We Make More Than Award-Winning CRM Products

The premise was simple. What tech stalwarts Paul Greenberg and Brent Leary wanted to do was have fun while stoking competition to round off a challenging year. Each band had to record three covers.

NexJ turned up with Nudge, alongside Jane Blonde & the Goldfingers from Elements.cloud, The Epi Encores from Episerver, Oracle Cloud CX Pistols, The Layer Cakes from Pegasystems, Crocobird from Pipedrive, The Profit from PROS, The Vlociters from Salesforce, The SAPremes from SAP, and RMD (Rendu Mani Dosa) from Zoho.

Here’s What We Did

Here Comes The Sun – The Beatles

“Little darling, it’s been a long cold lonely winter. Little darling, it feels like years since it’s been here…” The Beatles mysteriously understood what working from home during a pandemic would be like four decades before the rest of us caught on.  We couldn’t think of a more appropriate lyric as we look to a brighter future.

Take On Me – A-Ha

We chose A-Ha’s Take On Me because we still can’t figure out how they made that cool video back when their competitors had nothing but bad mullets and even more bizarre taste in fashion. We wanted to try and capture a bit of the song’s energy, which never seems to get old, and also because the lyric “Today’s another day to find you…shying away” captures what we all feel when we’re invited to another Zoom meeting.

Everlong – The Foo Fighters

Our final song was Everlong by the Foo Fighters because one of our band members loves the guitar parts and big drop-D chords and if we don’t indulge him occasionally, he has the proverbial “prima donna musician meltdown”. It’s a good song to welcome 2021 with though: “And I wonder, when I sing along with you, if everything could ever be this real forever, if anything could ever be this good again…”

Here’s What We Won

NexJ Systems walked away with the Bronze badge for Best Band, in what we were told by organisers was a really close battle. We have also been informed about another competition planned for 2021, and our band is more than ready.

Making great music together is about trust and comfort, and the ability to work well as a team. We bring this spirit of camaraderie to our day jobs, building the next generation of CRM products and solutions for markets that constantly evolve. To find out more about our company, get in touch with us today. If you’re lucky, we might even play you a song.

The post We Took On Some The World’s Biggest CRM Vendors. Guess What We Won? appeared first on NexJ Systems.

In January 2020, Anna Zakrzewski and Dean Frankle of the Boston Consulting Group published a whitepaper titled Outperform – Beat the Average: Key Levers for Top Performers in Wealth Management. It documents the shifting fortunes of wealth managers, from strong asset appreciation based upon economic growth in rapidly developing economies to an acknowledgement of the industry’s fragility when times got tough. Here, in a nutshell, are some of the issues they believe wealth managers must focus on in the future if they are to come up with effective strategies to counter them and thrive.

A Need To Redefine Growth

How wealth managers define growth can yield insights on how they react to market volatility. The emphasis for most firms is on asset appreciation, which always stands to be affected by everything from low interest rates and political uncertainties to increased market volatility and even trade tensions. 2020 saw its fair share of these, and there are signs that some amount of continuing unrest is certain. Markets traditionally rebound, of course, but wealth managers need to account for significant falls as well.

Paying Attention To Revenue Margins

Studies show that revenue margins have been declining over the past few years. There have been extended periods of relative stability, but these have been few and far between. Analysts lay the blame on these among other causes:

• Regulators mandating an increase in fee transparency
• More investors preferring lower-margin products
• Lower interest rates
• A global rise in political uncertainty
• Higher demand for cash-alternative solutions
• More calls for lower-margin fixed income
• The entry of more non-traditional companies in the FinTech space
• More financial firms focusing on wealth management
• Higher bargaining power by UHNWI leading to lower prices

Costs Will Continue To Rise

There is a limit to how costs can be kept down by wealth management firms. Lower investments in new technologies, for example, can lead to advantages for competitors. Firms need to strike a balance between escalating costs and the real need for investments in critical systems. Higher operating costs can be offset by investments in areas that drive growth and efficiency. An emphasis on digital capabilities, coupled with the intelligent use of data and analytics, can also help.

In our second blog on this topic, we will look at what the Boston Consulting Group learned from conversations with some of the best performers in the financial services industry, and how it identified key strategic profiles for wealth managers related to size, client and geographical focus, market presence, and organizational set-up.

To find out more about how NexJ is creating the next generation of Vertical CRM and digital assistants for wealth management to address some of these emerging challenges, get in touch with us today.

The post Here’s Why Wealth Managers Have More Challenges To Deal With Than Ever Before appeared first on NexJ Systems.

This is what makes integration such a crucial factor in the deployment of any CRM solution. When done efficiently, it allows firms to provide their users with seamless access to all data and functionality. It also brings together client, account, and company data directly from the source, reduces the risk of conflicting or out of date information, and empowers advisors with all enterprise knowledge about a customer in a comprehensive customer-centric view.

CRM In A Post-Pandemic World

The pandemic has affected every facet of the marketplace leading to advisor expectations that have given rise to specific trends:

Improve customer service:

This is more important than ever in our Age of the Customer. Firms acknowledge that the only sources of competitive differentiation that will matter in the months and years to come are good customer experiences. The need to make customers feel understood is more important than ever, and data-driven responses are easier when CRM is used effectively.

NexJ helps firms improve customer service with wealth management-specific solutions that enable advisors to track client attributes specific to their business. Our CRM helps manage complex relationships quickly, create and share notes quickly, and offer more efficient recommendations.

Generate empathy:

CRM makes this possible by translating data-driven insights into real-time actions and more informed financial advice. More and more studies show that this helps customers feel more engaged, prompting an increase in assets under management.

The pandemic has created the potential for unnecessary risk-taking or making poor financial decisions. NexJ offers solutions that help advisors re-assure clients pro-actively, based on an understanding of individual concerns and how they affect each member of a client’s household. We help advisors track all key conversations and investment decisions, creating a more comprehensive picture of a client’s financial health.

