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Recession, personalization, and ChatGPT, oh my! 2022 was a wild ride!
The tech industry was not immune to economic swings and, in many ways, seemed at its epicenter. So let’s reflect on what we learned in order to draw inferences on what awaits in 2023.
This year slowed tech’s long winning streak and, in some cases, brought it to a complete halt. The past few years gave us favorable macroeconomic conditions: a strong job market, a shift from the physical world to digital channels, cheap access to capital in a low-interest rate environment, and a generally favorable investment environment. However, current global and domestic events slowed down tech’s momentum.
Even though many economists aren’t officially using the “big R” word, the tech industry is feeling the effects of a recession. More than 90,000 tech employees were laid off in 2022. And companies will likely continue with more layoffs starting early in the new year.
These are stark indicators that growth has stalled. Companies are tightening their belts and making difficult decisions about how to navigate choppy waters ahead. Businesses in the tech industry, including many of our customers, are forced to answer difficult questions:
While each business will make decisions based on its unique circumstances, our customers have widely indicated that the momentum of their digital transformation initiatives will continue into the new year. While they won’t be at the same frenetic pace as immediately following the COVID pandemic, their 2023 initiatives will focus on areas that drive meaningful business outcomes through innovation focused on real-time personalization, fraud detection, and AI.
Our experiences in 2022 showed us that digital acceleration is not just a flash in the pan. Though most customers no longer see the same peaks as during the height of COVID lockdowns, it’s clear that digital is here to stay. Digital channels drive new revenue channels, meet user demands, and foster innovation. However, digital is not a free lunch. Digital channels require performance beyond the capabilities of legacy technology and introduce new market competition — often cloud native. Hence, enterprises now treat real-time latency at scale as a competitive advantage.
We have seen this ourselves, as Redis has helped customers perform real-time data analysis to improve product recommendations, fraud detection, and financial transaction scoring.
Real-time performance enables businesses to provide customers with a seamless and personalized shopping experience. By instantly analyzing data across multiple e-commerce domains, a shopper’s purchasing behavior is analyzed in microseconds. Using that data, online retailers can provide a personalized customer journey that is curated to the individual, which increases the likelihood of purchase conversion. The result: a better shopping experience for the customer and increased revenue for the retailer. It’s a win-win.
As an example, our Ulta Beauty case study showcases all of these elements.
felt that their security initiatives had not kept up with digital transformation and introduced new business risks. Introducing new digital channels also introduces new cybercrime attack vectors and security complexity that legacy solutions were not built to handle.
Because cybercrime is a cat-and-mouse game, real-time analysis can help financial institutions build better (and faster) mousetraps. Millions of transactions can be analyzed in milliseconds, as they occur to prevent fraudulent transactions at the point of purchase. When transaction-risk scoring and anomaly detection is performed in-flight, as part of the transactional path against known malicious patterns, these technologies can block the purchase before it’s committed or immediately refer it for forensic analysis. This saves financial institutions the expense, and consumers the headache, of remediations after fraud has already occurred.
Read our Simility case study on fraud detection and transaction scoring.
AI has come so far that you might have been asking if this blog was written by ChatGPT. (Just kidding, I’m not a robot.). That said: AI is now so advanced that it can write essays as well as an undergraduate student, negotiate lower utility bills, and even delve into a realm once thought untouchable by machines: creating works of art. AI is now everywhere, and its adoption continues at a rapid pace.
There are also tremendous business implications for artificial intelligence and machine learning. Machine learning can be used to analyze patient healthcare data and to power interactive customer chats. But to deliver on the promise of artificial intelligence and machine learning within an interactive digital environment, you must store vast quantities of data to be accessed in real-time.
To see how AI works in practice, see the iFood case study.
2022 was a wild ride, but through the ups and downs, there were many lessons. Let’s carry this insight into the future and work together to make 2023 a happy and successful new year.
Learn more about how Redis Enterprise can prepare your business for the times ahead.
Stop testing, start deploying your AI apps. See how with MIT Technology Review’s latest research.
