While we’re not completely cashless yet—I still pay for my morning coffee and tip my barista in cash—it’s certainly trending that way. That’s why, as a business, it’s important to think about credit card payment processing and not only how you handle it but how much it costs. So whether you’re shopping around for a new provider or reassessing your current tech stack to see where you can cut costs, it’s essential to take a look at fees for credit card processing, and how much it costs your business to accept payments. Key takeaways: Credit card processing fees typically range from 2% to 4%. Processing fees include set charges from card networks, card issuing banks, and credit card processor markups. Credit card processor markups vary and are where you can shop around for lower rates. Processing companies have all different kinds of fee structures, which adds complexity and makes it difficult to know what is the cheapest. Most businesses benefit most from flat-rate, interchange, or subscription models. What are credit card processing fees? Credit card processing fees are the costs the businesses and/or customers incur in a credit or debit card transaction. Businesses typically pay more in fees than consumers, and in some locations, charging consumers a processing fee is illegal. New Jersey, for example, only recently legalized businesses’ ability to pass along the processing fee to customers. Altogether, credit card processing fees refer to what businesses pay processors for their payment processing service. The majority of these fees come from the individual transaction fees, which consist of: Interchange fees. The customer’s issuing financial institution charges businesses an interchange fee. This is usually the biggest fee associated with credit card processing. The fee varies depending on the card used and the type of transaction. They can be flat rate or interchange plus, basically a smaller flat rate plus a percentage. Assessment fees. The customer card’s network—Visa, MasterCard, American Express, for example—also charges a fee. Each network has its own fees, mostly percentage-based. American Express is known for having high fees compared to many other networks. Payment processing fees. The payment processor, or merchant services provider, also charges a fee. This is the technology that enables the card transaction to be processed. Fee structures for payment processing have a lot more variety—they can be subscription-based, percentages, or flat fees, for instance. However, some processors also charge fixed monthly or other fees. What are credit card processing rates? Credit card processing rates refer to the percentage processors charge per transaction. The higher the rates, the more you pay in fees. What are the different credit card processing payment structures? Credit card processing payment structures generally take the following forms: Tiered Flat rate Interchange plus Subscription Let’s take a closer look at each one. What Pros Cons Tiered Tiered pricing is a percentage of the transaction plus a flat fee. Each tier of a transaction has its own associated fee: Qualified (lowest): debit cards; credit cards without rewards Mid-qualified (mid-range): cards with basic rewards; manually keyed-in transactions Non-qualified (highest): cards with generous rewards; corporate cards; international payments Could be helpful for brick-and-mortar businesses that take a lot of in-person card-present payments. Common among payment processors. Costly, especially compared to other payment processing fee structures. Unpredictable and inconsistent payment processing fees. Flat rate Businesses pay a single flat fee for each transaction, regardless of how much the payment is for or which payment method or type of card is used. Predictable transaction fees. Can save money if you have a high average order value (AOV). Can be costly if you have a low AOV and lots of transactions. Interchange plus Businesses pay a flat fee plus a percentage of the transaction amount. It essentially covers the interchange and assessment fees, plus the payment processor’s fee. Tends to be the most cost-effective. Offers transparency. Not available to all businesses—especially smaller businesses with low transaction volume. Subscription Businesses pay a monthly subscription fee to a payment processor in exchange for its services. This may or may not include an additional discounted percentage or flat fee per transaction, depending on the provider and plan. Arguably the most predictable way to pay for credit card payments. Typically, comes with limitations on the number of transactions you process in a month. Some processors charge an additional fee for each transaction, which is discounted. How much do credit card processing companies usually charge? The average credit card processing fee is difficult to pin down because card networks make it a bit complicated—I’d venture to say they do this intentionally. Generally speaking, the standard credit card processing interchange fee is somewhere between 1% and 3%, though it can fall