Fusion Theme Blog | Technology

Fuse Highlights: Coconuts, horses, and beer, oh my!

May 26, 2022

Every few weeks, we share insights with our Fuse subscribers along with news and trends we’re following across the web, including book recommendations. Here’s a compilation of some of our key insights from last six weeks. If you want content like this delivered directly to your inbox, we’ve got you covered. Subscribe to the Fuse here. Data is the Holy Grail In the classic film Monty Python and the Holy Grail, viewers hear King Arthur and his trusty servant Patsy approaching with a trademark “clip-clop, clip-clop” sound. When the duo emerges from the primordial mist, you see (spoiler alert) that the source of all this noise is not, as might be supposed, a horse. Rather, Patsy is banging two coconut shells together as the king trots about on his own two legs. The duo is getting from point A to point B in their quest, but not in the most efficient or effective way possible. Many companies follow that script. Equipped with buzzword mandates like process optimization and data-driven decision making, it’s all too easy to make small adjustments that sound like you’re headed in the right direction but aren’t necessarily getting you there any faster. How do you drop the coconuts and get on the horse (metaphorically speaking)? What does it look like to use data to drive optimization in real terms? We’ve got our eye on digital twins. Before you run away (how’s that for a deep cut Monty Python reference?) from yet another data buzzword, it’s worth another look at this practical application of machine learning and data analytics. Digital twins are most often used to optimize physical assets and processes like manufacturing, warehousing, and logistics. Using sensors to collect data on a product, machine, or physical process, the digital twin feeds real-time data to a machine learning algorithm to test variables and scenarios faster — ultimately leading to actionable process improvement insights. These days, we’re starting to see more businesses use digital twin frameworks to optimize and innovate non-physical business processes like accounting, HR, and marketing as well. A digital twin simulation can help you surface interdependencies and inefficiencies that might otherwise be blind spots, especially if they’re baked into your business culture as “the way we’ve always done it.” In the quest for digital transformation, don’t settle for coconuts. Instead, let’s talk about the ways your data can carry more of the weight for you. Get smart: If all this talk of Monty Python and the Holy Grail puts you in the mood for an old-school movie night, good news: it’s available on Netflix. And if you’re looking for a more literary scratch for your Middle Ages (ish) itch, we’re reading Cathedral by Ben Hopkins. It’s a fascinating look at the complex processes involved in constructing architectural marvels in the days before edge computing. We may handle optimization differently now, but human nature stays the same. Read the full Fuse: Data for April here. A horse is a horse, and other martech myths Martech is a crowded field, and a lot of the voices weighing in on your options have a horse in the race.