Oh, the deep world of artificial intelligence. There's an endless sea of content as it pertains to best practices, how to leverage AI for your business, how it will impact consumers, and the list goes on (way on). The truth is, some AI is too complex and unnecessary for businesses to be worrying about investing in for their brand at this stage, especially given current and short-term demands. But with constant talk of AI and automation in the media, the pressure is on, it's tense, and it's noisy. In an attempt at cutting through that exact noise, insights were gathered from 11 tech leaders that go deeper on where that focus should be (and why).
AI isn’t always the right solution
Businesses need to carefully and methodically weigh their options on whether or not AI is right for them and if so, methodically plan their roadmap accordingly to ensure both product and consumer longevity.
Jason VandeBoom, CEO and founder at ActiveCampaign, a marketing automation and CRM platform provider for small and medium-sized businesses.
A lot of times, artificial intelligence is marketed as the quick fix to solve all of your business problems. Businesses should be wary, however: AI is not a quick fix, and marketers shouldn’t buy into the jargon right off the bat. And a quick fix cannot, by definition, be a customized one. Above all, businesses need solutions that are tailored to their specific needs, not something that’s one-size-fits all.
Before exploring the realm of AI, businesses should first ask themselves if and why they need AI-powered solutions. If a business doesn’t have an issue that AI can solve, such as acting on mountains of data with human-like ability to reason, they would be smart to go beyond the solution-of-the-week mentality and find a customizable solution that’s right for their business.
Further, understand that garbage in equals garbage out. When approaching new technologies built on the foundation of your internal or external data, businesses can find themselves in a ‘walk before you run’ situation. They must take care of basic needs like data hygiene before exploring the technologies to be built on top of that data.”
AI is providing more actionable insights for B2B sellers in the supply chain
Leveraging artificial intelligence to optimize data for B2B, eCommerce players can be a powerful accelerant in efficiencies and gain invaluable consumer insights while automating inventory management.
Ray Grady, President and CCO at CloudCraze, the only B2B commerce platform natively built on Salesforce.
While still in its infancy in B2B, AI has the potential to radically transform eCommerce from both an operational and buyer experience perspective. AI puts endless amounts of actionable data into the hands of businesses, enabling them to analyze customer insights and contextual data to transform the way they do business with customers and improve internal operations. Combining customer browsing and buying habits with external data such as weather forecasts, regional trends, and crop reports can inform decisions for suppliers like product pricing, inventory replenishment, product recommendations, and promotions. On the flip side, AI makes it easier for B2B buyers to manage inventory and automate orders to guarantee stock levels stay balanced. AI helps buyers and sellers make more strategic decisions, save time, sell more and increase profits.”
AI is creating a more personalized digital experience
Retailers have a significant opportunity to improve the customer experience through either create or evolve front-end personalization with both AI and machine-learning being the driving layer behind it all.
Ed Kennedy, senior director of commerce at Episerver, a global provider of a single platform to smartly manage digital content, commerce, and marketing in the cloud.
Artificial Intelligence in commerce is often misunderstood and overhyped. Retailers should see AI as another tool to enhance and improve the customer experience. One key area AI enhances the customer experience is with personalization. There is no doubt retailers will start to see an increased demand for highly personalized commerce experiences from consumers. According to Episerver’s Reimagining Commerce report, more than a third of consumers feel brands do a poor job of personalizing the customer experience -- which will ultimately mean missed revenue for retailers. A subset of AI, machine-learning, leverages real-time consumer behavior and historical order data to automatically recommend products, promotions, and content that are unique to each individual consumer. What’s exciting is we’re now seeing tech-innovative retailers use AI-powered personalization experiences beyond web and mobile, and extending into physical stores such as shoppable mirrors or recommended assortments for associates to show customers."
AI is helping retailers solve three historically trying pain points in the buyer journey
Search, true inventory management, and real-time price management can all be positively impacted and is something retailers need to carefully weigh for optimization consideration.
Michael Fauscette, chief research officer at G2 Crowd, a business software and services reviews platform.
