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Is The Digital Customer Experience Relevant To B2B Brands?

Jennifer Arnold

Last year SAP Australia/New Zealand released the 2016 Digital Customer Experience research, which investigates consumers’ views about the quality of digital customer experiences they receive from local consumer brands and identifies the impact of this on brand loyalty and advocacy. In sharing our research with SAP customers, I meet with many business-to-business (B2B) organisations.

Often the first question I’m asked is, “Our customers are other businesses, so is this research relevant to us?” And my response is always, “Yes, because until Artificial Intelligence runs the world and all future business engagements are conducted chatbot to chatbot, your customer on the other side of the screen isn’t a business, it’s a real person, who is also a consumer.”

Our research with 6,000 ANZ consumers found they’re nearly 5 x more likely to remain loyal to a consumer brand if it provides them delightful digital experiences.

In a business setting, employees typically will have less or no choice about the businesses they have to engage with compared to the wide choice they have as an individual consumer. Therefore, we expect in the B2B segment the impact of an individual employee’s experience on brand loyalty will be less critical to the brand’s business.

However, based on conversations with customers and partners, we’ve found the quality of the digital experience does affect how an employee chooses to engage with a business, which impacts productivity, cost, and efficiency. For example, if a manufacturer wants customers to use an online ordering tool but the tool doesn’t work properly or is hard to use, the customer’s employees may choose to work around it by emailing or phoning in orders. This can increase the processing time and result in the manufacturer having to double-handle the order information to enter it into its system.

When it comes to the impact on brand advocacy, nearly 70% of ANZ consumers would be willing to recommend consumer brands that provide them with a delightful digital experience.

The feedback we receive from SAP’s customers also suggests that the impact on brand advocacy is slightly less for B2B organisations than for B2C brands. This is primarily because large numbers of consumers share feedback about their experiences and recommendations on broad social media platforms, and again, average employees often can’t choose the companies with which their employer does business.

That’s not to say advocacy is not important to B2B companies. An increasing number of B2B organisations, including SAP, use the Net Promotor Score rating, which measures advocacy as a key indicator for customer satisfaction and business health. Aspects of customer satisfaction typically measured include service quality, communication and responsiveness, customer support, the ease of doing business with the company, fit of products and services, and handling of issues. If these aspects aren’t well supported by a positive digital experience, customer satisfaction and the willingness to promote or advocate for the company will be impacted.

The size of a company may also matter when it comes to the the impact of the digital experience on loyalty and advocacy. For smaller B2B organisations, the impact may be greater because their customers are less likely to be locked into long-term contracts and would have more choice of providers, so risk of customer churn is greater.

Consider this: If you have a regular print supplier for your company documents, proposals, posters, and so on, and their online systems for booking, tracking your orders, and exchanging and checking artwork don’t work to your satisfaction, would you stay with them or go to a supplier that provides a better-functioning system? Which of them would you recommend to a friend or colleague who needs print services?

Improved loyalty and advocacy aren’t the only objectives organisations expect from improving customer experiences. More and more I’m asked to share our Digital Customer Experience research findings with our B2B customers and discuss what they can do to improve experiences because enhanced customer engagement is central to organisations’ overall digital transformation strategies. These transformations involve adapting their products and services and creating new channels to sell to and support consumers – all increasingly digital-only.

This is expanding the customer experience conversations outside of the marketing and customer service departments into all business functions and levels. The customer experience – especially the digital customer experience – is relevant and increasingly critical to every part of the business in every brand in the market.

If you’re from a B2B organisation, are you trying to improve your digital customer experience? If so, what are the most critical steps you’re taking? What changes do your customers want to see?

For more information about improving your digital customer experiences, read our 2016 Digital Customer Experience research paper and join us for the discussion at our four-city Art of the Possible roadshow across ANZ starting 7 March.

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Jennifer Arnold

About Jennifer Arnold

Jennifer joined SAP in May 2015 as Vice President and Head of Marketing, Australia and New Zealand. She is a member of the APJ Marketing Leadership Team as well as the ANZ Senior Executive Team (SET). She leads the ANZ Marketing team in supporting sales goals across SAP’s portfolio, building SAP’s brand and reputation ANZ, and driving SAP’s integrated marketing execution. Jennifer joined SAP from Unisys where she spent nearly nine years, most recently hold dual roles of Enterprise Services Global Portfolio Marketing Director and Enterprise Services Practice Business Consultant. She was responsible for developing and delivering global integrated demand generation campaigns and sales enablement materials. She also developed and rolled out the company’s global sales personas program. In her consulting role, she worked with clients to design persona-based business and technology strategies and develop enterprise services analytics projects. Prior to her global roles, Jennifer was the Unisys Asia Pacific Marketing Director, managing a team delivering campaigns focused on IT services, security, infrastructure and applications across the region. Prior to Unisys,

Flash Briefing: Turning Customers Into Promoters

Peter Johnson

Today, we’re talking about user-generated content and how it can be an easy way to help improve a brand’s reputation, social engagement, Web-site traffic and overall sales.

