Retail ExecutivesFrom

Exclusive Predictions

2018 TECHNOLOGY PREVIEW

For the sixth year, Retail TouchPoints has asked leading retail experts to share their insights on what to expect from new technologies and trends in the coming year. We have collected input from 17 executives who have shared fascinating, and sometimes surprising, predictions.

Each executive was asked to answer the following question:

Which technology trend will impact the retail industry most significantly in 2018 (and why)?

For the 2018 report, we have split the predictions into 6 different categories. Columns are listed alphabetically by company name within each category.

Let us know if you agree with these executives. Happy Reading!

RETAIL TOUCHPOINTS 2018 TECHNOLOGY PREVIEW

EXCLUSIVE PREDICTIONS FROM 17 RETAIL EXECUTIVES

Debbie HaussEditor-in-ChiefRetail TouchPoints

2018 TECHNOLOGY PREVIEWEXCLUSIVE PREDICTIONS FROM 17 RETAIL EXECUTIVES2

Retail ExecutivesFrom

Exclusive Predictions

Data/Analytics

1010data, Silvia Lacayo, Product Marketing Director 5

Aruba, Michael Brewer, Product Marketing Manager for Retail Solutions 6

Esri, Gary Sankary, Retail Industry Manager 7

Kibo, Haowen Chan, Director of Data Science and Data Architecture 8

Salesforce Commerce Cloud, Rob Garf, VP, Industry Strategy and Insights 9

Workjam, Mike Zorn, VP of Workplace Strategy 10

Digital Innovation

Oracle NetSuite, Matt Rhodus, Retail Director and Industry Principal 12

SAS, Dan Mitchell, Global Director of Retail and CPG 13

STRATACACHE, Manolo Almagro, Managing Partner, Q Division 15

Voysis, Peter Cahill, CEO 16

Zebra Technologies, Thomas Moore, North America Industry Lead, Retail & Hospitality 17

Store Experience

Precima, Graeme McVie, Chief Business Development Officer 19

ShopperTrak, Dave Berg, Global Head of Solutions Management 20

StoreForce Solutions, Dave Loat, President 21

Inventory Management

Radial, Stefan Weitz, Executive Vice President, Technology Services 23

Marketing

Zaius, Eric Keating, Vice President of Marketing 25

Payment/POS/Security

Toshiba Global Commerce Solutions, Kirk A. Goldman, Vice President, Business Strategy 27

About Retail TouchPoints 28

Data/Analytics

2018 TECHNOLOGY PREVIEWEXCLUSIVE PREDICTIONS FROM 17 RETAIL EXECUTIVES5

As a big data and analytics tech provider to large retailers, we have a unique view into which hot trends are actually taking hold in the industry. Personalization is one of those trends. In 2018, personalization — based on actual purchase behavior patterns across channels — will set data-obsessed retailers apart from the rest. There are two main reasons why the retail industry will see a notable shift in this space across the next 12 to 18 months.

A Compelling Strategy For Growth Designing, executing and measuring customized experiences and offers for shoppers is a clear growth driver. A recent BCG survey across industries cites revenue growth potential of 6% to 10% from personalization initiatives. For retailers, actual purchase behavior data — including transaction and CRM data — is the best kind for designing effective personalization programs. But the potential doesn’t stop there: combined with supply chain data, or external data such as geo-location or weather, retailers have an opportunity to build more sophisticated models that deliver even more impactful results.

The challenge many retailers face in developing and executing personalization initiatives isn’t attributable to a single roadblock. It’s a data analytics and predictive modeling challenge. It’s a supply chain challenge of ensuring the right product is being delivered to the right distribution center, the right store, or the precise customer’s home or workplace. And successfully fostering a company-wide analytics culture is arguably the biggest challenge of all.

Recent advances in big data are helping retailers overcome these challenges. Primarily, gathering all the data and making it available for meaningful analysis at scale is now possible in ways it wasn’t before. This includes CRM, transactional, promotional, inventory, store operations, location-based, and other data; all of which can be used to create personalized offers and experiences.

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Retailers can ask and answer as many questions about their business as needed and get answers quickly, so they can make more timely decisions about their personalization initiatives. The Harvard Business Review covered this concept well in a piece on how today’s marketing tech, automation and advanced analytical tools make it easier for companies to develop a “test and learn” culture in pursuit of effective personalization at scale. While they had marketers specifically in mind, the same principles apply across all aspects of retail.

Cross-Channel Expansion In 2017 Will Shape 2018The second reason personalization will be a key trend in 2018 is based on the retail expansion that shaped 2017: Amazon bought Whole Foods and Walmart acquired ModCloth, Moosejaw and Bonobos, and continued to invest heavily in e-Commerce operations. And online retailers like UNTUCKit and Warby Parker successfully opened more brick-and-mortar stores. This level of cross-channel expansion yields new troves of actual customer purchase data for retailers to analyze. If retailers can act fast on insights from these rich data sources, they can test and optimize personalization programs much more quickly than their peers.

Customers will continue to expect personalized retail experiences across channels. To derive useful insights about customer purchases and enable valuable, personalized experiences, retailers need the right technology. From the right tools to the right overarching IT systems, retailers who invest in scalable analytics will be able to make analytics and personalization a core part of their business culture. This bodes well for retailers who have both massive amounts of cross-channel data and the ambition to grow via customer-centric personalization efforts.

In 2018, personalization — based on actual purchase behavior patterns across channels — will set data-obsessed retailers apart from the rest.”

Cross-Channel Purchase Data Makes Personalization More Relevant

SILVIA LACAYO

PRODUCT MARKETING DIRECTOR

1010DATA

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The recent blog, When Good Customer Service Comes Down To One Simple Question, makes the case for relevant, personalized customer communications as a competitive differentiator. We couldn’t agree more. In fact, relevance and personalization are the motivators driving retailers’ accelerating embrace of location-based mobile technologies in 2018. Here’s what we’re seeing in the marketplace.

Driver #1: Personalized Navigation And Associate LocationMap-enabled, indoor, turn-by-turn navigation via your mobile app is swiftly becoming table stakes as forcing your customers to hunt for desired products now carries considerable risk. According to the aforementioned blog, almost half of North American and European consumers consider frustrating experiences the ultimate deal-breaker.

More sophisticated solutions go further by offering assistance empowerment, also known as location sharing. With this capability, customers can consult your shopping app to visually find nearby associates if they want help. Clicking an associate’s icon enables sending that individual a pre-defined text. If the associate is occupied, they can respond with an availability estimate while also suggesting the consumer continue shopping because the associate can find them.