Use automation and AI:

Our lives have been changed by social distancing and the rise of virtual interactions, which will have implications for the use of automation and Artificial Intelligence in CRM. This will lead to efficiency, time savings, as well as tailored advice to meet customer needs when, where, and how they want them. Solutions will have to keep up not just with how customer demands change, but with the expected increase in touchpoints and volume of data.

NexJ Nudge-AI, our suite of digital assistants, leverage AI, Machine Learning, and Natural Language Processing to make suggest next best actions, share relevant information and analyse data for insights that help advisors better engage and service their clients.

How NexJ Approaches The Idea Of Integration

NexJ CRM aggregates data from enterprise systems, applies business intelligence, and drives intelligent, proactive interactions, saving advisors’ time. We have always believed that CRM must lie at the core of an advisor workstation because managing clients in a central location will be more important than ever in a post-pandemic world. We rely on integration to effectively automate day-to-day tasks, personalize service, and encourage pro-active engagement, all via a single, seamless interface.

Our solutions streamline key tasks, leverage analytics (and social media), and display relevant KPIs and dashboards to influence advisor behavior. This allows our users in 60 countries to do their jobs effectively, offering their customers a superior level of service and engagement.

Advancements in technology are expected, of course, specifically in areas such as Artificial Intelligence to automate non-social tasks. There will also be an increased dependence on Machine Learning to process customer behavior across digital landscapes, and an emphasis on hyper-personalization to cater to more collaborative relationships between firms and customers. CRM will also learn how to parse data better, to create models that are more accurate and actionable.

Here’s another reason we take integration as seriously as we do. It improves user adoption rates, which is key to the success of any Customer Relationship Management software implementation. Firms may bring on board all kinds of new products and solutions to face the economic climate of tomorrow. Their success will depend entirely upon how their advisors take to these tools.

To find out about integration, how we approach it, and what the next generation of CRM will look like, why not send us a message?

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How Do You Choose Your CRM?

There are all kinds of reasons why RFPs are essential, starting with the fact that they are often required as part of corporate policy. There isn’t anything intrinsically wrong with relying on them either, provided firms acknowledge that they can also be time-consuming. They can prolong the process of selection by introducing bureaucratic hurdles. Worse, they may produce undesirable outcomes by not being engineered for new delivery models.

This is why NexJ believes the most critical step in any evaluation is a well-run Proof of Concept. We say this based on our experience with successful financial services firms and satisfied users in over 60 countries.

Navigating A Crowded Marketplace

Firms select solutions that best support their CRM strategy while navigating a market that evolves constantly. The possibilities can seem endless, especially when firms have specific requirements based upon the industry they are in. This is why NexJ’s product capabilities and features are always targeted towards specific users, job requirements, and departments within an organization.

The rise of deep vertical CRM vendors has also coincided with a need for solutions suited to specific ecosystems. In such a scenario, while traditional RFPs can help narrow down options, it is only Proof Of Concept trials that can help firms find the right solutions.

The Advantages of a POC

A POC answers four important questions. It allows firms to test vendor claims, validate assumptions and conclusions, and go beyond demos, brochures, and meetings with team members. These questions simply cannot be addressed by an RFP:

• What can this product do?
• What will our users think?
• How can we evaluate this vendor’s capabilities more effectively?
• How well can the product integrate with our infrastructure?

When an application or solution being evaluated isn’t created for specific software ecosystems, it is a POC that can help firms identify potential problems and narrow down options that display reasonable levels of efficiency.

Understanding The NexJ Approach

Not so long ago, one of our clients realized its systems were struggling to handle complex relationships well. They couldn’t handle workflows required to meet compliance regulations and were not flexible enough to meet changing customer demographics and regulations. What they needed was a financial services-specific CRM with robust integration capabilities to connect their systems, handle compliance workflows, and obtain a complete view of each customer.

We delivered a 4-week long POC to validate our technology and functional capabilities as part of an integrated solution. The aim was to show how our platform would be a suitable replacement for the existing Customer Data Management and CRM solutions, both of which were internal builds. Our POC featured distinct workstreams that covered our business modeling capabilities and flexibility, business processes, integration capabilities, and a migration prototype that converted data from the legacy client data master and relationship management platforms to the target model implemented in NexJ.

To find out more about our approach to a Proof of Concept, and why we believe this helps our customers understand our products better, get in touch with us today.

The post Here’s Why A Proof Of Concept Can Increase The Chances Of Your Project’s Success appeared first on NexJ Systems.

Everyone has a different understanding of what customer trust and engagement means, of course, but there is always a consensus on how this involves the building of trust between advisors and clients.

The Covid-19 Pandemic and Customer Engagement

The pandemic has created a genuine need for are solutions that allow advisors to interact with clients more efficiently, at a time when there are all kinds of questions or doubts, coupled with the constant potential for unnecessary risk-taking or making poor financial decisions. Clients who can rely upon their advisors will be more likely to trust and accept their advice, which will strengthen their relationship long after the crisis has faded.

Solutions that can help advisors must, first of all, enable them to re-assure clients pro-actively. Every client and household has specific concerns, which means a solution must also offer advisors an understanding of what these concerns may be, and how they affect each member of a client’s household.

Another important aspect of engagement is an advisor’s ability to track all key conversations and investment decisions, creating a more comprehensive picture of a client’s financial health, thereby enabling more efficient recommendations.

NexJ’s Applications For Continued Loyalty

NexJ has always believed that business is about relationships and places that principle at the heart of all its award-winning products. The solutions we offer advisors arm them with tools that help their clients have peace of mind and make better decisions.

We responded to the Covid-19 pandemic by coming up with a free trial of an application to help advisors pro-actively engage with customers. Here are some of the things our application helps advisors do:

Pro-actively re-assure clients: By keeping in touch with clients on a regular basis and sending them public news articles about their personal interests and concerns.

Understand the primary concerns of each client and household: By using householding to track each member of the household including their interests and concerns.

Track all key conversations and investment decisions: By using a journal to record all client activity and an interaction timeline to view recent events.

