have one. Having two is okay, especially if one is a “Learn More” link. However, three is pushing it in many circumstances, unless there is a clear and logical reason for it. On the Matt Gove Photo home page, that’s the case. The photos are clearly divided into three parts.

• The turquoise waters and sandy beaches on the Nevada side of Lake Tahoe
• The rugged cliffs and deep waters on the California side
• My hike up to the top of a cliff to take aerial photos.

Under no circumstance should you have more than three calls to action associated with your hero image. Even with three, you risk overwhelming and confusing your audience with too many choices. Unfortunately, when you have too many choices, the one you make most often is simply to leave.

Let’s back up in time for a sec. We’ll go back to 2013. At the time, I had little experience when it came to web design and web development. Not surprisingly, I tried to cram way too much into the home page. To say it overwhelmed you with choices is an understatement. Not to mention I needed a few lessons in color theory.

Back in the present day, I cringe big time looking at that. You should too. But we must learn from our mistakes and experiences. My how things have changed since then.

4. Too Much Text or Copy Muffles the Effectiveness of Your Hero Image

As we discussed at the beginning, your hero image should do most of the heavy lifting for getting your message across. Keep your text to a bare minimum. It should consist of no more than:

• Main Heading
• Subheading
• Short desciption – 1 or 2 sentences
• Call(s) to Action

There is once exception to this rule when it’s okay to write more than a couple short sentences: coming soon ore pre-launch pages. The reason why? You need to be able to describe both what is coming soon as well as the benefits your audience will get once it launches. If you can do it only one sentence, more power to you. But for most of us, it takes a short paragraph.

5. The Contrast of Your Hero Image is High, so You Can’t Read the Overlaid Text

If you’ve ever tried to overlay text over any photo of the outdoors, you’ve likely run into this issue. No matter which color you choose for the text, there’s part of the image where you can’t read it. Your first thought may be to make the text multiple colors, but that never looks professional.

Let me let you in on an industry secret. Okay, it’s not really a secret, as just looking at the “Videos Coming Soon” page in the screenshot above gives it away. If you put a semi-transparent overlay on top of the image, the overlay mutes the effect of the high contrast and lets you easily read the text without having to change colors or squint at it from a weird angle. You want to find the perfect balance where the text is easy to read, but you can still clearly see what the image is behind the overlay.

For comparison, here’s the same “Videos Coming Soon” page with the semi-transparent overlay removed. Quite a difference, isn’t it?

6. Your Hero Image is too Small

There are two ways you can go with regards to size. First, the resolution of your photo may be smaller than the resolution of your screen. For making a professional, trustworthy first impression, it’s a complete disaster if that happens. Not only does such a mistake make you look like an amateur, it also looks like you just don’t care.

Now, I intentionally shrunk the image in a development environment to generate that screenshot. However, if you are dealing with very high-resolution screens (larger than 4K), you may run into an issue where your large hero image starts to adversely affect your page performance. There are a couple ways around it, both of which I employ on the Matt Gove Photo home page.

1. Use JavaScript to asynchronously load the image at the same time the rest of the page loads. In other words, you load the page without the image and the image simultaneously.
2. Use the background-size: cover CSS property to ensure that both dimensions of the hero image remain greater than or equal to the dimensions of the screen. Be aware that this can make your hero image grainy if its resolution is not optimized for larger screens.

Second, your hero image may not be taking up enough real estate on the page. In that case, you can easily argue that it’s no longer a hero image, but that’s a discussion for another day. Your hero image should take up the entire screen regardless of its size, orientation, and resolution. If your viewer’s eye is not immediately drawn to it, you’re doing it wrong.

You Can Shrink Your Hero Image to Tease What’s Below the Fold

There is one scenario where it’s perfectly okay to shrink your hero image: to tease what’s below the fold (the content you have to scroll down to see). If you have a look the next time you buy something online, you’ll find many businesses and e-commerce sites apply this strategy. I use it on my business’ website, too.

7. Your Hero Image is a Low Quality, Under or Overexposed Photo

Your hero image should be one you would frame to hang up in your home or office. People should be oohing and aahing over it. Don’t use crappy images. Ever. If your photography skills aren’t up to snuff, you should either license a photo or hire a professional photographer to take and/or process your photos for you.