Perhaps the original associate isn’t the best person to assist, or the consumer responds with a request for more immediate attention such as “that won’t work, as I need to leave the store in X minutes.” No problem. The original associate can also use your app to find another staffer to help the customer.

Driver #2: Asset Tracking For Speedier AssistsLocation-based services can also help turn other delay-related annoyances around by providing you with asset tracking sensors for affixing to high-value items such as ladders, carts, POS devices or pallets of goods.

Consider a common out-of-stock scenario: A customer encounters an empty merchandise slot, consults an associate and learns the item just arrived. The associate walks the length of your store to the stockroom, where they’re confronted with multiple unworked pallets.

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Sans asset tracking, it’s most likely the associate will return to the waiting customer, after a dozen minutes or more, empty handed with the request to “check back later.”

Equipping pallets with asset tracking tags completely alters the paradigm. Searching for a pallet by goods type enables the associate to locate the proper pallet and retrieve the item for the shopper.

However, if the item isn’t on any of the pallets, the associate can notify merchandising and learn when the product will be re-shipped. Then, upon returning to the customer, the associate can provide accurate information and offer an incentive to return on the expected arrival date or order the item from your online channel.

This is just one way asset tags can assist with personalized and relevant customer service. Others include quickly locating a ladder to get items off of a shelf or finding a cart capable of handling bulky merchandise.

Driver #3: Leading-Edge AnalyticsAdopting the most advanced location-based solutions allows for an array of analytics to improve in-store experiences.

For example, the aforementioned blog discusses the frustration of joining a loyalty program only to discover a favorite product is chronically missing from its peg. With analytics from your loyalty app, you can focus attention on items your best customers purchase to keep them stocked.

On a traffic flow level, analytics help visualize what’s working, and what’s not — including seasonal variations — across your entire footprint. Combining such insights with “what-if” modeling can assist you with devising strategies for creating relevant experiences and boosting sales simultaneously.

Beyond the personalization and relevance benefits, here’s yet another reason to deploy BLE-enabled location-ready infrastructure: You can rapidly take advantage of new experience innovations already in the pipeline to keep you ahead of your competition.

Relevance And Personalization Drive Location-Based Services Adoption In 2018

MICHAEL BREWER

PRODUCT MARKETING MANAGER FOR RETAIL SOLUTIONS

ARUBA – A HEWLETT PACKARD ENTERPRISE COMPANY

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Disruption in the retail market is real and there is no shortage of technologies that are ready to help retailers manage the next phase of digital transformation. Artificial Intelligence (AI), machine learning, and virtual reality are all bleeding-edge technologies promising a lot of hype and hope for the retail market. However, for all that buzz, one single thing continues to be the currency that drives digital disruption: Data.

We’re talking about data in the hands of consumers as well as the data retailers have at their disposal. As new technologies come online and make even more data available, retailers are increasingly challenged with massive amounts of seemingly unconnected information about everything and anything. Inventory details, shopper traffic patterns, gift registry information, digital offer success rates, same store sales figures, employee productivity numbers, social media interactions, point of sale conversations — the list goes on!

Retailers know that to successfully execute a credible Unified Commerce strategy, they must be more relevant and more precise with offers and products than ever before. As the Internet of Things (IoT) and mobile commerce continue to mature hand-in-hand in the year ahead, the amount of data retailers will be forced to juggle will grow exponentially. The need to consume, analyze, connect, and find insights in vast amounts of unrelated data is the most important challenge in technology facing retailers today and there is no foreseeable end in sight to that trend. If there are consumers, there will be more data!

Consider these pieces of information: a store location, a weather report that includes snow in three days, seasonal items like shovels and scrapers, and customers’ home addresses. These are four disparate data sources that must be joined and interrogated to create a seemingly simple strategy like sending “get ready” notifications to customers about a promotional event at their local store before the storm arrives. This example in turn is repeated a thousand times a day in most retailer-consumer interactions. Every touchpoint is an opportunity to reach the right customers with precisely the right offer at precisely the right time. To know when the time and place are

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right, you must have not merely the data, but the tools and technology to make sense of that data in a way that the entire enterprise can leverage it and take advantage of it today.

The key to managing all this data and giving it context to work is location intelligence which enables:

Visual Data Mining: Intuitive, easy to understand images, graphics, and maps offer the fastest path to sharing and insight into customer data. If you have 40 million records in your customer loyalty database and you want to quickly understand the relationship between stores and customers, a map will bring more insights faster to more people than a static table or a chart.

Seeing The WHOLE Picture: The key to being relevant and successfully executing segmentation and personalization strategies is being nimble and flexible with the types of data being considered. Finding a common theme in disparate data is the key and often that common entity is a location. Location creates a point for otherwise unstructured joins of data so having technology that can consume, manage, and build against this data is critical.

Precision Everything: In merchandising, marketing, and almost every retail activity, precision and localization are absolutely critical to delivering on a Unified Commerce strategy. Maintaining and growing market share while competing against a host of innovative channels and messaging requires retailers to be more specific and more precise in their understanding of their customers. Technology that leverages data to help merchants and marketers be more localized and more relevant in their execution will be critical in the years to come.

Retailers are working hard to provide customers with more relevant products and experiences as they look to build loyalty and deepen relationships. In 2018, the technologies that can serve as the backbone to achieving those digital transformation initiatives and provide the unique experiences customers are after will be the priority.

Digital Transformation Needs Location Data

GARY SANKARY

RETAIL INDUSTRY MANAGER

ESRI

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The fundamental definitions of engagement in retail are changing as a result of the increased instrumentation and intelligence of modern systems. The industry still has a lot of ground to cover in terms of simple facilitation: making it easy to find and order specific products, increasing conversion rates, and so on. As catalogs and user bases grow, this problem will continue to be technically challenging. Technologies like search (using natural language, images, or inferring stylistic similarities) and personalized site experiences will get increasingly sophisticated.

However, the most advanced retailers are looking past that, toward concepts such as intelligent anticipation and inference of needs as well as direct engagement through social media and other online touchpoints. There are many potential applications of artificial intelligence (AI) in this space, for example intelligent sales or personal agents, and marketing agents designed to generate content specifically tailored to various online subcultures so a brand can be more than one thing to more than one person. Retail has always chafed under the fact that advertising and sales interactions are crude and heavy handed. The holy grail here is for brands to use technology to directly integrate into the lives of individuals in a positive way, so that the retailer or brand is almost like a partner in your life helping achieve your goals.