The practical uses of AI in retail / eCommerce is centered around solving 3 basic pain points: 1. helping customers find what they are looking for on and offline, 2. keeping the “right” amount of inventory at the “right” place in the “right” product mix, 3. real time pricing adjustments to stay competitive. The 1st pain point is addressed by AI enabled search, sometimes called “insight engines” and the use of chatbots to manage the interaction. The 2nd pain point is addressed through the use of AI to provide dynamic inventory forecasting or order velocity forecasting to predict needed inventory levels and locations, and dynamic products assortments to optimize the product merchandising plan. The 3rd pain point is managed through a dynamic pricing engine that constantly monitors pricing and dynamically adjusts to keep the product price optimized. In all three cases AI provides the ability to take large and dynamic data sets and product an output that gets better as the AI engine “learns” and adjusts from dynamic feedback."
AI is making customers’ voices heard
Consumers are now being presented with dynamic, AI-infused machine learning, natural language processing (NLP), and speech analysis feedback scenarios. This, along with deep social listening and enhanced 'sentiment analysis' is another clear opportunity to gather actionable consumer insights.
Spencer Morris, SVP, Data Science at InMoment, a Forrester-recognized online platform designed to give companies a way to listen to their customers and employees to optimize the customer experience.
AI is already being leveraged in very practical ways -- to streamline tasks, infuse relevance and personalization into people-machine communications, and augment human intelligence and action. Some of the brands we’re working with are already harnessing a variety of AI elements (machine learning, natural language processing, predictive, speech analysis, etc.) in feedback scenarios. Allowing the technology to dynamically adjust follow up questions based on what customers are saying results in very human-like conversation that also net significantly better data.
AI is also being applied to understand what customers are saying on social media posts, videos, and phone conversations -- in real and near-real time -- and then automatically alert multiple places across the business on everything from supply chain issues to legal-safety emergencies. Another practical and current business application of AI is in mining massive sets of every data type imaginable, instantly and continuously. The AI “watches” for patterns in human conversations that indicate something unusual is happening. Business users can pick up the thread and easily understand what, where and most importantly why the anomaly is occurring and then take intelligent action. This type of AI might be more accurately referred to as SAI -- super artificial intelligence -- because it can peruse a quantity of incredibly complex and divergent data in seconds and minutes, something even an army of the smartest, fastest human analysts couldn’t accomplish. By both automating the impossible and mundane, and giving humans a massive boost in their ability to exercise more informed judgement -- the area where robots fall down -- AI can make businesses smarter, faster and even more human.”
AI is revolutionizing how we provide customer service as a whole
Chatbots are no longer a 'fad' -- they're being incorporated in a variety of interaction, retailers included. With machine learning and the ability to 'cluster' messages together, it's only going to get smarter and more accurately understand, assess, and solve a customer's needs.
Greg Reda, Data Science Lead at Sprout Social, a social media management software.
The rise of chatbots is a crucial AI application for businesses to embrace, but the tech world is further away from a real AI chatbot than the hype would lead you to believe. It’s important for brands to focus first on the problem-solving aspect of this first, rather than the AI aspect, ultimately still utilizing a human-in-the-loop with the goal of making them more efficient.One thing we’ve heard from social customer service teams is that, when they’re fielding a high volume of inquiries day in and day out, they don’t have insight into which customer issues or questions they should prioritize when building a chatbot. That offers an opportunity to use machine learning to cluster similar messages together, ultimately surfacing most frequently discussed inquiries or topics. A human would be able to see those clusters and intuitively know which issues they should prioritize for their chatbot. This type of sorting, analyzing and qualifying using machine learning in tandem with human insight is where I see the biggest opportunity for brands.”
AI is a game-changer for helping us target smarter
Targeted advertising wrapped with AI can hyper-target customers further stretching the dollar.
Jason Nesbitt, VP of media and agency operations at Strike Social, a social advertising software & management company.
According to the Strike Social report, “The Big Picture: 2017 YouTube Advertising Benchmark Report,” the retail industry view rate for YouTube ads is 76 percent below the all-industry average. With 18- to 34-year-olds being the most influenced by YouTube, the retail industry needs to figure out how to better engage this group to drive sales. That’s where AI comes in. By using machine-learning algorithms, advertisers can identify new segments within the 18-34-year-old range — both demographically (e.g. gender) and related to the platform's targeting (e.g. different affinity audiences) — and optimize a campaign towards them. This method allows us to help brands find different ways to approach any struggling segments to achieve better performance."
