First things first: AI is not coming for your job. Instead, it can massively enhance and streamline your work by removing some of the drudgery and complexity. For your business, it can unlock new opportunities, grow revenue, assist with cost management, and wow your buyers.
I’m not referring to ChatGPT either. It may have grabbed all of the headlines, but the world is still figuring out (1) what the heck to do with it and (2) how to avoid some very real problems with accuracy and bias.
In the meantime, there are other AI solutions available today that are producing outsized returns. We’ll look at a few of them and share how API-first businesses can take advantage of the technology.
The world is producing 2.5 quintillion bytes of data every day. It might be higher; that statistic was from waaayy back in 2021. A quintillion has 18 zeros in case you were wondering. While your organization may only be responsible for a fraction of that, even a fraction of that is a lot.
The challenge for people is analyzing that information quickly. This is where machines and machine learning comes in. Over the past 10+ years with the rise of cloud computing we’ve been able to analyze greater amounts of information faster. Machine learning algorithms and predictive models are taking advantage of that computing power.
Artificial intelligence is a fancy term. Most technologists prefer to speak about the specialized machine learning algorithms involved. AI can refer to any number of machine learning technologies. ChatGPT is known for its use of large-language models (LLMs), but there are many other technologies or algorithms for machine learning such as vector embeddings, neural hashing, and approximate nearest neighbors (ANN). Machine learning is usually a multi-step process of transforming information into machine-readable data for faster processing, and then applying different models to interpret the data and deliver predictions. Models can be tweaked to improve performance and confidence levels.
The data you’re collecting from your customers — geo, gender, purchases, browser, member status, ratings, and even returns — is fuel for the fire. Recommendation features like customers who bought x also bought y can improve customer satisfaction and also generate a higher average order value. Machine learning can take this even further, however. By adding a layer of AI-driven personalization at checkout or post-purchase emails, brands can offer even more value.
There are many applications in production or coming soon, but I’ll highlight a few.
Imagine you could assign a real-time personal assistant to every shopper on your site to help them find exactly what they’re looking for. With AI search, it may feel just like that.
Traditional keyword search engines only work if the query is simple. Newer AI search engines are more human-like. It’s the difference between a search query for a “clock” versus a query for “the best rated eco-friendly clock that runs without batteries”. Regular keyword search engines will stumble on long tail searches, but newer AI search engines will understand. Even if there are misspellings or unusual terms — for example, if you were to replace “clock” with “timepiece” in the query — the query would still work. AI will search in the same way customers think!
Search AI can also eliminate much of the work you and your company currently spend writing rules, synonyms, and other workarounds that optimize search results. Better results, faster time to ROI, and less effort. And that’s just the start of how AI is going to transform ecommerce.
There are too many permutations for anyone to analyze — multi-channel visit history, purchase history, demographics, etc. — but machines are very good at this. AI can suggest relevant and tailored products for each buyer by assessing their browsing and buying patterns, prior transactions, interests, demographics, past search history, and other pertinent data. This enhances their likelihood of purchasing and connecting with the brand more regularly.
Machine learning algorithms have come a long way and can even personalize results on single-visit sessions based on data like landing page, browser, mobile vs desktop, on-site activity, geo, and other metadata. The more data you’re able to collect on customers (while still maintaining privacy restrictions), the better.
AI-powered personalization can be used everywhere, too, from your website to emails, and even AI-powered ads. For example, AI can help you analyze your demographics and assist to deliver highly-targeted, context-sensitive ads as someone browses your site.
Discovery and personalization can boost revenues. On the other side of the spreadsheet are costs. AI can help here too. For example, artificial intelligence-enhanced inventory management systems can detect buying patterns to provide more accurate forecasts and optimize warehouse replenishment strategies with real-time data.
From robots zipping around a warehouse to finding out-of-stock items to advanced AI that considers historical data and current buying patterns, machine learning can help stores, warehouses, distribution centers, and their external partners and suppliers manage inventory before it becomes an issue. AI is ready to lend a hand before you kick off your next promotion to ensure products are where they need to.
Inventory and supply chain management AI has huge potential to lower costs and disruption, saving businesses time and money. Combined with top-line revenue generating AI, like improved search, can help businesses optimize at every level.
Google, Amazon, and Netflix have invested billions of dollars and hired thousands of people to design proprietary AI-powered personalization, recommendation, inventory, and search solutions. Fortunately, it won’t cost you nearly as much. Newer API-first, cloud-hosted solutions can be deployed at a fraction of the price into a headless architecture in weeks or months.
When designing a composable solution, it’s good to park services as close to one another as possible. Reducing latency optimizes end-user experience. In other words, if your database and web application are in AWS-East, ideally your machine learning solutions are nearby, too. Thanks to modern computing and highly-optimized algorithms, machine learning is extremely fast, but any amount of delay can impact customer experience and brand perception.AI may be the most exciting and game-changing technological advancement in this era. According to McKinsey’s Notes from the AI Frontier, AI will drive as much as $13 trillion to global GDP by 2030. MACH-based applications are the fastest, most cost-effective and reliable way to build and implement solutions today.
Author: Jon Silvers, Director, Digital Marketing Algolia