outside of that range depending on the transaction. Here’s how the interchange fee is charged from the major card networks: Network Interchange fee Visa 0.05% + 21¢ to 1.9% + 25¢ MasterCard 0.19% + 53¢ to 3.15% + 10¢ American Express 1.1% to 3.5% Discover 1.35% + 5¢ to 2.5% Processors then take those interchange fees, add on the assessment fees, and then their own markup. Ultimately, total processing fees typically range from 2% to 4%. And if you’re curious about what payment processors charge, here’s a look at some of the fees from the top providers: Payment processor Fees Chase 2.6% + 10¢ tap, dip or swipe 3.5% + 10¢ manually keyed in or payment links 2.9% + 25¢ ecommerce plus monthly fee starting at $9.95 Dharma $20/month 0.20% + 11¢ Visa, Mastercard, and Discover 0.30% + 11¢ American Express 0.10% + 11¢ high-volume discount (>$100k/month) $25/chargeback $49 closure fee Helcim 1.83% + 8¢ Visa, Mastercard, and Discover 2.61% + 8¢ American Express 1.00% + 8¢ debit 2.27% + 25¢ Visa, Mastercard, and Discover online 3.01% + 25¢ American Express online 10¢ tap to pay iPhone $0 ACH $15/chargeback (free if successful) $5/ACH reject $30/month Smart Terminal $99 Card Reader PayPal 3.49% + 49¢ PayPal Checkout 2.29% + 9¢ QR code payments 2.99% + 49¢ invoice payments 2.59% + 49¢

How many times have you tried to configure a static IP address for a machine on your network, only to realize you had no idea what addresses were already taken? If you happen to work with a desktop machine, you could always install a network protocol analyzer to find out what addresses were in use. But what if you’re on a GUI-less server? You certainly won’t rely on a graphical-based tool for scanning IP addresses. Fortunately, some very simple-to-use command line tools can handle this task. I’m going to show you how to scan your Local Area Network (LAN) for IP addresses in use with two different tools (one of which will be installed on your server by default). I’ll demonstrate on Ubuntu Server. SEE: Top Commands Linux Admins Need to Know (TechRepublic Premium) The arp command The first tool we’ll use for the task is the built-in arp command. Most IT admins are familiar with arp, as it is used on almost every platform. If you’ve never used arp (which stands for Address Resolution Protocol), the command is used to manipulate (or display) the kernel’s IPv4 network neighbor cache. If you issue arp with no mode specifier or options, it will print out the current content of the ARP table. That’s not what we’re going to do. Instead, we’ll issue the command like so: arp -a The -a option uses an alternate BSD-style output and prints all known IP addresses found on your LAN. The output of the command will display IP addresses as well as the associated ethernet device. The arp -a command in action. Image: Jack Wallen You now have a listing of each IP address in use on your LAN. The only caveat, is that (unless you know the MAC address of every device on your network), you won’t have a clue as to which machine the IP addresses are assigned. Even without knowing what machine is associated with what address, you at least know what addresses are being used. NB. The arp command only works for IPv4. If you have IPv6, you’ll need to use the command ndp (which stands for Neighbor Discovery Protocol): ndp -a It’s easy to tell if you’re using IPv4 or IPv6 addresses. The former use periods, the latter use colons. IBM offers an explainer on the subject if you want more details. SEE: How to Add an SSH Fingerprint to Your known_hosts File in Linux (TechRepublic) Open source: Must-read coverage Nmap Next, we use a command that offers more options. Said command is nmap (which stands for Network Mapper). You won’t find nmap installed on your Linux machine by default, so we must add it to the system. Open a terminal window (or log into your GUI-less server) and issue the command: sudo apt-get install nmap -y Once the installation is completed, you are ready to scan your LAN with nmap. To find out what addresses are in use, issue the command: nmap -sP 192.168.1.0/24 Note: You will need to alter the IP address scheme to match yours. The output of the command, will show you each address found on your LAN. nmap will display each address found on your LAN. Image: Jack Wallen Let’s make nmap more useful. Because it offers a bit more flexibility, we can also discover what operating system is associated with an IP address. To do this, we’ll use the options -sT (TCP connect scan) and -O (operating system discovery). The command for this is: sudo nmap -sT -O 192.168.1.0/24 Depending on the size of your network, this command can take some time. And if your network is large, consider sending the output of the command to a file like this: sudo nmap -sT -O 192.168.1.0/24 > nmap_output You can then view the file with a text editor to find out what operating system is attached to an IP address. nmap will display each address found on your LAN. Image: Jack Wallen With the help of these two simple commands, you can locate IP addresses on your network that are in use. Now, when you’re assigning a static IP address, you won’t accidentally assign one already in use. We all know what kind of headaches that can cause. source