There are numerous applications for data in retail — multi-channel personalization and product recommendations; catalog management; order management and optimization; in-store and online data integration. The challenges facing retailers in the data world fall into several categories depending on the maturity and capabilities of the retailer.

Data collection and instrumentation typically manifests in smaller retailers who have basic e-Commerce systems, but no additional sources of data to leverage the more complex use cases like personalization. For example, customer registries may only track previous orders, and not site clickstream

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behavior. Perhaps marketing email open rates are not being monitored, and specific item engagement for these are not recorded. In each case, the goal is to add additional instrumentation to start gathering valuable data. Retailers face a data integration challenge when they have multiple systems generating data but no integration between the systems — manifesting in data silos. This limits the data applications deployable by the retailer and leads to increased cost of ownership of the data as each separate sub-store of data needs to be independently maintained. Investment in this is unfavorable to the retailer as many do not wish to develop data processing and maintenance as a core competency.

The solution is to have a single core system that collects all appropriate data from various subsystems and applies the appropriate cross references to identify data linkages across stores.

Once a retailer has achieved a high level of data integration, the challenge then becomes deriving maximum value from the data. The challenges here are twofold: (1) Does your application suite support flexibly adapting its models and behavior to take advantage of various types of incoming data?; and (2) If you have an in-house data team with highly specialized domain knowledge of the specifics of your market, does your application framework support customization and interfaces that allow your data team to inject the results of their modeling and analysis without significant integration costs?

How Data Science And Big Data Are Changing The Retail Space

HAOWEN CHAN

DIRECTOR OF DATA SCIENCE AND DATA ARCHITECTURE

KIBO

Retail has always chafed under the fact that advertising and sales interactions are crude and heavy handed.”

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Over the last several years, how many times have you heard a retail executive declare, “We must put the customer in the center of everything we do”?

Customer-centricity is a common goal, and indeed, many retailers and brands have invested significantly over the past two decades to enhance the consumer experience. But these efforts have largely fallen short.

Consumer Experience: Expectation Vs. RealityThe reality is that the consumer experience is broken. Despite the holy grail status bestowed on that experience, when we honestly look inside our organizations, we see the truth:

• Functions and departments aren’t aligned.• Information systems are neither agile nor fully governed.• Performance metrics vary, depending who you talk to.• Data — particularly about the consumer — is scattered

throughout the enterprise.

The Potential Of AI Rests On One Factor: DataThe promise of artificial intelligence (AI) and machine learning looms large for 2018, and with good reason. Three years ago, Amazon hadn’t even introduced its Echo in yet, and today nearly half of Americans use voice assistants. AI-powered personalization is already driving impressive business results for retailers, as data from 150 million shoppers’ activity indicates that 7% of e-Commerce site visits with recommendation clicks drove an astounding 26% of revenue.

The potential business impact of AI on internal operations and external consumer experience is massive, providing hope for retailers amidst the headlines. But no brilliant machines can help you if you don’t have the data.

Thus, the imperative for retailers in 2018 is to organize their databases to compete for today’s — and tomorrow’s — consumers. Consumer data belongs in the center of all customer centricity mantras.

2018 TECHNOLOGY PREVIEW

Accessible And Actionable Data Is A Retailer’s SuperpowerA unified consumer experience reaches beyond web site and store to affect the entire journey. That’s why teams across the organization must work together to consolidate, rationalize, normalize, cleanse, and syndicate data. Once that’s complete, create a virtual consumer profile that’s accessible and actionable for everyone who plays a role in crafting that journey.

Actionable customer data is nothing short of a retailer’s superpower, as it enables companies to know shoppers in the moment and grow intelligence with every click. Retailers must prioritize the most critical pieces of consumer data needed to enhance experiences in the short-term — and realize that clean, actionable data isn’t a one-time effort.

Excellent Experiences Require CollaborationOnce consumer data is consolidated, teams across marketing, commerce, and service can collaborate to build a unified experience. The brands and retailers that succeed in providing personalized journeys will be those that don’t view departments as silos, but rather, as opportunities to influence a longer journey.

For example, if a shopper hits any curated content on Stonewall Kitchen’s website, the specialty food producer leverages AI to display custom add-ons (like syrups, jams, and pancake mixes). This strategy, powered by Commerce Cloud Einstein, influenced 14% of customer purchases during the summer of 2017 — driving an average conversion rate of 44.3% and an average add-to-cart rate of 39.6%.

Similarly, Adidas CEO Kasper Rørsted shared at Dreamforce that the company is now selling 1.2 million pairs of shoes online every day, thanks to unifying its commerce, marketing, and service. Adidas has launched a new AI-enabled app to offer each shopper a unique experience, based on their tastes and past purchases. Mobile payments are simple with Android Pay or Apple Pay, and post-sale, customers can track orders or chat with service reps or bots.

All the talk about AI for retail is exciting — but harnessing consumer data is step one. If AI is the rocket ship, consumer data is the rocket fuel.

Putting The Consumer [Data] Back In The Consumer Experience

ROB GARF

VP, INDUSTRY STRATEGY AND INSIGHTS

SALESFORCE COMMERCE CLOUD

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Every day, retail stores collect millions of data points. From how many customers walk into a store to which items are most likely to sell out first to which employees are the most reliable and engaged, retailers have a gold mine of data they can use to make more informed decisions regarding staffing and operations.

But the data collected by store managers often comes from disparate sources, making it difficult for retailers to turn their raw data into actionable insights. However, aggregating data and using it to make managerial decisions speeds up the decision making process, freeing managers to focus on what is most valuable — developing strategy, coaching their employees, and by being more efficient — resulting in cost savings. Predictive analytics is about using available data points to make the workplace more efficient, more cost effective, and allows managers to focus on managing and leading.

Predictive Data Analytics Empower Retailers To Make Smarter And More Agile Business DecisionsNew technology, including digital workplace platforms, has made it possible for retailers to digitally monitor all parts of their business. Once that data is compiled and thoroughly analyzed, it surfaces useful information retailers can use to manage their workforce.

