To absolutely no one’s surprise, business owners’ appreciation for customer data, in one form or another, is as old as commerce itself. Keeping track of customer purchasing habits and preferences — even when done informally — has been central to the shopkeeper’s role since biblical times. But it wasn’t until the last century that the utility of customer data underwent a transition from a proprietor’s instinct to an operational necessity. And the ongoing digital revolution empowered organizations to go even further, extracting valuable, highly personalized insights and intelligence from massive collections of customer data with the help of artificial intelligence (AI).
Even before the advent of AI, there were companies that made thoughtful use of customer data — combining it with seasoned business judgment — and experienced remarkable success. In 2003, the CEO of Harrah’s Casinos wrote about his company’s collection and interpretation of data regarding patrons at its 13 gaming locations.
By studying customer engagement data and ignoring competitors’ traditional focus on high rollers and ersatz architectural replicas, Harrah’s discovered that the smaller slot machine players were the ones who largely drove the company’s revenue. That realization led to a series of focused reward and incentive programs that led to significant growth at a time when the gaming industry had largely stagnated.
Today, enterprises of just about every size and industry are struggling to embrace the power of data to improve their own production efficiency, business processes, planning and revenue. The tools for capturing and analyzing that data are becoming more affordable and improving all the time.
The use of customer data has been massively successful in many markets and industries, such as the airline industry, where big data implementations have made a convincing case for the technology’s business value. As a result, companies are making massive investments in acquiring, subscribing and otherwise implementing the digital tools needed to keep them ahead of the competition.
But artificial intelligence, which is often as powerful as science fiction fans imagine it to be, is by no means a simple technology. The algorithms that drive it are essentially opaque to all but the most advanced data experts, and they change as the system learns more.
As a technology, AI is still immature. It comes with limitations, and it can be expensive to implement. That means a thoughtful and strategic approach to adopting AI as an element of a company’s digital transition is essential. One strategy is to apply AI to projects where its ability to sift through mountains of data in search of useful insights can overcome the limitations on both human and conventional computer bandwidth to handle massive volumes of information.
For example, AI can be used to provide financial advice to customers. AI could periodically rebalance an investor’s portfolio to achieve a predetermined ratio of stocks to bonds or find a path to minimize the investor’s taxes. On the other hand, it would take an experienced financial advisor to do things that AI is ill-equipped to handle, such as understanding the client’s investment goals or providing retirement planning or coaching.
In the case of marketing, which has become a data-intensive industry, the marketer’s challenge is to use that data to predict the customer’s behaviors — the best times, locations and means to reach that individual with an appropriately crafted message or experience.
By extension, it can also peg other good prospects and high-value leads with similar behavioral patterns. Beyond that, AI can drill through piles of seemingly inconsequential data to unearth valuable patterns and business intelligence. But, alas, it is lousy at crafting creative assets and compelling messages. This is why AI is best applied as the interface between the data-driven and creative marketers and their voluminous datasets.
However, diving deep into the data — even with AI on your side — is not an amateur sport. Doing it successfully requires someone with an understanding of data science, and turning marketers into data scientists just isn’t practical. What’s needed instead is to bring the tool sets typically reserved for data scientists intrinsically closer to the marketing stack.
While in the past, data scientists may have lived in their own silos, data scientists and marketers now need to work together, using data in the same ways, to show the value of their efforts. AI technology, in combination with marketing analytics and intelligence tools, can be a massive enabler in this regard. But extracting the marketing value from AI takes more than just technology.
Successful AI implementation starts with executives and management teams, whether it’s the board or the CMO. It’s critical to set the organizational expectation that it’s not good enough to just do something; you need to be able to monitor, measure and interpret it to extract future value.
It’s also important to understand the performance of campaigns, channels and promotions over time and across customer segments. Additionally, you need to be able to generate visualizations that tell a story supported by facts. You need to build intelligence that can move an organization toward marketing success to accelerate sales, generate revenue and support mission-critical goals. In this regard, there can be no separation between AI-based solutions and the business leaders responsible for corporate strategies.
AI, machine learning and related cognitive systems are well equipped to perform tasks, but not jobs. And while they can perform those tasks impressively, they fall short of human creativity, savvy and instinct. Organizations that figure out how to combine the best of advanced AI technologies with the unique strengths of human intelligence will emerge as market winners.
This article was originally posted on – https://www.forbes.com/sites/forbestechcouncil/2020/01/10/in-marketing-ai-is-nothing-without-its-masters/#50fa6ac61958