* No one is out to skew the odds on purpose, but your organization is unique. Just because one solution is a front-runner doesn’t necessarily mean it’s a great fit for your business. So how do you sort the facts from the hype and decide where to place your bets? We rounded up a few martech myths as a starting point. Myth: One CDP is as good as another. Fact Check: Finding the right CDP (or CRM, or DMP, or any other solution you can think of) isn’t a simple box to check. And, once you make your decision, integrating and customizing your platform will also take time and attention. Myth: Everyone needs a CDP. Fact Check: Depending on your use cases, you might be able to do everything you need to do within your current tech stack. Myth: You should pick a platform and go all in. Fact Check: It probably goes without saying, but when it comes to technology there are no one-size-fits-all solutions. One product might be a great fit for your needs, but that doesn’t mean that vendor should supply your entire tech stack. Myth: Data and tech silos are just the way business works. Fact Check: Regardless of size, scope, or industry, today’s businesses can’t afford to be siloed. When you’re evaluating a tech solution or rethinking your entire customer data strategy, prioritizing integration is always a safe bet. On your mark. Ready to put your martech through the paces? Read on to find resources to help you optimize your stack and get your customer data strategy across the finish line. *Full disclosure: one of our team members won $100 when Rich Strike won the Kentucky Derby, but for the most part we are platform- and livestock-agnostic. Get Smart: We try not to be too on the nose with our book recommendations but couldn’t help ourselves this time. In Data Strategy, Bernard Marr collects a solid primer on the data landscape and how your organization can use it (legally and ethically) to advance your goals. Spoiler alert, though probably not surprising given the title, strategy turns out to be the foundational driver for effective data use. Whether you read the book, our Ultimate Guide to Customer Data Strategy, or just want to get a sense of potential next steps, we’d love to chat about customer data. Grab a time that works for you. Read the full Fuse: Marketing for May here. Three technology strategies walk into a bar If you’ve got a monolithic legacy system on your hands, sticking with the status quo isn’t a fun choice. But going nuclear and building back from scratch probably isn’t realistic. Wouldn’t it be great to find a middle ground? Meet the composable enterprise. It’s an iterative path toward digital transformation, with applications repackaged into components that can be used to build new solutions across the business. Piece by modular piece, you rebuild your technology ecosystem — becoming more efficient, effective, and scalable as you go. As the glue that holds those components together, APIs are key to building a composable business. And developing secure API solutions that accommodate shifting capacity demands and amplify your technology takes a hefty dose of strategy and expertise. That’s what we love about it! If APIs are your jam, too, or if you’re wondering if a composable system makes sense for your business, let’s talk. We’re talking APIs over IPAs in Cincinnati on June 16 and you’re invited. It’ll be fun! Get smart: You’ve probably spent the day wondering how speculative/sci-fi/literary fiction relates to API strategy and microservices (or maybe that’s just us). But, we’d guess, the same type of mind that enjoys transforming legacy monoliths into composable enterprises would also really track with a book like How High We Go in the Dark by Sequoia Nagamatsu. Modular pieces linked together by strong bonds leading to an intricate and ever-expanding whole? We’re here for it (the book and the technology strategy). Read the full Fuse: Technology for May here.

