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Marketers today have more data available to them than ever before. Applying artificial intelligence (AI) to that data is necessary to driving marketing effectiveness in today’s competitive digital world. When properly harnessed, AI provides insights that help achieve lower customer acquisition costs, greater lifetime spending per customer and better revenue outcomes in general. However, AI can often seem overwhelming for marketers that don’t have experience with the benefits that data science can yield.
Leveraging AI doesn’t have to be a byzantine process that’s only accessible to seasoned data scientists. Marketers and all business professionals can gain significant value from AI without being deeply technical. They should try to understand the advantages that AI provides. Let’s break down those advantages into three Ps:
- Patterns: AI can quickly detect patterns in vast amounts of data, allowing marketers to detect common customer attributes and understand segments of consumers
- Preferences: AI can discern customer preferences, helping marketers serve up the right content for a given audience
- Predictions: AI can provide a better view of what might happen next, helping marketers determine things such as the next best offer or who will be the next new customer ahead of their competition
AI applied to rich data yields patterns, preferences and predictions. By focusing on the three P’s, marketers can better understand how to apply AI to their marketing objectives, leverage its tremendous value and ultimately better understand their customers. Let’s take a closer look at each.
Analyzing data around demographics, web browsing behaviors, and past purchases is not new. What AI and machine learning (ML) bring is the ability to do this using immense volumes of data in real-time. Where humans would be overwhelmed, AI is capable of quickly identifying common attributes among segments of consumers. These shared attributes can then be scaled by identifying more customers that display similar behaviors.
Consider this real-world example: A few years ago, a women’s yoga apparel company began using AI to detect patterns in their customer behavior. Afterward, the company noticed that men accounted for a significant percentage of sales. Realizing there was an unmet need there, they rolled out an entire new line of men’s yoga apparel, doubling revenue in that product segment.
AI enabled this organization to uncover an important trend among a specific demographic. Without this insight, that behavior may have remained invisible to the company, and they would have missed the opportunity to better serve those customers and drive better business outcomes.
When applied to large data sets, ML not only helps isolate patterns, but also discern preferences. Ideally, digital consumers would provide their consent and preferences so that they are served the products and services that actually interest them. But that isn’t occurring as much as you’d think. As a result, consumers are often served irrelevant advertisements, such as new car owners continuing to see offers on the same car, maybe even for less than they paid. This inaccuracy wastes ad dollars and also hurts brand reputation; not only are consumers irritated by the irrelevant ads, but the brand wastes an opportunity to reach the consumer with more relevant messages, for example after-sales care.
AI is only as powerful as the data that feeds it. That’s one reason consumer consent is such an important topic for marketers to familiarize themselves with. Feeding AI stale third-party datasets will result in problems like the car ad after the purchase has been made (garbage in, garbage out). The more organizations can clearly prompt consumers to authorize use of their data while explaining how that data will result in a better experience, the more set up for the future those companies will be. When consumers understand how their data is being used, and for what purposes, the opt-in rate is likely to be higher and companies will benefit from more accurate and up-to-date information.
After that, AI can be applied to that data to give marketers nuanced, actionable insights on consumer preferences. That AI first creates accurate audience profiles based on variables such as age, gender, family, location, income, education, occupation and more. Then it shows what each audience buys, what brands they prefer, and what media they consume or sites they visit. Finally, AI provides detailed information on engagement, further breaking down audiences into passers-by, regulars and fanatics. With all these insights, organizations can better reach the right consumers with relevant ads.
By using AI and ML to create models, marketers can gain a better view of consumer behavior in the future. Think about it as having a sort of crystal ball: an opportunity to identify the next best offer, next best action, next new customer, etc. To do this, AI analyzes massive quantities of past data to identify historical consumer patterns, including both industry-wide and brand-specific, then uncovers the impacts of those trends. Which led to a sale? Which didn’t?
With that information, organizations can anticipate future patterns and decide how they should adjust and optimize advertising efforts when those trends occur.
For example, in the month after COVID locked us down in the spring of 2020, automotive dealer and car comparison websites saw a significant decline in traffic. This seemed to be a predictable outcome from economic and social uncertainty, which most in the industry likely expected to continue indefinitely.
However, after a month of decline, automotive dealer and car comparison web traffic suddenly began to spike, nearly reaching pre-pandemic levels by the following month, reportedly due to public transit and air travel being deemed risky and scaling down drastically. If car dealers had seen the data unfold earlier, they could have tapped into this new demand and acted on it.
AI Delivers Easy Insights
Making sense of massive data sets is difficult or impossible to do manually, especially for marketers that lack deep analytics or data science experience. But AI can provide clear, streamlined insights from data. They shouldn’t feel intimidated, though, as it doesn’t take technical expertise to grasp and act on these insights. Luckily, AI and ML tech is already embedded into a number of marketing and advertising technology platforms and tools out there today, and marketers need to primarily understand the three basic values from AI – patterns, preferences and predictions – to effectively employ this technology and improve key business outcomes.