Table of Contents
Introduction Jennifer Sherman Kibo Rob Garf Salesforce Commerce Cloud Brian Rigney Zmags
Omer Artun AgilOne
Peter Sheldon Magento
Juliana Pereira Smartling
About Retail TouchPoints
Bryan Chagoly Bazaarvoice
James Green Magnetic
Jim Davidson TurnTo
Jared Blank Bluecore
Maribeth Ross Monetate
Mihir Kittur Ugam
Allen Nance Emarsys
Oscar Sachs Salesfloor
Pete Olanday Vertex2018 E-COMMERCE TECHNOLOGY PREVIEW
RETAIL TOUCHPOINTS
2018 E-COMMERCE TECHNOLOGY PREVIEW
Retail TouchPoints is proud to introduce the third annual E-Commerce Technology Preview, featuring insights from 15 e-Commerce industry experts. This guide offers an exclusive and unique look at how retailers are gearing up for e-Commerce and omnichannel success in 2018 and beyond. This comprehensive collection of e-Commerce thought leadership will help retailers determine the most effective go-forward business strategies. Key topics include:
Artificial To
We hope you find a significant takeaway from each contributed article that you can share with your team to help make 2018 a most successful year!
Debbie Hauss Editor-In-Chief Retail TouchPoints
Intelligence (AI);
Personalization;
Beat Or Join Amazon; Science; and Strategies.
Data
Mobile-First2018 E-COMMERCE
TECHNOLOGY PREVIEW
OMER ARTUN CEO AGILONE
THE TRIFECTA: RETAILERS, CONSUMERS AND MACHINES
We have entered a time when technology advances have brought us to a place where computers are able to augment human understanding and behavior. While I don't think we have gotten to the point when artificial intelligence will replace human work entirely, there is a lot for retailers, in particular, to gain embracing the power of machine learning.
CLUSTERS & DECISIONS
Machine learning works best for marketing situations where the marketing mechanics, the automated response to customers, require human judgment and decisioning. A perfect example is clustering, where one of the key heuristics is representativeness. Representativeness is when humans naturally group things together to reduce complexity. When machines do this with unsupervised learning algorithms, marketers gain meaningful customer segments without the work it would take to develop those segments manually. Clustering also helps marketers recognize future customers earlier, predict whether someone likes a product or message, and so on. Machine learning manages these variances exactly as a human would: calculating and decisioning without explicit programming. Machine learning can accommodate for a wide range of human idiosyncrasies, such as: Complexity -- when tasks are non-linear, high dimensional, and go beyond the surface Low SNR (needle in the haystack) -- when events are few and far between, and filled with noise in the meantime Incomplete information -- when only some of the necessary information is present Probabilistic events -- when events following other events are likely, or not Memory -- when problems, conditions, and data change, the system learns and forgets
A MARKETER'S DREAM COME TRUE
By harnessing the power of machine learning, human neural pathways are replicated by machines. For marketing, the result can be the ability to personalize the consumer experience for a lot more people than could fit into a corner market. The experience feels authentic to the consumer and cultivates a real brand-consumer relationship, while creating lifetime value. So, what are the human intelligence heuristics that we can mimic with machine learning to create these kinds of results Here are some of the important ones: Anchoring -- Comparison of past and future Availability -- Calculation of probabilities Representativeness -- Grouping things together for patterns Gains and losses -- Providing opportunities to win and avoid losing (Note: People don't want to lose, they want to win. Loss aversion creates inertia to stick to your current status. People are twice as miserable for a loss than for a gain of the same item.) Status quo -- Providing a desirable default state (Note: People tend to stick to what they do. Trial subscriptions exploit this. This heuristic also highlights importance of defaults in a system.) Framing -- How the information is presented against an alternative Even last year, these sophisticated advances had marketers scrambling to harness all the data that was being generated by consumers opinions expressed on social media, having the insight into the products that consumers most recently purchased and what they were most likely to buy next. For the first time ever, we have the computing power to scale combining the structured data from CRM databases to unstructured data from social networks and free flowing real time data from devices and the Internet of Things (IoT).