8. Your Unoptimized Hero Image is Suffocating Your Website’s Load Time

When your audience logs onto your website or application, they expect it to load. Fast. If your site takes more than a few seconds to load, you can kiss your audience goodbye. They won’t wait around for it to load. And losing your audience isn’t your only worry. Search engines will punish your website if they detect it’s unnecessarily slow.

Unfortunately, we’re barreling right towards a Catch 22. Large, and often bloated, images are the #1 cause of slow websites. So how do you maintain that lightning fast load time while at the same time being able to use beautiful, high-quality hero images?

1. As we discussed a few sections ago, use JavaScript to asynchronously load the image. That way, the rest of the page loads without the extra weight of the image. At the same time, JavaScript loads the image and inserts it into the page at the same time the page is loading.
2. Save your hero image in jpeg format. Other formats, such as png, are much larger and are not optimized to be used as hero images.
3. Your hero image should not be larger than 4K resolution (3840 x 2160). This recommendation will likely change in the future, but screens larger than 4K are so rare these days, it’s not worth it yet. Consult your analytics to find out what screen sizes your audience uses. Then optimize your hero image for those screen sizes.
4. Use a separate hero image that’s optimized for mobile devices to speed up mobile users’ performance significantly.

9. You’re Using Multiple Hero Images

Hero images were designed to be used one at a time, and one per page. If one hero image is bogging down your website, imagine what several will do. In addition to the performance issues, you risk overwhelming your audience with choices if you use multiple hero images on the same page. And we all know what happens when you do that.

In addition, sliders and carousels stopped being popular in 2010. Don’t use them. Search engines have a brutally difficult time crawling them, which can have a profoundly negative effect on your search engine optimization. Most SEO and conversion experts agree that they have little use 99% of the time. In addition to bogging down your site, the statistics just don’t justify their use anymore.

10. A Hero Image May Not Be Right For You

If you feel you’ve done everything right with your hero image and still aren’t getting conversions, it may mean that hero images aren’t for you. There’s nothing wrong with that. Maybe you have home page content that is constantly being updated. Have a look at any news site out there. None of them use hero images. The same goes for certain e-commerce businesses. Amazon, Walmart, Home Depot, and Best Buy don’t use them, either.

If you don’t feel hero images right for you, don’t use them. Yes, they’re all the hype right now. And yes, they can be absolutely gorgeous. But they’re not for everyone. You’re the only one who can make the decision as to what’s best for you.

Conclusion

Well, that’s about enough butchering of my own websites as I can take. When used correctly, hero images can convert at an incredible rate and boost your credibility to levels you didn’t think possible. Unfortunately, that’s an incredibly difficult needle to thread. Hero images are astonishingly easy to screw up. I’ve been building websites since 2008 and I still find new ways to make mistakes.

However, we must continue to learn from our mistakes. Use analytics to your advantage. They’ll tell you why your hero image is not converting. Please reach out to us if you need any help. With our expertise in data science, web development, and graphic design, we’ll help you process your analytics and make sure that your hero image becomes a magnet for leads. The worst thing you can do is let it frustrate you.

Top Photo: A Desolate Road to Nowhere
Death Valley National Park, California – February, 2020

The post Does Your Website Make These 10 Mistakes with Hero Images? appeared first on Matthew Gove Blog.

Even if you’re just an average Joe planning a trip somewhere, you can use geospatial data science to get the best experience you can. You do not need to have any connections to the travel industry.

1. Plan Everything with Geospatial Analytics

Geospatial analytics are the heart and soul of any location-based data science. You don’t need to have any connection with the travel industry to take advantage of them, either. Heck, you don’t even need to have a business. Have you ever used Google Maps to get directions? What about comparing airline fares to find the best price for your next flight? If so (and I’m guessing you have), you’ve used geospatial analytics to plan your trip.

Regardless of whether you’re transporting critical supplies around the world or just planning your next vacation, your planning accounts for the same three factors.

