- 1 Content Creation and the Case of Evisu
- 2 Digital Advertising, Media Buying and the Case of Red Balloon
- 3 Conversational Commerce and the Bot Landscape
- 4 Behavioral, Predictive Analytics and the Case of Albert and Harley
- 5 A Few More Benefits of AI
- 6 What’s Next with AI and the Transformation of Marketing?
Today, we still think of artificial intelligence (AI) as the technology of tomorrow—and that makes me worry that many of us aren’t paying close enough attention to the incredible leaps happening in the field. In truth, AI has already started to transform life—and marketing—as we know it.
The adoption of AI is right now bringing companies the benefits of real-time audience segmentation, personalized messaging, predictable customer value, and optimized media buys. It has ceased to be an abstract idea located at some distant point in the next decade. It is instead a tool that marketers need to equip themselves with now in order to stay effective and relevant for the near future.
With explosions taking place in the amount of customer data that can be generated each second, deep insights are obtained regarding customer intentions and behavior. Deep learning—a subset of machine learning (ML)—makes personalized marketing-at-scale a present-day reality. Every day, tons of data are gathered from leads, prospects, and customers. With data flowing in from websites, apps, CRMs, marketing automation systems, social media, and even the Internet of Things (IoT), companies now have more substantive information to leverage than ever before. Optimal marketing ROI being a top priority for any business owner, once they pair with the power of AI, an enormous value is instantly created.
The value addition can be in the form of building enhanced capability to forecast sales, understanding customers more comprehensively, improved data points, ability to identify up-selling and cross-selling opportunities that traditional business intelligence tools were unable to track. All of this massively enhances customer experience, service, and retention.
Most marketing benefits of AI and ML fall into one of three categories:
- PRECISION: Engaging customers with precision.
- ACCELERATION: Accelerating the conversion of customer insight into action.
- OPTIMIZATION: Optimizing the return on marketing investment.
AI has ushered in a number of innovations in digital marketing, and at the same time a lot of new AI-powered tools were introduced into the marketing technology stack.
Let’s take a look at four very successful use cases where artificial intelligence is transforming marketing and creating breakthrough results:
Content Creation and the Case of Evisu
AI has incredible potential to curate and create massive amounts of quality content. The tools can further be used to proofread content and then distribute it to the right people, at the right time, on the right platforms at scale. Platforms such as Quill, Wordsmith, Articoolo and WordAI are leveraging machine learning to deliver targeted content. Many of these platforms deploy advanced natural language generation (NLG) to create meaningful content from data.
Consider the case of Evisu, a premium denim and lifestyle brand with a global presence.
Like all global brands, their goal is to engage their customers with the right message, at the right time, through the right channel and with content that’s relevant and timely. They have a very small online team, and managing all their digital marketing needs, specifically, SEM and social channels, is an overwhelming task.
So the brand turned to AI to perform many of the time-consuming, manual tasks which humans are unable to perform at the speed and scale required for efficient and effective consumer interactions. With content curation, Evisu marketers found that taking small steps to ensure personalization is correct and located in the right places was a critical success factor. You must take the time to see how your customers respond to the content you are sending to better understand what works and what doesn’t work. Evisu conducted experiments to see which personalization efforts yielded the greatest return and then doubled down on those.
Digital Advertising, Media Buying and the Case of Red Balloon
Artificial intelligence is already disrupting the way businesses buy media and advertise. New AI-enabled marketing entails placing personalized ads in front of a highly segmented target audience based on complex algorithms and big data. Although some form of segmentation and targeting is taken care of in today’s traditional digital marketing too, it is based on limited data sets often derived from traditional data sources such as customer touch points, CRMs, Website traffic, etc.
With AI, personalization rises to the next level. Brands today are beginning to use a number of artificial intelligence platforms and tools to intelligently identify and segment audiences, build advertising creative, test variations, improve campaign performance, and optimize ad spend. AI-powered ad tools detect patterns in advertising data and predict what changes to campaigns will improve performance against a specific KPI. All this happens instantaneously, often in real time and at a massive scale. Analyzing, testing, and iterating campaigns used to take weeks earlier.
The tools liberate marketers by eliminating the tedious manual tasks of tweaking business rules each time new customer information is captured. With fewer tasks that previously had to be done by hand, AI allows you to focus on more strategic and creative activities like campaign planning.
In the case of Red Balloon, an online experiential gift retailer, AI allowed them to break away from outdated advertising mediums, reach past their then-audience of Australia and New Zealand, and connect with their customers on a more personal level.
You can read more in HBR about how Red Balloon took their digital advertising campaign to new heights.
Conversational Commerce and the Bot Landscape
Smart chatbots can be found on plenty of websites. While some bots are nothing more than glorified command languages, others are driven by natural language processing, and the most intelligent bots are in the arena of self-driving cars.