Take, for example, decision making in workforce scheduling. Using a digital workplace platform, retailers can track how many hours an employee works each week, which are the most engaged, and analyze levels of productivity during each shift. Managers can then use this information to determine which employee is best suited for additional shifts and remove individuals whose past behaviors raise a red flag. If an employee is slated to work on a busy holiday but has missed their last few shifts, for example, predictive analytics can suggest to managers a different employee who is more

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reliable. On a macro level predictive analytics can help employers identify dysfunctional teams early on and address leadership and functional needs before it becomes a wide scale issue.

Retailers that are able to take advantage of their employee and sales data will better understand their business and identify ways to improve their retail and employee experience compared to their competitors. While some retailers may question a machine’s ability to make decisions currently entrusted to HR, inventory and sales managers, there is growing evidence that predictive analytics can effectively supplement the activities of retailers today. It is happening in 2017 and will explode in 2018.

The new frontier will be in using predictive analytics to grow company culture and create environments where managers are empowered to lead their hourly associates because technology is enhancing their decision making capacity.

As businesses continue to compete for the lion’s share of the market, retailers will need to not only embrace their store data but also go beyond data analysis to anticipate what the workplace needs next. With the assistance of predictive analytics, retailers can expand their ability to provide their customers the best shopping experience possible while maximizing their sales potential and growing the business’ bottom line.

How Retailers Can Use Predictive Analytics to Grow The Bottom Line

MIKE ZORN

VP OF WORKPLACE STRATEGY

WORKJAM

On a macro level predictive analytics can help employers identify dysfunctional teams early on and address leadership and functional needs.”

Digital Innovation

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For a sleep-starved new parent wandering through the aisles of a big box baby store in a delirium that is a mix of adrenaline, anxiety and awe, the so-called seamless shopping experience takes on a new meaning. I recently had that very experience. After logging a bunch of items with the scanning device to reconcile with our baby registry what we had and didn’t have, I realized the hefty gift card I wanted to use was sitting on my counter at home. The feeling of defeat quickly turned to delight when I realized I could simply sit down, log into our registry account at home, and accomplish the purchase of the items I’d scanned in the store in just a few clicks.

The registry — and its ability to pull those online and in-store experiences together — offers a simple example of a trend that I think will be increasingly important this year — delivering omni-device experiences to your customers. We talk a lot about the importance of a unified data source — having customer, inventory and order data in a single database — in empowering omnichannel experiences like buy anywhere, return anywhere, as well as personalized marketing campaigns. But omni-device extends beyond that — from enticing consumers and enabling their experience, to actually empowering them.

Omni-device strategies empower customers to solve problems. Outside of the wedding and baby realms, a registry is sort of akin to a wish list (without that added benefit of having other people reference it to buy a bunch of presents, or clothes, or even appliances). Consumers use wish lists to

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remind, compare and ultimately, help them make the best buying decisions — but the tool is by and large limited to online shopping experiences. By allowing consumers to log things they see in a store that they’d already researched online — or vice versa — and providing shared shopping cart functionality, you lend consumers the visibility and ability to solve problems and make purchases on their terms.

Retailers can take that a step further and offer in-store associates visibility into those lists, as well as online and in-store purchase history, served up through an easy to use POS interface that helps them to make relevant recommendations that convert sales. In such a way, omni-device strategies become the foundation for reimagining brick-and-mortar stores, and ensuring that they’re not sites for fire sales and instead, places that consumers go to augment personal research with expertise and test drives.

None of that can happen unless the same engine is driving the behavior of all devices. That starts by having a unified source of data on the backend — with reliable inventory, order and customer data the foundation for serving up omni-device delight. Think broader and bolder than omnichannel this year — down to the nuances of the matrixed online and physical worlds in which shopping occurs.

As Channels Blur, Omni-Device Will Be the Biggest Trend Of 2018

MATT RHODUS

RETAIL DIRECTOR AND INDUSTRY PRINCIPAL

ORACLE NETSUITE

Omni-device strategies become the foundation for reimagining brick-and-mortar stores, and ensuring that they’re not sites for fire sales.”

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The final line from the theme song of the long-running sitcom “Cheers” captures the spirit of omnichannel retailing: You wanta be where everybody knows your name.

Whether they’re shopping in a brick-and-mortar store, buying online, conducting merchandise research with help from Google, videos or a recommendation engine, customers expect retailers to know who they are and what they like.

Omnichannel Retailing Demands A New Breed Of AnalyticsRetailers have used analytics to enhance customer outreach for more than 30 years. But omnichannel retailing has stretched older forms of analytics to the breaking point.

When retailers began using analytics in the 1980s, they used them to determine how well TV, print or radio ads drove sales. To perform these analytics, advanced data scientists took the variables they knew and built a model that could predict the behavior necessary for best outcomes. Although this process was complex and time consuming, it was tolerable because retailers only used one model to reach all customers.

Over time, retailers began creating customer segments. Eventually they found themselves targeting hundreds of different target markets. In today’s omnichannel retailing environment, customers interact with retailers — both in store and online — in myriad ways: Digital signs deliver personalized promotions in-store; customers use a retailer’s mobile app; customers go to an ecommerce site or do product research via blogs, videos, reviews and recommendation engines.

Creating a personalized experience for each of these customer touch

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points requires the use of many different types of analytics, including: • Descriptive analytics to perform segmentation;• Diagnostic analytics to understand behavioral drivers;• Predictive analytics to understand communications channels, timing,

products and pricing options;• Natural language processing for social/text and voice-of-the-

customer analysis;• A/B multivariate testing; and• Marketing mix modeling.

Machine Learning And AI Make Omnichannel Retail EasierSo how do retailers deploy the right analytics to respond to each customer interaction in real time? This is a job perfectly suited to artificial intelligence (AI) and machine learning.

AI is the idea that machines can carry out tasks in a way that humans would consider smart — even by understanding and processing natural language. Machines with AI can act without relying on explicit instructions from a human software developer.

Machine learning is a subset of AI where computers look for structure in a set of data by performing computations in an iterative manner. The results from each successive wave of computations are fed back into the model to provide a “feedback” loop. The model takes the feedback and uses it to modify the approach it takes for the future. By using algorithms that iteratively learn from new data, machine learning allows us to find hidden insights without having to reprogram the machine about where to look.

Welcome Shoppers Across All Channels With AI And Machine Learning

DAN MITCHELL

GLOBAL DIRECTOR OF RETAIL AND CPG

SAS

Machines with AI can act without relying on explicit instructions from a human software developer.”

