The surge in technological advances has had a profound effect on today’s consumer behavior. With the wealth of resources, information and data available, modern consumers hold a lot more sway over the marketing game than they did in the past, when traditional and aggressive marketing tactics were the norm.
The early days of online marketing when shiny banner ads and emails that promised payoffs, but didn’t really deliver, no longer fly.
Consumer behavior is largely driven by instinct, research, and peer approval. Customers know what information they want, where to get it and how to go about using that information to make buying decisions. Technology has changed consumer behavior over the past few years:
The Need for Instant Gratification
The need for instant gratification is nothing new – as humans, we’re hard-wired to seek out the quickest and easiest means to satisfy our needs. However, with today’s tech-driven consumer base, this tendency has just become more intense. The time span between needing and finding the solution to a problem has diminished drastically.
Immediate Access to Information
As search engines have evolved, so has the consumer’s access to a massive pool of information and data. Searches have become more context-oriented, looking beyond search queries to provide results that take a user’s context and behavioral history into account. Search capabilities are no longer limited to text queries: voice search and image search have made it more fluid and organic.
Perpetually Connected and Mobile
Consumers are mobile and connected to the internet in near-perpetuity. Likewise, the introduction of personal assistant software like Siri and Alexa has escalated the integration of technology into their daily needs, from seeking directions to the nearest coffee shop to finding the best deals for a service or product.
Intelligent and Intuitive Consumption of Content
(Inbound) consumer’s journey is largely contingent on content that is delivered at the right time and in the right context. In other words, the content that users consume goes beyond providing information; it is tied closely with improving user’s experience and guiding their journey from visitor to buyer. Personalization is a key aspect that defines content experiences: from readily available content, incisive filtering capabilities and recommendations based on personal preferences, consumers can control exactly what they want to view.
Given how individualized and segmented the market is, the marketing game is no longer as cut and dry as it once was. We need to be able to strategize our tactics right down to the individual customer. As daunting as that sounds, however, technology hasn’t exactly left us marketers in the lurch.
If the consumer end of the spectrum has seen behavioral and contextual changes, the marketing end of the spectrum has gained a massive advantage in the form of Big Data. As consumers continue to integrate their lives with mobile and online technology, every behavioral aspect, from their browsing habits to their social media interactions, is preserved and translated into data. We know how big data in inbound marketing is crucial and how it helps us reach timely, growth-driven business decisions.
So you can try to crunch the numbers, or you can let a smarter, faster entity do it for you.
The Evolution and the Need for AI Marketing
As we began to gain access to raw, yet rich and extensive, data sets, it resulted in an influx of advanced and automated data analytics tools aimed at creating comprehensive reports to help businesses drive growth.
However, consumer behaviors are no longer static or predictable, and we need to tweak strategies and tactics to keep up with the audience: a process that is nigh impossible to do manually. There is a need for self-reliant marketing algorithms that can be trained to make intelligent recommendations or decisions and eventually, become entirely reliant on their own core intelligence, with no need for human intervention.
How is Artificial Intelligence Driving Marketing Today?
Artificial intelligence is already at the heart of many major businesses including Amazon, Netflix and Google. And, in time, more and more businesses are expected to integrate AI into their marketing strategies.
AI-enhanced Paid Advertising
Two of the dominant paid advertising channels – Google AdWords and Facebook – use AI at the heart of their operations, serving up highly-targeted advertisements to users based on their personal preferences and behavioral history. Today, marketers invest a chunk of in-house resources into managing and optimizing their paid advertising campaigns on both these platforms.
However, with AI, we can maximize PPC potential through multiple channels to create increasingly performing ad copy, improve targeting and place competitive bids.
AI-powered content creation
It has been a slow process, but as Gartner predicted back in 2015, AI-generated content – also known as Natural Language Generation – is here to stay. AI has the potential to streamline the efforts of content writers and remove the onus on them to constantly generate content that converts.
Currently, NLG tools can create content such as personalized emails, business reports, product descriptions, real-time stock insights and sports recaps. The content and writing styles can be designed to be readable and comprehensible, based on rules and formats established by the business.
Chances are that you’ve already encountered chatbots: those ubiquitously named assistants that pop up on websites. These chatbots are programmed to provide automated customer service through personalized responses to queries. Chatbots are built around a large pool of customer-centric data. Using this data, and using predictive models to detect patterns and problems, they can provide near-perfect solutions for individual customer problems in clearly-worded, readable replies.
Real time data analysis
On an average, 2.5 quintillion bytes of data are generated per day; and artificial intelligence can be used to sift through it, and help identify patterns and trends that we wouldn’t be otherwise able to spot. These insights, in combination with KPIs can help make intelligent, proactive decisions, take corrective measures for outcomes that have not happened yet and stay ahead of the competition.
