Algorithms are essentially collections of “if–then” principles that originated in computer science. However, several factors, including easy-to-use predictive analytics and data-visualization tools, the proliferation of mobile devices, and companies’ ability to track and measure customer behaviors, have enabled businesses to find an astonishing variety of ways to use algorithms, particularly in marketing.
Algorithms enable marketers to use customer-specific facts — demographics, prior behavior, and the preferences of other consumers — to create personalized offers and distribute them frequently in real-time. They assist businesses in tracking customers, cross-selling to them, and promoting items. Banks use algorithms to promote new items to consumers, by online shops to establish and adjust pricing, and by media, businesses to select and provide streaming material and adverts.
Nonetheless, despite the widespread use and expansion of algorithm marketing, businesses should exercise caution for four reasons.
1. Algorithms aren’t context experts
Messages that are tailored to the consumer are the foundation of effective marketing. Even the most ordinary of items, customer behavior, is susceptible to a plethora of ever-changing circumstances. Personal factors such as how well a person slept the night before, current mood, hunger, and previous choices, as well as environmental variables such as the weather, the presence of other people, background music, and even ceiling height, can all influence how a customer responds on any given occasion. Algorithms can only use a few variables, so a lot of weight is inevitably placed on those variables, and often the contextual information that matters, such as the person’s current physical and emotional state or the physical environment in which the individual is tweeting, Facebooking, or purchasing online, isn’t considered.
2. They create suspicion and have the potential to backfire.
Algorithmic marketing is based on the assumption that companies can predict — and influence — customer behavior. That’s a lot of responsibility. Companies that try to capture the value of predictive analytics may find customers turn against them. Customers have good reason to be skeptical: The companies that collect and use their data often do so without transparency and with a profit motive. When they’re confronted with how their data is used, customers will often feel deceived.
3. Algorithms miss essential signals that offline marketing can access.
Traditional marketers have always paid attention to context. They know that a hot day and a ripped dress increase a woman’s chances to buy a cold drink. They know that a customer’s mood in a bar may change the way she interacts with a waiter. That’s why companies invest in training staff members to learn how to respond to customers. Companies connect with customers by anticipating situations and responding directly, successfully, and personally.
4. But algorithms can’t replace human interaction.
One of the great promises of algorithmic marketing has been its potential to improve the targeting of marketing messages. Perhaps the most attractive element of algorithmic marketing is that it’s automated: The more people a company tracks, the more data it collects, and the more it can refine algorithms to prop up the most successful and effective interactions.
But a company’s ability to predict the activities and responses of individuals depends on the quality of the algorithms and on how much data the company has collected. Not every company has the resources to develop powerful predictive analytics or collect comprehensive data. Those who don’t will find it hard to construct a robust predictive model, which means they’re unlikely to craft highly targeted messages.
The marketing world is changing at an incredible pace. In the past, marketing used a simple one-way communication channel to target individual customers. Today, however, technology has made it possible for companies to communicate with their customers in new ways that are interactive and personal.
Technology can give companies the tools to personalize their interactions with customers, but it can’t build relationships. Companies will still need to hire and train the people who can become effective agents in these relationships.
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