Benefits That Help Build Trust

Our free application offers these benefits:

Build loyalty and differentiate on client experience: It does this with the help of a powerful news and research recommendations tool that uses a sophisticated Artificial Intelligence engine and matches news to client profiles, delivering tailored content from more than 15,000 publishers.

Track client attributes specific to your business: Our application’s wealth management-specific profile enables advisors to track client attributes including Wealth Stage, Segmentation, Investor Qualification, and Risk Tolerance.

Manage complex relationships quickly: The application’s Householding feature enables advisors to manage relationships for multiple customers living at different addresses to understand and service their clients. This includes referrals, family relationships, related parties, companies, and ad-hoc relationships such as associations, or professional affiliations.

Create and share notes quickly: This feature improves collaboration between teams working from home. It also helps with compliance, demonstrating how advisors are acting in a client’s best interest while providing a record for monitoring and auditing.

Save time: An Outlook Add-in easily logs sent and received emails directly from Outlook, enabling advisors to work faster while ensuring complete client history.

To find out more about this application, how it can help your advisors enhance customer loyalty, or how these tools can yield benefits long after the pandemic has faded from public memory, why not take a look at our offer today? View offer here

The post Unusual Times Call For Solutions That Can Help Build Customer Trust appeared first on NexJ Systems.

More and more financial services firms are beginning to recognize that the ability to understand a customer better, based upon an understanding of individual likes and dislikes, enables them to offer solutions tailor-made for specific needs. This leads to product differentiation, deeper client engagement and, ultimately, customer loyalty.

Hyper-personalization is about understanding needs and intent thoroughly, to tailor not just a service but its messaging and marketing to what a customer expects from a financial services organization. It separates CRM that can communicate with a customer from CRM that genuinely engages with and leverages that relationship.

Strategy Starts With Data

Financial services firms recognize the importance of data, of course, but what matters is not the amount of data as much as the right type of data being collected. This is critical for the implementation of any hyper-personalization strategy, because the effectiveness of data-gathering impacts results about customer journeys in real-time.

Strategy begins with the definition of a desired audience. Events must also be defined, along with the kind of personalized message or action that each of them triggers. Channels also play a role, because they trigger real-time messaging based on importance and a customer’s interaction history. The kind of data available has changed dramatically in recent years, incorporating visual and auditory information where there was once just the written word. This has changed how organizations interact with customers, and also created new opportunities for those willing to invest in technology that can analyse these emerging kinds of data.

Understanding Customers Better Through CRM

Personalization is impossible if financial services firms don’t have the means to understand what high-value customers need on an ongoing basis. This can happen only with analytical tools from powerful CRM solutions that can make sense of structured as well as unstructured data to identify patterns. What matters equally is how these solutions translate and feed relevant information to advisors using easy-to-use dashboards.

A solution like NexJ CRM comes with features such as Comprehensive Customer View, which provides financial services organizations with a 360° view of their customers in an easy-to-view and navigable format. It empowers them to make recommendations instantly without the need for collating data. It also offers features like Relationship Hierarchy, which can aggregate and roll up account and interaction information, giving advisors and bankers detailed information on each client, including transactions, coverage team, related parties, and documents.

Then there are features like our Interaction Journal, which logs all client interactions, including meetings, communications, events, etc. NexJ CRM also allows bankers and advisors to capture and view notes about the contact and collect extended profile information with the help of custom fields and categories. The result is information about the customer that can be used to drive referrals and opportunities to increase proposals and close deals, support business growth by driving client loyalty and, ultimately, provide insightful information that can be used to offer hyper-personalized client experiences.

Customer expectations are always on the rise, and trends show a preference for convenience as well as control. The successful implementation of CRM plays a significant role in meeting these expectations, provided financial services firms know exactly what they hope to accomplish.

Vertical CRM and Customer Expectations

When one refers to personalization or hyper-personalization in financial services, what one is referring to is the deepening of engagement. Vertical CRM solutions understand that industries have specific needs, as do customers engaging with these industries.

In Wealth Management, for example, personalization begins with understanding how and when an advisor should engage with a customer. The nature of this engagement is also important because this could be a conversation about a portfolio alone or market movements in general. In Private Banking, other questions involve how a customer is to be addressed, what their profile is like in terms of objectives and risk tolerance, and what their goals are.

In Corporate Banking, factors that need to be taken into account include an understanding of internal coverage teams, the lending situation and financing needs of clients, cash flow forecasting, prediction and treasury management monitoring, key financial statement data, and benchmark comparisons to competitors. Lastly, in commercial banking, there needs to be an understanding of internal coverage teams, the lending situation and financing needs of clients, cash flow situation and treasury management monitoring, key financial statement data, behavior patterns such as when salaries are paid, and benchmark comparisons to competitors.

Most modern CRM solutions make it easier than ever for firms to offer a personalized approach to customers. What gives NexJ an edge is its Vertical CRM created and deployed specifically for particular financial services organizations. To find out more about what makes us different, why not get in touch with one of our experts today?

The post For Financial Services Firms Interested In Hyper-Personalization, CRM Is Always The Key appeared first on NexJ Systems.

Innovation and a global approach

These are both crucial aspects of what it takes for an institution to become the Banker of Choice for corporate and institutional clients. Banks need to innovate with a global approach to the end-to-end client experience, which is possible only by leveraging the information and relationships available to their local coverage teams.

The aim, always, is to become the best cross-border coverage team, executing a proactive and client-focused engagement strategy. When this is managed successfully, an institution becomes a genuine product partner and solution provider, which contributes towards winning more clients, more deals, and deeper client relationships. Ultimately, it helps deliver the numbers.

First insights, then collaboration

The key to becoming the Banker of Choice lies in the right combination of insight and collaboration. To truly know clients, banks need to understand what they do, their industry, the market conditions affecting them, and their product portfolios in all geographies. Beyond the balance sheet, they also need to know about their client’s non-banking and non-lending interests and objectives, such as their philanthropic and corporate responsibility policies.

