For years, the role of marketers has grown dramatically. CMOs and marketing teams are now expected to drive demand, elevate their brand, anticipate shifts in the market, provide strategic counsel to the business, drive the pipeline and more. Following a year like 2020, nailing any one of these areas can feel overwhelming. When markets are flipped upside down, your customer demand is uncertain, and your marketing budget is at risk of declining, the job of a marketer gets even more complex.
To be clear, not all years see such dramatic twists and turns, but as the CMO of a company that offers AI-driven audience insight, targeting and measurement solutions, 2020’s volatility has made one thing certain for me: There is a strong need for marketers to understand their data and models to make predictions about the future. This may sound more difficult than it has to be. But thanks to data, artificial intelligence (AI) and machine learning, it’s doable. Let me explain.
Identifying Patterns In Customer Behavior
Throughout the pandemic, consumer behavior has changed dramatically, as McKinsey illustrates (download required), and reliable, first-party data on how your customers have evolved is invaluable. Past customer data is a treasure trove of insight that simply can’t be bought; it must be collected through a range of digital channels over time.
That said, collecting loads of data doesn’t do you much good unless you can analyze it, identify patterns and put it to use. To do that, data needs to be paired with AI and machine learning — technology that helps marketers identify patterns in behavior that the human eye can’t always see, which shapes how they reach audiences and then tailor content and offers.
For instance, as a lover of the outdoors and an avid outdoor gear shopper, I should see customized ads informed by my prior searches and purchases. After consenting to brands using my data for such purposes, it should be table stakes that my favorite brands, such as North Face and Patagonia, regularly incentivize me to buy their latest. At the same time, AI should be helping brands determine the appropriate cadence of ads and the level of personalization that works best for each customer.
A Real-Time Snapshot Of Audience Behavior
Throughout the past year, we’ve experienced unprecedented change, and the preferences that guide consumer decision-making have changed as well; relying on what you once knew about your audience is not as straightforward as it used to be.
For example, the person who once preferred fancy dinners out is now cooking at home (paywall), and the person who went to the mall on weekends may now be hiking with their dog. As a result, brands need to understand and act on that change. Brands seeking to reach the at-home chef or weekend hikers need to get in front of a much more diverse group of people.
To be successful, marketers must first recognize that their audience is constantly evolving. Second, they should identify and utilize real-time data about who their audiences are, what they’re looking for and where to reach them. Doing so will allow marketers to adjust how they reach audiences, as well as customer experiences, based on what consumers are dealing with right now.
Predictions That Go Beyond Your Gut
Without AI and machine learning, marketers are largely left with their gut instinct to predict the future. And regardless of experience, AI has the ability to make stronger predictions than humans. While going with your gut can prove valuable, AI can help augment your intelligence.
This past year, for instance, I’ve seen AI-based insights that could have helped predict that consumers were interested in buying used cars during the pandemic. With AI-powered forecasting tools and technology to personalize customer journeys, automakers and used car dealerships could have gotten a jump on the resurgence of automotive activity (paywall) following the initial spring 2020 lockdowns.
Other tools that can predict your next best action or identify the price that will drive the most sales help marketers make decisions grounded in data. Technology can help you figure out how new circumstances will impact your customers: what they want after purchasing a new hiking pack or how much they’re willing to spend on a new tent.
The Four Must-Haves For Getting Started
Technology is great, but AI is nothing without data. AI and machine learning act on data from a wide range of marketing and business data sources, including customer, revenue, social, digital and sentiment data — and even external data sources that help augment the businesses’ data to deliver the answers.
This is the second item: Marketers need to ask the right questions — frame the question so that it can be answered with AI. Questions about the next-best offer or next best action for a set of prospects or customers are questions that AI can answer. AI-powered platforms can help answer questions like where ads should run to effectively to reach the right audience. There are many adtech and martech solutions on the market that can help predict what will happen next.
The third item to consider is people. Having a savvy business analyst or marketing ops person in the marketing department is a good start — someone who can understand the right questions to ask and who can actually use AI/ML platforms or technologies. This person does not need to be a data scientist, although a lot of companies have central data science teams that their marketing departments can use.
And finally, the marketers need to embrace the culture of AI, which includes rethinking processes, deciding which technologies to invest in and determining how they make decisions.
One final note: I highly recommend that every marketer and business professional works to understand more about AI. One excellent course is “AI for Everyone” by Andrew Ng on Coursera.
So while 2020 put marketers in an extremely difficult position, it did do us one favor: make it abundantly clear that data and AI are foundational to our success. Now that we’re certain, we’re well positioned to meet, or even exceed, the lofty expectations in front of us.
This article is written by Ingrid Burton and originally published here