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What Changed To Make It All Possible Now? AI and machine learning principles have been well understood since the late 1950s. Why are they only now coming into widespread use? Two recent developments have made sophisticated analytics available to virtually any retailer:

1 Big Computing. The availability of big computing gives retailers the horsepower they need to handle data in a timely manner at an affordable cost.

A gigabyte of storage in 1960 cost the equivalent of $28.6 million in 2016 dollars. The cost of storing a gigabyte today? About 2 cents. With plummeting computational and storage prices, costs are no longer an obstacle to anyone wanting to take on big computational challenges.

Instant access to streaming data containing high-resolution customer behaviors can now feed data-hungry machine learning models.

2 Democratized Analytics. The availability of sophisticated tools that allow non-technical users to use analytics on a self-service basis means business users don’t have to rely on IT to understand data. Business users can efficiently explore a problem without waiting hours for each iteration.

Ensuring every customer feels known across all online and brick-and-mortar retail channels is a daunting challenge. By using analytics that take advantage of machine learning and AI, retailers can give customers personalized information, recommendations and promotions when and where they need them to dramatically improve sales and customer loyalty.

A gigabyte of storage in 1960 cost the equivalent of $28.6 million in 2016 dollars. The cost of storing a gigabyte today? About 2 cents.”

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Three essential retail technologies band together to help retailers better understand the needs of their customers in 2018. The combination of Retail IoT, Artificial Intelligence and Data Aggregation Platforms will create new and unprecedented opportunities for retailers and brands to ultimately help them meet the increasing demands of today’s consumer. This is a critical cornerstone in helping create strategies for meaningful and convenient customer experiences.

We refer to these core platforms as the “Holy Trinity” of Retail Tech. It is important to note, while each of these segments are equally important, the unification of the three will unlock new ways for retailers and brands to build data driven, connected commerce experiences.

The 3 technologies will be in the areas of:

Retail IoT IoT solution providers have been on a relentless path to connect anything and everything to the internet. New solutions to track in-store customer behavior using advanced sensors will more cost-effectively collect data on traffic, gender, age and sentiment, bringing the same type of analytics available with online commerce into the physical store space.

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Artificial Intelligence (AI) It’s true — AI is commonly depicted as the end-all, be all solution for retail. However, in this specific context, we refer to AI as a broader term to include; Machine Learning, Natural Language processing, and Machine Vision — all of which are critical to the process of automating vast amounts of data collected by Retail IoT platforms. AI will be critical to everything from Virtual Assistants and Chatbots to solutions that allow people to walk out the door without having to check out at a cash register.

Data Integration PlatformsThe quest for accurate and relevant data attribution with on-line (conversational commerce, e-Commerce, social shopping and mobile) and off-line data (traffic drivers, in-store shopping behaviors, conversion and sales) has been the main challenge every smart retailer and brand has been trying to tackle for the past 24 to 36 months. Newer, more sophisticated data aggregation platforms that better integrate data collected from both online and in-store Retail IoT (connected store) will become the secret weapons of the retail industry in 2018.

The ‘Holy Trinity’ Of 2018 Retail Tech

MANOLO ALMAGRO

MANAGING PARTNER, Q DIVISION

STRATACACHE

The combination of Retail IoT, Artificial Intelligence and Data Aggregation Platforms will create new and unprecedented opportunities for retailers and brands to ultimately help them meet the increasing demands of today’s consumer.”

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Between Amazon Alexa and Google Home, nearly 30 million voice devices have been sold in the U.S. alone. With more consumers using voice assistants, retailers have started paying attention as to how voice can improve their customer experiences.

At the beginning of 2017, roughly 1% of retailers and brands expressed that they were investing in voice experiences. As 2017 came to a close, that percentage rose to 50%, with many companies understanding that voice is playing a pivotal role in the consumer experience, particularly when it comes to searching for and buying items on a mobile device. Today, roughly 60% of online retail traffic comes from mobile devices, while mobile shopping conversion rates are around 20%. A number of factors contribute to this discrepancy but the leading one dates back to the advent of the smartphone: mobile screens are smaller and traditional visual UI experiences simply don’t translate well to mobile.

That’s where voice comes in.

In 2018, we’ll see more retailers investing in voice technology and we’ll also see voice experiences come to life in an omnichannel way. Web sites and mobile apps will be voice-powered, delivering real utility for customers to improve the shopping experience with natural language. And we’ll also see voice bring new opportunities for efficiency and engagement in the store.

Here are a few reasons why:

• Voice is orders of magnitude faster than typing;• 83% of consumers say that voice makes it easier to find products; and• Natural language allows users to overcome the limitations of

visual interfaces.

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But there are other forces at play here as well. Amazon has dominated the e-Commerce landscape, leaving many retailers wondering what their fate might be. On Black Friday in 2017, Amazon was responsible for 55% of online e-Commerce sales, selling Alexa devices as the top item for the second consecutive year. This is bad news for retailers, as Alexa owners are using their speakers to buy Amazon products, thus continuing the cycle of Amazon’s dominance in the market. And Amazon isn’t slowing down. They’re adding voice across their entire digital experience, including constant enhancements to the Alexa experience on their own mobile shopping app.

By 2020, 50% of search will be done via voice or through image search. And voice will be bigger than a cylinder with no screen — it will be the future of how consumers interact with retailers and brands alike. Consumers expect shopping to be a frictionless experience, regardless of platform or channel. They’ve experienced how voice has achieved this for music, and now they expect other verticals such as e-Commerce to follow. Voice is the next-gen equivalent of Amazon’s “one-click checkout”.

Voice Takes Center Stage

PETER CAHILL

CEO

VOYSIS

By 2020, 50% of search will be done via voice or through image search. And voice will be bigger than a cylinder with no screen — it will be the future of how consumers interact with retailers and brands alike.”

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We’re entering a crucial time in retail, as the blurring of the physical and digital worlds paired with the ubiquity of mobile connectivity are accelerating the adoption of the Internet of Things (IoT) in the retail sector. Retailers who don’t evolve quickly enough will fall out of favor with shoppers.

In the industry’s push towards a new way of retail serving all channels, IoT promises to deliver gains in productivity, efficiency, inventory visibility and customer satisfaction. According to Zebra Technologies’ recent Retail Vision Study, the three main factors negatively impacting shopper satisfaction are products being out-of-stock, lower prices available elsewhere and desired items being unavailable in-store.

Among the emerging trends being seen by forward-thinking retailers in today’s evolving landscape, the focus on unified commerce through the new concept of going “phygital” is one that will be key in 2018.

