It’s well known that AI, machine learning and data modelling are helping businesses become more cost effective, increasing productivity, and freeing up employee time to focus on innovation, creative problem solving and customer experience. However, there are other, less spoken about benefits to adopting these new technologies.
What’s more, AI, machine learning and data modelling are also becoming crucial tools for helping organisations manage and mitigate risk and build resilience into their business models.
Let’s take a look at how they are doing it.
Predicting and responding to events
Predictive modelling has been around for a while, but until relatively recently its uptake has mostly been out of reach for many businesses. Today, however, it is fast becoming a tool for preventing customer churn, projecting future cash flow and sales cycles, and predicting customer needs to name a few use cases.
Predictive modelling is helping retailers predict product shortages. In the financial sector, it’s helping banks assess customer’s credit scores. In manufacturing, it’s helping forecast demand and predicting machine and production line breakdowns before they happen. From real estate to healthcare to marketing, predictive modelling is helping businesses provide better products, services, care, and personalisation to customers as well as optimising various aspects of an organisation’s processes, marketing, and customer experience. At nearly every level, predictive analytics brings a benefit, which helps build organisations resilient to more profound macro changes that can rock the market but also able to respond quickly to those changes and bounce back.
Digital twins to monitor real-time performance and detect issues
Machine learning can help spot risks and opportunities that would otherwise go unnoticed in big data. One of the latest developments in this field is the use of an AI digital twin to recreate a virtual model of a real-life system, be it a supply chain, a manufacturing site, a machine (such as a car), a building, or even virtual customers based on target personas.
These digital twins can detect bottlenecks, simulate stress tests for machinery and buildings through to new products, manage and monitor sensor data, control inventory, remove operational redundancies, mimic a customer’s journey with a brand and more. With businesses able to test and optimise a variety of processes, products and experiences using digital twins, they are able to detect issues before they impact their real-life systems, avoid costly mistakes, and mitigate risk—all of which helps safeguard and future proof businesses.
Managing the omnichannel experience
Regardless of what channel a customer uses, they expect to have the same brand experience. However, from a marketing and operations side, this proposes a huge challenge. Which is where automation, AI and data centralisation helps enormously. From automated delivery updates to consistent branding and product quality and availability, AI and automation is helping businesses manage and optimise brand experiences across their customer journeys—from online or real-world to transaction and delivery.
What’s more, people no longer browse on one device. They might be checking Instagram, see an ad they like and click on it, then decide they want to see the online store on a bigger screen, so switch to their desktop computer or tablet. Later, they might go visit a physical store to see a particular product before they purchase it. Across all these instances, the consumer expects the brand to not only be consistent but also provide an easy, intuitive experience from research to browsing to purchase—be it via an online store or a physical shop or a mix of both.
Businesses that excel in delivering omnichannel experiences will keep delighting customers at every corner, which translates to repeat business, consistent cash flow and ultimately, resilience in the face of change.
Related content: Smarter Not Harder: Empower Your Business With Martech
Improved accuracy and data quality
With AI and automation beginning to handle repetitive and often tedious tasks, such as data entry, there is less chance for human error. Add machine learning into the mix, particularly deep learning, and businesses are fast finding ways to enhance their offerings and operations. For example, image recognition software is aiding organisations in medical diagnosis, crop disease detection in agriculture and developing self-driving technology. Meanwhile in, the finance industry, AI and robotic process automation is helping detect fraud, automate invoice processing and account reconciliation as well as compile reporting for tax, compliance, and more.
By offloading these tasks to machines, businesses are reducing the risk of any costly or customer-losing mistakes as well as speeding up traditionally time-consuming tasks. From an employee side, it also frees staff up to put their energy towards innovation, creativity and problem solving, which helps keep them engaged and your business resilient as a result.
It’s no secret that AI, machine learning and data modelling is fast changing business, and the topics explored in this blog are only scratching the surface of the impact it is having. In terms of baking resilience into a business, these technologies are not just a good idea, but essential—and likely to become even more so. With so many tools giving us the ability to make data-driven decisions, optimise business practices and mitigate risk, building business resilience has never been easier.
Marketing technology is not only changing how we market but the experiences customers receive and expect from brands. Learn how you can track, improve and grow your customer experience with our customer experience management software, Customer Monitor.