All of this information then needs to be incorporated into daily workflows, whether it is account planning, collaborating with product specialists, preparing pitches, or visiting clients. It is only with this depth of insight that a relationship can be grown to a point where an institution becomes the Banker of Choice, helping it increase wallet share to grow the global business.

The importance of CRM

Consider this simple example: A bank wants to be able to leverage a great in-country relationship between a client and the local coverage team, to earn an introduction into another region of the client business. It can use our CRM, which provides rich sales and legal hierarchies, to help coverage teams navigate the global relationship and collaborate on a cross-lines account plan. This is all managed centrally, but executed locally.

Another great example relies upon understanding a client’s legal structure to help banks manage risk and liquidity, or extend preferred pricing terms to a new entity within the structure of an established client relationship. This type of insight helps provide proactive lending solutions that support strategic client initiatives, developing a bank’s role as a trusted partner.

Finally, as the Banker of Choice, the aim is to be involved in each step of a client’s supply chain. For example, a client exporting product overseas will engage a number of suppliers, service providers, and counterparties to the transaction. High-quality data-driven insights, along with that deep client understanding from the right CRM, will help banks understand the trade corridors they are participating in. This helps them identify non-banked referrals, counterparty risk, and ultimately increases their participation in the share of trade between clients and global markets.

With the right tooling, banks can harness the information in our CRM to act on client triggers, identify client motivators, and proactively respond to evolving client needs as a trusted collaborator and solution provider. This ultimately builds connections between the bank and clients, helping them become a global Banker of Choice.

To find out more about how we, at NexJ, help some of the world’s most respected financial institutions build deeper relationships with their customers, get in touch with us today.

The post Here’s How Leveraging Client Information Can Help You Become The Banker of Choice appeared first on NexJ Systems.

If a world leader in Customer Relationship Management software wonders what integration has to do with its product, one has to start wondering if it is in the wrong business. We, at NexJ, have long recognised that our CRM provides real value through integration alone. It allows us to bring together client, account, and company data directly from the source, without the need for replication. It’s why we can claim to offer a comprehensive customer-centric view without the cost and complexity of data duplication.

Speaking of comprehensive customer view, here’s what made us spill a lot of our coffee this morning.

Salesforce announced the acquisition of big data firm Tableau Software Inc for $15.3 billion, the biggest in the company’s history, because it wants to offer “more data insights to its clients.” According to media reports, the decision was made to accelerate Salesforce’s roadmap for its Customer 360 initiative. The aim, apparently, is to help companies “gain a complete view of their customers.”

One could argue that the decision is based less on strategically enhancing its product and more on an attempt to buy growth. Keeping in mind that Salesforce came into being in 1999, one has to wonder if it isn’t a little too late in the day for them to try and understand a customer better.

What is NexJ’s Comprehensive Customer View?

Here’s our elevator pitch: It helps you spend less time toggling and more time actually servicing your clients.

What NexJ’s Comprehensive Customer View has always done (and done really well, going by our list of awards and accolades) is provide bankers with a 360° view of their contacts in an easy-to-view and navigable format. Our CRM empowers them to make recommendations instantly without the need for collating data, by consolidating information across company-wide sources — from profile information and demographic information to activities, and related account information — to create a single view of key data.

Why is Salesforce just waking up to this?

According to press releases announcing its acquisition, Salesforce made the decision to bring on board a partner that could help people see and understand data, in order to help it understand customers better. It refers to big data analytics as a complex process that can uncover hidden patterns, unknown correlations, market trends, and customer preferences, all of which can help companies make better business decisions.

What NexJ CRM has been doing since Day One, in order to drive results, is focus on Relationship Hierarchy. This allows bankers to access detailed information on each client, including transactions, coverage team, related parties, and documents. The functionality also models client relationships, including personal and professional relationships, ad hoc hierarchies, and referrals, all of which can be viewed for each relationship.

We also use an Interaction Journal to log all client interactions, including meetings, communications, and events, allow bankers to capture and view notes about every contact, and provide custom fields and categories for the collection of extended profile information. In short, our Comprehensive Customer View has always been geared towards driving referrals and opportunities to increase proposals and close deals, support business growth by driving client loyalty, enhance face-to-face client experiences, and provide bankers with insightful information.

If you have questions about understanding your customer, figuring how important integration is, or how these pieces come together to offer real value to your advisors, we suggest you give us a call. Or wait for the next 20 years until Salesforce finally figures it out.

The post 20 Years After Its Formation, Salesforce Finally Wants To Understand Customers Better appeared first on NexJ Systems.

What makes data migration critical?

There are all kinds of reasons that drive a financial services organization’s need for data migration. Some do it to overhaul an entire system, others to upgrade databases or establish a new data warehouse, still others to merge new data from an acquisition or other sources, or deploy another system alongside existing applications. Irrespective of why it is done, the process always involves the careful extraction of data followed by a series of functions in preparation, before re-loading into a target location or system.

It is imperative to follow best practices when it comes to data migration because data never sleeps, budgets can be overrun without a competent migration plan, and operations may end up functioning below expectations. When done in a professional manner, data migration can prevent loss of data, allow an organization to become more flexible, and increase revenue.

The NexJ approach to data migration

NexJ CRM approaches data migration via a high-level process that begins with data extraction. We start by using each third-party application’s native export function to extract data to a standard format for an export file such as CSV or Microsoft Excel, manually or using an automated script. The data is then loaded into a staging database for analysis.

This is followed by an analysis that allows us to identify the following:

• Structured data
• Security requirements for data and data sharing
• Entries already loaded into the system
• Unstructured data such as free-form note fields or unknown custom fields
• Attachments such as spreadsheets
• Data that needs to be discarded

The next steps are cleansing and mapping, where data elements in the source systems and NexJ’s CRM are mapped, and rules to transform the source data are determined. Some data must be parsed, merged, altered or standardized depending on enterprise requirements, and this is managed by configuring data manipulation scripts in SQL.

