Month: March 2021

Artificial intelligence is more than a buzzword when it comes to marketing. While there’s still plenty of unfulfilled AI promise (no robotic creative directors in sight), it’s also true that sophisticated marketers are using AI right now — and delivering results.

Sure, we all know about AI-powered tech, like chatbots, voice recognition and automated ad bidding. But there are other, more direct applications of AI in marketing quietly driving the decisions of some brands.

Right now, marketers are using AI to tackle challenges like:

1. High-value customer segmentation: What characteristics define my most valuable customers, and how can I find more of them?

2. Churn risks: Which customers are most likely to churn, and how can I retain them?

3. Product propensity: Which consumers are most likely to accept my latest offer?

Answering these questions requires examining why a consumer behaves a certain way. Whether we seek to identify the common characteristics of high spenders, churn risks or high-propensity audiences, we must paint a picture of the consumer and understand the attributes (known as “features” in data science) that influence the way they behave. Marketers can use AI to build these portraits. Specifically, AI offers a framework to build predictive models that are powered by the attributes that predispose consumers to behave a certain way.

AI For Targeted Marketing

Here is a walkthrough of the predictive modeling process as it relates to marketing to shine a light on how consumer attributes can be used to predict future behavior.

Data scientists apply AI techniques (machine learning and predictive modeling) on consumer data to figure out which consumer characteristics are most likely to be associated with a specific behavior. The resulting characteristics are considered to be “predictive features.”

Predictive features can be created from demographic characteristics (age, gender, profession and marital status), or behavioral characteristics (international traveler, marathon runner or high-end shopper). If you choose them carefully, the right set of features can identify whether (or not) someone is likely to take certain actions.

The “action” depends on marketing goals, and it could be either a desired outcome or one that needs to be prevented: booking a trip, signing up for a particular kind of service, account cancellation and so on.

With a carefully selected set of predictive features in hand, data scientists can use AI techniques to analyze past behavior and find patterns that are predictive of the outcomes they want to drive.

Case In Point: AI’s Predictive Characteristics In The Field

To understand in more detail how this actually works, let’s look at a real-world example of a food delivery company (a former client of my company, an AI- and data-based consumer intelligence solution). With the goal of growing its revenue, this company sought to better understand its highest-value customers, increase the percentage of these valuable customers within its customer base and develop acquisition and retention strategies for them.

To achieve this, the company employed a predictive modeling technique known as recency, frequency and monetary (RFM) modeling. RFM modeling helped define the predictive features of a high-value customer in a manner that created actionable insights for marketing campaigns.

Step 1: The Starting Point

Like most businesses, the food delivery company maintained robust data about its customers and their order history. In data science, this internal data is known as first-party data. Marketers need to start by understanding the first-party data available to them in their organizations and taking an inventory of what they have access to. Depending on the organization, access to data is highly variable. There will be some data that is readily available and unfortunately, there will be data that you will never get your hands on.

First-party data is incredibly valuable, but it does not typically provide the kinds of insights needed for efforts like audience segmentation or programmatic ad buying. For the delivery company, it was relatively easy to define its best customers using first-party data, but these “best customer” characteristics couldn’t be applied to the general consumer population. You cannot create a market segment of middle-aged women who like to order Italian food after 7:00 p.m. (although I could be over-personalizing this).

Step 2: Data Enrichment And Feature Selection

After inventorying the data that is available to you, determine where you have gaps. What sort of information would be useful to better understanding customers and prospects? What data elements will build that elusive 360-degree profile?

In our example, by enriching its first-party data with third-party consumer data (external data acquired through a data marketplace), the food delivery company was able to create a fuller picture of the customer and generate a set of predictive features that defined its best potential customers: young (25-34 years old) couples with kids in which both partners worked and had lengthy commutes.

Step 3: Taking Action On Insights

Once you have enriched your data, you are ready to act. You can use this data as part of your predictive modeling, advanced segmentation and personalization to increase the effectiveness of your efforts.

Predictive features in hand, the food delivery company worked with their marketing partners to identify and target audiences of dual-income professionals who had kids and long commutes, which made it possible to send campaigns directly to the people who were most likely to become high-value customers.

The AI Payoff

The application of AI in marketing is a nuanced process that requires the right volume and type of consumer data. It can be less time-consuming and substantially more effective than traditional methods. Gathering the depth, breadth and scale of data for AI is just one of the challenges (but possibly the biggest) that marketers may encounter. Another common challenge is with internal understanding and skill sets. Does your organization really understand the benefits of AI, and do you have the right people to successfully to implement it? Finding a trusted partner for the near term is one way to overcome these challenges if you lack internal resources.

