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The Dynamics CRM Sales Process: Everything You Need to Know
For any business, small or large, success depends entirely on sales figures. And no matter how great the product is or how much was spent on marketing, the primary goal for any organization is to increase revenue. is designed to support the sales process from beginning to end – right from acquiring a new lead through the close of a sale. By relying on a set of steps and actions, it enables you to close more deals and increase revenue.
What is the Goal of the Dynamics CRM Sales Process?
The sales process aims to generate potential sales opportunities and nurture leads for businesses. It is designed to support the sales process from acquiring a new lead through the close of a sale and to generate accurate sales forecasting. By automating and optimizing several stages, it helps streamline the sales process while improving the rate of closure. It also helps track and measure every sales activity and understand every number and component of the sales funnel in order to grow income. Using the Dynamics CRM sales process, you can zero-in on the right leads and build an extraordinary sales pipeline.
Microsoft Dynamics CRM Sales Process Life Cycle
The Dynamics CRM sales process life cycle provides a streamlined process to generate potential sales opportunities for your business. Since Dynamics CRM stores all of the information of new leads, it helps track follow-up communication including phone calls, emails, and appointments, and aids in qualifying leads into accounts and opportunities. Here’s a look at the Dynamics CRM sales process life cycle:
1. Lead Capture
A lead represents any person or organization that your organization might have the potential to do business with. Leads are generally captured through online or offline advertising, trade shows, direct communication, and other marketing campaigns. In Dynamics CRM, in order to create an entry in the system, you need to enter the name of the lead under the “lead entity” tab and mention information about the lead under the “topic” tab. Once a lead has been captured, follow up activities that include emails, phone calls, and appointments need to be conducted to capture more information about the lead and to proceed to the next stage of lead qualification.
2. Account Creation
Accounts are entities with which your organization has a relationship. In Dynamics CRM, this is where all account information is stored in the database. Accounts can include prospects, vendors, business partners, and more.
3. Contact Setup
Contacts are individuals with whom your organization has a relationship; these are generally customers or contacts of customers. Contacts are often related to an account, but certain organizations and businesses may serve or sell to individual consumers as well. Since contacts in the Dynamics CRM integrate with contacts in Microsoft Outlook, any contact you set up or any change you make to the contact record fields in will automatically reflect in Outlook depending on the synchronization settings.
4. Opportunity Management
Opportunities in Dynamics CRM represent potential sales for a specific customer; when an opportunity is created, it gets included in the sales pipeline. An opportunity is said to be won when a customer agrees with the quotation; however, when a customer disagrees, the opportunity is lost and the sales life cycle ends. Opportunities allow you to measure the success of marketing efforts by tracking sales back to the original lead source and source campaign. When an opportunity is lost, reasons for the loss can be tracked.
5. Product Catalog
Dynamics CRM enables you to maintain a product catalog with multiple customizable prices and discount lists for all of the business’ needs. You can create quotes, invoices, and orders directly from Dynamics CRM and include various factors including territory-based pricing, discount lists, product or price bundles, and more. You can also list product relationships to facilitate product substitutions, highlight cross-sell and up-sell opportunities, as well as mention write-in discounts.
6. Quote Management
A quote contains the list of products or services with a defined price list and discounts the customer is interested in. After reviewing, the customer can either agree or disagree to place the order. In Dynamics CRM, you can create quotes in two ways: from an opportunity using system-calculated pricing, or as a new quote. Multiple quotes can be created from one opportunity to include special pricing offers.
7. Order Management
An Order is a confirmation of a sale that is to be invoiced and placed for further processing in terms of logistics. Since they are accepted quotes, they document what specific products or services the customer is buying. In Dynamics CRM, orders can be created by selecting the “Create order” button on an active quote.
8. Invoice Management
Invoices represent the final stage of the sales cycle. After an order is placed successfully, an invoice is generated in the Dynamics CRM system. You can either create an invoice directly from a specific order screen or navigate to the invoice section and select a new invoice. You can create more than one invoice for an opportunity or an order.
9. Sales Business Process
Every organization follows a specified business process to capture sales information and close a sale. The Dynamics CRM business process flow enables users to follow the guided process without any confusion. You can either choose the out-of-the-box business process flow or custom define your own in accordance with your sales process life cycle.
How Can Your Business Optimize the CRM Sales Process?
Dynamics CRM is intended to help boost your sales by guiding you through the journey from prospect to customer, as well as helping marketers to create lead generation campaigns and monitor where leads are coming from. Here are 5 ways your business can and the sales process using Dynamics CRM:
1. Goal Management
Microsoft Dynamics CRM benchmarks performances against KPIs to inform sales directors, managers, and other sales professionals with easy to follow charts and reports for real-time monitoring of individual and team progress towards sales goals.
2. New Sales Opportunities
Using Dynamics CRM, you can take advantage of the data, and segment it based on user behavior such as the pages they have visited, or the products they are interested in. This way, you can identify new sales opportunities with ease.
3. Sales Dashboards
Dynamics CRM provides live reporting sales dashboards, which enable you to monitor active leads and sales opportunities and react with timely, and informed decision-making. These dashboards, which include charts, statistics, sales metrics, and KPI graphics, provide real-time visibility that can help raise productivity, increase sales, improve operational efficiency, and optimize business.
4. 360-Degree Customer View
Dynamics CRM provides full information about sales data to anyone who needs it – from sales and marketing to finance and management. You can align your sales activities with your business and achieve better sales. By leveraging contextual information available in Dynamics CRM, you can also create up-sell and cross-sell campaigns within minutes.
5. Mobile Sales
Dynamics CRM’s mobile app gives field sales teams the ability to work efficiently on-the-go. With any-time access to updated sales information, you can make sure they stay in the loop. Mobile CRM helps sales agents update CRM data in real-time, even in locations with intermittent or no internet connectivity.
Improve Sales Efficiency
A well-defined sales process is key to successfully managing your sales staff as well as your sales pipeline. Successful companies are far more likely to have a formal and structured sales process in place, that increases the chances and efficiency of sales, ultimately increasing revenue in the long run. The Dynamics CRM sales process aims to prioritize, collaborate, and organize sales activities in an efficient manner to directly impact sales pipelines and business results.
Using the , you can move from one stage of the sales process to the next with ease, set the right goals using built-in dashboards, and get that help you close more deals. You can drive more consistent sales interactions with leads and prospects, improve sales efficiency, and provide your business organization the competitive advantage necessary for success in today’s fast-paced, ever-changing environment.
Learn more about how to properly leverage to improve your sales processes by contacting an expert at Synoptek.
10 machine learning algorithms you need to know
If you’ve just started to explore the ways that machine learning can impact your business, the first questions you’re likely to come across are what are all of the different types of machine learning algorithms, what are they good for, and which one should I choose for my project? This post will help you answer those questions.
There are a few different ways to categorize machine learning algorithms. One way is based on what the training data looks like. There are three different categories used by data scientists with respect to training data:
- Supervised, where the algorithms are trained based on labeled historical data—which has often been annotated by humans—to try and predict future results.
- Unsupervised, by contrast, uses unlabeled data that the algorithms try to make sense of by extracting rules or patterns on their own.