1. Location or Route Optimization
2. Cost Optimization
3. Time Optimization

Your activities and goals will dictate how much weight each factor gets in your analysis. Here are just a few examples.

2. Perform a Sentiment Analysis with Social Media

As much as we love to hate social media, take a step back. It truly is a remarkable resource from a data science perspective. Not surprisingly, it’s a treasure trove of information for the travel industry. And that’s without having to get into any debates about privacy.

Why Do We Post So Much Content from our Trips?

Think back to the last time you took a trip. In the shadow of the COVID-19 pandemic, you likely have to go back a ways. Did you post pictures of that trip to social media? Did you blog about your experience or upload material somewhere else on the internet? You probably included data with that content, too.

• Where were you?
• What were you doing?
• Who were you doing it with?
• How was your experience?

More importantly, why did you publish that material in the first place? Aside from the business reasons, I do it because it gives me a platform to tell a story, recommend activities I enjoyed to others, and relive fond memories. Those memories became even more precious following the COVID-19 lockdowns.

What Can Businesses in the Travel Industry do with this Data?

Businesses in the travel industry are very interested in what people are posting on social media when they travel. Put yourself in a business owner’s shoes. Now look at the four questions in the previous section. How would you use that data to improve your business?

Know exactly what your clients want and offer finely tuned experiences that are specifically tailored to each client. Being able to offer each customer a truly personalized experience creates a positive feedback loop. When they have an exceptional time, they’re more likely to return and recommend you to their friends and family. New customers drive more revenue to your business. When they have a great experience, they bring in even more customers. It has the potential to snowball quickly.

Create a better community. Easily spread reviews of businesses, locations, attractions, events, and more. In an inclusive community, everybody wins. There is no better way to build a great community that than recommending that your clients patronize other businesses in your community who aren’t direct competitors.

If you run a museum, wouldn’t you want to know that there is a really incredible restaurant next door? Why not partner together and offer a package deal or some other incentive for customers to patronize both businesses? Everyone wins in that scenario.

This doesn’t even begin to scratch the surface of the what the travel industry can do with this data. However, I could easily write an entire post just on that alone.

3. Fine Tune Your Business Operations in Real Time

Geospatial data science offers incredible insights into real-time information, especially in the travel industry. Because of the modern design of networks in the travel industry, one minor hiccup can easily cause significant disruptions rippling through your company. The airline industry is particularly vulnerable to this risk.

I once sat next to a couple on a plane whose connecting flight from Phoenix to Los Angeles was canceled because of a snow storm…in Chicago. And who can forget when a middle-of-the-night power outage in Atlanta led to Delta Airlines grounding over 2,000 flights around the world over the course of three days? Delta is far from the only airline to be crippled by computer glitches.

Reduce Risk and Protect Against Losses

If there’s one thing we can learn from the airlines’ mishaps, it’s the importance of monitoring your operations in real time. The airlines are some of the best in the business at optimizing and monitoring routes in real time. Those same glitches could easily impact bus or train networks, shipping routes, and delivery operations.

Do you need more motivation to monitor your operations in real time? With a proper analysis, precisely identify the exact risks that post the greatest risk to your business. With this information, you can reduce your insurance bill and establish policies to protect your assets against loss.

Laser-Focus Your Marketing

Real-time analysis is critically important in the tourism industry. Even without social media, you’ll be able to identify both long and short-term opportunities for deals that you can offer to very specific groups of customers. Look for patterns in past customer data, and you’ll be able to anticipate their needs and serve them better.

4. Anticipate Future Trends

Mathematical models are one of the smartest and most lucrative investments in which a company can make. When you run past client data through a mathematical model, your predictive analysis will yield significant benefits.

• Identify periods of peak demand and charge higher rates during those times.
• Offer special discounts to bring in a shot of revenue when things get slow
• Precisely plan sales and promotions to maximize your profits and accomplish other goals. There is a reason Black Friday occurs when it does.

A quick side note. If you’re looking to do a predictive analysis and don’t know where to start, we are experts in mathematical modeling and would love to help you get started.

