37% of businesses have implemented some form of artificial intelligence within their organization. That’s an increase of 270% over the last four years! Nonetheless, AI can seem like an intimidating topic and difficult to implement for many business owners.
We’re happy to tell you it’s not that way at all.
In fact, integrating AI into your agency is much more flexible and simpler than you might think. Common problems like not understanding your highest revenue channels, best traffic sources, and similar can be quickly diagnosed thanks to this technology.
Furthermore, one important field within artificial intelligence is predictive analytics. Make sure to read our guide to predictive marketing if you need a refresher. On that note, predictive analytics focuses on using data and forecasting to help businesses make better decisions, save time, and reduce stress.
Wouldn’t you like that? If so, you’re going to find the predictive analytics case studies we’re outlining ahead very inspiring.
These will show you examples of predictive analytics and how they have revolutionized both businesses and the world.
Ready? Let’s go!
1. A 98% delivery rate thanks to predictive analytics
As your business scales, so does your complexity. Consider the predictive analytics company Seebo, for instance. They specialize in using AI to help companies within the manufacturing industry improve their production lines and predict disruptions.
That’s why a global biotechnology company reached out to them for help. They were experiencing the need to constantly clean equipment, stoppages, high levels of waste, lower capacity, and a longer time to market. Not good.
I’m sure you’ve experienced many hiccups and interruptions like this while operating your own business, right?
Well, listen to this.
Seebo’s opted to take a predictive analytics approach to solve the client’s problem. They leveraged big data, machine learning, and forecasting to discover problems before they happened.
Historical and real time data were used to create models which helped Seebo discover the most optimal operating temperature, distillation time, and mixing duration. This is the equivalent of finding the best PPC ads, SEO keywords, and customer segment for a digital agency, for example.
The result? An incredible 83% reduction in downtime, 72% savings in downtime costs, 98% delivery time, and 5.1% improvement in production capacity.
That means this predictive analytics manufacturing example proves AI can save a business money and make them increasingly productive at the same time.
2. Microsoft and Graemener make the world a better place with AI
AI helping to preserve wildlife, who would’ve thought? We’re big on seeing artificial intelligence and tech being used to help people and nature, so we’re excited to share this case study.
The Nisqually River Foundation is a nature conservation organization that assisted a local tribe in monitoring the fish of the Nisqually River. It’s an 81 mile long river in west central Washington as you can see here:
Manually processing and identify all of the species of fish in a river that size is daunting to say the least. That’s why the foundation partnered up with Gramener and Microsoft to create a predictive analytics solution.
AI models were created with Microsoft Azure, other tools, and video feeds of the Nisqually river. Frames with different species of fish were tagged using one of Microsoft’s visual tagging tools for segmentation. This data was then used to train the model to do all of the heavy lifting for the foundation.
They were able to increase the accuracy of how fish were identified by 73% and they believe the system given to the client will further result in an 80% cost savings in the future.
Think about the struggle of segmenting users for an email list, CRM, or a tool your agency uses. It’s the same thing.
An AI model can be created — just like what we offer here at Morphio — to find the highest performing combinations of creatives and channels.
3. AirBnB’s 43,000% growth in five years
Yep, you read that number right. Since its launch, Airbnb has, in fact, literally grown by 43,000%. Most of that is also thanks to our good friend predictive analytics.
Riley Newman, apart of AirBnB’s data science team, said it best by stating “The trick has been to manage scale in a way that brings together the magic of those early days with the growing needs of the present — a challenge that I know we aren’t alone in facing.”
What was the secret for this explosive growth? Humanizing data. It’s so easy to forget that all of your analytics and metrics are the result of real people taking real actions.
Or, as Riley put it, “If you can recreate the sequence of events leading up to that decision, you can learn from it; it’s an indirect way of the person telling you what they like and don’t like.”
They used a four step process in creating a predictive analytics system that lead to rapid and consistent growth:
- Take historical and real time data to create models and theories about what will work the most effectively again.
- Create a plan to use this data in the most impactful way possible.
- Use A/B split tests and experiments to confirm if findings are correct and how they can be maximized.
- Measure the results of the data and repeat the process.
4. Amazon’s new predictive ordering patent
Have you ordered a product on Amazon and they always seem to nail the recommended items? Well, they’re about to take it a step further.
Amazon took out a patent on a new predictive ordering technology that will be able to order products you want before you’ve purchased them yourself. Yeah, that’s right. Their predictive AI is becoming so advanced they know what consumers want to buy before they do.
Also known as “anticipatory shipping,” this technology will take advantage of Amazon’s massive customer data pool to determine what consumers want ahead of time. These items will then be sent to a shipping hub to speed up delivery time when they’re actually ordered.
This means the customer gets what they want faster, improving their experience and potential to continually shopping through Amazon. It also automates the work Amazon and their employees has to do, making the business-side less of a headache.
5. Noah Kangan’s hands-on approach to predictive analytics
Noah Kangan is a successful serial entrepreneur and the founder of OkDork. Before the launch of this website, he was the market director for Mint.com when his boss through a new task on his desk; acquire 100,000 users in six months.
Seems like an impossible task, right? Noah thought that, too. And while under that pressure, he devised a predictable system that would allow him to crush that goal and more.
I bet you’ve been in this situation before; you need to hit KPIs and grow your agency but it seems out of reach. Well, consider what Noah did.
He took a barebones approach to creating a predictive analytics model for Mint. Noah took the end result — which was 100,000 users in six months — and reverse engineered it.
The first step was to set up a goal. What are you trying to achieve with your agency? Don’t shoot in the dark. Set a milestone for website traffic, users, recurring revenue, or similar metrics. Break down the goal into quarterly, monthly, and weekly achievements to make it digestible, too.
Secondly, choose a timeframe. When would you like to achieve this goal by? Shoot for the stars but be realistic at the same time. It might be one year, six months, or less depending on exactly what you’re trying to achieve.
Noah created a spreadsheet with this information as the third step and it looked like this:
As you can see, it’s composed of the channels he used along with goals for metrics that were important for Mint.
Noah then made a scoring system based on ease of implementation and potential impact. These numbers were added up to create a final score, giving him the most ideal combination of strategies and channels.
Do you know how well Noah did in his pursuit of hitting 100,000 users in six months? He hit 10x the target, bringing in one million users for Mint.
And, guess what? You can be achieving similar numbers for your business by using predictive analytics software that does all of this on your behalf.
Sure, Noah’s system is impressive, but AI platforms will be constantly running in the background finding opportunities and predicting issues while your scale your company. Say goodbye to spreadsheets and manual data entry.
Final thoughts on today’s predictive analytics case studies
Predictive analytics is like having a crystal ball. You can see into the future, preventing problems in your business from snowballing and finding lucrative opportunities.
Despite this, many businesses are still relying on dated technology. They aren’t leveraging AI which is resulting in them missing chances to scale faster and run their businesses with fewer migraines.
That’s exactly why you need to start your free trial with Morphio today! Our AI platform is capable of helping your business make smarter decisions by taking small steps with AI that compound into huge results over time.