- Semi-supervised, which is a mix of the two above methods, usually with the preponderance of data being unlabeled, and a small amount of supervised (labeled) data.
Another way to classify algorithms—and one that’s more practical from a business perspective—is to categorize them based on how they work and what kinds of problems they can solve, which is what we’ll do here.
There are three basic categories here as well: regression, clustering, and classification algorithms. Let’s jump into each.
Regression algorithms
There are basically two kinds of regression algorithms that we commonly see in business environments. These are based on the same regression that might be familiar to you from statistics.
1. Linear regression
Described very simply, linear regression plots a line based on a set of data points, called the dependent variable (plotted on the y-axis) and the explanatory variable (plotted on the x-axis).
Linear regression is a commonly used statistical model that can be thought of as a kind of Swiss Army knife for understanding numerical data. For example, linear regression can be used to understand the impact of price changes on goods and services by mapping the sales of various prices against its sales, in order to help guide pricing decisions. Depending on the specific use case, some of the variants of linear regression, including ridge regression, lasso regression, and polynomial regression might be suitable as well.
2. ARIMA
ARIMA (“autoregressive integrated moving average”) models can be considered a special type of regression model.
It allows you to explore time-dependent data points because it understands data points as a sequence, rather than as independent from one another. For this reason, ARIMA models are especially useful for conducting time-series analyses, for example, demand and price forecasting.
Clustering algorithms
Clustering algorithms are typically used to find groups in a dataset, and there’s a few different types of algorithms that can do this.
3. k-means clustering
k-means clustering is generally used to segregate groups with related characteristics and group them together.
Businesses looking to develop customer segmentation strategies might use k-means clustering to better target marketing campaigns that groups of customers should respond to. Another use case for k-means clustering would be detecting insurance fraud, using historical data that in the past had showed tendencies to defraud the insurance provider to examine current cases.
4. Agglomerative & divisive clustering
Agglomerative clustering is a method used for finding hierarchal relationships for data clusters.
It uses a bottom-up approach, putting each individual data point into its own cluster, and then merging similar clusters together. By contrast, divisive clustering takes the opposite approach, and assumes all the data points are in the same cluster and then divides similar clusters from there.
A timely use case for these clustering algorithms is tracking viruses. By using DNA analysis, scientists are able to better understand mutation rates and transmission patterns.
Classification algorithms
Classification algorithms are similar to clustering algorithms, but while clustering algorithms are used to both find the categories in data and sort data points into those categories, classification algorithms sort data into predefined categories.
5. k-nearest neighbors
Not to be confused with k-means clustering, k-nearest neighbors is a pattern classification method that looks at the data presented, scans through all past experiences, and identifies the one that is the most similar.
k-nearest neighbors is often used for activity analysis in credit card transactions, comparing transactions to previous ones. Abnormal behavior, like using a credit to make a purchase in another country, might trigger a call from the card issuers fraud detection unit. The algorithm can also be used for visual pattern recognition, and it’s now frequently used as part of retailers’ loss prevention tactics.
6. Tree-based algorithms
Tree-based algorithms, including decision trees, random forests, and gradient-boosted trees are used to solve classification problems. Decision trees excel at understanding data sets that have many categorical variables and can be effective even when some data is missing.
They’re primarily used for predictive modeling, and are helpful in marketing, answering questions like “which tactics should we be doing more of?” A decision tree might help an email marketer decide which customers would be more likely to order based on specific offers.
A random forest algorithm uses multiple trees to come up with a more complete analysis. In a random forest algorithm, multiple trees are created, and the forest uses the average decisions of its trees to make a prediction.
Gradient-boosted trees (GBTs) also use decision trees but rely on an iterative approach to correct for any mistakes in the individual decision tree models. GBTs are widely considered to be one of the most powerful predictive methods available to data scientists and can be used by manufacturers to optimize the pricing of a product or service for maximum profit, among other use cases.
7. Support vector machine
A support vector machine (SVM) is, according to some practitioners, the most popular machine learning algorithm. It’s a classification (or sometimes a regression) algorithm that’s used to separate a dataset into classes, for example two different classes might be separated by a line that demarcates a distinction between the classes.
There could be an infinite number of lines that do the job, but SVM helps find the optimal line. Data scientists are using SVMs in a wide variety of business applications, including classifying images, detecting faces, recognizing handwriting, and bioinformatics.
8. Neural networks
Neural networks are a set of algorithms designed to recognize patterns and mimic, as much as possible, the human brain. Neural nets, like the brain, are able to adapt to changing conditions, even ones that weren’t originally intended.
A neural net can be taught to recognize, say, an image of a dog by providing a training set of images of dogs. Once the algorithm processes the training set, it can then classify novel images into ‘dogs’ or ‘not dogs’. Neural networks work on more than just images, though, and can be used for text, audio, time-series data, and more. There are many different types of neural networks, all optimized for the specific tasks they’re intended to work on.
Some of the business applications for neural networks are weather prediction, face detection and recognition, transcribing speech into text, and stock market forecasting. Marketers are using neural networks to target specific content and offers to customers who would be most ready to act on the content.
Deep learning is really a subset of neural networks, where algorithms ‘learn’ by analyzing large datasets. Deep learning has a myriad of business uses, and in many cases, it can outperform the more general machine learning algorithms. Deep learning doesn’t generally require human inputs for feature creation, for example, so it’s good at understanding text, voice and image recognition, autonomous driving, and many other uses.
Other algorithm types
In addition to the above categories, there are other types of algorithms that can be used during model creation and training to help the process, like fuzzy matching and feature selection algorithms.
9. Fuzzy matching
Fuzzy matching is a type of clustering algorithm that can make matches even when items aren’t exactly the same, due to data issues like typos. For some natural language processing tasks, preprocessing with fuzzy matching can improve results by three to five percent.
A typical use case is customer profile management. Fuzzy matching lets you identify very similar addresses as the same so that only one unique record ID and source file would be used for the two similar addresses.
10. Feature selection algorithms
Feature selection algorithms are used to whittle down the number of input parameters from a model. A lower number of input variables may lower the computational cost of running a model, as well as improve the performance of the model.
The commonly used techniques like PCA and MRMR are useful for picking up as much information as possible from a reduced subset of features. Using a subset of features can be beneficial because your model may be less confused by noise and the computation time of your algorithm will go down. Feature selection has been used to show business competitor relationships, for example.
If you want to dive deeper into machine learning, including how to get your first project off the ground, check out RapidMiner’s Human’s Guide to Machine Learning Projects.
How Do You Know Your Power BI Security Roles are Right?
You need insights from your data, but you don’t want the wrong people seeing data that’s confidential or outside their responsibilities. In the Power BI service, there’s a robust way of ensuring that the right people get the data that they need for their purposes: row-level security (RLS). It’s a set of filters that only gives people those rows that they need according to their role. These roles are creating in Power BI Desktop and mapped in Power BI Service to users, or better, security groups. I don’t want to talk about the technical aspects of setting up RLS. There’s plenty of resources online on how to set this up (I’ve learned a lot from Reza Rad, for example). Instead, I’d rather open up a discussion about auditing security in Power BI reports.