5. Offer Your Clients a Completely Personalized Experience

Offering personalized products, services, and experiences is one of the best way to set yourself apart from your competition. Most importantly, it drives customer loyalty and shows your clients that you care about them. Take the time to establish relationships with your customers. By doing so, you’ll only be able to personalize their experience more. This strategy is particularly effective in the hospitality sector, which includes the tourism side of the travel industry.

So how does data science come into all of this for the travel industry? Look no further than your own customer database. You should at a bare minimum be able to see your customers’ names, what they’ve purchased, and where they’ve purchased them. From just that data alone, the potential to personalize their experience is enormous.

• Get to know your customers by name. This can be exceptionally powerful when you can greet them by name when they enter your establishment.
• Identify your most loyal customers. Reward them with exclusive deals you don’t offer your general customer base.
• Offer customers personalized deals based on items they frequently purchase or stores they most often shop at.
• Do any of your customers own businesses? If so, consider doing business with them.
• Educate your customers with free classes and demonstrations

The more data you collect from your customers, the more personalized experience you can offer them. There’s no need to be creepy about data collection either. Ask a few questions from time to time. Give away free items of value in exchange for this data. Most importantly, let your clients maintain their own data, and be transparent about how you use the data that they give you.

6. Optimize Your Operations Through Machine Learning

If you’re unfamiliar with machine learning, it’s most commonly defined as “the field of study that gives computers the capability to learn without being explicitly programmed”. It allows computers to learn what its users interests and activities are and make decisions with little to no intervention from humans. Machine learning powers everything from self driving cars to Netflix recommendations to keeping food from spoiling.

Machine learning through data science carries an enormous potential not just in the travel industry, but across all markets and sectors.

• Decision support for business operations
• Identify and eliminate bottlenecks in your business
• Recommend products and/or services to your customers
• Identify when relationships with customers or vendors begin to sour
• Increase security, and detect fraud quicker and easier
• Conduct better market research
• Use dynamic pricing tactics to maximize your revenue
• Analyze your finances to ensure you’re getting the best bang for every buck you spend
• Identify patterns hidden in your company data. Use that data to reduce risks and eliminate unnecessary expenses that come with unexpected failures.

Again, this doesn’t even begin to scratch the surface of the potential that machine learning can bring to your organization. I hope it can at least give you somewhere to start.

Conclusion

Even though so many companies have realized the importance of data science in their operations, there is still so much untapped potential out there. Data science gives the travel industry a particularly high potential. Seizing that potential lets businesses maximize the value they can offer their customers.

In return, customers are much more likely to recommend your business to their friends and family, which further boosts your revenue. Word-of-mouth marketing is the most powerful form of marketing, and it’s completely free. It doesn’t get much better than that. By integrating geospatial data science into the travel industry, we can make it a more inclusive and a better place for everyone who wants to participate in it.

Top Photo: Rafting the Colorado River
Grand Canyon National Park, Arizona – June, 2015

The post 6 Ways the Travel Industry Can Use Data Science to Step Up Its Game appeared first on Matthew Gove Blog.

As an expert in data analytics myself, I can’t help but notice an interesting, but disturbing pattern that has emerged recently. Let’s put the statistics side-by-side and let the numbers speak for themselves.

If most of your data is going unused, or even worse, if you’re not using analytics at all, you are failing yourself and your company. The lack of analytics can bite your wallet on both sides of your business. You’re likely missing out on leads and sales. In addition, you won’t be able to optimize your operations, which will cost you money in extra, unnecessary expenses.

What are Geospatial Analytics?

Geospatial analytics are one of the most powerful tools your company can use in today’s globalized world. In short, they are simply analytics that are focused around location-based data. They are also one of the least used and most underutilized forms of analytics around the world today. The geospatial analytics market is expected to grow from $52 billion in 2020 to around $200 billion by 2027. You don’t want to miss out.

Not surprisingly, there has been no single event that has exposed the need for geospatial analytics more than the COVID-19 pandemic. Businesses that used analytics to track the movement of COVID-19 around the globe were able to precisely target specific geographic areas to keep cash flowing in during the crisis. Many of those that didn’t, have since gone out of business.

