Corporate pioneers in artificial intelligence (AI) want what all business owners have always wanted—an efficient and inexpensive way to generate and increase sales. Today, CEOs are much closer to achieving these goals in a revolutionary way thanks to artificial-intelligence platforms, which are making robust, granular personalization in sales and communications not just available but inevitable.
Successful early adopters see AI solutions as a means to boost the bottom line as well as the brand by making digital interactions across all virtual and physical channels fluid and as close to flawless as possible. And executives aren’t simply automating processes; they are using AI to extract new value, as illustrated by the three companies below.
Breaking Down Walls At A Home-Improvement Giant
Lowe’s Companies Inc., the world’s second-biggest home-improvement-store chain, wants to be number one, and it sees the strategic use of AI as an important step in that direction. One of its efforts in particular seeks to define an individual customer’s personal style in a unique way.
While a counterintuitive idea to some, not everyone knows what their style is, which is why most people shop with a trusted friend when it comes to major projects like renovating one’s kitchen. If that trusted friend is unavailable though, it can be difficult for a store employee to close a sale, simply because they don’t know a customer well enough to make winning recommendations.
To overcome this barrier, Lowe’s is testing a hardware/software service in its kitchen-remodel section to help customers see inspiring and appealing options. To get a deeper feeling for the client, the service first leverages an AI solution to analyze a buyer’s Pinterest board. It then helps narrow down ideas and shows three-dimensional digital model kitchen options using virtual-reality lenses. By allowing customers a detailed glimpse into what the end-product could look like, Lowe’s is empowering customers and giving them the tools they need to make informed decisions.
The Liebherr Group Goes Deeper
Global manufacturer the Liebherr Group is another company whose AI initiatives are extracting new value. Manufacturing a broad range of big-ticket and industrial goods, including cranes, aircraft parts, mining equipment and household appliances, the Liebherr Group may not be the first company that comes to mind when discussing firms concerned with how best to use social media, but the company sees its promise and value.
Liebherr executives not only understand the tactical side of social media—brand protection—but also the strategic side, which is why the company made social media a core component of its larger AI platform focused on purchase, upgrade and support processes.
The vision for this initiative was far-reaching: Use social media to monitor industry developments (competitors’ strategic moves, new products, etc.), independently prompt sales and better manage support cases. By integrating a social-engagement platform with Liebherr’s existing customer-relationship-management and collaboration systems, the company reaped several advantages.
Once the systems were integrated, executives were able to react more quickly to customer comments on social media by assessing what that client had bought (using Liebherr’s databases) and locating service partners near the customer—within hours. The installation’s internal reach was unprecedented for Liebherr. Teams in marketing, research and development, and sales now use it to plan product updates, and they’re able to contact potential buyers during product development.
QuarterSpot’s Full-Measure AI Proposition
Online lender QuarterSpot Inc. is another good example of a company leveraging AI to push its business forward. In the wake of the 2008 financial crisis, QuarterSpot began making loans to small firms, a sector that was largely being ignored by major lending institutions yet one that creates far more jobs than midsize and large businesses.
QuarterSpot’s operations depend on huge amounts of data crunching, and the company’s success depends on speed. Once employees have reviewed loan applications and think the chances of default are tolerable, they approve an application and assign it an interest rate using predictive models written for different industries, risks, etc. Investors can then buy portions of the debt.
This model works only if risks can be assessed quickly and accurately, and QuarterSpot was not hitting either demand well enough manually. Even major banks at the time needed a year to 18 months to write, test and implement predictive models. Economically, much can change in that time.
To help address this, QuarterSpot brought in a big-data analytics platform. One component of this platform is a machine-learning solution that develops and tests multiple complex lending algorithms. With the help of this software, a novel business can now apply for a loan from QuarterSpot, and the machine-learning software can go from algorithm development to deployment instantly—a task that had previously taken a month for QuarterSpot to do. What’s more, the models can be retrained automatically in the face of new data.
The result has been eye-opening. Default rates have been halved even as borrower approval rates—meaning new business—has risen 15%.
As these case studies prove, fully integrated AI platforms can bring companies closer to their customers and closer to revenue goals. The key is to get started now.
Take this brief online assessment to help determine your preparedness for implementing artificial intelligence (AI) in your processes and products.