Today, despite the flourishing e-Commerce business model, brick-and-mortar stores continue to pull in 91% of all retail sales generated across all platforms, an indicator that the physical retail is a turf to safeguard. To make the in-store shopping experience more intuitive and seamless, retailers are supplementing technology in their physical stores, turning the entire space into a smart store that automatically senses and records the location and movement of virtually everything — merchandise, associates, shoppers and assets — and translates that data into easy-to-read actionable intelligence that delivers real competitive advantage. The phygital retail space combines the best of two worlds — the tactile satisfaction of physical retail and the intuitiveness of ecommerce.

To do so, 81% of retailers surveyed will implement security sensors; 75% will install sensors tracking status of inventory; and 71% will deploy sensors for tracking customer footpath. IoT device and networking monitoring (73%) is also gaining popularity among retailers.

2018 TECHNOLOGY PREVIEW

In the physical space, enterprise-class mobile devices are playing a critical role in enabling sales associates to achieve customer success by helping transform them into a business development role that supports both in-store and online sales. Empowering associates with mobile computers can enhance the customer experience and improve the efficiency of in-store operations by providing associates with the right information at their fingertips allowing them to provide sound advice and accurate product information to customers.

Personalized customer shopping experiences are another key digital retail offering. The promise lies in knowing what customers want in their precise moment of need. Micro-locationing technologies are also being used by retailers to customize a personalized experience for customers through sensors that interact with customers’ smartphones via low-energy Bluetooth signals and can deliver contextually relevant, in-store offers for different customers. Other locationing technology can help track the footprint of a given customer in the store, using data to predict customer behavior, generating actionable insights on customer shopping habits and purchasing patterns. From there, retailers can make smarter decisions on store lay-out strategies that best match their customers’ patterns.

Personal Shopping Solutions (PSS) are another way retailers are engaging customers in real-time. PSS solutions allow customers to scan their own items as they shop, meaning they only have to pack their bags once and can check out without a queue. Enhanced benefits also include access to lists, reminders and recipes, help locating products and personalized promotions.

As the next wave of digital retail is upon us, the industry will continue to see the convergence of physical and online stores. For retailers who want to strive and thrive in 2018 and beyond, adoption of IoT devices, tracking sensors, connectivity, and data analytics tools are essential.

Going ‘Phygital’ Is Key To Retail Success In 2018

THOMAS MOORE

NORTH AMERICA INDUSTRY LEAD, RETAIL & HOSPITALITY

ZEBRA TECHNOLOGIES

Store Experience

2018 TECHNOLOGY PREVIEWEXCLUSIVE PREDICTIONS FROM 17 RETAIL EXECUTIVES19

The extended retail value chain has been using category management as its marketing and merchandising backbone for nearly 30 years. It launched hundreds of new careers on both the retail and brand sides, spawned dozens of consulting and technology firms and generally shook up the world of retail. 2018 will see the start of a new approach, Total Store Optimization.

The industry has long needed to reevaluate the old, generic category-by-category approach because it restricts the ability of trading partners to understand and satisfy shopper needs while reaching sales and profit goals. Other important considerations are that category management in its current form doesn’t allow for efficient support of ecommerce or the implementation of an integrated approach to personalization.

To move to the next generation of category management, the industry needs to support an approach that systematically deploys enhanced shopper-driven insights that are attainable with today’s technology solutions. This methodology needs to go well beyond product and category optimization to be customer-centric for the entire shopping experience. And, it must take into account a total store, cross-category technique to marketing and merchandising.

By deploying Total Store Optimization, retailers will directly translate actionable shopper insights into price, promotion and assortment actions that make sense for categories across channels of engagement. Some categories are better at driving price perception and volume, while others are more suited to driving revenues and profits, so retailers will adopt a portfolio approach that enables them to achieve their varied objectives while better satisfying the needs of different customers. The key is to balance all activities between the store, categories and individual products, with the aim of satisfying shoppers’ needs across multiple elements of the retailer’s offering.

Retailers and their trading partners will deploy Total Store Optimization to collaborate on decisions across price, promotion, assortment, space and

2018 TECHNOLOGY PREVIEW

shopper marketing, aligning the resources of the supplier community with the specific needs of the shoppers in the retailer’s specific stores rather than applying a generic, cookie-cutter approach. They will take competition into consideration and adjust prices to improve value perception. For assortment, they will determine which categories need more depth and breadth to satisfy shopper needs. For promotions, they will adopt a three-pronged strategy to identify which promotions are not working (and stop running them), which can be fixed (by changing the promotion mechanics to improve shopper engagement) and which can be slanted (towards loyal customers to minimize cherry picking).

Total Store Optimization allows you to optimize merchandising and marketing decision across the entire store for all categories simultaneously, the same way your customers shop the store, helping you reach your overall comprehensive sales and margin goals.

With the shopper at the center of your strategy, it helps you define and operationalize category roles by using advanced analytics to understand customer behavior and recommend a balanced set of actionable shopper-driven price, promotion, assortment and personalized marketing recommendations across your category and customer portfolio.

Some retailers will want to take a big-bang approach to Total Store Optimization and embark on transformational change; others will opt take a phased approach, starting with Crawl (make the most impactful changes and build momentum), then move on to Walk (tailor category targets and objectives and implement total store price, promotion, assortment and personalized marketing decisions) and finally Run (evolve the organizational structure, roles and responsibilities to be shopper-centric). Moving from a category focus to a shopper focus delivers rapid and meaningful results while laying the foundation for longer-term sustainable competitive advantage; having visible buy-in from the management team and collaborating with trading partners increases the speed to value and magnifies the overall impact.

Total Store Optimization Puts The Shopper At The Center Of Every Decision

GRAEME MCVIE

CHIEF BUSINESS DEVELOPMENT OFFICER

PRECIMA

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Online retailers can pile-on a dizzying array of technologies to deliver significant amounts of data and personalize the customer journey in real-time. But this isn’t necessarily the case for brick-and-mortar retailers, who have traditionally lacked the insights to make decisions based on real-time data. While there is a considerable amount of existing technologies that add efficiencies to current retail store operations, brick-and-mortar retailers should invest in technology solutions that can provide a meaningful impact. The greatest area of opportunity for retailers is both their largest expense and their distinct differentiator over online competitors: in-store staff.