 

Tune in Monday through Friday for more Digitalist Flash Briefings on disruptive technologies and trends on your favorite device or app.

  • Amazon Echo or Dot: Enable the “Digitalist” flash briefing skill, and ask Alexa to “play my flash briefings” on every business day.
  • Alexa on a mobile device:
    • Download the Amazon Alexa app: Select Skills, and search “Digitalist”. Then, select Digitalist, and click on the Enable button.
    • Download the Amazon app: Click on the microphone icon and say “Play my flash briefing.”

Find and listen to previous Flash Briefings on Digitalistmag.com.

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Peter Johnson

About Peter Johnson

Peter Johnson is a Senior Director of Marketing Strategy and Thought Leadership at SAP, responsible for developing easy to understand corporate level and cross solution messaging. Peter has proven experience leading innovative programs to accelerate and scale Go-To-Market activities, and drive operational efficiencies at industry leading solution providers and global manufactures respectively.

Why $4.6 Trillion Was Left In Abandoned Online Shopping Carts In 2016

Aaron Solomon

Nearly 70% of online shopping carts are abandoned without the customer ever completing the purchase. According to Business Insider, that added up to over $4.6 trillion in the global economy in 2016. So, what can online retailers do to fix this problem? Keep reading to learn why cart abandonment is so prevalent, and the steps you can take to recapture potential sales in your business.

Top 3 (preventable) reasons for cart abandonment

Customers abandon online carts for a variety of reasons, ranging from issues with your online store to simply getting distracted and leaving their computer. Studies show three common, and often preventable, reasons customers do not complete the checkout process.

1. Shipping cost sticker shock

For a whopping 61% of U.S. shoppers, the number one reason they don’t complete the checkout process is unexpectedly high shipping costs. Customers may have found your product prices acceptable, but high shipping costs can change their view completely. Not every company can subsidize shipping, and even fewer can on all orders, but here are a few things you can do to try to reduce your customers’ shipping cost shock:

  • Price fairly: Review what you are charging your customers for shipping against what your actual costs are. It’s no sin to turn shipping into a revenue stream, but if these costs are excessive, it may be costing you more in sales than it’s worth.
  • Offer options: Depending on your carriers, consider offering customers slower, but more cost-effective options, such as UPS 3 Day Select instead of Next Day or Second Day Air.
  • In-store pickup: If you also have physical stores, consider offering in-store pickup as an option.

2. Lack of trust in your site

Whenever a first-time customer makes a purchase, they are demonstrating trust in your ability to fulfill their order, charge them accurately, and most importantly, protect their data. There are several website attributes that could cause customers to consider shopping elsewhere:

  • Site maintenance: If your site has blurry images or broken links, customers may doubt your ability to meet their needs.
  • Online security: Include a “Trusted Site” logo from your certificate authority on your site to tell customers that you have properly secured your site.
  • Return policy: Having a complete and accessible return policy on your online store can provide customers with the reassurance that if the product does not meet their expectations, there will be a way for them to address this issue.
  • Shipping clarity: A shipping information page provides customers information they will want to know before committing to a purchase, such as how long after an order is placed it will be shipped or what shipping carriers and delivery options are available.

3. Frustration during the checkout process

The main perk of online shopping is convenience. If your checkout process is slow or tedious, customers get frustrated quickly. Take the following three points into consideration to mitigate this concern:

  • Guest checkout: In 2016, 33% of U.S. shoppers abandoned their carts when forced to create an account. Having a customer create an account can be beneficial for your business, but, if customers are forced to create an account to make a purchase, is it worth it? Consider leaving the option for them to check out as a guest to simplify their shopping experience.
  • Coupon codes: If you offer promotions with coupon codes, make sure that all your marketing information has the correct coupon codes and expiration dates for these codes.
  • Make it easy for customers to reach you: As a best practice, online stores should always have a “Contact Us” page to allow customers to easily reach out. If customers are experiencing frustration, being able to reach you can be the deciding factor on whether they give up or not.

Successful online retailers manage these issues to ensure that when customers abandon carts, it is not due to failures of the business. Taking these steps can reduce the amount of lost revenue, as well as increase your business’ reputation with both current and prospective customers.

For more insight on selling through digital channels, see Primed: Prompting Customers to Buy.

This blog was originally posted on the SAP Anywhere Customer Success Portal, and has been reposted with permission.

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Aaron Solomon

About Aaron Solomon

Aaron Solomon is the head of Training and Content Development for SAP Anywhere. With a dedicated history in knowledge management and consulting, he is driven to provide quality information to customers and help them understand how best to grow their businesses. His areas of expertise include e-commerce management, data analysis, and leveraging technology to improve efficiency.

