part 3: artificial intelligence - thought leadership...delaying artificial intelligence...

Part 3:

Artificial IntelligenceThe majority of marketing leaders think AI will revolutionize how brands go to market, but few are confident they understand how to leverage it.

Digital Trends:Predictions for 2019 and Beyond

A seven-part series brought to you by the Insight Lab at MNI Targeted Media Inc.mni.com

2Digital Trends: Predictions for 2019 and Beyond

[ Artificial Intelligence / mni.comFor more strategic predictions go to mni.com, or email [email protected].

Artificial Intelligence (AI) has found itself a coveted place in the evolutionary technology space. Its promise is simple—automate everything, from driving to ad buying. But as good as machine-learning algorithms may be at recognizing cats in your online photos, researchers know that neural networks can’t make sense of the world without help from a large amount of meticulously labeled data sets.

Here’s what’s in it for marketers and advertisers—AI-enabled attribution tools can analyze search and consumer behavior patterns, utilizing data from content platforms such as social media and blog posts to help brands understand how customers find and use their products and services. This is exactly what Facebook messenger bots are doing—helping brands optimize and automate their customer service. And this is just the tip of the iceberg.

Uber employs conversational chatbot technology to communicate with customers, making it simpler for them to hire cars, even on channels like Facebook Messenger, Slack or Google Maps. Passengers can use the menu to choose the type of ride, make a request, track the location of the car, send friends a time estimate of their arrival, and even make payment.

As with any new technology, the industry is fragmented in its readiness to embrace AI. While marketers are intrigued by new developments in emerging technologies, many feel unprepared for such advancements. A November 2017 survey by Conductor revealed that 34% of marketing executives felt unprepared to deal with AI in 2018—the highest rate of any technology mentioned, which includes virtual/augmented reality, voice search and new social networks 2.

This does not seem to dampen the spirit of the overall industry, where several players are keen to invest in and experiment with novel AI applications

ranging from smart lead nurturing to automated content production. We will see and hear a lot more of AI-driven technologies this year, and some will impact us more than others (hint: smarter chatbots.).

According to Gartner, 30% of companies worldwide will be using AI in at least one of their sales processes by 2020 3.

Machine learning and AI…will empower and improve every business, every government organization, every philanthropy – basically there’s no institution in the world that cannot be improved with machine learning.”

- Jeff BezosCEO, Amazon

By 2020, 20% of companies will dedicate workers to monitor and guide neural networks 1.

Artificial Intelligence:The Journey into the Unpredictable is Already Happening

What is ‘Deep Learning’Deep Learning is an advanced branch of machine learning, where a computer learns with minimal programming. Deep learning is driven by data and computational power. Given adequate quantities of both, marketers can make the most of data and apply it to make predictions about consumer behavior.

For example, you might train a deep learning algorithm to recognize cars in a photograph. This would be done by feeding it millions of images, some with cars and others without. The program will then establish patterns by classifying and clustering the image data to inform predictive models that is able to look at a new set of images and predict if they contain cars or not, based on the model it has created using the training data.

3Digital Trends: Predictions for 2019 and Beyond

[ Artificial Intelligence / mni.comFor more strategic predictions go to mni.com, or email [email protected].

Making AI Work for YouWhile chatbots are adept at scheduling appointments, making peer-to-peer payments, and checking in for a flight, they will evolve to become a crucial lead generation tool. Brands looking to attract more consumers can leverage chatbots or virtual assistants to collect essential consumer information, especially when deployed via social media channels or through mobile apps. The data and leads collected are used to push the brand’s products and services via the same chatbot, over the course of the conversation. Smarter chatbots will have the ability to detect at what stage of the purchase funnel the individual consumer is, and accordingly tweak its communication (for example, offering a time-limited discount coupon when the consumer has added an item to their cart).

Software developers like HubSpot, Chatfuel, and Botsify are making it easy for brands to create bots that interact with potential customers on their websites and on Facebook.

What to Expect in 2019

For more strategic predictions go to mni.com, or email [email protected].

4For more strategic predictions go to mni.com, or email [email protected] Trends: Predictions for 2019 and Beyond

[ Artificial Intelligence / mni.com

4 Examples of Big Brands Leveraging AI in MarketingBeyond customer service and ad targeting/delivery, AI has tremendous applications in other industries, such as retail. Face-recognition software alerts staff when loyalty program members enter the store, and “magic mirrors” assist consumers with beauty products and allow them to virtually try on clothing and accessories. With such technologies going mainstream, retailers will find themselves armed with more data that will help them identify popular products during the shopping stage as opposed to the post-purchase stage.

Saks Fifth Avenue has used facial recognition since 2016 to identify VIPs—as well as shoplifters.

CaliBurger, the regional fast-food chain, uses facial recognition to identify loyalty members, with an aim to enable face-based payments.

Amazon leveraged automation to reduce ‘click to ship’ time by 225% or down to 15 minutes 3.

Netflix saved $1 billion in lost revenue in 2017 by using machine learning to make personalized recommendations 3.

The idea that AI technologies can be up for grabs as finished products without needing further human investment is a recipe for failure. Neural networks require retraining every time new data is available, which is almost always. In fact, neural networks only offer value to an enterprise when put in an endless retraining and reinforcement loop. CIOs will need to make the business case to ensure the any AI-driven project is provided with necessary resources.

More Advanced Ad PersonalizationCustomer service levels will improve, and a new bar will be set for brand-driven conversations, thanks to AI. Since the technology can bring up past order history as well track current shopper behaviors, the two can be layered to offer unparalleled consumer personalization and—with a comprehensive data set—allow for predictive audience analysis. This will invariably increase a brand’s value among loyal consumers and attract new consumers.

25% of brands with active customer service will use chatbot technology by 2020, up from less than 2% in 2017 4.

Adopt AI and Win Programmatic AuctionsAd agencies and tech vendors will increase adoption of AI to restructure their data sets and increase their odds of winning programmatic auctions. Cloud computing giants such as Salesforce, IBM, Oracle, and Nielsen are all investing in AI products, a wise decision considering that 83% of companies with more than 1,000 employees use or plan to use AI in the next 12 months 2.

44% of businesses worry about delaying artificial intelligence implementation as a vulnerability to new, disruptive start-ups 3.

What to Expect in 2019Artificial Intelligence[

YOUR AI DECODER RING Should you find yourself talking AI with an engineer or enthusiast, below are a few key terms to keep the conversation going:

Machine learningUsing example data or experience to refine how computers make predictions or perform a task.

Deep learningA machine learning technique in which data is filtered through self-adjusting networks of math loosely inspired by neurons in the brain.

Supervised learningShowing software labeled example data, such as photographs, to teach a computer what to do.

Unsupervised learningLearning without annotated examples, just from experience of data or the world—trivial for humans but not generally practical for machines. Yet.

Reinforcement learningSoftware that experiments with different actions to figure out how to maximize a virtual reward, such as scoring points in a game.

Artificial general intelligenceAs yet nonexistent software that displays a humanlike ability to adapt to different environments and tasks, and transfer knowledge between them.

Source: The WIRED Guide to Artificial Intelligence

part 3: artificial intelligence - thought leadership...delaying artificial intelligence...

Documents