THE TRIFECTA: MARKETERS, HUMANS, AND MACHINES
So, how does machine learning help relationships between brands and customers It takes the 1:1 dynamic and propels that dynamic to a massive scale. Building high quality customer relationships with high lifetime value can't be done simply from machine-human (the old spray and pray dynamic), and it can't be human-human (this doesn't scale). The answer instead is in the human+machine-human dynamic. For marketers, humans AND machines are better than humans OR machines. We've come a long way in harnessing machine learning for effective marketing. It will be exciting to see where machine learning continues to take us.2018 E-COMMERCE
TECHNOLOGY PREVIEW
BRYAN CHAGOLY VICE PRESIDENT OF TECHNOLOGY BAZA ARVOICE
AUTOMATION, AI AND MACHINE LEARNING WILL IMPROVE 1-TO-1 PERSONALIZATION IN E-COMMERCE
To stand out in today's noisy e-Commerce environment, retailers are working toward making the online shopping experience as convenient, relevant and enjoyable as possible for consumers. From a technology standpoint, one area where we're seeing retailers focus and innovate is improving the customer experience through personalization -- recognizing individual shoppers, recommending relevant products for them, and directly marketing to them based on their past shopping activity. With the combination of new technology and customer data, retailers are getting closer to providing a 1-to-1 shopping experience for consumers -- an experience where the retailer can communicate, recommend products, and provide relevant offers as if the shopper were interacting with a live customer service representative or sales associate at a physical store.
USING SEARCH, BROWSING AND BUYING DATA TO IMPROVE PERSONALIZATION
Even with technology and automation, true 1-to-1 people-based personalization is incredibly hard to achieve. For retailers to be successful, they must focus on building comprehensive people-based models that include more robust signals than what can be seen from just one e-Commerce site. Consumers tell us what they are in-market for every day, but retailers must pay attention. Based on the websites they read, retailers they shop at, reviews they leave, and places they go, consumers are signaling intent everywhere -- it's up to retailers and their technology partners to synthesize this information to find these consumers and provide relevant content and enjoyable shopping experiences for them. Retailers should leverage systems that can combine, analyze, model and automate these data signals to get closer to true 1-to-1 customer interactions. In addition to collecting a rich set of shopper data, retailers must also understand what qualifies as relevant shopper data because high-quality and fresh real-time signals of intent are essential for personalizing the shopping experience and providing timely recommendations. Marketers must evaluate the freshness of their first-party data to remain relevant and ensure that their digital efforts are identifying consumers with the greatest propensity to buy. Retailers must better define relevance and use data that point to a recent and immediate intent to buy, such as product pageviews, search terms or interactions with ratings and reviews within a reasonable time frame, which is usually days, not weeks.
AI, MACHINE LEARNING AND AUTOMATION IN PERSONALIZATION
Machine learning and AI techniques have made it possible to build robust 1-to-1 people-based personalization, but acting on it remains difficult. Signal enrichment, automation and activation become key to building timely and inspiring interactions with consumers. For example, with machine learning, we can correlate signals from various sources, identify patterns, and infer strength of purchase intent. When observing the shopper journeys of hundreds of millions of shoppers across the retail ecosystem, patterns of intent emerge, and intelligent systems can understand which behaviors lead to conversion. AB testing, and even multi-variant testing, is no longer sufficient; instead, intelligent systems can optimize an individual's experience in real-time to provide the best content for that shopper wherever they are in their personal shopper journey. This type of personalization done well leads to a closed loop learning system that optimizes the entire purchase funnel toward maximal conversion rates and revenue.2018 E-COMMERCE
TECHNOLOGY PREVIEW
JARED BL ANK SVP OF DATA ANALYSIS AND INSIGHTS BLUECORE
2018: THE YEAR TO EMBR ACE AMAZON'S DISTRIBUTION CHANNEL
Retailers desperately want to combat the force of Amazon, but they lack the strategy, the marketing dollars and the digital resources to do so. In 2018, smart retailers will stop battling the ecommerce and personalization giant and instead leverage the digital powerhouse as a very effective distribution channel. Retailers have always had a complicated, ever-changing relationship with Amazon. In the past, retailers across industries held firm that Amazon would not encroach on their territory. They believed that consumers were looking for a more brand-specific experience. However, one by one, retailers in categories from apparel to jewelry to consumerpackaged goods found that consumers will in fact trade a branded experience for the convenience that Amazon offers. Now, many retailers are mulling their strategies around free shipping, in-store service and low prices to mimic what Amazon