3 Ways Artificial Intelligence is Disrupting Marketing

The Hubspot State of Inbound 2017 report was just published today, and as always, there are some preeeetty interesting insights there.

Between the usual data on marketer’s biggest pain points this year, and where most marketing spend is going, there was one section that particularly caught my eye in this report: Disrupters.

While “disrupters” may seem like just another buzzword, it would be a mistake to not take these incredible innovations seriously. Disrupters do just that: they change the game, flip the script, make you re-think how you approach old practices. And one disrupter, in particular, is promising to alter inbound marketing as we know it: Artificial Intelligence.

As marketing increasingly becomes data-driven and technology-reliant, artificial intelligence finds a natural place to live here. While in recent years, marketing and branding trends have stressed the human side of business, and human-to-human marketing approaches, AI is going expedite the processes for companies to understand their place in the market, more efficiently connect with and procure customers, and increase overall customer experience.

Based on the State of Inbound Report and my own personal experience in using AI, I’ve rounded up three ways AI is already disrupting marketing, how you begin to use it, and where it could go next.

1. Artificial Intelligence for market and competitor research

When Hubspot unveiled their Growthbot chatbot last fall at INBOUND 2016, I was extremely excited to try out my first bot in my marketing practice. While I used some bots before, like Slackbot, Growthbot is designed for gaining insight and information specifically for web traffic, keyword research, inbound links, and similar topics related to marketing and website optimization.

What I ended up finding was that Growthbot is crucial in helping myself and clients conduct market and competitor research. Whether it’s finding out what are the best inbound links a site has, what it’s monthly traffic is, or what keywords it ranks for, Growthbot can lend some powerful insight into website data instantaneously. I particularly like it for finding out a website’s spend on Adwords and which words a company is bidding on.

I’ve been amazed at how easily and quickly I can get this information, though the app isn’t perfect yet. Sometimes I hit snags where it can’t find details on a specific site, and I run into more trouble sometimes when I’m searching international companies and URLs.

But I think in the future, while Chatbots are already really good at increasing engagement with customers, and qualifying leads, the competitor and market analysis capabilities will really become a crucial tool to my business.

What I hope to see in the future is greater capabilities for prospecting as well, including finding information about which social media networks a company or individual is most engaged on, or if a new company is already using a marketing agency. By accessing this information, I can be even better at targeting prospects myself. Get on that Hubspot….

2. Artificial Intelligence for content production and curation

One example in the Inbound Report is how content can now be curated and even produced using artificial intelligence. While I don’t personally believe AI will be taking over my job anytime soon, I do think it’s a positive step for efficiency in content marketing.

Fact or data-filled pieces of content that typically follow a basic formula are what we’re seeing today, but the implications of what we might be able to “teach” AI programs in the future are enormous. What I think may be possible in the future is creating optimized structures and formats of content that are tailor-made based on lead scores or buyer personas. Content, especially sales collateral, can then be created in a “plug and play” way, filling in posts with graphics, data points, and information that is most relevant to whomever it is delivered to.

Personalization will be the name of the game with AI in content production, and I think the same can be said for content curation. While there are already automations you can use for doing this type of thing, I think it will become better quality in the future.

You know how you can always tell when a piece of content is found and posted on Twitter by an automation tool? There’s always a stilted or unnatural text that accompanies it, or simply just the title copied and pasted.

While content curation is still a great tool for marketers, I think these automations are just hurting curation’s reputation. I’d like to see AI help curation automation get some credibility back by potentially pulling from real commentary you’ve made in the past, or crafting copy based on your tone and personality. If AI can do this, you can add freshness and relevance to your curated posts, and shorten the process for content production.

3. Artificial Intelligence for customer-personalized product recommendations

Wanting to find out more about Artificial Intelligence and its applications to marketing, I recently attended an event at Journey XP in Copenhagen called AI Garage. There, a series of speakers provided insights to the AI projects they were working on, and while some of it was quite frankly a bit too technical and dense for me, there were some incredibly interesting things that I learned.

One speaker was ren Lind Kristiansen, a Data Scientist and Machine Learning Engineer at Vivino. In case you haven’t heard of or used Vivino, it’s an online marketplace for wine. But they differentiate themselves from other online wine retailers by using comprehensive data insights and artificial intelligence to make smart recommendations to new and returning customers.

While this company had me at “wine,” the ability for them to use smart technology to actually make accurate recommendations and increase customer trust and experience was almost head spinning. I started thinking about how many other applications for technology like this there may be.

And though Vivino borrows from Amazon’s program for making recommendations (which has been used since back when people were still freaked out by this sort of thing), they’ve turned it into something that is really engaging the customer. The AI seeks to continuously improve, which then, in turn, improves the entire customer experience. As an inbound marketer, I really like that.

In the future, I think this type of technology might be used across the board in all B2C industries to help customers make even more informed buying decisions.

I still have questions about the program’s bias, and how data scientists can work to prevent the same products being recommended over and over, simply because they are popular with first-time buyers – or are they popular because the program recommends them more? (See, this is the problem I have).

But I think these programs will be much improved if we can also use more insights like which products were bought repeatedly by customers. Or even better, pull from other historical data outside of the site-specific information, like in the case of wine recommendations, looking at recipes that have been saved on Pinterest. Then we’re onto something really crucial in the next phase of e-retail customer service.

When I think about machine learning and artificial intelligence, I often find it hard for me really wrap my head around it. But what is crystal clear to me is that it’s crucial to get on board with AI for marketing strategy and improved customer experience. I can’t wait for what this disrupter is going to do next.

Interested in learning more about how I use AI to find information on your competitors? Contact me now.

 

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