Cleansing, loading, and conversion

Data cleansing is easier to accomplish when data already exists in a semi-structured format and is not yet subject to stricter requirements of the enterprise database.  After cleansing, data is appropriately structured and NexJ platform scripts are used to load and/or update data in NexJ CRM. This then leads to conversion, starting with live data transformation and import into the NexJ business model. Branch based nightly batch process migration during off hours allows NexJ to streamline data inserts, followed by custom ETL procedures that convert migration data into the NexJ core data structures.

The importance of best practices

Data migration is a huge driver of success when it comes to deploying any CRM solution, and is what powers our comprehensive customer view, enabling us to empower advisors and customers with the right kind of information.

NexJ’s practices have enabled us to successfully complete extremely large one-time data conversions, including one of the largest in the history of wealth management firms. This happened when we converted 100 million records for Wells Fargo Advisors. The conversion migrated more than 10 years of A.G. Edwards customer and interaction data and included over 12 million contact and household records and related interactions and notes. This data included everything from contacts, companies and addresses, to phone, email, relationship hierarchy, tasks, schedule items, file attachments, accounts, holdings and transactions. We also have experience engaging with clients to take ownership of as much of the conversion process as they are comfortable with.

We adopt best practices in our approaches to data migration because we understand that world-class tools and techniques can fail without the right process of implementation. To find out more about our processes and practices, get in touch with us today.

The post If You Get Data Migration Wrong, How Will You Get Your CRM Right? appeared first on NexJ Systems.

What exactly is Machine Learning?

Let’s start by tackling what AI means. Without beating about the bush, it refers to a field of computer engineering that focuses on creating systems capable of gathering data and making decisions and/or solving problems. Machine Learning also works towards getting computers to learn and act like humans but isn’t the same as AI. It improves learning over time by being fed data and information in the form of real-world interactions and observations.

The definitions of Machine Learning differ depending upon whom you ask. Some say it is about using algorithms to parse data and learn from it to make predictions. Others believe it is about computers acting without being explicitly programmed or relying on rules-based programming. Still others define it as generalizing from examples to perform certain tasks. Machine Learning also refers to learning processes and how computer systems can automatically improve with experience.

How Machine Learning has evolved

Machine Learning began with the premise that computers could learn without being programmed, based on pattern recognition. It has evolved along with computing technologies, slowly shifting focus by exposing models to new data for them to independently adapt and learn from previous computations to produce repeatable, reliable results. Interestingly, how we define it has also evolved based on our ability to apply complex mathematical calculations automatically to big data (extremely large data sets) faster than ever before.

The goal is to get machines to learn a task, and the process depends upon the kind of task, as well as the kind and amount of data available. This lies at the heart of everything from self-driving vehicles and online recommendations to fraud detection and social media listening tools. There is a common misconception that Machine Learning and automation are the same, but what differentiates them is processing power that enables the quick highlighting or finding of patterns in big data that may be missed by human beings. This makes problem-solving and informed inferences possible.

The limitations of Machine Learning

It all boils down to data, which is the biggest advantage of Machine Learning as well as its biggest limitation. A common problem is overfitting when a bias is exhibited by a model towards training data and new data is not generalized. Then there is variance when new data leads to the learning of random things. Dimensionality occurs when algorithms with more features work in higher or multiple dimensions, making understanding the results more difficult.

The successful testing of training data does not guarantee the success of a Machine Learning algorithm. Models are usually tested only on reserved data set aside, followed by the whole data set. Feeding a learning algorithm more data can solve more problems, but also lead to issues with scalability when an algorithm doesn’t get enough time to learn new data.

What is Machine Learning used for?

If an industry works with large amounts of data, chances are it can benefit from Machine Learning because of the insights to be gained in real time. Machine Learning has made huge advances in healthcare, helped marketing and sales departments by generating recommendations, analyzing buying history and creating personalized experiences, and helped government agencies mine multiple sources of data for increased efficiency and significant savings. It has also helped the oil and gas industries and enabled the transportation industry to make travel more efficient.

Financial services industries stand to gain from Machine Learning in all kinds of ways, starting with its ability to help detect fraud and minimize identity theft. Banks can use data to identify important insights and prevent fraud, while commercial and investment banks can identify investment opportunities. As for wealth management organizations, they can use it to help investors know when to trade, identify clients with high-risk profiles, or pinpoint warning signs of fraud.

Where does Deep Learning come in?

Deep Learning is a Machine Learning technique that tries to teach computers how to learn by example, which comes naturally to human beings. It has evolved in recent years, and often reaches state-of-the-art accuracy that can exceed human-level performance, making it responsible for everything from driverless cars to voice control. The technique involves teaching a computer model to perform classification tasks directly from images, text, or sound, using a large set of labeled data and neural network architectures that contain many layers.

Understanding how Deep Learning works

Deep Learning came into being in the 1980s, as a theory, and slowly became popular with the availability of high-performance Graphics Processing Units (GPUs), cloud computing and large amounts of labeled data that reduced training time. Recognition accuracy is what gives it an edge, enabling it to outperform humans in tasks such as classifying objects in images.

The use cases are plenty, from driverless cars to medical research and electronics. Driverless cars, for example, use it to detect signs, traffic lights, and pedestrians automatically, while the aerospace and defense industry uses Deep Learning to identify objects or unsafe zones from satellites. In medical research, it has been used to detect cancer cells, while industrial automation uses it to improve worker safety by detecting when people or objects are within an unsafe distance of heavy machinery. In electronics, it helps in automated hearing and speech translation as well as home assistance devices.

Machine Learning and Deep Learning: What’s the difference?

Deep Learning is a specialized form of Machine Learning, which is what gives rise to the confusion between the two. There are differences though, starting with how workflows are constructed for each. In Machine Learning, a workflow starts with relevant features manually extracted from images to create a model categorizing the objects in the image. A Deep Learning workflow has relevant features automatically extracted from images and subjected to end-to-end learning where a network is given raw data and a task and learns how to do it automatically.