Digital transformation: Our predictions for 2021 and beyond

Mar 16, 2021

What will the future hold when it comes to digital transformation? We don’t have a Magic-8 ball or special spidey sense, but our team does anticipate sizeable change. We asked a few of our team members what they thought based on their work and personal experiences. Here’s what they’re envisioning for 2021 and beyond. The cloud gains more ground I expect cloud-based business application platforms such as Dynamics 365, Salesforce, ServiceNow, and Workday to drive significant digital transformation within modern workplaces in the next year. Following on the heels of many core infrastructure services moving to the cloud — such as email, servers, files, and data — the next major lift for many organizations will be to modernize and automate their core business processes. I anticipate areas like finance, HR, production, and other critical business operations and workflows will be the next major shift to using cloud-based business application platforms. Moving away from legacy, on-premise solutions is not always a simple task, but in doing so, employees can then work remotely without being tethered to an office environment. Greg Deckler, Vice President, Cloud Services Connect with Greg on LinkedIn Remote workers collaborate differently The work-from-anywhere model has been proven to work and it will continue. However, right now, a Zoom meeting is about the extent of what most people see as remote teamwork — and we all know those can be exhausting. I predict greater adoption of tools like Miro and Mural. These online workspaces allow for active collaborating and co-creating in real time. The need to move quickly and keep pace with digital transformation will require these types of tools, and those who know how to leverage them, to make the most of a remote team’s time together. Doug Scamahorn, Solution Director, UX Design & Innovation Connect with Doug on LinkedIn Cookie compensation I think 2021 will be the year when businesses and marketers confront the pending deprecation of the third-party cookie. Google is driving the industry towards new solutions for retargeting and attribution following the announcement that Chrome will cease to support third-party cookies in 2022. While industry players debate over a long-term replacement, expect to see a scramble to shore up first-party data in the meantime. At a tactical level, this will look like increased pushes for “registered” online experiences where users must explicitly identify themselves, as well as the integrations that power these points of data collection. In the background, businesses will be pushing to connect the dots between online and offline touchpoints using a variety of identifiers, from email to devices to data from “walled gardens” like Amazon, Facebook, and even Walmart and Target. Companies may opt for a CDP (consumer data platform) solution on top of their existing data stack to manage data points specifically for targeted marketing campaigns. When reporting on campaign success and attribution, analysts may need to adopt new tools and strategies for managing “fuzzier” readouts on customer behavior and journey identification. Amy Brown, Solutions Director Connect with Amy on LinkedIn Augmented reality becomes actual reality As mobile processing and bandwidth progresses and matures, we can expect more augmented reality (AR) apps to provide visual assistance in a huge range of applications. I fully expect we will see vehicles with heads-up displays, smart glasses (remember Google Glass?), and other clear displays to be adopted by more companies and thus, individuals. Visual processing in itself is gaining in popularity. Retailers like IKEA are already using AR with their IKEA Place app to enable customers to “see” furniture in their spaces. Microsoft’s recent HoloLens release is a good example of where we’re headed. Jeremy Keiper, Competency Lead Connect with Jeremy on LinkedIn B2B marketers will get more creative There’s always been an understanding that marketing is both an art and a science. Over the last decade, marketers have leaned into the science. Data provided marketers with information about customer behavior that was never available before. Even before the pandemic, B2B marketers were relying heavily on digital channels to engage customers. But pandemic office closures caused marketers to rely on channels like email, webinars, social media, and search engine marketing (SEM), in an attempt to reach prospective buyers who were now working from home. And they had to get creative. Marketers had to be willing to test new ideas and try things that haven’t been “proven,” and to think creatively about how we connect with and engage prospects and customers. I anticipate this to continue, and marketers will use customer data to make sure they understand consumer goals and motivations, then get creative about how to reach out and connect. Kristin Raikes, Sr. Director of Digital Strategy Connect with Kristin on LinkedIn Looking ahead Thinking about the year ahead, we do know that even after offices reopen and things get back to “normal,” the new “normal” will look different than it did before. If people continue to work from home or prefer to engage with brands virtually versus physically, then technology will have to adapt. Are there any major trends not listed above that you think will be a key to digital transformation for this year? If you have questions about specific trends, you can also connect with our team via their LinkedIn profiles above. Our consultants and team members work with clients to improve, streamline, and create actionable change. We create exceptional customer experiences by leveraging data insights, experience design, and technology to transform the way you connect with your customers. Interested in learning more? Let us know, or sign up for our newsletter to get to know us.