Across Europe, skills shortages are emerging as a key challenge. The Council of the European Union says this is driven by demographic change, demand for new skillsets, and poor working conditions in some sectors. Adding to that, a recent report highlighted that around 42% of Europeans lack basic digital skills, including 37% of those in the workforce. The rapid advancement of AI is adding more pressure. While AI offers the EU a shot in the arm to strengthen the bloc’s innovation and competitiveness, there is still a gap between the skills required, and the skills available. 5 jobs to discover this week Full-stack developer, Haystack People, Rotterdam Systemengineer DBMS DB2 DEV, belastingdienst, Apeldoorn Scrum Master, Artisans, Papendrecht IT Systemtechniker (m/w/d), SanData IT-Gruppe, Warstein Stage Logiciel: Développement d’outils de simulation et de tests en GO F/H, MBDA France, Le Plessis-Robinson In the Netherlands, new analysis from De Nederlandsche Bank (DNB) has found that the growth of the labour force will decline sharply in the coming decades. It says that in the Dutch labour force, there are more vacancies than those unemployed, and that this trend is set to continue. How Startup Amsterdam Boosts Innovation and Growth at TNW Conference Discover how the City of Amsterdam partnered with TNW to amplify its startup ecosystem, attract global talent, and foster innovation that drives economic impact. Germany is also experiencing a similar fate, with Indeed’s Jobs & Hiring Trends Report for 2025 finding that demand for labour continues to cool, particularly among professional groups with top salaries. The report also found that in the medium term, Germany is heading for a shortage of skilled workers. In France, Indeed says the picture is broadly similar. “In 2025, wage and purchasing power gains are expected to remain limited while the French unemployment rate will remain close to its current levels,” it notes in its report. The unemployment rate in France is hovering around 7.4% thanks to a mismatch between supply and demand for workers, in part fuelled by the skills deficit. On a macro level, these sluggish European labour markets aren’t a great sign, and it is clear that there is much work needed to be done to fix the wider issues around skills gaps. But on a micro, or personal level, software engineers and tech professionals have a lot of scope. Top skills and programming languages If you have the right skills, particularly around artificial intelligence and software development, then opportunity knocks. Stack Overflow’s most recent developer survey found that the most popular programming roles are for full-stack, back-end, and front-end developers. These were followed by desktop or enterprise developers, and mobile and embedded applications. For its community of developers, JavaScript retained its long running spot in first place followed by SQ, HTML/CSS, Python, and TypeScript. On the other hand, GitHub’s recent Octoverse report found that on its platform, JavaScript has been knocked off its previous perch by Python. This is a language with many uses, notably in the in-demand fields of data science and machine learning, thanks to its simplicity and extensive libraries. GitHub says this is, “the first large-scale change we’ve seen in the top two languages since 2019—and it speaks to the rise in Python that’s accompanied the generative AI boom we’ve seen over the past two years.” 3 more roles to discover The rise of cloud computing, IoT, and AR/VR technologies has also created demand for languages that can efficiently handle these environments. Think Kotlin, which is gaining traction as the preferred language for Android development. Go (Golang) is popular for building scalable network servers and concurrent systems due to its performance and simplicity. Older languages are seeing a resurgence too. According to data from Developer Nation, Java, for example, gained over eight million new developers from 2021 to 2023. It may be more than 20 years old, but its recent rise in popularity is due to its use and versatility across cloud and IoT. C++ remains popular according to the TIOBE Index, where it is currently in second position. This is attributed to its performance and scalability, particularly in domains like embedded systems, game development, and financial trading software. However, not everyone is happy with C++, notably the US government, which issued a report this year urging programmers to move to memory-safe programming languages. This has led Rust’s user base to triple recently. The memory safe language is particularly appealing for systems programming due to its focus on safety and performance, and as a result it can offer a strong alternative to C++. As the big programming languages battle it out for supremacy, there are always the underdogs waiting in the wings. In its 2024 report, GitHub put a spotlight on the fastest-growing languages. These ones-to-watch include Go, HCL (HashiCorp Configuration Language), Kotlin, Dart, Trust, Luna, TSQL, and Blade. Ready to find your next software job? Check out The Next Web Job Board source