If you can ask, how do we know that we are improving, you should also be able to ask how do we know that the security roles are implemented correctly. Data culture is not just for the business, but for the reporting team as well. I haven’t seen much discussion of auditing security roles in Power BI circles, so I’m genuinely curious about how others tackle this issue. Does everyone simply work hard and hope for the best? Or do you restrict everything at the database level and use different apps for different groups instead? There may even be regulatory reasons which require you to restrict it at the database level. But even if you do restrict everything at the database level, you still need to validate that security as well.
Personally, I LOVE Power BI RLS (and folks on Twitter agree). It enables me to carve out the data for those who need it without the report creep which happens with maintaining dozens of siloed reports. To ensure that roles are correctly configured, however, I’ve had to do some significant work. Wrong row-level security can be not only embarrassing, but costly as well. It can expose an organization to legal liability or put the organization at a competitive disadvantage. Because security roles can be high stakes, they need a comprehensive suite of approaches to administer them. This is definitely an area for growth in the Power BI offering.
Here are some ways that I’ve reviewed Power BI security roles:
- an Excel workbook with each role, table, and DAX rule (Excel to the rescue!).
- Double checking in Power BI Desktop that each role is correct by comparing the rules for each role and table to the Excel workbook. Should we take screenshots of each rule?
- As report creator, testing as Role for each role in Power BI Desktop.
- As administrator testing as Role for each role in Power BI Service.
- Create measures specifically for RLS that are put onto a tab in the dataset containing the data (either a hidden tab or a tab on the original dataset). This makes Testing as Role quicker.
- Power BI reports that get data from Active Directory to show what users are in what groups, powered by unique naming conventions to show only Power BI security groups. This makes it easy for any team member to notice anomalies. Example: Red Team PBI role is mapped to Red Team PBI security group.
- Automatic testing with process automation. There are many possibilities with this tactic.
How would I like to verify role security in Power BI?
- View as role in Power BI Service for anyone in a role where there are no filters on any tables. This would enable others who aren’t report authors to check security, making the roles transparent to business users who need to verify the correct roles but aren’t report creators or Power BI administrators.
- Export security roles from Power BI Desktop. This would facilitate rapid review at the time of report creation and updating.
- Administrators extract the security roles from Power BI Service. This would enable auditing of the security roles in place.
The good news is that I’ve figured out how to do #2. I can now export security roles for Power BI Desktop. I have template file called RLS localhost.pbit on GitHub. It works like this:
- Open a report in Power BI Desktop
- Open up DAX Studio (the latest version with External Tools makes this a breeze).
- Run a query to get the catalog_name and look at the localhost number at the bottom of DAX Studio.
- Open up RLS localhost.pbit and put in the localhost number and catalog_name.
- You’ll need to authenticate your access with your Office login.
- Wait for report to refresh.
- Most likely, you’ll want to enable native queries and ignore privacy in the settings to prevent having to ok each native query and to avoid the formula firewall error.
Security role report looks like this:
The report lets me see if the roles make sense and are consistent. On the page shown, there’s one table for Role Name, with the tables that are directly filtered by that role. If tables filtered is less than the max for roles, then conditional formatting shows the gap. The second table lists each table in the model, how many roles reference it, if it has a direct restriction or not, and what tables indirectly filter it. Date table is highlighted to show that it’s not filtered directly or indirectly. Region and Salesperson tables are both filtered directly by three roles each. Sales is filtered indirectly by Region and Salsesperson tables. The cards at the top of the page show number of roles, total tables, and how those tables are filtered.
The report documents security roles for 1-to-many relationships in Power BI models. Many-to-many relationships not supported. Bi-directional filtering and bi-directional security not supported. Inactive relationships not supported. I can finally export all of my security roles from this report, including a local timestamp of when they were extracted. The report uses a method described by Chris Webb: Analysing Power BI DMV Queries In Power BI Desktop. The report uses a live connection and can only be refreshed in Power BI Desktop.
What about you?
How do you review your security roles? How would you like to review them? Are there things you do that I haven’t mentioned? How can we help each other to be more transparent, more accountable, and more evidence based in our reporting practices?
YOU KNOW THIS; I KNOW THIS….BUT IT’S STILL COOL
Condors can go 100 miles and never flap their wings:
Weighing up to about 16 kilograms and with a wingspan of roughly 3.3 meters, the condor is the largest flying bird in the world by combined measurement of weight and wingspan. With that in mind, it’s almost impossible to believe that it can stay airborne for at least five hours and cover a distance of over 100 miles without flapping its enormous wings once. But that was the most interesting finding of a study published by researchers at the University of Swansea after monitoring a group of condors for five years.
Between 2013 and 2018, biologist Emily Shepard and her team monitored eight Andean condors near Bariloche, Argentina by attaching flight-recorders capable of logging every single flap of the wings during the birds’ flight. They aimed to study the effects of different weather conditions on the condors’ flight, but they ended up learning something far more surprising.
In the most extreme example logged by the flight recorder, one of the condors spent over five hours soaring through the air without once flapping its wings, during which time it covered a distance of 106 miles or 172 km. Even over mountains, where the condors have to mitigate complex airflow conditions, they were able to navigate air currents with very little movement.
“The extraordinary low investment in flapping flight was seen in all individuals, which is notable, as none were adult birds,” the study authors wrote. “Therefore, even relatively inexperienced birds operate for hours with a minimal need to flap.”
Researchers obtained more than 230 hours of flight data from the eight tracked condors, and out of all that, only one percent of it was spent flapping wings. Most of that one percent was during take-offs. Because of their size and weight, it takes a lot of energy for condors to take off, but once they become airborne they conserve their energy with maximum efficiency.
Moving to the Cloud – What You Need to Know
Perhaps you’re contemplating moving your data and business processes to the Cloud, or maybe you’d like to improve your organization’s cloud strategy. Either way, you probably have questions about costs, the migration processes, or even the terminology surrounding cloud technology. As has supported many organizations that have moved their operations to the Cloud. We’ve heard all the questions, and we have answers.
A successful cloud migration begins with a plan
Every successful project starts with a plan. is no different. We’ve identified the steps that make up a successful cloud migration plan:
Step 1: Involve all the key players
For most businesses, migrating your data and processes to the Cloud is a significant change, and it will require broad organizational input and support. The key people in your organization need to be fully on board and willing to actively champion the project. Representatives from owners, users, and IT working together for the project’s success will lead to a smoother, faster cloud migration process that meets everyone’s goals.
Step 2: Inventory the physical and virtual servers you already use.
While your current management tools may provide a good representation of the hundreds—maybe thousands—of applications your organization is running, you need an inventory mechanism that can feed data into subsequent steps. With , you’ll have a complete inventory of servers with metadata for each—including profile information and performance metrics—allowing you to build your cloud migration strategy. Using this knowledge, map your servers to represent your on-premises applications. This will help identify dependencies or communication between servers so you can include all necessary application components in your cloud migration plan—helping reduce risks and ensuring a smooth migration. Then group your servers logically to represent the applications and select the best cloud migration strategy for each application based on its requirements and migration objectives.
Step 3: Evaluate your on-premises applications.
