16-Oct-2019 12:56 PM Digital Marketing
From image recognition to predicting crop yield, verifying your identity and unscrambling pixelated images, AI can just about do anything nowadays. The AI buzz is ubiquitous and you cannot go a day without encountering some business offering “AI solutions.” This buzz has reached such a feverish pitch that businesses on the lookout for new solutions now filter their searches with “AI-enabled.” Whether the AI system is a long sequence of if-elif statements or a massive supercomputer in a super cooled basement capable of wrangling millions of datasets is irrelevant. As long as the “AI” tag is present, you’re guaranteed to encounter some form of it in your daily life.
Lack of industry guidelines as to what exactly is classified as AI-enabled technology is an issue here. Assuming that the vast majority of these AI solutions are in fact legitimate, the question remains as to what AI can do for your marketing team. If you’re reading this in 2019, chances are that you have stumbled across this article at the right time. By 2020, AI in marketing will have spread like a virus across business verticals. This is the right time to get on the AI bandwagon. If you’re reading this any later than 2020, you may have arrived too late to the party. Your competitors have already sailed away in the AI cruise ship for greener pastures.
Let’s get down to the nitty-gritty of AI in marketing.
Whether you’re a business owner starving for leads, a service provider in search of clients or a marketing strategist unable to make any headway, you suffer from one common problem — data.
Data is king. And s/he who can conquer data and beat useful insights out of this data will be crowned emperor. With petabytes of data generated every day, companies are leveraging big data analytics to analyze buying decisions, predict customer behavior and model marketing strategies. But that’s where big data stops. While big data refers to the raw input, structured or otherwise that needs to be processed to obtain useful information, AI refers to the output part that can take in input data generated using big data frameworks and arrive at intelligent insights. So, while big data can make use of multiple tools to analyze data, using AI tools seems to be an intelligent option.
Whatever your big data analytics tools can do, AI can do it better. AI is capable of mimicking human cognitive functions like acting and reacting to inputs. Traditional computing systems can do this too, but only in the ways it has been taught to do. If you offer them a hitherto unknown scenario, it will just throw up a scripted error. But with AI, it can learn from past experiences with similar data and rely on its robust computing to come up with a solution, albeit after several cycles of trial and error.
Not to say that big data and traditional computing is a huge waste of time. Data still needs to be prepared before going through the AI pipeline. In a sense, AI bridges the gap between big data analytics and strategy execution. This will save you a lot of time and effort.
A well-trained AI platform is capable of identifying themes across huge data sets in record time. These systems are also capable of understanding human-generated content that is rich in emotions and context as is usually seen on social media. Think of Gmail’s smart compose feature which can read your email and craft suitable responses before you even finish reading the message.
- Love ‘em or hate ‘em, chatbots are here to stay. With AI-enabled chatbots coming very close to passing the Turing Test, customers are finding it increasingly difficult to say whether they are talking to a man or a machine.
- Propensity modeling is the process of predicting future behavior based on past performance. Driving enormous data sets through machine learning algorithms results in deep insights about what product a customer will look for in the market a week from now.
- If that scares you, wait till you hear about propensity modeling’s evil cousin predictive analysis. Armed with statistical algorithms, customer data, and machine learning models, predictive analysis is a much broader tool that can predict market trends, customer behavior and forecast business outcomes.
- Content curation is another way in which AI has finagled its way into our daily lives. A great example is Netflix’s content curation system which suggests movies based on your interests. Either I’m a being who’s too complicated for AI to understand or this is still a work in progress, but this feature leaves much to be desired. However, similar content curation algorithms can be used to guide users to articles that they might find interesting. In fact, Ad networks now claim to use AI-powered programmatic advertising to curate the best ads for website visitors. This means more bang for your buck as your ads will only be shown to consumers who have a proven interest in your product.
- Dynamic pricing is another avenue where AI is being deployed effectively. Demand is not the only metric that guides these price fluctuations like in the olden days. AI is capable of factoring in several more metrics like time of the day, users’ socio-economic status, and impending holidays to offer a dynamic model like no human could ever conceive.
It wouldn’t be a complete picture if we didn’t talk about some of the obvious disadvantages of AI.
Whatever your business vertical, expect AI-based features to seep into your daily tools. Being a marketer who is required to make sense of data, it is in your best interest to keep up-to-date with the latest AI marketing tools like Acquisio Turing, Atomic Reach, and Conversica, to name a few.
If you feel confused and lost in the marketing clutter, maybe it’s time you turned to some experts who have intimate knowledge of digital marketing. Hey! Wait a minute. That’s us. Why don’t you visit us at iverbinden.com and learn more about what we can do to upgrade your marketing experience?