While Machine Learning algorithms plateau at a certain level, Deep Learning algorithms scale with data and continue to improve with the size of data. Techniques and methods are chosen for Machine Learning based on the size of data being processed, the problem that needs to be solved and the application. A Deep Learning application requires large amounts of data to train the model, as well as GPUs for the rapid processing of this data.

To find out more about how AI, Machine Learning and Deep Learning can help you in the financial services industry, contact us today.

The post Here Are A Few Things That Differentiate Machine Learning From Deep Learning appeared first on NexJ Systems.

AI and Customer Relationship Management

The financial services industry has, unsurprisingly, been able to deploy AI in all kinds of ways. Products such as NexJ’s CRM empower customers to differentiate on service by anticipating customer needs to deliver a tailored customer journey, all enabled by AI.

AI lies at the heart of NexJ’s CRM, allowing it to improve customer experience and revenue outcomes, drive suggested next actions and proactive engagement. Our product also uses rules in marketing, sales, and customer service, as well as to manage processes such as generating customer insights or automating research distribution to clients. Another important cog in our wheel is backward chaining, also referred to as backward reasoning, which is an inference method that works backward from the goal and is used in a number of AI applications. It is, along with forward chaining, one of the most commonly used methods of reasoning with inference rules and logical implications.

The evolution and future of AI

AI began with the top-down model in the 1950s and abandoned it in favor of bottom-up machine learning methods only because technology hadn’t advanced enough to make the former approach possible. Research and computational techniques have now made it possible for a revival of those early approaches.

Today, AI makes workplaces more productive and efficient by capturing data better to discover meaningful insights and deploy those benefits. There is more data-driven decision-making, changes in how data is shared internally and externally, and an increased emphasis on real-time analytics. Also, there are shifts in how employees function as implementers of strategic initiatives based on data parsed by analytic tools.

There are still limitations to data-hungry neural networks, from trouble identifying anomalies to misidentification of objects that have been slightly altered. There are also privacy issues involved when it comes to using large amounts of data that belongs to citizens. There is also the worrying possibility of data being manipulated in unethical ways to suit specific purposes.

The future of AI lies in faster, more efficient systems that will rely on intelligence rather than volumes of data. One of the ways this is being done is through more efficient reasoning, training robots to have a human-like conceptual understanding of the world. Machines are also being taught to navigate the world using common sense, even though this is one of the most difficult tasks for machines.

Where does CRM come in?

The biggest reason why AI has increasingly transformed CRM platforms is that the platforms themselves now do much more than simply function as a repository for customer data. Award-winning CRM solutions such as NexJ effectively empower advisors and financial services employees to engage with customers better. This continuous engagement in real time plays a critical role in delivering service as well as revenue goals, and AI is the foundation of what smarter working environments such as our Intelligent Customer Management platform have been built upon.

Customers are increasingly comfortable with the use of AI, Machine Learning and Deep Learning tools that lead to more effective experiences and interactions with their financial services organizations. They engage with everything from chatbots to voice technology, allowing CRM solutions to introduce more AI into their offerings and workflows.

Managing data more effectively

The quality of data sets is what defines the effectiveness of AI in any CRM solution. It is only by studying patterns of behaviour from data that algorithms can learn and make predictions, thereby cutting down manual processes and boosting efficiency. As customer data becomes richer and more robust, a CRM solution that effectively deploys AI and Machine Learning can drive better insights at lower costs. Machine Learning algorithms can now leverage insights from big data at great speed, empowering advisors with recommendations in real time based on comprehensive views of their customers.

Interestingly, non-traditional sources of data such as call center recordings and posts on social media platforms can also be used to generate deeper insights that, in turn, deepen relationships between advisors and customers.

If you have questions about how AI, Machine Learning or Deep Learning can have a positive impact on your approach to CRM, why not get in touch with our experts today?

The post If Your CRM Is Smarter Than Ever Before, You Can Thank Artificial Intelligence appeared first on NexJ Systems.

Artificial Intelligence
A field of computer engineering that focuses on creating systems capable of gathering data and making decisions and/or solving problems.

Active Learning
This is a training approach in which an algorithm chooses some of the data it learns from. An active learning algorithm selectively seeks the particular range of examples it needs for learning, rather than blindly seeking a diverse range of labeled examples. Active learning is particularly valuable when labeled examples are scarce.

Algorithm
Math formulas and/or programming commands that inform non-intelligent computers how to solve problems using AI.

Artificial Neural Network (ANN)
A model used in AI based on the human brain that consists of neural layers.

Bayesian Neural Network
This is a probabilistic neural network that relies on Bayes’ Theorem to calculate uncertainties in weights and predictions. It predicts a distribution of values and can be useful when it is important to quantify uncertainty.

Bucketing
This refers to converting a feature into multiple binary features called buckets or bins, based on value range.

Classification Model
This is a type of Machine Learning model for distinguishing among two or more discrete classes.

Clustering
This refers to the grouping of related examples, particularly during unsupervised learning. A human can optionally supply meaning to each cluster once all the examples are grouped.

Deep Learning
This is a Machine Learning method that consists of a many-layered Artificial Neural Network and uses many layers of nonlinear processing to extract features from the data before transforming it into different levels of abstraction.

Data Analysis
This refers to how an understanding of data is obtained by considering samples, measurement, and visualization. It is crucial in understanding experiments and debugging problems with the system.

Data Augmentation
This is when the range and number of training examples are artificially boosted by transforming existing examples to create additional examples. It can produce many variants of the original example, possibly yielding enough labeled data to enable training.

Dataset
A collection of examples.

Decision Tree
A model represented as a sequence of branching statements.

Dynamic Model
A model trained online in a continuously updating fashion.

Heuristics
Common-sense rules based on experience.

Input Layer
The first layer that receives the input data in a neural network.

Layer
A set of neurons in a neural network that processes a set of input features, or the output of those neurons.

Metric
A number that you care about, that may or may not be directly optimized in a Machine Learning system.

Machine Learning
A subset of AI that uses algorithms to learn from identified patterns in data, then adjusts actions accordingly, without explicit programming.

