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5 Sure Steps for a Successful Mobile Strategy

Mutual Mobile

strategy-steps-hero Over the last several years of helping companies leverage mobility to both benefit customers and further their business goals, we’ve learned a few things – five things, to be precise. These are the five steps we’ve found to be essential to creating a mobile strategy that will enable you to successfully deliver exactly what your customers want and what your business needs. 1. Remember: technology ≠ strategy Too many companies today let technology – instead of strategy – drive their mobile investments. They see a new mobile technology and start looking for a way to apply it, rather than the other way around. In our experience, the key to benefiting from mobility is first identifying customer needs, and then identifying the mobile technology to address them. Case in point: Innovative financial institutions saw that customers were frustrated by having to go to the bank to deposit checks, and identified the technology to enable mobile check deposit. 2. Listen to users Data can tell you a lot about what customers are doing with their mobile devices, but you have to dig deeper to find out why they do what they do – and figure out how to respond to it. We had a retail client who saw from the data that customers were abandoning their mobile carts mid-experience. But it turned out to be for different reasons than they thought. Customers were using their shopping cart as a wish list, and they’d make their purchases in-store. Then the company was able to move toward the right mobile solution to better enable customers’ multi-device journey. 3. Be nimble The mobile landscape today is changing constantly; that’s why the traditional long-game approach to strategy just doesn’t work anymore. You have to rely on a more agile approach, where you take a shorter-term view that foregoes the big releases in favor of small, iterative cycles. We work with clients to quickly identify mobile opportunities, prioritize them based on alignment with business goals and act on them – and then be ready to instantly shift to a completely different direction. 4. Choose your battles Just because you can do something doesn’t mean you should. That’s a rule of thumb that applies in many endeavors, and mobility is no exception. We work with clients to evaluate the range of opportunities in mobile that are available to them based on 1) the value to the customer and 2) the value to the business. Then we measure that value against the ability to execute, and we use that information to establish priorities for what to do now and what to think about down the road. 5. Stop debating, start doing It’s easy to succumb to “analysis paralysis” as you navigate through the complex and constantly evolving world of mobile. But as long as you’re spending more time trying to figure out what to do than actually doing it, you’ll never win. Everything is changing too fast to afford you that luxury. We typically take clients through a quick three-step plan – investigate, formulate and activate – that lets them develop an informed strategy quickly and act on it immediately. Whatever your industry, whatever size your business, these five steps will move you toward a mobile strategy that really pays off for your customers and your business.

Want to take a deeper dive?

Check out “Best Practices for Building Your Mobile Strategy,” a free replay of a webinar featuring Mutual Mobile and Forrester Research.

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Why New Technology Has An Adoption Problem

Danielle Beurteaux

When 3D printing became a practical reality, in the sense that the actual printers became more efficient, less expensive, and more accessible to the average consumer, there was an assumption that the consumer 3D printing market was going to take off. We’d all have printers at home printing…. what? Our clothes? Toys? Spare organs?

That has yet to happen. 3D printing company MakerBot just went through its second employee layoff this year, driven by a market that’s developing much slower than predicted.

That same thinking is in play with a somewhat more prosaic technology – digital wallets. Apple Pay was released this year, as was Samsung Pay. There’s also Google’s Android Pay. During an earnings call, Apple CEO Tim Cook said: “We are more confident than ever that 2015 will be the year of Apple Pay.” But that expectation has yet to be realized, at least vis-à-vis consumers.

Consumers aren’t using any of the digital wallets en masse. According to Bloomberg, payments made via mobile wallets – all of them – make up a mere 1% of retail purchases in the U.S. The reason is that consumers just don’t see a compelling reason to use them. There’s no real reward for them to change from SOP.

Both these instances highlight a problem with assumptions about mass adoption for new technology – just because it’s cool, interesting, and accessible doesn’t mean a market-worthy mass of people will use it.

Who is more likely to use mobile wallets? Emerging economies without a stable financial and banking systems. In those environments, digital payments present a more secure and quicker method for purchasing. These are the same areas where mobile adoption leapfrogged older technologies because there was a lack of telecommunications infrastructure, i.e. many never had a landline phone to begin with, and they went directly to mobile. The value-add already exists. (But there are also security issues, to which consumers are becoming more sensitive. A hack of Samsung’s U.S. subsidiary LoopPay network was uncovered five months post-hack. Although one was expert quoted as saying the hackers may not have been interested in selling consumer financial info but instead in tracking individuals.)