4 ways banks can leverage the power of machine learning

Jan 19, 2021

The financial industry has faced waves of changes over the last two centuries. Emerging nations, the American gold rush, the power of the stock market, and even the Great Depression have all shaped how banking works and what consumers expect from their banks. Notably, from 2015 onward, bankers began to list technology risk among their top five concerns1. While these changes have increased banking access and options for the average consumer, they also brought in more tech-savvy competition and greater regulatory scrutiny as heaps of data have become digitally accessible. Ironically, the very technological disruption that has so upended the financial industry will also be what brings new opportunities for growth and increased wallet share. This is especially true with advanced data tools such as artificial intelligence (AI) and machine learning (ML); according to one source2, 83% of early AI adopters have already achieved substantial (30%) or moderate (53%) economic benefits. In light of the benefits machine learning can bring, we’ve compiled four major areas where we’ve seen ML used to reduce costs, increase revenue, and mitigate risk for banks. 1. Acquire new customers Gone are the days where marketing was limited to just a few channels; now banks must maintain an omnichannel presence in order to reach younger consumers who may not listen to the radio or watch TV. Acquiring new customers means reaching them where they are with messaging that’s highly targeted and relevant. Yet as margins get slimmer and budgets are squeezed, reaching these consumers with targeted messaging without breaking your budget can be a challenge if you’re not careful. How machine learning can help Making the most of your marketing involves making the most of your data. Machine learning can help you identify trends in consumer behavior and interests, which can help you deliver the right marketing messages in the right channels at the right time. ML opportunities Identify which existing bank customers will buy another bank product Score your commercial leads based on risk, profitability, and probability to close ON-DEMAND WEBINAR: Learn how to turn data into insights that drive cross-sell revenue 2. Deepen relationship with customers Digital transformation has affected every business in profound ways, especially in the area of reaching customers and managing the customer relationship. Today’s users want a more seamless experience, more targeted messaging, and on-demand access to information, and they’ll move to the bank that can meet their digital demands. How machine learning can help AI and ML allow you to combine your leadership’s decades of experience with customer engagement data. So not only will you have a gut check of what customers want, you’ll have quantifiable data to back it up. Which means your sales and customer relationship initiatives will ultimately be more effective at targeting customers ready to upsell and at cross-selling more of your products to hungry buyers. ML opportunities Identify high-value customers early and engage with them differently Predict the likelihood of a customer taking their deposits elsewhere Identify which disputed purchases are legitimate Project a customer’s lifetime value for those with a limited history with the bank 3. Reduce Financial Risk Consumers are becoming both more credit averse and less credit worthy, which extra pressure on banks and credit unions of all sizes. On top of that, banks face increased risk caused by data breaches, fraudulent activity, and increased costs brought on by regulatory compliance3 . These challenges make maintaining adequate cash reserves more difficult than ever before at a time of increasing market volatility. How machine learning can help Machine learning can give you the insights needed to reduce your overall financial risk by helping you identify fraud and financial liabilities early – so you make and keep more of your profits. ML opportunities Clarify the liabilities on your balance sheet and determine which are the greatest risks Detect fraud and misuse of the company’s finances Project cash reserves to reduce excess bank cash GET THE USE-CASE WORKBOOK: The ultimate guide to machine learning use cases for banks 4. Optimize investment offerings The investment management arm of today’s banks continues to change rapidly as industry challenges increase. Today’s investment managers deal with increased market volatility, capped organic growth, and increasing fees. Because of these challenges, they struggle to keep up with shifting expectations of clients who demand a better investment turnover. How machine learning can help Machine learning can be used to detect patterns hidden in a bank’s historical investment data combined with external financial data. These patterns produce actionable insights that can increase the accuracy of key investment decisions. ML opportunities Match securities to investors based on trade history and market conditions Dynamically price securities based on competitive offerings, market saturation, and risk profile See how one institution used ML to predict their deposit customers' likely deposits on a daily basis, freeing $40,000,000 in excess cash reserves The up-and-coming (and existing) opportunities for financial institutions to win with ML are staggering. One source estimates that advanced data initiatives like AI and ML are predicted to boost overall business profitability by 38% and generate $14 trillion of additional revenue by 20354. And while it’s true that digitally savvy industry newcomers may take advantage of these trends faster than their legacy peers, legacy banks and credit unions hold something the younger competition doesn’t: mountains of historic data that, when mined for insights using AI and ML, can give them a leg up in retaining customers, increasing their wallet share, and reducing their overall financial risk. To adequately leverage these four opportunities, banks will need to embrace the very technology disrupting the industry. Banks that view their data as one of their most important assets and embrace AI and ML to create new insights will likely see growth, whereas those who don’t will struggle to keep up. 1) 2015 Banking Banana Skins Report 2) 2017 Deloitte 3) 2017 Financial News 4) n.d., Accenture