Neural Network
A network designed to be similar to the human nervous system and brain that gives AI the ability to solve complex problems.

Natural Language Processing
AI trained to interpret human communication.

Objective
A metric that an algorithm is trying to optimize.

Reinforcement Learning
A method of teaching a machine that involves running scenarios and reporting results, then using the feedback to achieve better results.

Supervised Learning
A learning method that provides the machine with the correct answer ahead of time.

Selection Bias
This refers to errors in conclusions drawn from sampled data due to a process that generates systematic differences between samples observed in the data and those not observed.

Sentiment Analysis
The use of statistical or Machine Learning algorithms to determine a group’s overall attitude, be it positive or negative, toward a service, product or organization.

Unsupervised Learning
Feeding the machine data and allowing it to find whatever patterns it is able to.

Turing Test
Developed by Alan Turing in 1950, this tests a machine’s ability to behave in an intelligent manner that is indistinguishable from human behavior.

For more information on any or all of these terms, or how Artificial Intelligence, Machine Learning, and Deep Learning can help your business, talk to us about our award-winning CRM solutions and Intelligent Customer Management platform today.

The post What, Where & How: A Glossary Of Terms Related To Artificial Intelligence, Machine Learning and Deep Learning appeared first on NexJ Systems.

Choosing between a single and multi-tenant cloud is an increasingly important decision, given lower data storage costs facilitated by cloud-based infrastructure. This allows more financial organizations to eliminate the need to purchase and maintain the infrastructure required to manage big data better. Here’s what you need to know before you make your choice.

Single or Multi-Tenancy?

Why you need a cloud can tell you a lot about whether single or multi-tenancy is right for you, and the pros and cons of each approach can be defined by this need.

It makes sense to start by evaluating challenges related to hardware, software, and human resources, followed by an examination of costs for upgrades, the need for fewer disruptions, decreasing internal IT staff, extensive risk management solutions, and flexibility with vendor selections. The aim, eventually, is deployment that allows an organization to use its resources more efficiently, speed up innovation, and optimize costs.

Understanding what single tenant and multi-tenant means is fairly simple. The former describes a single instance of software and supporting infrastructure serving a single customer, while multi-tenancy refers to a single instance of the software and its supporting infrastructure serving multiple customers. Single tenancy offers each customer an independent database and instance of the software. In multi-tenancy, each customer shares the software application as well as a single database. The data is tagged in the database as belonging to one customer or another, and the software tracks and manages who the data belongs to.

Benefits of Single Tenant Deployment

There are three reasons why single tenancy always scores over multi-tenancy — security, reliability, and customization. Check out this infosheet for more information.

A single tenant approach will always be more secure because each customer’s data is completely separate from the other. In terms of security, data is isolated, which can be the key to meeting compliance needs in a post-GDPR era, especially for payment-related transactions or in the case of regulations related to data residency.

Reliability stems from the fact that customers using an application don’t affect one another. This boosts consistency and performance, because dedicated infrastructure can provide the highest and most consistent level of performance for an application, as opposed to the multi-tenant approach that divides resources between customers. Also, as the only user, all resources in that environment belong to you, which means intensive applications that demand consistently high performance have higher chances of being met.

Single tenancy also allows customers to do more in terms of configuration and customization because the software is unique to a customer and allows for modification. This applies to network and storage infrastructure as well, because it offers users the ability to architect an environment exactly the way they need it, from the amount of memory required to the type of network switching to serve traffic, to local and external storage solutions that best meet capacity and performance needs.

Then there is the “noisy neighbor syndrome,” a reference to problems faced by cloud services when more than one user comes on board. Physical resources are now shared, and the performance of applications may be impacted if other users in that environment have high performance or usage spikes. The failure of one application may lead to a grabbing of resources that affects others, leading to a potential weakening of the entire edifice.

There are other advantages, such as economics. Dedicated hosting may actually be cheaper, in terms of total cost of ownership, as opposed to public cloud depending upon the steady running and number of applications being deployed on a platform. Managed infrastructure and support is another factor, given how complicated this can be and how it can call for additional resources and training. Lastly, a single tenant approach comes with dedicated resources equipped to tackle varying technical requirements and deal with scalability far better than in a scenario where resources have to be divided between many customers.

The Case Against Multi-Tenancy

There are all kinds of reasons why multi-tenancy scores fewer points when compared with single tenant deployment, starting with the “cascade effect” that can occur when a major public cloud service suffers an outage. If and when this happens, it isn’t just individual customers of a provider that suffer but the many customers of each SaaS provider using that platform.

Another possibility that can have an adverse effect on growth is a service provider’s move to delay or remove features to satisfy the requests of larger customers. Backing up databases is also a nightmare, which is why few multi-tenant systems offer users the option.

Finally, there is the issue of data sovereignty, which is increasingly important as requirements vary from country to country and are often subject to modification. The ability of public cloud providers to move data around for purposes of efficiency and recovery could put users in jeopardy in the absence of strict requirements in the users’ contracts. There are implications for security as well, because every customer’s data resides inside a single database, making them all more prone to risks.

The NexJ Approach

NexJ CRM offers flexible cloud-based deployment options that can be tailored to meet any needs and has real experience with cloud-based software for the financial services industry that allows companies in these sectors to combine information from multiple systems to create a singular, comprehensive view of the customer. Our open and flexible architecture can securely integrate into all enterprise systems and empowers customers to customize the solution, while offering them all the flexibility they need.

Our single tenant private cloud deployment delivers the integration, customization, data ownership, and security that financial services organizations need, enabling them to retain ownership and control over data, and leverage the wealth of data across the company, while taking full advantage of the cost savings and agility of cloud computing.

If you have questions about single tenancy and the advantages it offers over multi-tenancy, do get in touch with us.

The post Advantages of Single Tenant Cloud Deployment appeared first on NexJ Systems.

What does CRM have to do with integration?