2,500,000,000,000,000,000 bytes of data are generated per day
Predictive marketing (Predictive Churn rate)
Artificial intelligence can be used for predictive marketing, which relies on information from existing customer data sets to build models that determine future outcomes and trends. These models can be particularly useful in assessing customer engagement levels and identifying segments that are about to churn or leave for a competitor. These algorithms may also identify what level of churn the customer is at, and in turn, trigger the corresponding nurturing and re-engagement tactics to prevent churn.
Also known as audience-of-one marketing, behavioral marketing involves building customized campaigns to match the behavioral trends and habits of the individual customer. This type of marketing, which requires the development and deployment of thousands of campaigns simultaneously, is obviously beyond the scope of manual marketing technologies. AI, with the aid of deep learning algorithms, is used to collect and analyze behavioral data, and dissect it to create campaigns personalized for each individual.
Audience segmentation is one of the fundamentals of Inbound Marketing: by breaking your audience into segments based on known attributes such as their demographic, behavior and other traits, you can tailor your marketing to be incisively relevant and personalized for prospective customers. The more specific the segments, the higher the chances for successful conversions.
Artificial intelligence can identify and create highly specific segmentation criteria, based on patterns and trends that it recognizes in customer data.
We often spend valuable resources and time into creating and scheduling multiple personalized email campaigns for our various audience segments. By mapping the users’ behaviors on a website, social channels and any engagements that they have had with content, self-learning algorithms can help create personalized emails for each user in a specific segment and make our job easier.
What’s even more interesting, and exciting, is that AI email marketing can create subject lines so perfect that the recipient can’t help but open the email. Phrasee, which we featured a few blogs ago, is leading the AI train in B2B email marketing.
Competitive Intelligence (CI)
Competitive or business intelligence involves the collection and monitoring of data and activity within the market to gather insights into how a business can beat out its competition. Big data has transformed competitive analysis into a widespread tactic that businesses have come to rely on: however, the ability to make sense of and derive intelligent insights from the data is beyond human capacity. Therefore, many businesses are now turning to AI to help accomplish it.
Sales Intelligence (SI)
Similar to CI, sales intelligence involves the gathering and monitoring of data to provide insights to help sales teams increase their chances of closing deals and converting leads into customers. AI automates most of the processes leading to a sale, only leaving the sales teams with leads that are close to conversions.
Lead-scoring AI algorithms can recognize behavioral patterns and trends in customer datasets that indicate the likelihood of conversions, score them, and initiate nurturing and re-engagement tactics for disengaged leads. This ensures that only leads that have a definite chance of converting are handed over to the sales team.
Enhance customer experience
In addition to helping marketers make intelligent business decisions, AI can also enhance a customer’s experience in real-time. AI-backed product selectors and recommendations are one such example: these algorithms take a user’s responses into account to provide them with the best solutions or suggestions for products they should buy.
As an example, 1-800-Flowers.com has a “gift concierge” that uses a customer’s real answers to its questions in conjunction with the data accumulated over time to tailor the best suggestions for the customer’s needs.
Enhance shopping experience
AI-based solutions for eCommerce and retail are nothing new: however today, AI solutions can be used to further streamline customers’ experiences right from product browsing to checkout to closely mimic human experience. Using customers’ previous buying history, social activity and contextual data, AI eCommerce solutions can tailor what customers see on websites. These include product recommendations, cross-selling and up-selling. Amazon’s recommendation engine has been credited as one of the primary driving factors behind the eCommerce giant’s success.
Immediate Benefits of Artificial Intelligence in Marketing
- Real time actionable insights: AI algorithms can be used to derive real-time insights from mountains of data. These insights can be turned into strategies to increase customer engagement and conversions.
- Better leads (a.k.a. no more bad marketing leads): Artificial intelligence can be used to score leads to close precision through algorithms that are trained to recognize patterns and trends indicative of the lead’s sales readiness. Since the algorithm also improves upon its previous performances, and learns as it goes, with each passing cycle, only highly-qualified and precisely sniped leads will be left at the bottom of the sales funnel.
- More sales: AI helps businesses generate more sales by providing them with insights on how potential leads are likely to behave. We can use these insights to determine what kind of message or tactics these leads will respond to, thereby increasing the sales efficiency: generating more sales in less time.
- Higher ROI: AI can drastically cut down your investment in resources needed across the marketing and sales cycle, and in turn, generate a much higher return through campaigns that generate qualified traffic, leads and ultimately, conversions.
- Time Saving: Since AI automates nearly the entirely marketing cycle, it can cut down the time your marketers spend in repetitive tasks, allowing them to focus more on tasks that require human intervention.