Once you have mapped your application groups, evaluate how best to move each on-premises application. Again, for resource recommendations and migration strategies for your application servers.
Automated cloud migration tools will also provide insight into your environment and dependencies to build out your cloud migration project plans. Assess your situation now to build a template for future use that aligns with individual applications, locations, or groups within your organization. Start with apps that have fewer dependencies so you can get your migration off to a quick start. provides step-by-step guidance.
If the process seems daunting, our experts at BroadPoint can help. Our team is gold certified in . We can lead you through your inventory assessment or perform it for you.
With an effective plan in place, you’re prepared to migrate to the Cloud.
Cloud migration approaches
What’s involved in the migration? That depends on the approach you take. There are various approaches:
- Rehost allows you to move your existing applications to the Azure Cloud quickly. All applications are moved as-is without code changes.
- Refactor may involve changes to the application design but no wholesale coding change.
- Rearchitect allows you to modify or extend your application’s codebase to scale and optimize it for the cloud. You can modernize your app into a resilient, highly scalable, independently deployable architecture and use Azure to accelerate the process.
- Rebuild has you rebuilding an application from scratch using cloud-native technologies. With this migration strategy, you’ll manage the apps and services you develop, and Azure will manage everything else.
If you’re unsure which migration approach is best for your company, work with our experts during the planning process. Together we can create a multifaceted application strategy to determine when rehosting, refactoring, rebuilding, or replacing applications will deliver the most value. Additional applications can be built using cloud-optimized and cloud-native design principles. Request that we contact you.
Migration complete; what’s next?
Congratulations! You are ready to operate in a secure and well-managed cloud environment using Azure security and management services to govern and monitor your cloud applications. If you begin using these services during your migration, you can continue full speed ahead to ensure a consistent experience across your hybrid cloud. You can also monitor and manage your cloud expenditure with tools like . This solution allows you to track resource usage and control costs across all your clouds with a single, unified view. Take advantage of rich operational and financial insights to make informed decisions.
What does it all cost?
Moving to the Cloud will likely reduce costs over time. can help you calculate costs. Before cloud computing, organizations would purchase computing hardware and software and locate them in on-premise data centers. An IT staff was required to maintain those data centers. Computers were like other capital expenses: usually, large one-time purchases followed by several years of depreciation. Once you move to the Cloud, you are not responsible for those costs, and you pay only for usage. Your capital expense is now an operating expense with a monthly charge. Your BroadPoint partner will be able to give you a firm price for operating your cloud solution.
There’s a lot to consider when it comes to cloud migrations. Our BroadPoint experts are here to guide you and answer your questions. at BroadPoint today.
By BroadPoint,
4 Things You Need to Know for Successful Enterprise CRM Integration
The enterprise IT environment is complex. Many systems, technologies and practices that were developed at various times coexist in the same world. With expectations for technological advancements at their peak, we’re tasked with enabling these systems to work together harmoniously to support the continuous sharing of information.
Systems and data must connect to allow full use of capabilities as if all information were native to each. There also must be many ways to present information to end users, though data is evolving on a constant basis. Given this complexity, it’s not surprising that an intelligent CRM system in an enterprise environment requires specialized insight and know-how to ensure a seamless integration that’s both relevant and current.
What does an enterprise intelligent CRM solution look like? Any smart solution first and foremost must be designed as an enterprise-first application: one that is flexible, scalable, upgradable and ultimately easily deployable.
To a great extent, success in the enterprise environment depends on the use of “enterprise literate technology,” as opposed to reliance on “technology literate enterprise” concepts that were common in the past.
Enterprise literate technology understands an organization’s business needs and is built with the standards of what the company needs to accomplish in mind. It can help predict outcomes, better facilitate operations, and even automate day-to-day tasks. With today’s functional and technical complexity, heterogeneous integration requirements, and need for scale and security, CRM integration success depends on an enterprise literate technology approach.
From an enterprise management standpoint, the first task is to find a platform designed for this purpose. The following four considerations are critical when sourcing intelligent CRM software for enterprise systems.
1. Flexibility and Context Matter
Enterprise CRM solutions should be tailored to meet business specific needs. The primary goal for any CRM solution should be a seamless user experience, delivered through a fully integrated single application that is responsive across all devices and platforms.
A good choice will offer a comprehensive customer view by integrating enterprise and third-party data sources through a customized, context-aware system. Systems like this automatically manage all communication and context passing between the CRM system and other third-party portlets needed to ensure everything works together as a single integrated system.
What’s more, enterprise intelligent CRM with built-in artificial intelligence has the ability to incorporate additional data sources to allow users to leverage available information effectively in order to better service customers.
From automating workflows to eliminating burdensome administrative tasks, the flexibility of an intelligent enterprise solution provides a deeper understanding of the customer, which helps with decision-making processes and simplifies day-to-day operations.
2. Is It Scalable?
CRM is more than just a software. It’s an essential strategic growth tool that all enterprises need. One of the main motivators for any enterprise CRM provider should be to equip an organization with the simple tools needed in the early stages of a business, as well as the capabilities to scale up as the business expands and grows.
Implementing a reliable, fully scalable CRM system in the beginning as part of an overarching business strategy ensures seamless growth down the road. After all, a good foundation — which includes a company’s ability to scale up to meet the demands of growth and challenges as they arise — is fundamental to any organization’s success.
With an enterprise-first approach to CRM, organizations can rest assured that the simple coordination of all the different customer touchpoints in a business — from ales and marketing to face-to-face interactions with customers — will be facilitated effectively through one centralized platform as the business grows.
This is what makes ongoing collaboration, information sharing and task management simple and efficient. Keep in mind all of this must be done with a robust and complex security system, too. A truly scalable solution allows organizations to configure security to protect their customers and match their requirements as the business expands, while allowing information to be shared across the company dependably.
3. What are the Integration Capabilities? Is It Upgradable?
When CRM integration is an afterthought, failure is inevitable. Instead, the technology must allow for integration with a wide variety of client platforms, data sources and applications, since every organization has different needs.
Without integration for items such as social media and news feeds, for instance, it is next to impossible to take customer management and experience to the next level. For organizations and CRM solutions alike, a comprehensive understanding based on the client’s complete relationship with the company is essential for customers to receive the expected level of personalized, continuously evolving service.
Look for and integrate a system that understands the nature and concerns of enterprise operations — including its ability to upgrade as needed. Technical and functional challenges are to be expected, but a well-integrated system supports upgradability from the beginning.
Among other things, it is important to not force unnecessary data replication or compromises on data security, visibility and residency. Instead, organizations should deploy an enterprise-first way of thinking that provides configurable, world-class functionality along with the business controls needed to take operations to the next level.
4. Deployment Should Be Simple
A purpose-built enterprise CRM software system should make deployment simple. It should be compatible with a variety of open source and common software platforms. An intelligent platform takes the “how”out of the equation by translating processes easily. It should be easy to tailor it to specific business needs.
The user’s job should be simply to dictate what the system should do, not how it will be done. Both the functional and technical complexity should be handled by the software itself, which should be sophisticated enough to render information-sharing appropriately across all common Web applications and technology devices.