We’ve all heard the old proverb about “doing more with less,” but when it comes to in-store operations, it may be more important for retailers to be “doing less with more.” When sales associates interact with in-store shoppers, they need to focus on providing a superior in-store experience. Retailers should equip sales associates with solutions to help spend less time on manual, rudimentary activities and make the most of more technology. In 2018, one of the most meaningful uses of technology will enable sales associates to put the “engagement” back in “customer engagement.” Here are some ways how:

Restructure Hours & Activities By leveraging in-store sensors and traffic insights, retailers can understand their “power hours” and allocate labor based on opportunity. In particular, traffic data enables retailers to determine peak hours and shape staff schedules and tailor tasking plans (e.g., restocking, straightening, etc.). This ultimately allows associates to better serve shoppers, enhance customer service and drive sales.

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Review Industry Performance Having greater insight into how individual stores are performing against competitors, other franchises or even different zip codes can help retailers discern unique trends based on region and market segment. This, in turn, allows regional and store managers to better evaluate their own store operations (marketing, promotions, staffing, etc.) and make knowledge-backed decisions.

Understand Your Own Store We foresee more innovation when it comes to interior analytics and improved real-time insights. For example, retailers will be able to gain insights from sensors and Wi-Fi within various aspects of their store and create meaningful comparisons. For example, how is grocery comparing to the pharmacy? What about women’s versus the men’s department? How are shoppers moving throughout the overall store? By having these valuable insights, retailers can spend less time guessing about staffing and merchandising and instead, allocate the proper resources to better meet customer needs and increase sales.

This year, retailers should focus on making technology investments that provide actionable insights, rather than leveraging legacy platforms, whose raw data leaves decision makers wondering, “but what does this mean for store operations?” By making smart investments, sales associates can spend more time focusing on customers and having meaningful (and profitable) interactions.

Narrowing Tech Investments Spark Action And Enhance In-Store Customer Service

DAVE BERG

GLOBAL HEAD OF SOLUTIONS MANAGEMENT

SHOPPERTRAK

When it comes to in-store operations, it may be more important for retailers to be ‘doing less with more.’”

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In a Unified Commerce world, stores should be thought of as markets. And the technology supporting the stores and head office needs to embrace and leverage this view in order to enable effective decision-making.

The brick-and-mortar store remains the retailer’s single most important brand touch point. Customers spend more time making an in-store purchase than an online purchase, and the in-store experience is personal and physical. In Unified Commerce, there are generally two avenues to demand generation (store and online), two avenues to order fulfillment (instant at the store or delayed via shipment from DC/another store), and two avenues for returns (store and ship to DC).

To address the various combinations of demand generation, fulfilment, and return options, retailers should look at the store as a market. This means: • Ascribing a market (store) to each online sale based on geolocation; • Having multiple sales ‘views’ for a store: brick-and-mortar sales, market

online sales, and aggregated total market sales; and• Tracking both sales and return metrics for each view.

A store that excels at being a brand ambassador will see a lift in their aggregated Market sales, even if their brick-and-mortar sales are declining. Measuring how this shift is occurring in each Market allows Field Managers to make better, more informed decisions on performance opportunities.

There is built-in friction between brick-and-mortar and e-Commerce channels, and it will be important to align goals to drive performance across all channels. E-Commerce is viewed as the rising star with annual double-digit growth, while declining traffic at brick-and-mortar stores can lead to year over year comp brick-and-mortar decreases. Further, some more antiquated POS systems can’t separate returns generated by the store versus returns generated by online purchases. When e-Commerce sales ranged between 1% and 5% of a retailer’s sales, the headaches caused at store level were barely noticeable — now that the percentage

2018 TECHNOLOGY PREVIEW

of online sales has increased significantly, the impact of online returns on a store’s sales (if not separated out) is so material that store managers are worried about losing their bonuses as a result of online returns.

The ability to align goals relies on how a retailer will use technology to measure and reward performance within each of the channels. While the jury is still out on some of the finer details, the trends we see are two-fold: 1) more retailers are shifting towards rewarding stores for demand generation, regardless of the method of fulfillment; and 2) the old method of expecting stores to deal with external fulfillment (both click & collect and ship from store) out of existing labor budgets has given way to selectively adding labor hours based on a combination of an external fulfillment forecast as well as each store’s available capacity to address orders within the existing labor budget.

Technology, specifically sales performance coaching and reporting technology, will need to break down the data to view stores as markets. This will give retailers the ability to understand both the impact and the relevance of stores in a Unified Commerce world, and ultimately drive the performance and customer experience needed that will allow them to continue to compete and thrive.

Stores As ‘Markets’ In The World Of Unified Commerce

DAVE LOAT

PRESIDENT

STOREFORCE SOLUTIONS

The ability to align goals relies on how a retailer will use technology to measure and reward performance within each of the channels.”

Inventory Management

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Let’s face it — inventory is a problem. Sure, it’s the lifeblood of your business but having too much kills your margins and having too little means disappointed customers. In fact, 75% of consumers have experienced an out-of-stock situation in the past year. And while they may not complain to you, 91% will never come back1 — costing retailers $630 billion in lost revenue per year (and that’s up 39% from 2012). To make matters worse, markdowns cost retailers another $470 billion per year with the average retailer losing 4.1% of revenue.2

Welcome to retail’s secret Trillion Dollar problem.

The problem isn’t solely having too much or too little inventory, it’s also about inventory location — having it in the wrong places causes billions of dollars of excess cost as retailers are forced to ship products across long distances to meet customer demand.

The good news is there is hope and it starts with better forecasting and planning. Done well, forecasts and visibility can help retailers recapture 50% to 70% of losses from inventory inaccuracy.3 And there are several areas where there can be improvement:

• Product Availability: Improve availability by identifying from historical data on where, when and which particular SKUs need to be in stock.

• Geographic Placement: Placing inventory in the right places, based on product type and demand to reduce fulfillment and freight costs.

• Consumer Insights: Understand your customers’ shopping and buying behavior to identify which products will sell, when.

• Excess Inventory: When you have the consumer insights and historical data, identify the exact amount of products needed, with little to no excess.

• Market Knowledge: You first need to understand what your customers expect so you can meet their demands and place your inventory appropriately.

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Unfortunately, traditional methods of forecasting have not kept pace with the changing ecommerce landscape. Much of the forecasting technology was built on old paradigms and lacks access to the robust data required to actually forecast. Much of the “state-of-the-art” planning tools are limited and expensive and so planning models were built based on those limitations — not a way to adapt to today’s commerce reality.