By Adam Lidgett ( December 23, 2024, 4:40 PM EST) — A judge at the U.S. International Trade Commission has found Amazon was infringing a series of Nokia patents related to video technology…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Slack will deeply integrate Salesforce’s Agentforce AI agents into its workplace collaboration platform, emphasizing contextual intelligence as the key differentiator in the increasingly crowded AI agent market. “There’s so much of your organization’s knowledge context [there]…Slack’s channels typically reflect your organization’s structure, but also your priorities for that given moment,” said Rob Seaman, Slack’s chief product officer, in an exclusive interview with VentureBeat. “That is just such rich context for agents to be able to answer questions and reason through whether or not they need to be able to take action.” Why context matters for enterprise AI The integration, part of Salesforce’s Agentforce 2.0 launch scheduled for tomorrow, December 17, aims to make AI agents more effective by giving them access to the vast troves of conversational and organizational data that flow through Slack’s channels daily. Seaman outlined three critical capabilities that define these next-generation AI agents: comprehensive contextual knowledge, reasoning ability, and action-taking power. What sets Slack’s implementation apart is its unique position as what Seaman calls a “searchable log of all communication and knowledge” — effectively making it the central nervous system of modern enterprises. Inside Slack’s new AI agent library The platform will introduce a library of customizable AI agents that can perform various tasks, from onboarding new employees to managing complex cross-functional projects. “You’ll see the library of agents in Slack. And it’s pretty magical to see humans and agents together, and to think of this world where humans continue to work with humans, but agents are there as part of the team,” Seaman explained. A key focus is user trust, and another is data governance. Seaman emphasized that all agents will operate with “user context,” meaning they can only access information that the user has permission to see. “Our goal ultimately is to honor user context for every system that an agent and a person [have] interacted with,” he said. The platform includes robust safeguards through what Salesforce calls a “trust layer,” which handles sensitive information appropriately and ensures compliance with business rules. Users can test agents in real time and observe their decision-making processes through a transparent builder interface. How AI agents could transform enterprise software For enterprises struggling with fragmented software stacks, this integration could signal a shift in how organizations approach their technology infrastructure. While Seaman avoided specific predictions about which tools might become obsolete, he suggested that many manual processes currently “spaghetti-ed across numerous systems” could be streamlined through these contextually-aware agents. One concrete example Seaman highlighted was employee onboarding: “Taking you from new hire to productive, is something that the company cares about, and it’s also, from an end-user perspective, it’s kind of a lonely, scary experience in your first several months as you’re trying to find your way.” The race for enterprise AI dominance The integration represents a strategic move by both Slack and Salesforce to position themselves at the forefront of the enterprise AI revolution. While companies like Anthropic and OpenAI have launched their own AI agents, Slack’s deep integration with enterprise workflows and access to organizational context could provide a significant competitive advantage. The development comes at a crucial time as organizations grapple with how to effectively implement AI tools while maintaining security and trust. With this launch, Slack and Salesforce are betting that contextually-aware AI agents, deeply integrated into existing workflows, will prove more valuable than standalone AI solutions. The question remains whether enterprises will embrace this vision of AI agents as team members, but with Slack’s widespread adoption in modern workplaces, the platform is well-positioned to drive this transformation. As Seaman notes, “We’re pretty lucky, frankly, that we’re in this moment, and we have a lot of the primitives that are required to make this possible.” source

