5-year trends in artificially intelligent marketing




  • How will artificial intelligence transform marketing over the coming years? Columnist Daniel Faggella dives into the results from a survey exploring the major trends and opportunities in AI for marketers.






    Artificial intelligence has been making headlines over the last 12 months in domains like health care, finance, face recognition and more. Marketing, however, doesn’t seem to be getting the same kind of coverage, despite major developments in the application of AI to marketing analytics and business intelligence.


    Five or 10 years ago, only the world’s savviest, most heavily funded companies had a serious foothold in artificial intelligence marketing tech. As we enter 2017, there are hundreds of AI marketing companies all over the world (including some that have gone public, like RocketFuel). These companies are making AI and machine learning accessible to large corporations and SMBs (small and medium-sized businesses) alike, opening new opportunities for smarter marketing decisions and approaches.


    Over the last three months, we surveyed over 50 machine learning marketing executives (email registration required for the full report data) to get a sense of the important trends and implications of AI over the next five years.


    Below, I’ve highlighted three major trends that impact the theme of “Intelligent Content.”


    Recommendation and personalization predicted to be greatest profit opportunity


    While most of our executives voted “Search” as the AI marketing tool with the highest profit potential today, “Recommendation and Personalization” topped the list for ROI potential in the coming five years.


    While search requires users to express their intent in text (or speech), recommendation pulls from myriad points of data and behavior — often bringing a user to a) what they were truly looking for, or b) what the advertiser wanted them to find.



    The implications of recommendation in content marketing are numerous. Below I’ll list just a few:


    First, recommendation engines help serve the content most likely to engage readers. In the past, this was done with simple text analysis or tools like elastic search. The “recommended” content was better than a random guess, but it was by no means truly optimized for user engagement.


    Companies like Boomtrain and Liftigniter are developing technologies to tailor content to individual visitors, displaying material most likely to keep them on the site based on their previous engagements, purchases, clicks and more.


    Second, programmatic advertising (like that used on giant platforms like Facebook and Google AdWords) is often used to drive users directly to content before seeing a product page or being asked to book an appointment. Many ad networks (Facebook included) don’t allow for direct lead generation and instead prefer to engage users with the right content first before looking for a conversion.


    Ad networks are partial to keeping user experience high in addition to driving engagement on ads, which is a delicate balance. Companies that can leverage these programmatic platforms to target the right prospects with the right content are the most likely to win.


    Third, we see entire content marketing platforms at the heart of business models. One such example is Houzz.com, a site that hosts millions of articles and photo albums about home improvement and decoration. This content ecosystem links to and references millions of home goods products (from throw rugs to couches and more), and “recommendation” drives the entire experience.


    Houzz is one of the best current examples of “intelligence content” directly tying to sales, and I suspect that in the coming five years, we’ll see elements of their business model become much more prevalent.


    Intelligent content might be content that makes itself


    Content generation is a complex machine learning problem, and until recently, it’s been relegated to big-budget media firms working in quantitatively oriented domains (namely sports and finance). Yahoo Finance uses natural language generation (NLG) to turn information about stocks and bonds into coherent, human-readable articles, saving time for Yahoo’s writers so that they can complete more important and creative tasks.


    NLG is now being used in a vast number of business applications including compliance, insurance and more — and a quick visit to the “solutions” page at Narrative Science shows a plethora of use cases and case studies for machine-written content.


    While domains like finance and compliance involve strict, formulaic transformations of cold data into readable text, executives in the field are excited about its profit potential, too. Rather than simply saving costs on human writers, intelligent content generation will alter existing content (and/or create new content) to help driving marketing goals. As Laura Pressman, manager of Automated Insights, explained in our survey:


    Content generation has high profit potential in the coming five years. Personalization and segmentation can be achieved through altering the content text to speak to certain groups of people, across different platforms, highlighting unique and targeted features that are most important to each specific segment.

    B2C companies may have an advantage in intelligent content


    When we polled our batch of executives about the most meaningful applications of artificial intelligence in marketing, we didn’t want to leave out their opinions about which businesses or industries would be most able to take advantage of AI’s advancements in marketing.



    “Industry” didn’t seem to have much to do with the predicted success that a company might have with AI marketing tech. Much more important was the way the company did business and sold products. For a business to take advantage of AI, the most important traits (as predicted by our batch of executives) include:



    • Data collection: Ability to quantify customer touch points across all marketing activities.

    • Transaction volume: Reaching the marketing “goal” more often helps to train marketing algorithms and provide better predictions and recommendations.

    • Uniformity: Businesses that pool their marketing and sales data into a single stream are more likely to succeed in applying AI.

    The above three qualities repeated themselves again and again in our survey responses, along with strong predictions that “Digital Media” companies and “E-commerce/Consumer Retail” companies would be most poised to take advantage of AI in marketing. As Lisa Burton, chief data officer of AdMass, explained in the survey:


    Advertisers and e-commerce businesses have the highest potential gain from machine learning because of the ease of measurement and quick feedback needed to train and improve machine learning algorithms.

    While B2C and retail companies seem to have an edge on “quantifiability” and attribution to sale, some of our respondents also hinted at the strong opportunity in B2B. Leveraging the many content and interaction touch points in a B2B sale will aid greatly in “cracking the code” on B2B marketing attribution, which is undoubtedly valuable.


    In the coming five years, it may be possible that attribution and recommendation take off quickly in retail, while adoption in services and B2B sectors will provide more of an “ahead of the curve” advantage in industries where tech adoption is slower.


     


    [Article on MarTech Today.]



    Some opinions expressed in this article may be those of a guest author and not necessarily Marketing Land. Staff authors are listed here.









     


    Marketing Land – Internet Marketing News, Strategies & Tips

    (5)