Here’s some interesting data and a good point made: mobile payments are most popular in situations where the buyer already has his or her phone in hand and the transaction is made even quicker than swiping plastic. For example, purchases made for London Transit rides are responsible for a good portion of the U.K.’s mobile payments.

Mass technology adoption is no longer driven simply by the release of a new product. There are too many products released constantly now, the market is too diverse, and the products often lack a true raison d’être.

Learn more about how creative and innovative companies are finding their customers. Read Compelling Shopping Moments: 4 Creative Ways Stores Connect With Their Customers.

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Mobile Marketing Continues To Explode

Daniel Newman

If your brand isn’t among those planning a significant spend on mobile marketing in 2016, you need to stop treating it like a fad and step up to meet your competition. Usage statistics show that today people live and work while on the move, and the astronomical rise of mobile ad spending proves it.

According to eMarketer, ad spending experienced triple-digit growth in 2013 and 2014. While it’s slowed in 2015, don’t let that fool you: Mobile ad spending was $19.2 billion in 2013, and eMarketer’s forecast for next year is $101.37 billion—51 percent of the digital market.

  1. Marketers follow consumer behavior, and consumers rely on their mobile devices. The latest findings from show that two-third of Americans are now smartphone owners. Around the world, there are two billion smartphone users and, particularly in developing regions, eMarketer notes “many consumers are accessing the internet mobile-first and mobile-only.”
  2. The number of mobile users has already surpassed the number of desktop users, as has the number of hours people spend on mobile Internet use, and business practices are changing as a result. Even Google has taken notice; earlier this year the search giant rolled out what many referred to as “Mobilegeddon”—an algorithm update that prioritizes mobile-optimized sites.

The implications are crystal clear: To ignore mobile is to ignore your customers. If your customers can’t connect with you via mobile—whether through an ad, social, or an optimized web experience—they’ll move to a competitor they can connect with.

Consumers prefer mobile — and so should you

Some people think mobile marketing has made things harder for marketers. In some ways, it has: It’s easy to make missteps in a constantly changing landscape.

At the same time, however, modern brands can now reach customers at any time of the day, wherever they are, as more than 90 percent of users now have a mobile device within arm’s reach 24/7. This has changed marketing, allowing brands to build better and more personalized connections with their fans.

  • With that extra nudge from Google, beating your competition and showing up in search by having a website optimized for devices of any size is essential.
  • Search engine optimization (SEO) helps people find you online; SEO integration for mobile is even more personalized, hyper local, and targeted to an individual searcher.
  • In-app advertisements put your brand in front of an engaged audience.
  • Push messages keep customers “in the know” about offers, discounts, opportunities for loyalty points, and so much more.

And don’t forget about the power of apps, whose usage takes up 85 percent of the total time consumers spend on their smartphones. Brands like Nike and Starbucks are excellent examples of how to leverage the power of being carried around in someone’s pocket.

Personal computers have never been able to offer such a targeted level of reach. We’ve come to a point where marketing without mobile isn’t really marketing at all.

Mobile marketing tools are on the upswing too

As more mobile-empowered consumers themselves from their desks to the street, the rapid rise of mobile shows no signs of slowing down. This is driving more investment into mobile marketing solutions and programs.

According to VentureBeat’s Mobile Success Landscape, mobile engagement—which includes mobile marketing automation—is second only to app analytics in terms of investment. Mobile marketing has become a universe unto itself, one that businesses are eager to measure more effectively.

Every day, mobile marketing is becoming ever more critical for businesses. Brands that fail to incorporate mobile into their ad, content, and social campaigns will be left wondering where their customers have gone.

 

For more content like this, follow Samsung Business on InsightsTwitterLinkedIn , YouTube and SlideShare

The post Mobile Marketing Continues to Explode appeared first on Millennial CEO.

photo credit: Samsung Galaxy S3 via photopin (license)

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About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

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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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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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.