Analytical customer relationship management software offers advanced analytics and reports to help businesses make data-based decisions for improved business operations and customer experience. Choosing the right analytical CRM system to match your organization’s operations and goals can bring several benefits, like increased productivity and data integrity. 1 monday CRM Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Any Company Size Any Company Size Features Calendar, Collaboration Tools, Contact Management, and more 2 Creatio CRM Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Medium, Large, Enterprise Features Dashboard, Document Management / Sharing, Email / Marketing Automation, and more 3 HubSpot CRM Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Micro (0-49 Employees), Small (50-249 Employees), Medium (250-999 Employees), Large (1,000-4,999 Employees) Micro, Small, Medium, Large What is analytical CRM software? An analytical CRM is a type of CRM software that collects, organizes, and stores internal and external data. It then uses that data to generate comprehensive reports and dashboards for the user to turn into actionable insights. This data is collected throughout the entire CRM cycle, from lead generation to post-sale and everything in between. Converting valuable data into digestible insights allows businesses to better strategize nurturing techniques and ultimately retain customers. What are the benefits of analytical CRM? An analytical CRM provides all the general CRM features and benefits you’d expect from this type of software. But beyond serving as a hub for customer data, a major benefit of an analytical CRM is all the ways the data helps users make impactful business decisions. Here are four distinct benefits of an analytical CRM: Data integrity: Make sure all the information generated from customer data is accurate, complete, and valid. Increased productivity: Spend more time interfacing with customers instead of tediously pulling data and manually turning it into comprehensive reports. Boosted resource allocation: Understand precisely where resources are best allocated depending on season, quarter, or year. Enhanced customer satisfaction: After identifying where you historically lose customers, provide more personalized and appropriate support in stages of the selling cycle. Analytical CRM use cases Medium to large teams or organizations Medium to large businesses can greatly benefit from advanced CRM analytics. When a business is scaling up and anticipating more growth, it is critical to have secure and regular data mining and recording. For smaller companies or startups, users might have more bandwidth to manage manual reports or less complex workflows. However, in the case of large companies and enterprises with vast quantities of dense data, an analytical CRM can efficiently do all that heavy lifting in the back end. Service-based industries Service-based industries like banking, real estate, or e-commerce can greatly benefit from an analytical CRM. These industries rely heavily on insights into consumer trends and forecasts, which can be gleaned from CRM data. The same can be said for industries that require a project management CRM, specifically one with real-time reporting and activity tracking. Businesses looking to leverage AI AI CRM software typically offers generative AI content, AI assistants, and AI-powered analytics such as forecasting and performance reports. But beyond generating reports with just a click, these technical CRMs can also make insightful suggestions based on the generated reports, such as the best time to cold call. Types of data analysis in CRM Sales cycle length A standout example of CRM data analysis is measuring sales cycle length. The sales cycle length is the exact amount of time a customer takes to be identified and nurtured into a closed sale. The goal is to have as short a sales cycle as possible so leads are less likely to back out or get stale. By keeping an eye on the average sales length, ruts or delays in sales can be identified and solved quickly. Data warehousing Data warehousing is when software stores and analyzes enterprise data sourced from multiple sources—including integrations. It allows the CRM system to act as a central repository and hub for all the consolidated data. This is critical for businesses to have a single source of truth with efficient input and output. Activity tracking Activity tracking monitors all sales activities and tasks completed by sales reps. This can include sales calls, emails sent, customer support tickets completed, and new leads identified. As a subcategory of performance management, this same monitoring can be done on an individual scale as well as team and department performance. Lead scoring and segmentation With a clearly defined ideal customer profile (ICP), businesses can configure the CRM to automatically assign scores to incoming leads ranging from low to high quality. Once a lead is identified as high quality, it signals sales representatives to start nurturing immediately to capitalize on the high probability of a sale. Sales forecasting Analytical CRMs can use all historical data on profits, closed vs. lost deals, and churn rates to generate predictive analytics for potential sales revenue and wins. These CRM systems include internal information, consumer trends, and competitor analysis. Having a reliable sales forecast helps decision-makers plan quarterly initiatives or