Here's how companies of all breeds can grow their business through artificial intelligence and machine learning and differentiate the products and services they offer for a competitive advantage.
Artificial intelligence (AI) is still in its infancy and may seem like a futuristic game to some observers. The reality is that the technology is increasingly prevalent across industries. While a majority of users don't realize they're using aspects of AI when they book tickets for vacations, search for products, or chat with their favorite brand, the applications of the technology become more common every day. In fact, companies of all types – from tech companies to retailers to car manufacturers – are looking to AI and machine learning to differentiate the products and services they offer.
Just like an athlete needs the right equipment, nutrition and training to win, in order for AI to have the greatest impact on a business – and be well-received by customers – it requires the right technology, oversight, and high-quality data. If the tools, techniques, and data you're using to develop your AI, analyze interactions, and automate decision have weaknesses, it will impact the outcome.
The right tools always matter. When considering AI partners, it is necessary to clearly understand what outcomes you are trying to achieve. Just like a marathon runner trains differently than a sprinter, and a hockey player's equipment and regimen differ greatly from those of a soccer player, AI solutions are developed to achieve the best outcomes across various capabilities. Understanding the goal of your AI is critical. For example, are you modeling lift based on a number of probabilistic outcomes (positive or negative) or creating look-alike/act-alike models? Identifying specific objectives, then testing vendor and platform capabilities to achieve those outcomes are the first steps in preparing to win the AI game.
The next key to success is constantly and adequately feeding data to the solution. Every day, 2.5 quintillion bytes of data is created, so much that 90 percent of the data in the world today has been created in the last two years alone. Discerning the signals from the noise requires a methodical and ethical approach, coupled with refinement using data quality and identity resolution, to produce an accurate and consistent set of data.
So, what makes data “high-quality” nutrition for AI? Whether using data from inside your company (aka "first-party") or leveraging second- and third-party data sources to accelerate and enhance the customer experience, marketers must ensure data is accurate, timely and unfragmented in order to drive desired consumer behavior.
Capturing as much first-party data as possible is critical for understanding who your customers are, what they are buying and how they engage with your brand. Adding third-party data to fill the gaps in your understanding is paramount to successfully interacting with customers and prospects as well as measuring the outcomes of your marketing activities.
Absent of the right dietary parameters, campaigns are likely to miss the target, or experience worse outcomes. The adage, "garbage in, garbage out," comes to mind here and is still relevant. With the vast amount of consumer information available that AI applications can consume, it's important for companies to create the right data "nutritional plan" to ensure the outcomes are, indeed, intelligent.
The last element of AI training is critical and often overlooked. As brands look to leverage the power of AI to enhance engagement with consumers, it's incumbent upon marketers to consider the ethical aspects of the solution when preparing their strategy. Organizations like IEEE are helping define standards for automated systems, AI, and the use of data in solutions in order to protect people's identity and maintain safe barriers for protected classes in society, such as children and the elderly. Having a clear set of rules that follow not just current laws but reasonable expectations of your brand by consumers creates a fair and ethical playing field for all involved.
Consumers today expect a voice in how their data is used, and they also want to know what they will get in return. Consumers are more likely to provide personal information to brands they trust, so demonstrating respect – for example, by anonymizing and encrypting personal data when appropriate or clearly communicating the value provided in return – is a data governance best practice. Brands that fail to deliver on this implicit promise are likely to lose access to the playing field and resources needed to compete, ultimately putting revenue and growth at risk.
The trend lines are clear: AI will continue to grow and be more commonplace in our professional and personal lives. For brands, the prospect of smarter and faster insights is thrilling and can empower marketers to make better decisions and communicate more effectively. As consumers, we are poised to benefit in numerous ways from these solutions, and the way we shop, engage and research will continue to evolve. To gain the most from these innovations, brands must start with the right AI technology for the solution, gain access to more of the right information and establish a clear set of ethical guidelines and principles that are code enforced in the system.
Through vigilant development, analysis, and self-regulation of your AI services, your brand will never have to risk sacrificing valuable consumer trust, affinity, and loyalty. In today's age of AI and the empowered customer, there is huge potential upside for brands that enable, feed, and actively manage their AI with care.