The technology behind the types of bots varies greatly in its complexity, application, and maturity level. For the purpose of this landscape, we’ll use the bot classifications provided by botnerds.com: Script Bots, Smart Bots, and Intelligent Agents.
Customers expect easy processes, minimum wait time, and self-service options and faster turnaround time wherever possible. Chatbots perform exactly these tasks in a more efficient, reliable and secure way. They are available 24/7 and they understand multiple languages.
Lastly, they can be accessed through customer’s most preferred platforms such as Facebook, WhatsApp or even through voice assistants like Amazon’s Alexa.
Behavioral, Predictive Analytics and the Case of Albert and Harley
Today, there is so much data available for marketers that they alone can’t possibly try to analyze it all. When datasets from all sources are analyzed, predictive analytics can’t be accurate.
Common examples of predictive analytics would be the product recommendations on Amazon or a movie recommendation on Netflix, price optimization through key insight into the impact of price change on revenue, creation of ads based on demographic segmentation and past consumer behavior and trends, predictive lead-scoring by B2B companies to improve their lead conversion rates, etc. These examples just scratch the surface. It’s hard to exactly quantify the impact of AI on behavior analysis and predictive analytics.
A Harley-Davidson motorcycle dealership transformed its lead generation efforts with Albert, the first artificial intelligence marketing platform for the enterprise.
Albert performs many of the time-consuming, manual tasks that people are unable to perform at the scale required for efficient and effective consumer interactions. The tool enhances overall marketing productivity by complementing the role of marketers.
A Few More Benefits of AI
So far, we discussed some of the early high impact use cases of AI in marketing and analytics. Here are a few more examples of how AI is helping streamline many other key processes of marketing:
- Forecasting call volumes to call centers more accurately enhances customer satisfaction and reduces the stress of high call volumes on agents.
- Using text mining to scan and classify thousands of responses to an open-ended question in a customer survey.
- Allowing advertisers to test out newer ad platforms and optimize targeting, algorithms can optimize ad bidding and find the best cost per acquisition ads for businesses.
- Analyzing hundreds of data points on a single user (location, demographics, device, interaction with the website, etc.), AI can display the best-fitting content. This leads to a much better customer experience for all website visitors.
- Ai can design highly-personalized dynamic email campaigns based on previous website interactions, previously read blog articles and content, time spent on a page, wish lists, interest of similar visitors and previous interactions with branded emails.
Although the above cases give just a glimpse of the degree of penetration AI can have into marketing, in reality AI can have a positive impact on every aspect of marketing.
What’s Next with AI and the Transformation of Marketing?
Today, artificial intelligence is fast becoming the backbone of existing marketing technology stacks. With the help of AI, marketing is becoming more and more data driven. 360 degree prospect profiles are being generated with the help of a massive pool of data available for each prospect, and a hyper-personalized campaign has replaced or is in the process of replacing what we once called “personalized campaigns.”
Looking ahead, we see AI transforming the depth of analytics which in turn is transforming marketing. With the help of big data analytics, companies can engage with their customers meaningfully throughout the buying journey in an unprecedented manner. In an online retail model, for example, machine learning can help retailers identify patterns of casual browsers and differentiate themselves from serious buyers. This insight generated with machine learning and advanced analytics, can allow the retailer to run hyper-personalized campaigns for a set of buyers out of the millions of casual browsers.
Just consider the case of online retailer Vineyard Vines. Clothes, as much as they might seem separate from marketing, form the building blocks of individualism in modern society. Fashion categorizes our personality, our ecosystem, and our status. It only makes sense, then, for a retailer to market to their customers as unique individuals who want to be treated as such. In a recent article for Harvard Business Review, I shared the research and analytic strategies that sent that little pink whale swimming toward success, but let’s take a step back. Vineyard Vines might have optimized their strategy to react to analytics and earn customers’ trust, but how did they know how to use their data? How did they know where to set their goals? Driving sales is at the root of all marketing decisions, but why did they employ their analytics data the way that they did?
The answer is simple: They used analytics and automation to narrow in on specific personas based on purchasing habits, interests, and personal engagement with the brand, a feat that would have been impossible without the help of the retail marketing automation platform, Bluecore. Out of the millions who visit a retailer’s website, Bluecore helps marketers can narrow it down to a small target group by identifying which stage each buyer is in, scoring level of interest based on their overall interactions. Layering on geolocation capabilities, and smart chatbots make the buyer journey even more smooth for the customer. At the end, it’s the retailer who gains the much needed competitive advantage over their peers who are still counting on older business intelligence tools based on structured data.
AI is proving to be a powerful tool in enabling innovation by creating efficiencies and competitive advantage while opening up human resources to focus on more rewarding and higher-value strategic work. Big data, together with machine learning will not only make it easy for marketers to get more out of their marketing efforts but assure their return on investment.