When you think of geospatial analytics, what companies come to mind? Shipping companies such as UPS and FedEx? How about airlines, trains, and bus systems? Sure, the travel and transportation industries rely much more heavily on geospatial analytics than the mom-and-pop coffee shop on the corner. However, every company can greatly benefit from geospatial analytics, regardless of their size or industy.

1. Stop Overpaying for Insurance

In 2019 alone, Americans overpaid for their auto insurance by nearly $37 billion. While algorithms that quote insurance rates are becoming exponentially more complex, location still plays a major role in those calculations.

Consider a scenario where a company is in business delivering goods to people’s homes. For this example, they can be delivering anything from pizzas to furniture. As they expand throughout the country, where you think they’ll pay more to insure their vehicles?

• The narrow, crowded streets of a major city such as Boston or New York
• The wide open prairies in rural Texas or Oklahoma

If your business doesn’t use vehicles, the same concept applies to any type of insurance. Geospatial analytics will allow you to precisely identify the exact risks you need to protect yourself against, wherever you are operating. That way, you don’t have to spend extra money to insure your business against risks it’s not exposed to.

Ironically, the insurance companies also use these exact same analytics and strategy. By evaluating risk based on geospatial analytics, they can maximize the amount they can charge you for coverage.

2. Easily Analyze the Impact of Severe Weather Events

A major natural disaster can take down even the most prepared organizations. With proper geospatial analytics, you can greatly minimize the impact of severe weather and natural disasters. You won’t be able to completely eliminate the risk. However, if your analysis done correctly, you’ll be able to continue operations largely uninterrupted when a natural disaster strikes.

Using geospatial analytics, there are so many ways you can reduce the impact of natural disasters.

• Diversify storage of assets and inventory so if one group gets destroyed, you can still use the others.
• Optimize travel and shipping routes to avoid areas that are under imminent threat of severe weather.
• Secure or move property, assets, and inventory that are under imminent threat of severe weather.
• Target your insurance coverage. Don’t pay for coverage your geographic area doesn’t need.
• Create and implement precisely targeted plans to protect your property and business should severe weather strike

Companies that are more prepared for disasters are far more likely to survive should disaster strike. Just have a look at the restaurant industry during the COVID-19 pandemic.

Restaurants that were able to quickly pivot away from relying on dine-in service for income made it through the pandemic largely unscathed. Innovative ideas, such as these restaurants that essentially turned into grocery stores, were a huge hit. On the other hand, many establishments that fought restrictions and continued to rely on sit-down dining are no longer in business.

3. Improve Fraud Detection and Prevention

I was actually able to use geospatial analytics to detect fraud on one of my credit cards before the bank alerted me to it. I had been out running errands and was at my final stop of the day at the grocery store. At the checkout, I discovered that my credit card had stopped working.

No matter how many times I swiped the card, it was declined every single time. Thankfully, I had a second card I could use to pay for my groceries. When I got back to the car, I logged onto my online banking. Low and behold, someone had tried to charge over $1,500 to my credit card at a gas station in Utah. At the time, it had been over 2 years since I had stepped foot in the state of Utah.

As simple as this is, it’s a classic example of using geospatial analytics to detect credit card fraud. How could I be making a series of legitimate purchases in the Phoenix, Arizona area and less than 10 minutes later use the same card at a gas station in Utah? Thankfully, I could easily retrace my steps. My credit card had been used in a skimmer at a gas station a week earlier when I ran an errand in a part of Phoenix I don’t regularly visit.

In addition to credit card fraud, geospatial analytics can easily detect fraud in other industries, especially when cross-referenced against timestamps.

• Insurance Fraud
• Healthcare Fraud
• Abusing government programs such as food stamps or social security
• Tax Fraud and other kinds of financial fraud

4. Optimize Your Marketing and Sales

Geospatial analytics hold an incredible potential to boost your marketing and sales. Segment your target market and organize your customers in to groups with common, unique traits. You’ll be able to promote specific messages to very targeted groups based on demographics.

• Gender
• Age
• Race
• Income Level
• Education Level
• Employment
• Homeownership
• Much More

What does this have to do with geospatial data? After defining the specific demographic groups you want to target, have a look at census tracts or postal codes. For each group, look for census tracts where your target demographics overlap. That’s where you run those very targeted ads and promotions.