Easy deployment insulates a software investment from the shifting underlying technology landscape, and ultimately makes the development and maintenance of a CRM solution more manageable over time.
There is unmatchable value in deploying an enterprise CRM system that can be customized for an organization’s specific business needs to give the ROI and improved customer experience companies seek.
Integrating an enterprise intelligent CRM system can seem daunting, but it doesn’t have to be. Every business has a unique set of needs, and selecting robust software that allows for thoughtful integration of data and applications can improve operations and help businesses expand and grow, while making the transition seamless and mitigating unnecessary stress.
Adam Edmonds is VP of products at
NexJ Systems, which specialiazes in CRM products for global financial services institutions. With almost two decades of experience developing customer management solutions in financial services and insurance, Edmonds is responsible for establishing overall product vision and designing easy-to-use solutions that solve financial advisors’ problems.
What Midsize Companies Should Know About The Customer Experience In Times Of Intense Change
Part of the “Navigating Disruption Today, Planning for Tomorrow” series
Midsize businesses are known for understanding what customers want and meeting them where they are – in good and bad times. But what happens when a major event creates a wave of significant change that upends every aspect of our social infrastructure?
Within weeks, we have evolved from a society that routinely gathers in large numbers, values physical togetherness, and has access to products and services on demand. Now, we are bound by lifestyles dictated by social distancing, supply chain bottlenecks, and operational setbacks.
As these social norms break down and the longer such an experience persists, the more people reevaluate their role as consumers of an interconnected economy. This reassessment is not happening because consumerism is failing; instead, it’s a lesson of its fragility.
For midsize businesses, this is the perfect time to reconsider the effectiveness of their services and redesign them around customer needs – no matter how they change.
A shake-up in the customer experience
Today, customers are on the verge of a new trend: the isolation economy. Now more than ever, people are forced to travel less to complete everyday activities, from going to work and sending children to school to shopping at the local grocery store.
It is well-understood that structural social changes lead to how businesses should engage and serve their customers. But this transformation will require a deep, lasting shift in business models, so companies can innovate and build products and services that resonate with their customer’s lifestyle transformation. The expectation is that the goods and services come to the customer, not the other way around.
But don’t be fooled: social connection is still a basic human need that needs to be addressed. Fortunately, we live in a time when technology allows us to remain in constant touch with family, friends, and colleagues. And perhaps more surprising is the ease of creating new social circles that span generations.
A call for flexible, creative, and empathic experiences
This pandemic won’t last forever, even though it may feel like it. However, most of the buying behaviors and preferences that people are now adopting will become deeply ingrained in their expectations for the customer experience.
At some point, customers will one day resume traveling, going to the movies, participating in sports events and concerts, and even visiting their favorite stores. However, midsize companies must remember that such activities will not happen at the same rate and intensity as before.
So besides quickly putting up e-commerce sites and using social media to engage customers, there are other ways to adjust to this “next normal” for the customer experience. It just takes a little more flexibility, creativity, and empathy to achieve it.
Take, for example, chatbots. Albeit a relatively simple technology, the digital assistant provides a human-like experience that resonates well with customers. More importantly, artificial intelligence can help midsize companies analyze changing customer behaviors as the devices learn about them by engaging in very focused dialogue.
Another approach is the ability to provide an experience that complements a growing do-it-yourself (DYI) movement. No more than ever, customers are engaging in the world of DYI to occupy time their time being sheltered-in at home. The new trend is ripe for new spaces, services, and products that support doing things for oneself – such as events, classes, and educational resources.
Customer experience today, building block for tomorrow’s
Whether facing a government shake-up, natural disaster, or war, people naturally resort to survival tactics to ensure that their basic needs are addressed. But over time, those behaviors shape their expectations for the customer experience – sometimes with such speed and intensity that most businesses find challenging to adjust.
In many ways, the recent pandemic has unquestionably become a much-needed wake-up call for the customer experience. But midsize companies shouldn’t fear it. Instead, they should embrace it with the flexibility, creativity, and empathy that customers increasingly desire.
For further exploration of how midsize companies can meet changing customer expectations, we invite you to join our Webinar, “Evolve Your Customer Engagement for Business Continuity and Future Growth.”
This blog is part of a series offering suggestions to help small and midsize companies weather the pandemic challenges. We invite you to join the other blogs in the series as well.
This article originally appeared on Forbes SAP BrandVoice.
Tempdb – Here’s a Problem You Didn’t Know You Have
Over the past few years, I’ve worked to maintain and optimize a database environment, and I’ve faced all sorts of problems, but one pain point common in almost all SQL Server environments is tempdb.
The usage of tempdb in SQL Server is well known, and I don’t want to explain the same list of issues and recommendations already covered very well in many articles all over the internet. Here are some of the known issues you could easily google to get more information about:
- Object allocation contention on metadata pages used to manage space on tempdb data file
- Metadata contention for pages that belong to system objects used to track the temporary object’s metadata (btw, this is improved on SQL Server 2019)
- Issues with codes that are not optimized to the benefit of tempdb cache of system metadata
- Spills of hash and sorts
In this article, I would not like to focus on those common things, but in other interesting problems I found while I was supporting my customers.
Some tempdb Problems You are Very Likely to Have
The following is a list of a few tempdb problems you are very likely to have in your SQL Server environment, that you may not know about.
- In versions prior to SQL Server 2014, eager writer is slowing down your inserts and wasting I/O when a flush operation is not needed.
- On SQL Server 2014 (backported to SQL Server 2012 SP1+CU10 or SP2+CU1), the eager writer was relaxed to not flush for tempdb; this fixed one problem and caused another one.
- Indirect checkpoint (target recovery time) enabled by default on tempdb for SQL Server 2016. The idea was to clean up dirty pages created by the change in SQL2014, but it doesn’t really do a good job fixing it as it doesn’t clean up dirty pages allocated on minimally logged operations.
Eager Writer and tempdb
One of many performance improvements that came with SQL Server 2014 is that it doesn’t flush dirty pages created in a minimally logged operation on tempdb. This gives you the benefit of having faster (compared to prior versions) inserts, but it caused another problem as those allocated pages may take a lot of time to be removed from the buffer pool data cache. Before discussing the problem, quickly look at the benefit and then understand some important concepts of flush dirty pages on tempdb.
Use the following query which inserts around 520MB of data into a temp table:
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
USE tempdb GO IF OBJECT_ID(‘#TabTest1′) IS NOT NULL DROP TABLE #TabTest1; CREATE TABLE #TabTest1(Col1 CHAR(500)); INSERT INTO #TabTest1 SELECT TOP 1000000 CONVERT(CHAR(500), A.name) AS Col1 FROM master.dbo.sysobjects A, master.dbo.sysobjects B, master.dbo.sysobjects C, master.dbo.sysobjects D OPTION (MAXDOP 1); GO |
When I ran it on in my local instances with both tempdb configured with same details (same disk, initial sizes, Trace Flags, etc.), I see the following results:
- On SQL Server 2008 R2 (10.50.6220.0) it took 32 seconds to run.
- On SQL Server 2017 (14.0.3238.1) it took 1 second to run.