Running Future Cities on Blockchain

Dan Wellers , Raimund Gross and Ulrich Scholl

Building on the Blockchain Framework

Some experts say these seemingly far-future speculations about the possibilities of combining technologies using blockchain are actually both inevitable and imminent:


Democratizing design and manufacturing by enabling individuals and small businesses to buy, sell, share, and digitally remix products affordably while protecting intellectual property rights.
Decentralizing warehousing and logistics by combining autonomous vehicles, 3D printers, and smart contracts to optimize delivery of products and materials, and even to create them on site as needed.
Distributing commerce by mixing virtual reality, 3D scanning and printing, self-driving vehicles, and artificial intelligence into immersive, personalized, on-demand shopping experiences that still protect buyers’ personal and proprietary data.

The City of the Future

Imagine that every agency, building, office, residence, and piece of infrastructure has an entry on a blockchain used as a city’s digital ledger. This “digital twin” could transform the delivery of city services.

For example:

  • Property owners could easily monetize assets by renting rooms, selling solar power back to the grid, and more.
  • Utilities could use customer data and AIs to make energy-saving recommendations, and smart contracts to automatically adjust power usage for greater efficiency.
  • Embedded sensors could sense problems (like a water main break) and alert an AI to send a technician with the right parts, tools, and training.
  • Autonomous vehicles could route themselves to open parking spaces or charging stations, and pay for services safely and automatically.
  • Cities could improve traffic monitoring and routing, saving commuters’ time and fuel while increasing productivity.

Every interaction would be transparent and verifiable, providing more data to analyze for future improvements.


Welcome to the Next Industrial Revolution

When exponential technologies intersect and combine, transformation happens on a massive scale. It’s time to start thinking through outcomes in a disciplined, proactive way to prepare for a future we’re only just beginning to imagine.

Download the executive brief Running Future Cities on Blockchain.


Read the full article Pulling Cities Into The Future With Blockchain

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Dan Wellers

About Dan Wellers

Dan Wellers is founder and leader of Digital Futures at SAP, a strategic insights and thought leadership discipline that explores how digital technologies drive exponential change in business and society.

Raimund Gross

About Raimund Gross

Raimund Gross is a solution architect and futurist at SAP Innovation Center Network, where he evaluates emerging technologies and trends to address the challenges of businesses arising from digitization. He is currently evaluating the impact of blockchain for SAP and our enterprise customers.

Ulrich Scholl

About Ulrich Scholl

Ulrich Scholl is Vice President of Industry Cloud and Custom Development at SAP. In this role, Ulrich discovers and implements best practices to help further the understanding and adoption of the SAP portfolio of industry cloud innovations.

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Are AI And Machine Learning Killing Analytics As We Know It?

Joerg Koesters

According to IDC, artificial intelligence (AI) is expected to become pervasive across customer journeys, supply networks, merchandizing, and marketing and commerce because it provides better insights to optimize retail execution. For example, in the next two years:

  • 40% of digital transformation initiatives will be supported by cognitive computing and AI capabilities to provide critical, on-time insights for new operating and monetization models.
  • 30% of major retailers will adopt a retail omnichannel commerce platform that integrates a data analytics layer that centrally orchestrates omnichannel capabilities.

One thing is clear: new analytic technologies are expected to radically change analytics – and retail – as we know them.

AI and machine learning defined in the context of retail

AI is defined broadly as the ability of computers to mimic human thinking and logic. Machine learning is a subset of AI that focuses on how computers can learn from data without being programmed through the use of algorithms that adapt to change; in other words, they can “learn” continuously in response to new data. We’re seeing these breakthroughs now because of massive improvements in hardware (for example, GPUs and multicore processing) that can handle Big Data volumes and run deep learning algorithms needed to analyze and learn from the data.

Ivano Ortis, vice president at IDC, recently shared with me how he believes, “Artificial intelligence will take analytics to the next level and will be the foundation for retail innovation, as reported by one out of every two retailers globally. AI enables scale, automation, and unprecedented precision and will drive customer experience innovation when applied to both hyper micro customer segmentation and contextual interaction.”

Given the capabilities of AI and machine learning, it’s easy to see how they can be powerful tools for retailers. Now computers can read and listen to data, understand and learn from it, and instantly and accurately recommend the next best action without having to be explicitly programmed. This is a boon for retailers seeking to accurately predict demand, anticipate customer behavior, and optimize and personalize customer experiences.