major marketing campaigns with confidence. SEE: Download our detailed guide to CRM features and solutions. Types of CRM software Even though some generalized CRM providers can offer more than one type of CRM, most fall into the analytical, collaborative, or operational category. Analytical: Focus on collecting, warehousing, and analyzing gathered data. Collaborative: Emphasize cross-departmental collaboration and supporting internal and external communication. Operational: Help manage the day-to-day operations of a business, from marketing campaigns to post-sale tasks. Top analytical CRM tools Beyond the data warehousing, mining, and forecasting an analytical CRM can provide, there are some other factors to consider when selecting the best analytical CRM for your business. I recommend familiarizing yourself with which integrations the CRM offers, how its pricing structure works, and how that pricing accommodates your allocated budget. Here’s a snapshot of the best CRM software with advanced

By Nadia Dreid ( December 20, 2024, 9:34 PM EST) — TikTok is set to be removed from app stores in the United States in less than a month, but two senators have urged President Joe Biden to kick the can down the road with an extension that would prevent “serious hardship” to both creators and users…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

A customer relationship management (CRM) system is an important software solution that focuses on creating lasting relationships between your business and its existing clientele, while also assisting your sales team with identifying prospective leads and converting them into paying customers. CRM systems increase sales productivity by organizing client information, simplifying workflows, and automating the routine tasks carried out by your sales, marketing, and customer service teams. 1 monday CRM Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Any Company Size Any Company Size Features Calendar, Collaboration Tools, Contact Management, and more 2 Creatio CRM Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Medium, Large, Enterprise Features Dashboard, Document Management / Sharing, Email / Marketing Automation, and more 3 Pipedrive CRM Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Any Company Size Any Company Size Features Calendar, Collaboration Tools, Contact Management, and more CRM tips and tricks With the right CRM software, sales team members can quickly access key details like past customer interactions and their buying history, all within a centralized hub, making it easier to personalize communication and focus on their top leads. This organizational method reduces the time spent searching for information, giving sales representatives more time to build relationships, improve customer interactions, and close more deals. A CRM tool also benefits sales by automating follow-ups, sending timely reminders, easing redundant data entry, and reducing administrative work to keep sales processes running as smoothly as possible. Included below is a list of the top CRM techniques to help get the most out of your CRM system. These CRM hacks can streamline your sales team’s work processes, enhance their productivity, and ultimately strengthen their efforts. CRM tip 1: Streamline data entry A CRM can streamline data entry by centralizing customer information, automating repetitive tasks, and reducing the need for manual input. With features like auto-fill fields, data validation, and integration with other systems, CRMs minimize errors and ensure consistent information across all records. Many CRMs also allow bulk imports and exports, which speeds up the process of updating records. At the same time, automated workflows and triggers ensure that customer data is kept up-to-date without requiring constant manual input. SEE: Download the TechRepublic guide for developing a machine automation policy. CRM tip 2: Segment customers A CRM enables effective customer segmentation by organizing data into detailed categories based on demographics, behaviors, purchase history, and other criteria. By collecting and analyzing these data points, the CRM allows businesses to group customers with shared characteristics, making it easier to tailor marketing and target outreach efforts. CRM tip 3: Leverage CRM analytics CRM analytics provide valuable insights to shape effective sales strategies by highlighting trends, identifying high-potential leads, and uncovering customer preferences. With CRM data, your sales reps can analyze patterns like peak buying times, most requested products, and customer lifetime value, helping prioritize efforts toward high-value opportunities. Analytics tools also track sales cycle lengths, conversion rates, and responses to past campaigns, providing a clear view of what resonates with your customer base. By segmenting customers based on engagement levels or purchase behavior, your team can tailor outreach strategies to fit each group’s specific needs, driving more relevant and targeted interactions. Best CRM Software CRM tip 4: Personalize