As you can see, the difference is huge; this is pretty much because of the following:
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1 2 3 4 5 6 7 8 9 10 |
SELECT Page_Status = CASE WHEN is_modified = 1 THEN ‘Dirty’ ELSE ‘Clean’ END, DBName = DB_NAME(database_id), Pages = COUNT(1) FROM sys.dm_os_buffer_descriptors WHERE database_id = DB_ID(‘tempdb’) GROUP BY database_id, is_modified |
|
SQL Server 2008R2 |
SQL Server 2017 |
|
|
|
As you can see, checking the DMV sys.dm_os_buffer_descriptors shows that most of the allocated pages on SQL Server 2017 are dirty, and on SQL Server 2008R2 the eager writer process took time to clean and write the pages on the tempdb data file. This is where the time difference comes from. On SQL 2008R2, the command has to wait for the flush, and on SQL2017 it doesn’t.
What does Checkpoint do on tempdb?
Before moving forward, here’s a quick recap of some important concepts you need to know to understand the problems I’ll mention in this article.
When you insert a row in a table, SQL Server does not immediately write it in the data (mdf) file. It has a control to write it on the log (ldf) and keep the row in a page in memory. The pages that are in memory and were not physically written to the data file are called dirty pages. A dirty page is written to the data file by one of the three processes: checkpoint, lazy writer or eager writer. These are the very basics of I/O writing on SQL Server, and you need to understand it to be able to troubleshoot and explain complex problems related to tempdb and the whole SQL engine.
Rows may exist only on the log file and dirty pages in memory. If an unexpected crash happens and your SQL Server stops, once it is back, SQL engine must redo any transaction that was written to the transaction log file and apply those changes to the data (mdf) files. This whole thing doesn’t make sense for tempdb, right? Since tempdb is recreated on SQL Server start, there is no need for tempdb to recover transactions from the log file into the data file, therefore, there is no need to flush dirty tempdb pages to disk. Actually, this is why SQL Server doesn’t (there are exceptions, when the log reaches 70% of space, manual checkpoint…) do automatic checkpoints for tempdb.
Understanding Why the Change on SQL2014 Caused Another Issue
You may be wondering, considering the case we’re looking at, why this whole checkpoint thing is important? Well, let me answer this with another question.
Using the query inserting data on #TabTest1 as a sample, consider that on SQL Server 2014+, dirty pages won’t be flushed by the eager writer; would this behavior cause another problem? In other words, what would be the impact of having many dirty pages on tempdb?
Well, the problem is that you could have a scenario where tempdb dirty pages are using a lot of memory of your precious buffer pool. If you consider there are scenarios with heavy usage of temporary tables, this can quickly turn into memory pressure. The main problem of this memory pressure is that there will be no automatic checkpoint to flush those dirty pages, that means that lazy writer will be the one flushing those pages. After it flushes the pages, then it will free other buffers available by removing infrequently used pages from the buffer cache.
As Bob Dorr mentioned in his blog post on the improvement, “the pages used in these operations are marked so lazy writer will favor writing them to tempdb”. What he is saying here is that those pages will be written to tempdb data file by lazy writer. So far, so good. However, if there is any memory pressure, lazy writer will flush those pages and put them in the free list and will do it for all dirty pages. That includes pages that were allocated in a session that are not active anymore. Say you have a session that created a 10GB temporary table. Once you’re done, you can drop the table and close the session, but the allocated pages will still be there in memory. Right? All those nasty 10GB of dirty pages of a table that does not exist anymore. As soon as you have memory pressure and need to find space on buffer cache to put some data in memory, lazy writer will waste time to flush those useless dirty pages that will never be used. In my opinion, the correct concept should be that once the table is dropped or session is done, lazy writer should be able to put these pages on the free list to avoid using up precious buffer pool cache and triggering unnecessary write I/O operations on tempdb data file.
Ok, Fabiano, I don’t get it, can you show me a sample on where this could be a problem? Yep, sure. Stay with me.
Now to prepare the scenario to simulate the issue. First, set the instance (you could use any version equal to or greater than SQL Server 2014, I’m using a SQL Server 2017) to use only 8GB of memory:
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USE master GO – Set MaxServerMemory to 8GB EXEC sys.sp_configure N‘max server memory (MB)’, N‘8192’ GO RECONFIGURE WITH OVERRIDE GO |
Then, create a 5GB table to run the tests. For this demo, I’m using the AdventureWorks2017 database:
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1 2 3 4 5 6 7 8 9 10 |
USE AdventureWorks2017 GO DROP TABLE IF EXISTS AdventureWorks2017.Sales.SalesOrderDetailBig GO SELECT TOP 50000000 a.* INTO AdventureWorks2017.Sales.SalesOrderDetailBig FROM AdventureWorks2017.Sales.SalesOrderDetail a, AdventureWorks2017.Sales.SalesOrderDetail b OPTION (MAXDOP 1) GO |
Now, create a procedure that will insert 5GB of data into a temporary table using an INSERT + SELECT command.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 |
USE AdventureWorks2017 GO IF OBJECT_ID(‘st_1′) IS NOT NULL DROP PROC st_1; GO CREATE PROC st_1 AS IF OBJECT_ID(‘tempdb.dbo.#TabTest1′) IS NOT NULL DROP TABLE #TabTest1; CREATE TABLE #TabTest1 ( Col1 CHAR(500) ); INSERT INTO #TabTest1 SELECT TOP 10000000 CONVERT(CHAR(500), A.name) AS Col1 FROM master.dbo.sysobjects A, master.dbo.sysobjects B, master.dbo.sysobjects C, master.dbo.sysobjects D OPTION (MAXDOP 1); GO |
Now, before running st_1 to populate the temporary table, make sure to run it using a cold cache:
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– Manual checkpoint on tempdb to flush pages and start with a cold env. USE tempdb GO CHECKPOINT;DBCC DROPCLEANBUFFERS(); DBCC FREEPROCCACHE() GO |
Now, open a new session and run the procedure st_1 to allocate some pages on tempdb. Once you’re done you can close the session; I know that the temporary table is destroyed after sp execution, but just in case you think it is not, go ahead and close the session.
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– Should take about 15 seconds as long it has memory to keep – all 5GB of inserted data EXEC AdventureWorks2017.dbo.st_1 GO |
At this time, if you look at the sys.dm_os_buffer_descriptors, you’ll see that about 666k (scary number) of newly allocated pages are dirty:
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1 2 3 4 5 6 7 8 9 10 11 |
SELECT Page_Status = CASE WHEN is_modified = 1 THEN ‘Dirty’ ELSE ‘Clean’ END, DBName = DB_NAME(database_id), Pages = COUNT(1) FROM sys.dm_os_buffer_descriptors WHERE database_id = DB_ID(‘tempdb’) GROUP BY database_id, is_modified GO |
Now, here is the fun part. If you try to read the 5GB table (Sales.SalesOrderDetailBig) created on AdventureWorks2017 DB, what happens? Remember, it’s a 5GB temporary table. Pretty much all those pages are in memory and dirty. If you try to read another 5GB, there will not be enough memory available (max server memory is set to 8GB), and lazy writer will have to run and put some pages on the free list. Before it does that, it will flush all those dirty pages. While it is doing it, your query will be put to sleep and may take a long time to run.