For example, it can be used to automate:

  • Personalized product recommendations based on data about each customer’s unique interests and buying propensity
  • The selection of additional upsell and cross-sell options that drive greater customer value
  • Chat bots that can drive intelligent and meaningful engagement with customers
  • Recommendations on additional services and offerings based on past and current buying data and customer data
  • Planogram analyses, which support in-store merchandizing by telling people what’s missing, comparing sales to shelf space, and accelerating shelf replenishment by automating reorders
  • Pricing engines used to make tailored, situational pricing decisions

Particularly in the United States, retailers are already able to collect large volumes of transaction-based and behavioral data from their customers. And as data volumes grow and processing power improves, machine learning becomes increasingly applicable in a wider range of retail areas to further optimize business processes and drive more impactful personalized and contextual consumer experiences and products.

The transformation of retail has already begun

The impacts of AI and machine learning are already being felt. For example:

  • Retailers are predicting demand with machine learning in combination with IoT technologies to optimize store businesses and relieve workforces
  • Advertisements are being personalized based on in-store camera detections and taking over semi-manual clienteling tasks of store employees
  • Retailers can monitor wait times in checkout lines to understand store traffic and merchandising effectiveness at the individual store level – and then tailor assortments and store layouts to maximize basket size, satisfaction, and sell through
  • Systems can now recognize and predict customer behavior and improve employee productivity by turning scheduled tasks into on-demand activities
  • Camera systems can detect the “fresh” status of perishable products before onsite employees can
  • Brick-and-mortar stores are automating operational tasks, such as setting shelf pricing, determining product assortments and mixes, and optimizing trade promotions
  • In-store apps can tell how long a customer has been in a certain aisle and deliver targeted offers and recommendations (via his or her mobile device) based on data about data about personal consumption histories and preferences

A recent McKinsey study provided examples that quantify the potential value of these technologies in transforming how retailers operate and compete. For example:

  • U.S. retailer supply chain operations that have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Using data and analytics to improve merchandising, including pricing, assortment, and placement optimization, is leading to an additional 16% in operating margin improvement.
  • Personalizing advertising is one of the strongest use cases for machine learning today. Additional retail use cases with high potential include optimizing pricing, routing, and scheduling based on real-time data in travel and logistics, as well as optimizing merchandising strategies.

Exploiting the full value of data

Thin margins (especially in the grocery sector) and pressure from industry-leading early adopters such as Amazon and Walmart have created strong incentives to put customer data to work to improve everything from cross-selling additional products to reducing costs throughout the entire value chain. But McKinsey has assessed that the U.S. retail sector has realized only 30-40% of the potential margin improvements and productivity growth its analysts envisioned in 2011 – and a large share of the value of this growth has gone to consumers through lower prices. So thus far, only a fraction of the potential value from AI and machine learning has been realized.

According to Forbes, U.S. retailers have the potential to see a 60%+ increase in net margin and 0.5–1.0% annual productivity growth. But there are major barriers to realizing this value, including lack of analytical talent and siloed data within companies.

This is where machine learning and analytics kick in. AI and machine learning can help scale the repetitive analytics tasks required to drive leverage of the available data. When deployed on a companywide, real-time analytics platform, they can become the single source of truth that all enterprise functions rely on to make better decisions.

How will this change analytics?

So how will AI and machine learning change retail analytics? We expect that AI and machine learning will not kill analytics as we know it, but rather give it a new and even more impactful role in driving the future of retail. For example, we anticipate that:

  • Retailers will include machine learning algorithms as an additional factor in analyzing and  monitoring business outcomes in relation to machine learning algorithms
  • They will use AI and machine learning to sharpen analytic algorithms, detect more early warning signals, anticipate trends, and have accurate answers before competitors do
  • Analytics will happen in real time and act as the glue between all areas of the business
  • Analytics will increasingly focus on analyzing manufacturing machine behavior, not just business and consumer behavior

Ivano Ortis at IDC authored a recent report, “Why Retail Analytics are a Foundation for Retail Profits,” in which he provides further insights on this topic. He notes how retail leaders will use new kinds of analytics to drive greater profitability, further differentiate the customer experience, and compete more effectively, “In conclusion, commerce and technology will converge, enabling retailers to achieve short-term ROI objectives while discovering untapped demand. But implementing analytics will require coordination across key management roles and business processes up and down each retail organization. Early adopters are realizing demonstrably significant value from their initiatives – double-digit improvements in margins, same-store and e-commerce revenue, inventory positions and sell-through, and core marketing metrics. A huge opportunity awaits.”

So how do you see your retail business adopting advanced analytics like AI and machine learning? I encourage you to read IDC’s report in detail, as it provides valuable insights to help you invest in – and apply – new kinds of analytics that will be essential to profitable growth.

For more information, download IDC’s “Why Retail Analytics are a Foundation for Retail Profits.

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Joerg Koesters

About Joerg Koesters

Joerg Koesters is the Head of Retail Marketing and Communication at SAP. He is a Technology Marketing executive with 20 years of experience in Marketing, Sales and Consulting, Joerg has deep knowledge in retail and consumer products having worked both in the industry and in the technology sector.