It’s becoming clear that we’re headed toward a new vision for our devices: the Phone as a Service (PaaS). Yes, sounds crazy, but look at the parallels between your phone and how/why other “X”s have become services:
X-as-a-service (XaaS) is delivery of X directly via the internet, eliminating the need to use and manage multiple and independent solutions on locally hosted devices, right? So, PaaS is the delivery of personalized media via the phone, eliminating the need to use and manage multiple and independent, locally hosted apps. We’re already seeing that happen.
At the beginning of the year, we like to take time and speculate on which data science trends will make the biggest splash in the year. Now that we’re entering the second half of 2019, it is a good time to take a look at our initial assumptions regarding these trends and re-evaluate each one’s impact on the industry.
In a recent column, Recode founder and New York Times columnist Kara Swisher cut to the core of what would seem to be concessionary calls for regulation from Big Tech firms, summarizing their attitude like this: “We make, we break, you fix.” She’s right, and with Google, Amazon, Apple, and Facebook doubling their combined lobbying spending from 2016 to $55 million in 2018, it is worth taking a closer look at the kinds of arguments the companies are trotting out to avoid responsibility for the outcomes of the technology they produce and sell. We should be particularly concerned about the arguments tech firms are making about AI, which is already remaking our society, replacing steps in crucial human decision-making processes with machine-generated solutions.
For an example of how tech firms are attempting to get away with peddling potentially dangerous AI-based tech to powerful entities like law enforcement agencies while accepting minimal accountability, consider Amazon’s Rekognition.
Street Fight is rolling into July with the monthly theme Disrupting Retail: a look at how retail continues to transform, driven by competition from Amazon and key trends like “retail-as-a-service.”
But why is this important to Street Fight (and to you)? As we continue to evolve the definition of “local,” one key component of its market opportunity is offline brick-and-mortar shopping. After all, about 90% of all U.S. retail spending, to the tune of about $3.7 trillion, is completed offline in physical stores. And that’s usually in proximity to one’s home (thus, local).
More than half of consumers are frustrated by customer-service situations in which they can only interact with automated agents, and nearly one in five even reporting feeling angry in those situations. That’s per a new survey of U.S. consumers conducted by The Harris Poll and commissioned by call tracking and analytics firm Invoca.
According to Gimbal’s SVP of location platforms Adrian Tompsett, the key to the location business is having a long-term and holistic view of customer value. That means using location intelligence to go beyond just triggering promotions to increase the customers’ basket size, instead using the technology in ways that will provide additional value in the long term.
It’s a brave new advertising world. The algorithms are taking over, whether human advertising managers like it or not. Our best bet is to understand how the algorithms work and to give them the freedom, the data, the budgets, and the creative assets they need for optimal performance. The Facebook algorithm will take away budget lever from humans when Campaign Budget Optimization becomes mandatory in September 2019.
There’s no time for the future of retail like the present. That is the motto at Walmart’s Intelligent Retail Lab, a live experiment in AI-driven shopping experiences that is now open to the public at a Walmart Neighborhood Market in Levittown, NY.
New technologies (and new spins on old ones) are the modern company’s ally in merging digital and traditional marketing. The brands that find a sensible balance between the two are the brands that will outperform the competition. Let’s take a look at four major examples of innovation in this arena.
With the reviews and other content being posted online about brands coming from an increasingly wide swath of sources, manual techniques for reputation management are no longer viable on a large scale. At the same time, the volume of online opinions bombarding potential customers is making it more important than ever for brands to constantly monitor what’s being said about them online. How are brands coping with the challenge?
The task Facebook must take up as it attempts to police hateful content is one inseparable from political values, human judgment, and the interpretation of statements that need to be parsed by well-trained eyes and bright minds with a stomach for horror to boot. While machines will play an indispensable role in content moderation on a platform of Facebook’s scale, they will be far from sufficient. That’s because monitoring hate speech touches on nothing less than some of humanistic inquiry’s age-old questions: the debatable violence, status of truth, and foundations of meaning in language.
Google’s Knowledge Graph ambitions are expanding to include obviating heavy reliance on secondary sources like Wikipedia and being able instead to classify and cross-reference information as a native, self-sustaining activity on web pages themselves. That’s what makes a recent patent filing different from the evidence of the Knowledge Graph we’ve already seen in the wild.
While this more ambitious way of surfacing information about entities is not yet standard, in researching Google’s new interface for hotels, I think I’m seeing evidence of a real-world example.
What do Google’s AI-fueled search results, 5G, and marketing champagne all have in common? They’re the central topics of a roundtable discussion on the latest episode of Street Fight’s podcast, Heard on the Street.
As we do quarterly, this is a bonus episode that puts aside our typical interview format and instead invites the leading thinkers from the Street Fight newsroom and executive ranks to discuss news and insights that are top of mind.
Rodion Yeroshek: The majority of restaurant businesses, especially the small ones, remain slow adopters and non-adopters of AI technology. People may think that the introduction of AI in small restaurant operations is nothing more than jumping on the bandwagon. However, research on the impact of AI on the world economy by McKinsey Global Institute warns the naysayers. The research predicts that by 2030 active adopters of AI technologies could double their cash flow, while non-adopters could lose up to 20% of theirs. This is a hint for restaurant managers who plan to stay in business for the next 10-15 years that it’s time they embrace AI tools or prepare to lose a big part of their market share for good.
On this week’s Location-Based Marketing Association podcast: 180byTwo’s eCHO, Outdoorsy the AirBnB for RVs, Outer, Tide launches 24/7 laundry service, LG builds Amazon Dash into all appliances, Baidu builds AI cat shelters. New research from Blis.
Greater customer expectations and technological advancements are driving big changes in delivery. What’s more, the delivery experience has emerged as a differentiating factor for customers when choosing one retailer over another. eCommerce retailers that operate solely online and omnichannel retailers that offer a physical and digital presence are both beginning to expand their delivery options to meet customer demand. Here are seven trends that will define retail delivery during 2019.
Beyond the star ratings lies a wealth of information. Sentiment and opinions can be used to shape the way brands develop their highest-selling products. Given the volume of reviews posted each day, however, it would be impossible for most major brands to analyze every customer reaction individually. Instead, a growing number of brands are utilizing artificial intelligence (AI) technology to extract and analyze the sentiment from product reviews. Here are five examples of platforms that offer this type of AI technology for analyzing customer feedback posted online.
Damian Rollison: Among hundreds of sessions, exhibits, and demos, one theme came through clearly at IBM Think this month in San Francisco: for large enterprises especially, the AI-driven future for which we’ve been told to prepare is already here. In fact, enterprise companies are using IBM’s Watson technologies today to address a myriad of challenges inherent in the scale of those businesses.