does best: Provide highly personalized and convenient experiences for customers. But while better data and experiences are important, it's time to stop fighting the inevitable and embrace the opportunity to sell products through Amazon. In 2018, brands cannot continue to treat Amazon like the enemy. With the number of Amazon Prime customers soaring and more consumers than ever beginning their product searches on Amazon, it's simply impractical for retailers to think they're going to succeed alone. Even the world's largest brands are using Amazon as a distribution channel, and their revenue and earnings reports are seeing a sizeable uptick. Just look at Nike, which reversed its long-standing policy against selling products directly on Amazon in June 2017. As Nike's experience illustrates, the key for retailers is learning to co-exist with Amazon. So how exactly can retailers think of Amazon as a distribution partner It will depend in large part on their business as well as the approach they take to working with Amazon. For example, there are currently three different ways retailers can sell goods on Amazon:
Wholesale: In this case, Amazon buys the goods from a retailer and then sets the price, selling the products to consumers directly. Fulfilled by Amazon (FBA): With the FBA program, retailers still own the goods, but Amazon handles the product listing and fulfills the orders. Amazon Marketplace: Finally, if retailers list on the Amazon Marketplace, they handle everything themselves, including listing and shipping. Each of these options allows for varying degrees of control over the process as well as varying degrees of competition with other products sold on Amazon. For instance, the wholesale option provides the least control, but might deliver better placement within Amazon in comparison to similar items. On the other hand, if a product is exclusive and no one else sells it, FBA or the Amazon Marketplace might be the better option because it costs less and offers more control. As we head into 2018, retailers have a choice to make: Compete against a company that spends more than $10 billion annually on R&D or use Amazon as a distribution channel. Increasingly, retailers are choosing the latter. Who will be next
In 2018, brands cannot continue to treat Amazon like the enemy.2018 E-COMMERCE
TECHNOLOGY PREVIEW
ALLEN NANCE CHIEF MARKETING OFFICER EMARSYS
BRIDGING THE GAP BET WEEN DATA SCIENCE AND EXECUTION
As we approach October, the desire to revamp becomes common among individuals and often time, organizations. Perhaps it's because the New Year is only three months away. But as Chief Marketing Officer of a company that provides cloud marketing software for B2C organizations, I am seeing firsthand that many marketers are starting to sketch out -- and even finalize -- their marketing strategies for 2018. Each year, marketers must keep their ears and eyes open to the latest trends, all with the common goal of meeting consumer demands for a more tailored, unified experience. Today, the average consumer has 4 connected devices at hand. So, brands must integrate the omnichannel approach in their marketing strategies to personalize the customer experience across different touchpoints and keep their target audiences engaged. So how is this done How can retail marketers bridge the gap between data science and execution, allowing brands to better map their customer's journeys In 2018, we'll see the accelerated adoption (amongst brands) of AIpowered marketing strategies. By integrating AI, retail marketers can easily target segments of customers to better address their individual needs. Studies show that some brands have already started to jump on the AI bandwagon, or plan to, in the near future. A recent survey conducted by Forrester on behalf of Emarsys, sheds light on how major organizations have started investing in AI, and what companies in the e-Commerce space can start doing to better harness these data crunching platforms. The figures uncovered that 78% of executives
planned to spend at least 5% more on AI marketing technologies in the next 12 months to better personalize the customer experience across different channels. According to a 2017 study, 48% of U.S. marketers reported that personalization on their websites or apps lifted revenues in excess of 10%. The list of potential uses for AI in retail marketing is almost limitless, and the technology continues to mature. Instead of giving marketers another report, AI can help brands execute a campaign that resonates with its consumers. E-Commerce retailers such as LuisaViaRoma and Cosabella have already dipped their toes in integrating AI across their marketing strategies and have seen satisfying results, including customer retention and win-back rates. As we move into 2018, retailers will look to cutting edge, machine learning technologies to receive an abundance of actionable insight into consumer trends, improved marketing metrics and campaign success. The intersection of machine processing and human brainpower is empowering markers all over the globe to better connect with their customers. In a world where drones can deliver packages and vehicles can drive themselves, AI is here to stay. Now it's time for marketers to harness its potential and get ready to embrace success.
By integrating AI, retail marketers can easily target segments of customers to better address their individual needs.