customer interactions With a CRM, team members can personalize their customer interactions by leveraging detailed customer data, such as past purchases, preferences, communication history, and engagement patterns. This information allows them to customize messages, offers, and recommendations based on individual interests and behaviors, making each interaction feel relevant and more thoughtful. CRM tip 5: Integrate CRM with sales tools Integrating a CRM with sales tools improves efficiency by creating a smooth flow of data between platforms, giving a complete view of customer interactions. Connect your CRM with email marketing tools to automate updates and follow-ups based on lead scores and purchase history. Adding sales tools for document sharing also lets teams track engagement directly in the CRM. CRM tip 6: Improve team collaboration A CRM enhances collaboration within a sales team by centralizing all customer data, communication history, and tasks in one shared platform. Team members can access real-time updates on leads, track each other’s progress, and assign tasks, which ensures that everyone stays informed and aligned on their ongoing goals. Another benefit to collaborating within CRM software is its transparency and work history, especially for remote teams or sales reps who spend a significant amount of time outside the office. CRM tip 7: Provide ongoing training and support Start by offering hands-on training sessions covering key CRM functions, then follow up with regular workshops focused on advanced features or new updates. You should also encourage team members to use the CRM daily to reinforce learning and build confidence. Providing access to self-paced learning resources, like video tutorials and user guides, is also a very useful strategy. Key takeaways Using the CRM hacks outlined in this guide can greatly boost your sales team’s productivity and efficiency. By simplifying data entry, automating tasks, and personalizing customer interactions, your team can focus on building lasting customer relationships. Integrating a CRM with other existing software tools and offering regular training for team members will keep their workflows running smoothly and improve collaboration. As your sales reps gain more confidence with using the CRM, they’ll gain insights that lead to smarter, data-driven decisions. These best practices will enhance efficiency, drive sales growth, and build customer satisfaction. FAQs What is the most common CRM mistake? The most common CRM mistake is not keeping data clean and updated. Duplicate or outdated records make it difficult for sales teams to trust and use a CRM system, which lowers productivity. Regular maintenance, like periodic clean-ups and clear data entry rules boost sales and prevent a CRM tool from being unreliable. It’s also crucial to understand your unique business needs, goals, and the size and

As the competitive landscape changes and the mix of cloud services available continues to grow, organizations are moving deeper into the cloud to stay competitive. Many are adopting a cloud-first strategy.  “Organizations are adopting more advanced, integrated cloud strategies that include multi-cloud environments and expanded services such as platform as a service (PaaS) and infrastructure as a service (IaaS),” says Bryant Robinson, principal consultant at management consulting firm Sendero Consulting. “This shift is driven by increasing demands for flexibility, scalability, and the need to support emerging technologies such as remote collaboration, real-time data processing and AI-powered diagnostics.”  Recent surges in cyberattacks have also accelerated these changes, highlighting the need for adaptable digital infrastructure to ensure continuity of business processes, enhance user accessibility, and protect sensitive customer data.  “Companies that are succeeding with cloud adoption are investing in improved security frameworks, focusing on interoperability, and leveraging cloud-native tools to build scalable applications,” says Robinson. “In addition, certain industries have to prioritize technology with regulation and compliance mechanisms that add a level of complexity. Within healthcare, for example, regulations like HIPAA are [considered] and prioritized through implementing secure data-sharing practices across cloud environments.”  Related:Best Practices for Managing Hybrid Cloud Data Governance However, some organizations struggle with managing multi-cloud complexity and the resulting inability to access, share, and seamlessly use data across those environments. Organizations may also lack the in-house expertise needed to implement and operationalize cloud platforms effectively, leading to the inefficient use of resources and potential security risks.  “Organizations should develop a clear, long-term cloud strategy that aligns with organizational goals, focusing on interoperability, scalability, and security. Prioritize upskilling IT teams to manage cloud environments effectively and invest in disaster recovery and cybersecurity solutions to protect sensitive customer data,” says Robinson. “Embrace multi-cloud approaches for flexibility, simplifying management with automation and centralized control systems. Finally, select cloud vendors with a strong track record and expertise in supporting compliance within heavily regulated environments.”  Following are more trends driving cloud service shifts.  1. Innovation  Previously, the demand for cloud data services was largely driven by flexibility, convenience and cost, but Emma McGrattan, CTO at Actian, a division of HCL Software, has seen a dramatic shift in how cloud data services are leveraged to accelerate innovation.   Related:Top 5 Infrastructure for AI Articles in 2024 “AI and ML use cases, specifically a desire to deliver on GenAI initiatives, are causing organizations to rethink their traditional approach to data and use cloud data services to provide a shortcut to seamless data integration, efficient orchestration, accelerated data quality, and effective governance,” says McGrattan. “[The] successful companies understand the importance of investing in data preparation, governance, and management to prepare for GenAI-ready data. They also understand that high-quality data is essential, not only for success but also to mitigate the reputational and financial risks associated with inaccurate AI-driven decisions, including the very real danger of automating actions based on AI hallucinations.”  The advantages of embracing these data trends include accelerated insights, enhanced customer experiences, and significant gains in operational efficiency. However, substantial challenges persist. Data integration across diverse systems remains a complex undertaking, and the scarcity of skilled data professionals presents a significant hurdle. Furthermore, keeping pace with the relentless acceleration of technological advancements demands continuous adaptation and learning. Successfully navigating these challenges requires sound data governance.  Related:Tech Goes Nuclear “My advice is to focus on encouraging data literacy across the organization and to foster a culture of data curiosity,” says McGrattan. “I believe the most successful companies will be staffed with teams fluent in the language of data and empowered to ask questions of the data, explore trends, and uncover insights without encountering complexity or fearing repercussions for challenging the status quo. It is this curiosity that will lead to breakthrough insights and innovation because it pushes people to go beyond surface-level metrics.”  2. Cloud computing applications  Most organizations are building modern cloud computing applications to enable greater scalability while reducing cost and consumption costs. They’re also more focused on the security and compliance of cloud systems and how providers are validating and ensuring data protection.  “Their main focus is really around cost, but a second focus would be whether providers can meet or exceed their current compliance requirements,” says Will Milewski, SVP of cloud infrastructure and operations at content management solution provider Hyland. “Customers across industries are very cost-conscious. They want technology that’s good, safe and secure at a much cheaper rate.”   Providers are shifting to more now container-based or server-free workloads to control cost because they allow providers to scale up to meet the needs of customer activity while also scaling back when systems are not heavily utilized.   “You want to unload as many apps as possible to vendors whose main role is to service those apps. That hasn’t changed. What has changed is how much they’re willing to spend on moving forward on their digital transformation objectives,” says Milewski.  3. Artificial intelligence and machine learning  There’s a fundamental shift in cloud adoption patterns, driven largely by the emergence of AI and ML capabilities. Unlike previous cycles focused primarily on infrastructure migration, organizations are now having to balance traditional cloud ROI metrics with strategic technology bets, particularly around AI services. According to Kyle Campos, chief technology and product officer at cloud management platform provider CloudBolt Software, this evolution is being catalyzed by two major forces: First, cloud providers are aggressively pushing AI capabilities as key differentiators rather than competing on cost or basic services. Second, organizations are realizing that cloud strategy decisions today have more profound implications for future innovation capabilities than ever before.  “The most successful organizations are maintaining disciplined focus on cloud ROI while exploring AI capabilities. They’re treating AI services as part of their broader cloud fabric rather than isolated initiatives, ensuring that investments align with actual business value rather than just chasing the next shiny object,” says Campos. “[However,] many organizations are falling into the trap of making strategic cloud provider commitments based on current AI capabilities without fully understanding