Top 3 reasons to invest in machine learning for mobile

Aug 24, 2020

Artificial intelligence (AI) and machine learning (ML) have completely transformed mobile development. Mobile app users today are often looking for an easy and relevant user experience — one that has been customized for them. The best way to get there? Machine learning. Machine learning identifies anomalies and patterns that ultimately optimize the user experience. If your technology conversations have stalled at the brainstorming or ideation phase, consider why. If you don’t have a clear answer, you’re not alone there either. “Strategic decision makers across all industries are now grappling with the question of how to effectively proceed with their AI journey,” says Marianne D’Aquila, research manager, IDC Customer Insights & Analysis. Despite questions about how to proceed, organizations know they need to invest in ML for mobile before current competitors, and those waiting in the wings, figure out how to profit from it first. Considering the speed at which machine learning is being adopted and spreading, and its potential to quickly help companies on multiple fronts, the time for execution and implementation is now. Here are the top three reasons that make machine learning development for mobile important right now: 1. Machine learning for mobile increases app security “Facial recognition” ($4.7 billion, 6.0%) and “fraud detection and finance” ($3.1 billion, 3.9%) were among the top five categories of AI global investment in 2019, according to the AI Index 2019 Annual Report (an independent initiative at Stanford University’s Human-Centered Artificial Intelligence Institute). It’s not surprising. From TikTok’s recent security flaws to Target’s $18.5 million settlement, app vulnerabilities and potential data breaches are breaking news, and there are few signs of a slowdown. While the short-term financial impact can hurt, the long-term cost of losing the trust of customers and partners can be even more painful. Companies that receive users’ personal information (e.g., passwords, billing addresses, answers to security questions) for processes such as app authentication or making purchases must continually optimize how the data is used. Through machine learning and automating parts of the process, you can identify anomalies faster, allowing you to see patterns and manage potential weaknesses more quickly. Operationally, ML can detect and staunch security issues related to data inside your company, such as logistics or pricing anomalies, that could be a drain on resources. For example, if one of your products is selling faster than usual via a shopping app, it could be related to a pricing error. Do you really want that $450 device on sale for $4.50? The mobile application landscape is comprised of a wide variety of operation system versions, devices, and software systems. This creates a much greater number of attack surfaces that attackers can target. (A first step to optimizing security is risk evaluation and awareness. Contact Fusion to hear more.) 2. Machine learning leads to increased mobile privacy It could be argued that the recent news cycle around privacy indicates a real desire for clarity, if not outright skepticism. In more than 3,600 global news articles on ethics and AI from mid-2018 to mid-2019, the dominant topics were “framework and guidelines on the ethical use of AI, data privacy, the use of face recognition, algorithm bias, and the role of big tech.” You’ve heard about Russia’s role in the 2016 election and the use of personal information for ad targeting. These sorts of debacles haven’t led consumers to give up on digital. Instead, they are demanding more privacy oversight and are being more cautious about the apps they use. Privacy concerns are complementary to security issues. While security comprises keeping personal data from hackers, trolls, or criminals, privacy is more related to keeping personal data in a person’s own hands, away from any individuals or organizations that don’t need to be privy to it. For example, if you use an activity tracking app to record runs, you might appreciate a note when you hit a milestone: “You had a personal record today!” Machine learning makes it possible for the mobile app to directly detect this activity and send a congratulatory message without any human intervention. There’s no need for a stranger to know you clocked a fast 10K. Machine learning on the edge further increases privacy by eliminating the need for data to be sent to the cloud. When ML on the edge is in place, individualized data never leaves the device, keeping the user’s personal information in their own hands at all times. Amazon, Alexa, and Google Home employ ML on the edge, as some functions are offloaded to a device while others have to go to the cloud. In addition to supporting privacy, the reduced travel time for data makes these apps and devices faster. 3. Machine learning for mobile helps create personalized customer experiences Consumers expect their demographic, behavioral, and other personal data to be secure and private, while they also want increasing levels of personalization. Delivering on these demands can be a delicate, real-time balancing act for companies, but machine learning helps make it possible to juggle data acquisition with protection and those prickly questions around how to use the data to everyone’s advantage. But is there a clear business case to pursue personalization? According to a 2019 Salesforce report, the answer is yes, as 75% of 8,000 consumers and business buyers surveyed expect companies to use new technologies to create better experiences. Machine learning for mobile enables you to make user-experience headway on several fronts. First, it can help you build a baseline of customer app usage. Once you have that baseline, you can see patterns in user behavior. Next, particular behaviors or deviations from the baseline can trigger delivery of a relevant coupon, suggested product to explore, or a reminder to revisit an abandoned shopping cart. Even more sophisticated, ML can serve up colors, screen layouts, and language that appeal most to a particular user. And with machine learning, the reactions are in real-time. The more your user engages with your mobile app, the more refined and personalized the experience becomes. Through machine learning, your brand becomes more closely aligned with the customer experience that your customer desires. Getting started can feel uncomfortable at first, but at Fusion, we’ve found that organizations often have low-hanging fruit ripe to benefit from machine learning for mobile. You just need to be able to see and then act on those opportunities. Working alongside you on this journey should be people who understand data science and machine learning, and who can uncover weaknesses to target. Now is the time to move forward on machine learning for mobile initiatives. Current market conditions indicate a shortage of professionals in machine learning and data science. Fusion fills this gap. If you’re interested in hearing more about machine learning for mobile, let us connect you with one of our experts.

Articles about Technology

Fuse Highlights: Coconuts, horses, and beer, oh my!

Digital transformation: Our predictions for 2021 and beyond

4 ways banks can leverage the power of machine learning

Top 3 reasons to invest in machine learning for mobile

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