2018 E-COMMERCE
TECHNOLOGY PREVIEW
JENNIFER SHERMAN SVP PRODUCT & STRATEGY KIBO
2018: THE YEAR OF PERSONALIZED OMNICHANNEL EXPERIENCES
In this increasingly crowded space, what everyone is fighting for is differentiation, discoverability and shopper loyalty. We all want to stake out our little piece of our target consumer's attention and, once we do, deliver the best possible experience. Now as I look at what we see successful retailers doing in their quest to deliver that great experience, I see a few trends: 1. Moving from Personalization to Individualization. Old school approaches that segment customers into categories and serve up recommendations and content accordingly will only get you so far. Consumers don't always fit cleanly into segments and these approaches are a waste of the wealth of data we've been collecting on shoppers for years. Forward thinking sellers are looking at how to individualize the experience for every shopper. 2. Site-wide personalization. Product recommendation widgets are a commodity now. Sites that will drive higher conversion rates and, in turn, higher AOV are those where the entire user experience is personalized (actually individualized). When these are powered by Machine Learning (an application of AI), these systems can do an amazing job of predicting what will interest a user based on all their past history and their current intent. Using these technologies to personalize everything from recommendations, to category sort to search allow a merchant to move from being demand sensing to demand shaping. When you shape demand, you build a better experience for the consumer and a more fruitful transaction for the seller. 3. Omnichannel. I think all too often we hear the term omnichannel and we think BOPIS. But omnichannel is so much more. It starts by recognizing that shoppers don't think about channels as silos. The store, the website, the mobile app are all part of their experience of
the brand. Those experiences need to be consistent, personalized and engaging. And yes, that means I should be able to order everywhere and receive my product anywhere but more than that, it means all those experiences should be individualized and offer consistent product assortments, pricing and service. Every great shopping experience we have as a consumer forever changes our expectations for all other shopping experiences going forward. That means every seller can raise the bar for the entire industry with every client interaction. That puts sellers on a flat-out sprint to keep up and shoppers do not care if you don't have the right technology, team size or skill set to get there. In the journey to deliver great individualized experiences, I suggest that each merchant look at the shopping experience across all channels and identify where it is disjointed, not personalized or delivering subpar results. Use those challenge areas to drive your personalization strategy. Challenge your technology partners to explain to you why their solution is different from the others, how they use predictive learning technologies and make sure their technology allows you to treat your customers like people, not segments. Make sure the solution can be used online, in store, and in your call center. Look for solutions that can pull data from stores, loyalty programs, legacy orders etc. to ensure that you have a 360-degree view into every client. Look for solutions that think beyond product recommendations. Look for solutions that you can use to shape demand. As for omnichannel transformations, find a partner who has experience integrating your online and store channels and who will guide you through the process, understanding both your business and industry best practices. Find a partner, not a vendor. This is going to be a hardenough journey and you don't need to do it alone.2018 E-COMMERCE
TECHNOLOGY PREVIEW
PETER SHELDON VP STRATEGY MAGENTO
PERSONALIZATION AT SCALE: HOW AI IS RESHAPING THE RETAIL CUSTOMER EXPERIENCE
Elon Musk created a stir when he claimed that artificial intelligence is a fundamental existential risk for human civilization. But whether we like it or not, AI has made exponential strides in the last couple of years and is poised to make significant progress in the next five. The retail industry will look entirely different as a result. By 2020, more than 60% of organizations will use AI for digital commerce, and AI will account for 30% of digital commerce revenue growth, according to Gartner. As customer touch points and data explode alongside customer demands for relevance and personalization, retail innovation has moved beyond human capacity. To deliver on sky-high expectations, retailers must rely on a world of data science, machine learning and AI to round out their strategies. Here are three ways AI will fundamentally reshape the retail customer experience, finally bringing true personalization at scale.
AI RETHINKS MOBILE SHOPPING
Mobile-only apps have created a frictionless user experience that customers now expect. Data-oriented functionality will continue to enrich mobile customer experiences by detecting customer location and behaviors. For example, take Uber, which understands where passengers are standing and proactively suggests the ideal spot to wait for the driver, ultimately removing all friction associated with getting to a destination from hailing a cab to paying for the ride. Where AI truly revolutionizes the mobile experience is from a product discovery perspective. The task of sifting through 4,000 SKUs is daunting for any consumer. But the experience is immediately more efficient and enjoyable if the retailer can automatically suggest 15 products tailored to the customer's preferences and past browsing activity, optimizing the mobile experience for quick purchase.
AI MAKES CUSTOMER ENGAGEMENT INTUITIVE AND HYPER-RELEVANT
Channels are getting murkier. As mobile, wearables, and hands-free devices evolve and become more advanced, channels for customer engagement will continue to explode. As a result, shopping will become less about channels and more about environmental experience. Machine learning and AI will be critical in mining information and providing intellectual recommendations on how, when, and where retailers should engage customers. Retailers who take advantage of data-driven analysis will not only increase conversion rates and average order value, but also refine the overall customer experience so it is more intuitive and relevant.
AI ADDS ANOTHER WEAPON TO THE MARKETER'S ARSENAL
The resource side of retail is not growing with the pace of technology. AI will never fully replace marketers because it lacks the empathy and emotional intelligence of real humans. However, under-resourced teams stand to benefit from AI by automating everyday merchandising functions, such as discount offers, loyalty incentives and A/B testing. Ultimately, the merchandiser could assume the role of moderator and leave the heavy lifting to AI. Through social channels, AI can help identify conversations about products and preferences that brands previously never knew existed. With AI, marketers can identify how and where people are talking about their brands and cultivate those conversations for marketing purposes. Marketers will be able to curate authentic content and, over time, gain feedback on how content moves purchasing forward. AI is not in itself the ultimate solution for retail's untapped opportunities. In fact, AI's success will hinge on its ability to fade into the background. At best, AI will be invisible. It is the next secret weapon of retail, driving powerful experiences that are truly personalized at the core.2018 E-COMMERCE
TECHNOLOGY PREVIEW