Additionally, look beyond just demographics to establish your target market and ideal customer.

• Lifestyle Data
• Behavioral data, such as buying patterns and purchase history
• Relationships between the postal codes of your customers and the location of your stores

When you plot these data on a map, you’ll discover patterns you had no idea existed. Armed with this information, you can execute a laser-focused marketing campaign that maximizes the efficiency of your marketing and boosts your sales.

5. Enhance Logistics, Shipping, and Transportation

Optimizing location-based operations is the holy grail of geospatial analytics. To get started with this optimization, all you need are GPS sensors and a GIS mapping program. Your goal is to minimize cost while maximizing the efficiency of your transport operations.

Unfortunately, you can’t just stick GPS sensors in your vehicles expect them to optimize themselves. You’ll need geospatial analytics for your optimization. You’ll need to consider several factors.

• Normal traffic patterns
• Current traffic hazards, such as accidents, rush hour congestion, construction, or road closures
• Weather patterns
• Civil or political instability
• How long you plan to be en-route
• How long you plan to stay at your delivery or pick-up point
• Historical data of traveling these routes

It’s also important to note that your optimum route will change depending on what day of the week it is. Additionally, it will even change depending on the time of day. A direct route that goes right through the middle of a major city may be okay in the middle of the day, but it’s likely not the best route at rush hour.

6. Track and Manage Your Assets

Any company that sends technicians into the field should be taking advantage of geospatial analytics. Easily manage your network, quickly respond to outages, and anticipate future needs.

Geospatial analytics really shine when it comes to managing workers in the field.

• Utility and telecom companies optimize the placement of field technicians so they can be dispatched to quickly and efficiently fix problems.
• Delivery and rideshare companies place their drivers to maximize the amount of goods delivered or rides given.
• Law enforcement agencies easily maximize the coverage of their patrols while at the same time minimizing their response times.
• Everyone from the military to farmers and beyond can use geospatial analytics to manage their assets.

7. Augment Safety and Situational Awareness

It’s a scenario that managers, executives, and business owners dread. You arrive at work one morning only to discover that your servers, which contain all of your data, are under a cyber attack. You don’t know where it’s coming from, how widespread it is, or what has already been compromised.

Since you know so little, you have no choice to shut everything down. Such a decision can have a devastating effect on your business. Costs can easily soar into the millions of dollars per day. Without a running server and data, your employees likely won’t be able to work, either, forcing you to either send them home or pay them for doing nothing.

Geospatial analytics cannot prevent such an attack from happening. However, they will let you respond in such a manner so that the impact on your business is hardly noticeable.

Here’s how drastically different this scenario could play out if you used geospatial analytics. After discovering the attack, you’d cross-reference the incoming IP addresses to determine where the attack is coming from. Once you know the geographic scope of the attack, simply blacklist or block the locations that the attack is coming from. Your employees would still be able to show up for work and be productive.

Unfortunately, in the real world, it’s never this simple. Cyber attacks are becoming exponentially more complex. However, with a proper plan, they can still be foiled without having to shut down your entire operation.

I Want to Use Geospatial Analytics. Can You Help Me?

Absolutely! As experts in data analytics and Geographic Information Systems (GIS), geospatial analytics are our bread and butter. If you’re part of that 47% who doesn’t use analytics, stop holding yourself back. Let us help you get started with geospatial analytics today.

Conclusion

We have barely scratched the surface of what you can do with geospatial analytics. There’s so much you can do across all sectors and industries, regardless of how big or small your organization is. Unfortunately, far too many companies are missing out on both boosting revenue and reducing expenses when they shun data analytics.

Without geospatial analytics, you’re missing out on so much potential to improve your company. Even worse, you’re probably wasting money, too. The geospatial analytics market is expected to quadruple in size over the next five to ten years. Don’t get left behind. Let’s get started today.

Top Photo: Morning Sun on the McDowell Mountains
Scottsdale, Arizona – October, 2016

The post 7 Ways Geospatial Analytics Give Your Business a Competitive Edge appeared first on Matthew Gove Blog.