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SELECT COUNT(*) FROM AdventureWorks2017.Sales.SalesOrderDetailBig OPTION (MAXDOP 1) GO |
While the query is running, this is what you can see:
As expected, lazy writes/sec performance counter is showing lots of activity:
The query is waiting on SLEEP_TASK, and there are several pending I/O requests.
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SELECT * FROM sys.dm_os_waiting_tasks WHERE session_id = 51 GO SELECT * FROM sys.dm_io_pending_io_requests GO |
My point is that the query on Sales.SalesOrderDetailBig is taking 3 minutes to run, and most of the time it is waiting for lazy writer to flush some of the dirty tempdb pages and put pages on the free list. If lazy writer didn’t have to flush those pages, the query should be able to run in just a few seconds.
But wait, SQL 2016+ indirect checkpoint on tempdb fixed this problem
On SQL Server 2016 indirect checkpoint, an outstanding feature initially introduced on SQL Server 2012, is enabled by default on model system database and set to 60 seconds. Since the tempdb database is derived from model during startup, it inherits this property and has indirect checkpoint enabled by default. (Note: TF3468 can be used to disable this)
If you haven’t heard about indirect checkpoint, the following is a quick explanation of it from this MS document:
“The conventional or automatic checkpoint algorithm assumes fixed time interval to recover each log record without accounting for the number of pages dirtied by a database transaction. In simpler terms, when (number of log records * fixed time interval to recover a log record) exceeds recovery interval set at the instance level, automatic checkpoint is triggered on the database. As automatic checkpoint doesn’t account for the dirty pages from each transaction (an important influencer of recovery time), it makes the recovery time less predictable. With indirect checkpoint, the engine maintains partitioned dirty page lists per database to track the number of dirty pages in the buffer pool for that database from each transaction. When the background recovery writer polling the dirty page manager detects the condition, where the number of dirty pages exceeds the dirty page threshold (calculated by heuristics), the recovery writer would trigger the flush of dirty pages to ensure database recovery would complete within the target_recovery_time set”
As I mentioned before, there is no recovery need for tempdb as it is recreated on SQL service start, so indirect checkpoints on tempdb may not make much sense from a recovery perspective. However, the indirect checkpoint feature is still important to ensure the dirty pages in tempdb do not continue to take away buffer pool pages from user database workload as there is no automatic checkpoint to flush pages for tempdb.
One of the reasons to enable indirect checkpoints on tempdb is to help scenarios with dirty tempdb pages take away the buffer pool data cache and cause pressure on lazy writer while it flushes dirty pages, the exact scenario we are analyzing.
If indirect checkpoint had previously flushed dirty pages on tempdb, a query wouldn’t need to wait (SLEEP_TASK) for lazy writer to flush it.
But, wait, don’t be happy yet; there is a small problem with indirect checkpoints on tempdb. Any non-logged “bulk” operation that qualifies for an “eager write” in tempdb is not a candidate to be flushed by the recovery writer (the internal thread that runs the indirect checkpoint).
This raises an important question: which data load operation is minimally logged on tempdb? This is important to know because minimally logged operations on tempdb will not be flushed by the indirect checkpoint. The following list can be used to assist you in understanding which load operations on tempdb will be minimally logged and which will not.
|
Command |
Is minimally logged? |
|
INSERT INTO #TMP + SELECT |
Yes, since local temporary tables are private to the creating session, there is no need to use TABLOCK to get an ML operation. |
|
INSERT INTO #TMP WITH(TABLOCK) + SELECT |
Yes. Note that TABLOCK is required to get + SELECT an insert operator running in parallel on SQL2016+. |
|
SELECT INTO #TMP |
Yes. Since tempdb DB recovery model is always set to simple, a SELECT + INTO will be ML. |
|
SELECT INTO tempdb.dbo.TMP |
Yes. |
|
INSERT INTO tempdb.dbo.TMP WITH(TABLOCK) + SELECT |
Yes. Since this is a regular user table that can be accessed by any session, it requires a TABLOCK to be ML. Same applies for a global (##) temporary table. |
|
INSERT INTO tempdb.dbo.TMP + SELECT |
No. So, the only way to don’t qualify for a ML operation on tempdb is to use a regular or a global (##) temporary table and don’t specify the TABLOCK. |
As you can see, pretty much all of the insert commands you can use on tempdb will be minimally logged, and pages won’t be flushed by the indirect checkpoint.
Summary
Tempdb troubleshooting can be very complex. It requires you to understand many components of the SQL Server engine. If you need an answer to the “why” question, make sure you took some time to study SQL Server internals.
The current behavior of SQL Server is just “the way it works today.” I honestly expect it to improve on this area, as it is not rare for me to see an environment where tempdb is the TOP 1 database on data cache usage. I don’t want my environments to have my precious productions tables doing physical reads because someone ran a report that created a 20GB temporary table, or, in a worst-case scenario to wait for lazy writer to flush some “never will be used again” dirty pages. It would be good to have something like a “max buffer pool usage” setting on tempdb. What do you think?
The Main Street Lending Program: What Your Business Needs to Know
By Justin Biel, trends editor at Grow Wire
The Federal Reserve Board on April 9 released new details of the Main Street Lending Program, designed to deploy loans to “small and mid-sized businesses” — firms with up to 10,000 employees and less than $ 2.5 billion in annual revenue — affected by COVID-19.
Unlike the Small Business Administration’s Economic Injury Disaster Loan and the Paycheck Protection Program, both of which are currently open for application and aimed at smaller businesses, the Main Street program will be the first federal relief program also aimed at larger companies. (Note: If you already applied for a loan with the Paycheck Protection Program, you can also apply for a Main Street loan.)
The Federal Reserve Board first announced the program in a March 23 press release. However, unlike the relief programs aimed at smaller businesses, the program has yet to accept loan applications.
This article covers what we know so far. We’ll update it with new developments as they arise.
What is the Main Street Lending Program?
After Congress allotted $ 454 billion to the Department of the Treasury to support the Fed’s programs in the wake of COVID-19, a number of lending programs developed to support businesses across the U.S. and stabilize the economy.
The Fed developed one such program, the Main Street lending program, to provide relief for both small and middle-market companies.
Mary Daly, president of the Federal Reserve bank of San Francisco, explained the rationale behind the program in an interview with Yahoo Finance.
“There is this whole swath of institutions that don’t access capital markets very easily and don’t qualify for small business lending,” she said. “So if you think about it, that’s a part of Main Street that still needs to be treated and directly employs millions of workers in the United States.”
Who is eligible for the Main Street Lending Program?
Per the Fed’s latest guidelines announced on Thursday, a business must meet the following requirements to qualify for a Main Street loan:
- Have up to 10,000 employees or revenues of less than $ 2.5 billion. (Previous reports said businesses would need to have a minimum of 500 employees in order to apply, yet the Fed’s new guidelines don’t mention that.)
- “Commit to make reasonable efforts to maintain payroll and retain workers.” (Again, previous reports provided more detail, saying that businesses who get loans would need to retain at least 90% of their workforces, at full compensation and benefits, until Sept. 30, 2020.)