AI is having a huge impact both in-and-out of stores
Brick-and-mortar retailers are leveraging in-store chatbots, increasing customer engagement and their shopping experience. As long as the bots are providing relevant shopping navigation, consumer willingness to the notion may be sticky instead of a novelty that wears off.
Matt Fleckenstein, CMO at Nintex, a workflow and content automation (WCA) software and platform.
Increasingly, retailers are applying AI tech to the in-store experience by using robots and in-store chatbots to increase engagement and help consumers find exactly what they need within the store. Today, these experiences are largely centered on using robots to accept and understand consumer voice commands to help navigate a consumer to the right area of the store for the products of interest, and retailers are even starting to test robots picking and packing customer orders on behalf of the customer. Additionally, ecommerce has long leveraged AI for product recommendations - according to McKinsey, 35 percent of Amazon purchases come from recommendations based on algorithms that correlate past customer purchases, searched product, and what others have purchased to determine what should be shown to any given consumer. And it’s only a matter of time (likely measured in days or weeks) until voice-controlled assistants like Amazon’s Alexa go beyond just order execution to start making product recommendations."
AI is successfully optimizing the online shopping experience
Barry Pellas, CTO & Chief Business Technologist at PointSource, a digital transformation agency, part of Globant.
AI should be incorporated as an amplifier to existing operations and optimizing the shopping funnel -- determining the most practical use is where retailers need to invest time to ensure they don't deploy a solution without strategic merit.
The key to adapting AI in retail is to focus on a very practical integration of the technology into what the storefront already does well. Retailers know more about their customers every day through analytics, and conversion funnels are driving their success metrics. Practical applications of these two key factors of success integrated with AI can help deliver a more personalized experience to the end users and assist with conversion. Retailers can use AI to start predicting conversion funnel paths and make the jump to that next step even more seamless than what you see today. If a shopper’s conversion will take four steps, they can predict with a certain level of accuracy the likelihood that this customer will make it to each of those steps and drive them further towards the conversion goal. This type of exercise will form a loop in which the analytics further drives the prediction and the machine will learn the most optimal way to assist the user down the conversion path.
The key to a successful AI integration into retail systems is and will always remain with the users of the system. If you are not improving the lives of internal and external users, another approach is likely necessary to take advantage of this new breed of technology.”
AI’s barriers-to-entry will lessen as opportunities expand
As more players jump into the AI game, it not only validates opportunity, but also mitigates risk associated with expanding the existing AI roadmap further -- the path is paved.
Erik Brown, technology director at West Monroe Partners, business and technology consulting firm.
The sheer volume of available data and the proliferation of accessible Machine Learning libraries like Google's TensorFlow and Spark MLLib have given developers far more options to build software that can actually learn and react. And as barriers to AI are lowered, it's opening doors for retailers and other organizations that typically don't have significant R&D budgets. For example, product recommendations can now be driven by more intelligent models that consider an individual at a point in their journey. Perhaps learning from recent transactional history can predict a life event and offer associated recommendations, or analysis of recent shipping delays can trigger a real-time interaction or offer to prevent attrition or a support request. Just as Netflix aims to predict what users want to watch as they fire up their app, retailers need to find opportunities to learn from individual behaviors and other relevant data to provide meaningful engagements through the appropriate channels.”
AI will allow an unprecedented level of personalization
Personalization will be expected to reach new heights and in streamlining full end-to-end eCommerce operations, both the retail and customer experience will be expected to yield significantly higher output & delivery.
Ken Yontz, Global VP of Transformation Management at 1WorldSync, the global leading product information network.
Artificial intelligence has the potential to transform product data throughout the entire eCommerce ecosystem, from the depths of the supply chain to customer-facing content. Over time, this technology will give manufacturers unprecedented insight into the products and criteria that are most important to consumers, which they can use to optimize sourcing and strategic product development. On the consumer side, we will see more robust information targeted to each individual’s specific interests, instead of the ‘one-size-fits all’ model. On both sides, product data will be informative, personalized and actionable for every party involved.”
The world of AI will continue to evolve -- you just need to hone in on how you want to take part in that journey or sit back and watch it unfold. Either way, it should be a fascinating experience on either side.