While AI can seem intimidating, hopefully this explanation demystifies the overlap between marketing, data science and machine learning. Marketers who team up with data scientists to use AI to determine predictive attributes and create predictive models can yield impressive campaign results.

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This article is written by Laurie Hood and originally published here

There are only a few other marketing innovations that shocked business owners, marketers and developers over the last decade like artificial intelligence. Artificial intelligence (AI) has made leaps and bounds since the 1950s when it was first theorized and created. Now, in 2021, AI has the potential to help everyone involved in the marketing process perform their jobs exponentially better than they could ten years ago.

You may be wondering, “How exactly does AI improve the way we reach and engage with the people who visit our website?”

Today, we will look at several ways to use AI to get more value from your marketing campaign. With this information, you’ll have options to build an enjoyable, user-friendly experience for your visitors.

Let’s get started.

Use Predictive AI For Personalization

The main reason businesses use AI is for data collection. Instead of manually going through every customer interaction, businesses can use AI programs to compile this information into actionable reports. Consequently, business owners and marketers can make smarter decisions about their campaigns.

One smart way many people are using AI for this business is for personalization. Consumers expect comprehensive experiences from brands. A big part of this puzzle includes personalized offers and content.

Think about the last time you visited Amazon. You likely noticed a page that shows products you may enjoy, along with featured selections under each product. This is an excellent example of how AI personalization improves customer experiences.

Amazon’s algorithm analyzes the products you’ve purchased in the past and recommends products across all categories that fit your needs. Adding a similar feature to your website that works off your customer personas can help you create personalized offers and content selections for users on your website, email, and even social media. The result of a successful personalization campaign is more sales, engagement, and site traffic.

Improve Content Creation With User Insights

Next, let’s discuss how AI helps marketers improve the quality of new content they post on their website. Blogs are an essential part of most businesses, especially when you consider that research suggests over 77% of internet users read blog posts.

E-commerce business owners and marketing teams put even more value into their blogs because most consumers read a company’s blog before making a purchase. There are clear advantages offered through content marketing, but brainstorming topic ideas can pose a challenge.

Using the power of AI, you can brainstorm topic ideas and improve your existing posts faster than ever before. Machine learning can analyze consumer behavior on your blog pages and give you helpful feedback about improving future posts.

For example, if you notice that many of your users leave before they make it a quarter of the way through your post, you can start thinking about ways to extend their time on your site with a video or images for more engagement.

Similarly, you can rewrite the content to see if the problem is with your text copy. This type of testing was possible before, but it’s more effective than ever before with the lightning-fast data collection offered with AI.

Chatbots And Natural Language Processing

Natural language processing (NLP) is a subset of machine learning that can understand, analyze, compile and create human speech. Business owners use it for a variety of reasons to improve marketing. In many cases, NLP is used in chatbot technology to communicate with visitors that land on a website.

Chatbots can understand basic requests from consumers, communicate efficiently and even make changes to their accounts. There’s no doubt that this feature has improved the way business owners sell products to their customers.

Chatbots can put product information on display for people when they land on your website and type a specific keyword or phrase into the system. Tools like chatbots make it easier than ever before to thrive in the world of digital marketing.

NLP also plays a pivotal role in voice search. More consumers use voice-activated devices than ever before. Consequently, website owners now have to use natural language on-site instead of keywords that may sound clunky or forced.

Using long-tail keywords and creating content that answers questions will ensure that Google’s AI program will find your result when someone users their voice-activated device to complete a search. So, even when it’s out of your hands, AI still plays an essential role in your business’s success.

Back To You

Despite the fear that AI will take work from others, it’s clear that we won’t see a significant shift in the tech job market. Instead, marketers will use AI to improve customer experiences by offering personalized and tailored content and top-notch customer support.

All these feats are accomplished with AI that improves on what the marketer is doing and doesn’t replace them entirely. At the end of the day, marketing decisions come from human beings. Using the power of AI, we can make smarter decisions and grow our websites through data-driven reports.

It’s up to you how you’ll implement this technology on your website. But one thing is clear: The future is here. If you plan on running a successful online store in the coming years, AI-powered marketing tools are a must.

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This article is written by Chris Christoff and originally published here

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.

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This article is written by Ingrid Burton and originally published here