- “Follow compensation, stock repurchase, and dividend restrictions that apply to direct loan programs under The CARES Act,” the federal stimulus package that sparked the Main Street Lending Program. (Law firms have more information on those restrictions.)
Neither the Fed nor banks have yet to issue exact details regarding applicants and how to apply. Earlier reports based on analysis of The CARES Act stated that businesses who get Main Street loans will likely be required, at least on good faith, to rehire at least 90% of any employees laid off since January 2020.
Also, according to the Act, those that get loans will “make a good faith certification” including to-dos like not outsourcing jobs while repaying the loan and for two years afterward.
How much money can I get from the Main Street lending program?
CNBC reports that loans would have a minimum of $ 1 million and a maximum of either $ 25 million or an amount that “when added to the Eligible Borrower’s existing outstanding and committed but undrawn debt, does not exceed four times the Eligible Borrower’s 2019 earnings before interest, taxes, depreciation, and amortization,” whatever is less.
What are the terms of these loans?
CNBC reports that interest rates will be just 0.01%, plus 250-400 basis points with a four-year maturity. Principal and interest payments will be deferred for one year, announced the Fed.
Who serves as lenders for the Main Street lending program?
Lenders for the Main Street program will be financial institutions, such as large banks. The government will purchase 95% of the loan while the financing institution will hold the other 5%.
How will I apply for a loan?
Businesses will apply directly with the lender, though neither the Fed nor banks have released details about the process. It’s usually recommended to apply for a loan at the bank you currently work with.
“The application process will certainly vary by bank – larger banks may leverage a portal to take in applications, while smaller banks may require an application and supporting documentation to be submitted to a specific branch or through email,” said Brandon Koeser, senior manager and financial services senior analyst with RSM US LLP, a middle-market consulting firm.
If your bank is using a portal to collect applications for the Paycheck Protection Program, it’s “more likely they’ll use a portal” for Main Street loan applications, he added.
When will I be able to apply?
The Fed’s much-anticipated Thursday announcement provided new details about the Main Street Lending Program, but it didn’t outline an application process.
Koeser acknowledged that “we are still in wait-and-see mode” with regard to a program launch date. It could be later rather than sooner, he added, referencing the fact that many banks weren’t ready to accept loan applications when the Paycheck Protection Program launched last week.
“In light of how the rollout of the Paycheck Protection Program has gone with the SBA and participating lenders, it is possible that the Fed will take all of those weeks to craft the necessary rules and lay out the application and funding process,” he said.
Is there anything I can do now to prepare?
Koeser expects the Main Street loan application to require similar documents to the Paycheck Protection Program.
To get prepared now, he recommends gathering:
- Payroll filings reported to the IRS for 2019
- Payroll records supporting your compensation expenses and total workforce numbers.
“The important thing to remember is that this stimulus is designed to get into the hands of those who need it,” Koeser added.
So, while explicit details of the paperwork required aren’t released yet, he recommends preparing any and all information related to payroll that supports your business’s need for a loan.
“The more detail you can pull together on your payroll costs, including IRS filings and payroll reports, the better it will be when it comes time to complete the application,” he added. “Information-gathering could prove to be the most time-consuming part of the application process, so gathering what information you have could save time when it really counts.”
Posted on Thu, April 9, 2020
by Barney Beal
Automated Screening: Know More About Companies You Do Business With
Part 8 in the “Controls and Risk Management” series
In addition to traditional post-process controls, more and more organizations have to enforce proactive screening and monitoring of the organizations and people they are doing business with.
Whereas some organizations simply decide to screen their vendors against countries ranked as the most “risky” in Transparency International’s Corruption Perceptions Index, this is definitely no longer sufficient to comply with the variety of associated regulations such Anti-Bribery and Corruption (AB&C or ABAC), Know Your Customer (KYC), Anti-Money Laundering (AML), Counter-Terrorism Financing (CTF), Modern Slavery Acts (MSA), embargos, etc.
Indeed, behind this extensive list of regulatory initiatives and acronyms are lists of foreign or domestic sanctioned parties, politically exposed persons, companies or people that have been associated with negative media coverage, and so on. These are the lists that companies need to screen against. Now, extend this to family members, known associates, and sub-holdings, for instance, and the scope that needs to be monitored increases exponentially.
Depending on the industry and geographical reach, organizations have to comply with local, national, and international lists, or a combination of all three, thereby making the process even more complex.
What’s the exposure?
Implications of not complying with the legislation mentioned above are multiple: from adverse media coverage to civil and criminal penalties or even revocation of a license to operate that can signify the end of a business.
Focusing on penalties, just for 2019, the total U.S. Office of Foreign Assets Control (OFAC) Civil Penalties amounted to over US$ 1.2 billion for 26 settlements. This is even above the “record” years of 2014 ($ 1.2 billion) and 2012 ($ 1.1 billion).
And these figures are just from the Office of Foreign Assets Control (OFAC) of the U.S. Department of the Treasury that:
“administers and enforces economic and trade sanctions based on U.S. foreign policy and national security goals against targeted foreign countries and regimes, terrorists, international narcotics traffickers, or those engaged in activities related to the proliferation of weapons of mass destruction, and other threats to the national security, foreign policy, or economy of the United States.”
Companies that operate worldwide would be exposed to scrutiny from many other governments, security, and law enforcement agencies.
Based on these 2019 figures, is an average settlement of close to $ 50 million a risk exposure that your organization would favorably consider? I’m pretty sure the answer would be no.
What can be done?
When you look at finding a needle in a haystack, there are three options available to you:
- Manually go over every straw in the haystack: This is very effective, but also very inefficient. And keep in mind that the haystacks will continue to pile up while you check the first one.
- Burn each haystack and go over the ashes with a magnet: Not only does this assume that the needle will be in metal (or to put it in GRC terms, that your control will be looking for the right deficiency criteria), but this also means that you basically lose your production. Yes, it is effective and efficient, but it yields a different outcome than what is really needed.
- X-ray each haystack in a scanner: This will be effective since the needle will be found, but it will also be efficient since it will be done automatically. Finally, it will be proactive since anything that is not “straw” will also be detected.
For third-party screening, SAP Business Integrity Screening acts as the X-ray scanner for Option 3:
Once the lists of people, organizations, countries, search terms, etc. have been created or uploaded in the tool, users can start the monitoring.
Where it becomes even more interesting is that you can then apply three modes:
- Online screening that is triggered when the individual business processes are executed (such as a payment for an instance)
- Mass screening for batch screening of business partners
- Delta screening for monitoring of business partners after list updates that can be run either during the list import or on-demand
Furthermore (and this is where I believe software supports the process even better than a manual check), aliases, address variations, initials, and more options can also be included for the screening.
Doing this manually would be possible, of course, but would require significant time and resources.
Learn more
Along with my colleague Michael Hecker from GRC Centre of Excellence for EMEA North, I am including a presentation of this solution in one of the pre-conference workshops of the SAP Conference on Internal Controls, Compliance and Risk Management taking place this week in Copenhagen. I look forward to reading your thoughts and comments on Twitter @TFrenehard.
This article originally appeared on SAP Community and is republished by permission
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