To make matters worse, traditional supply chain planning solutions require users to figure out a lot on their own. So, if you want to advance, you need experience in crunching billions of data points across your supply chain to make everything work. The most sophisticated retailers use goal-driven inventory optimization to define the desired results and keep up with fast paced consumers rather than having to manually build rules that could be outdated by the time they are deployed.

But if you don’t have machine learning experts and big data iron in house to handle the information analysis required to plan well, you need technology. There are inventory optimization technology solutions that leverage AI to provide the insights that planners and marketers need in order to improve the overall business and optimize their inventory. Partnering with a provider who can leverage order and fulfillment data, with artificial intelligence, you can reach the optimal level of inventory management.

1 Retail Touchpoints 2017 Holiday Guide

2 IHL Group – 2015/*GT Nexus – 2016

3 GT Nexus – 2016

Retail’s Trillion Dollar Problem

STEFAN WEITZ

EXECUTIVE VICE PRESIDENT, TECHNOLOGY SERVICES

RADIAL

Marketing

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There’s a martech revolution happening in retail, and in 2018 it’s going to be impossible to ignore. For the first time in many years, we’re going to see significant consolidation in this crowded technology landscape.

Over the past few years, the number of new consumer devices and marketing channels has exploded — from mobile phones and tablets to email, push, social media, search and more. In an effort to keep up, solution vendors rapidly developed device- and channel-specific solutions to help marketers reach those audiences.

Today, the average marketer uses an average of 12.5 different marketing systems. While each system may work well for one specific channel or device, they rarely work well together. Many of these marketing tools are completely siloed, each with their own set of data and little to no communication with each other. As a result, the marketer struggles to understand their customers and deliver the cohesive omnichannel retail experiences that today’s consumers demand.

We’ve all seen the overwhelming map of the martech space today — this level of fragmentation clearly can’t continue. Many B2C marketers today already feel exhausted trying to manage so much software and are struggling to unify their customer data effectively to analyze their marketing results. It’s clear that the solution is consolidation: a single system that can unify all of your customer data and execute on your marketing strategies across channels.

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This system is the B2C CRM. Unlike a B2B CRM, the B2C CRM is able to collect and unify the high volume of data that retailers deal with on a daily basis in order to segment and personalize customer communications across channels, execute automated campaigns, and analyze your successes. Your customers don’t care if you’re talking to them via email or through social media — what they care about is whether you’re communicating with them in a way that is relevant and consistent. A B2C CRM allows you to serve your customer needs first — rather than focusing on the channel or device.

The Martech Retail Revolution

ERIC KEATING

VICE PRESIDENT OF MARKETING

ZAIUS

The average marketer uses an average of 12.5 different marketing systems.”

Payment/POS/Security

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Cryptocurrencies are all the rage, with Bitcoin in particular spurring huge price volatility based principally on speculation. While exciting, the interesting question is the applicability of these currencies to the retail industry. In this context, cryptocurrencies have a long way to go for wide-spread market acceptance but it’s worth watching.

The backbone of cryptocurrencies is blockchain. Blockchain utilizes a distributed ledger to eliminate the need for middlemen in settlement processes. Everyone involved in the transaction has access to the same ledger. This transparency creates trust and therefore no middlemen. Because middlemen are eliminated, transaction costs are much lower. While blockchain is very secure, today real world usage presents challenge to retailers and consumers.

From a consumer perspective it is currently a complex process to set up a digital wallet and start accumulating coins. Startups have emerged that aim to make this process easier, however there are still major obstacles such as significant currency volatility. Cryptocurrencies will need to remain relatively stable in order to gain wide spread adoption.

Bitcoin may be the most well-known cryptocurrency, but may not be cryptocurrency of choice for retailers in the future. The primary reason… speed. Transactions on Bitcoin take well over 10 minutes per block (each step of the transaction) to complete, making a block of 6 take about an hour. According to experts, the average retail transaction could take in excess of 300 minutes. None of these time spans would be acceptable in a retail environment where transactions are measured in fractions of seconds. Without faster transaction speed, wide spread adoption in retail will never materialize and Bitcoin could get relegated to a peer-to-peer role.

Litecoin, for example, may be better suited for retail payments. It has faster transaction times, 2 minutes versus 300 minutes for Bitcoin. Litecoin also has four times as many coins in circulation. Although faster, 2 minutes per transaction is still not acceptable.

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Ethereum’s blockchain is mainly geared toward “smart contracts” which execute when conditions are met. Smart contracts work like a vending machine where you put in a dollar and press the button to get a can of soda. There is a promise made. Once the first party upholds its part, the second party automatically delivers their end. For example, a retailer making payment immediately upon receipt of the delivery by a consumer products manufacturer.

Some retailers have already waded slowly into the use of crytocurrencies. Retailers currently accepting cryptocurrencies include Subway, Overstock.com, Microsoft, and Expedia. Amazon has reportedly registered three related domain names: amazoncryptocurrencies.com, amazoncyrptocurrency.com and amazonethereum.com.

Generally, in retail, we expect to see blockchain technology emerge on a large scale in back office functions, beginning with the supply chain where transaction speed is not always as vital. Trust versus speed make this a prime area to start. Marketing attribution models may be another area that will allow marketers to determine campaign effectiveness by tracking personalized campaigns from offer to purchase with the retailer paying vendors immediately on performance. While too slow for in-store payments, retailers may consider using cryptocurrencies and blockchain for online transactions.

Although blockchain offers little that will offset the speed of transactions today, it will improve over time. Cryptocurrencies for consumers in-store will need to overcome a few factors: consumer wallet ease of use, transaction speed and valuation volatility. That’s a lot to overcome, however we would not advocate taking your eye off this space any time soon.

The Potential To Transform Retail With Cryptocurrencies Is On The Horizon

KIRK A . GOLDMAN

VICE PRESIDENT, BUSINESS STRATEGY

TOSHIBA GLOBAL COMMERCE SOLUTIONS

Retail TouchPoints is an online publishing network for retail executives, with content focused on optimizing the customer experience across all channels. The Retail TouchPoints network is comprised of a weekly e-newsletter, special reports, web seminars, exclusive benchmark research, an insightful editorial blog, and a content-rich web site featuring daily news updates and multi-media interviews at www.retailtouchpoints.com. The Retail TouchPoints team also interacts with social media communities via Facebook, Twitter and LinkedIn.

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