“We looked and said, this is integration software, what does that have to do with CRM?” That is what the co-CEO of Salesforce said after his company acquired MuleSoft for $6.5 billion last year.  This statement surprised our product and development teams.  His statement sounded strange to us, given that integration is what helps us provide value to our customers and is the key to user adoption.

Integration is embedded in our CRM architecture, allowing financial services organizations to provide users with seamless access to all data and functionality thanks to more approaches (data at source, messaging, synchronization), technologies (DB connectors, security adapters, messaging protocols), and integration points. Integrated data is immediately made available in every aspect of the system.

It is through integration that our CRM provides real value by bringing together client, account, and company data directly from the source, without the need for replication. The risk of conflicting or out of date information is also eliminated. Organizations can present all enterprise knowledge about a customer in a comprehensive customer-centric view without the cost and complexity of data duplication.

Understanding NexJ’s Data Management Services

This is really the engine under the hood, for lack of a better metaphor. What it does is provide application-level integration with external systems that support data access through message-based interfaces. The NexJ Integration Layer enables integration and communication between mission-critical systems via services, messaging, or direct persistence bindings to Oracle, MS SQL, Sybase, DB2 or others with custom adapters.

The Integration Layer facilitates all patterns of message-based integration, supports unlimited integration points, and provides support for custom APIs. It also allows messages of any format (XML, JSON, CSV, Copybook, …) to be sent or received over a variety of channels (HTTP(s), TCP, UDP, MessageQueue, Mail, File, …), and provides a transformation engine to convert messages to and from any of these formats.

The NexJ Portal Server can expose any or all of NexJ CRM’s functionality as portlets, as well as integrate in functionality from third-party portal applications. It provides a context-aware interface, encapsulates an organization’s data model, business rules, security and privileges, and enables communication between portlets to ensure the complete picture and accurate data is presented to end users.

What makes NexJ different?

An integral aspect of what defines our CRM is our data management solutions, where integration occurs at an attribute level and, once resolved, allows the attribute to be assigned to many views. Our data management services natively publishes attributes to associated views so changes in operational systems, updates from streaming services or the latest messages are reflected.  This is currently the backbone of our reporting and searching capabilities.

Does integration matter to user adoption?

This is like asking a helicopter pilot if the rotors are of much use while flying. User adoption is key to the success of any CRM, and integration can improve user adoption rates dramatically simply because users start to recognize that they are getting more out of the solution than they are putting into it.

NexJ’s Integration Layer provides these extensive integration capabilities and tools to support all kinds of integration scenarios, including secure API access to the business model, contract-based web services to integrate with client enterprise SOA standards, flexibility for custom service development, context sharing and UI mashups in the presentation layer, REST API, real-time connectivity to databases and services for a virtualized view of enterprise data, bulk data ingestion and extraction, direct SQL integration and real-time access to any audited changes in the business model.

User adoption drives home the importance of integration to CRM projects. Without integration, are you getting a comprehensive view of your customer and are you getting the most out of your CRM investments? Think about that the next time you speak to your CRM vendor.

The post If Your CRM Doesn’t Take Integration Seriously, How Can It Offer Seamless Access To All Data? appeared first on NexJ Systems.

The co-CEO of Salesforce said he had “some healthy skepticism” about buying MuleSoft before his company’s largest deal ever. “We looked and said, this is integration software, what does that have to do with CRM?” he asked in an on-stage interview at CNBC’s Capital@Work Summit in San Francisco.

For us at NexJ, this report generated some amusement, given how we have always placed integration at the heart of our vertical-specific CRM products.

What makes integration important?

This question, presumably only just being asked by established CRM vendors, is fundamental to how NexJ builds its solutions. Without the integration of enterprise data sources, applications, and external data sources such as social media and news feeds, you cannot incorporate intelligence into customer management. In other words, if you treat integration as an afterthought, you are doing everything in your power to make sure your CRM project fails.

Let’s say tactical products such as siloed CRM solutions deliver intelligent capabilities. How can you be sure your sales and service decisions are correct, without a comprehensive understanding of each client based on the totality of the client’s relationship with your company? How can your client’s expectations for a personalized omnichannel experience be met? Every client-facing user needs to tailor interactions to each client’s unique needs because personalized service builds trust, and trust leads to a greater share of customer wallet.

In other words, without integration, you have no complete customer view and, to cut a long story short, not enough information to offer your customer the expected level of service.

Can integration boost productivity?

It can, it does, and it helps users of NexJ CRM do a lot more. In terms of increasing productivity, integrated systems can be monitored for key client events when they have access to back-office systems and intelligent capabilities like alerts. When users are alerted to events, they can take the right action and deliver personalized service at scale that captures more assets under management and drives more share of wallet.

Integration also enables firms to successfully incorporate machine learning into customer management. This happens with real-time data loading of structured and unstructured data into a data lake and semantic normalization of that data to create a consistent understanding of data conflicts (e.g. legal name, nickname, maiden name). This allows firms to quickly provision data to analytics and machine learning. Integration also enables firms to embed the results in CRM in the form of dashboards and alerts, allowing users to take action on them immediately.

Integration can boost user adoption

The successful implementation of any Customer Relationship Management solution depends upon user adoption, and integration improves adoption rates. This happens when users realize they are getting more out of their CRM than they are putting in. When done right, integration gives users access to massive amounts of data in one view, empowering them to deliver superior client experiences.

Why NexJ CRM takes integration seriously

As I said earlier, we place integration at the heart of our CRM solution, not as an afterthought. Our products are open for integration and have many ways of configuring integration with other systems, from secure API access to contract-based web services to integrate with client enterprise SOA standards. Our goal is always to provide the most flexibility for custom service development which, in turn, allows us to offer customers powerful tools such as our Integrated Desktop.

NexJ’s Integrated Desktop allows users to quickly and effectively leverage enterprise data and applications to better understand, sell to and service their customers. This sees substantially higher productivity than the use of multiple systems, stand-alone CRM, or CRM with limited integration, by turning users into net receivers of information. Our users don’t need to toggle through systems and re-key data, because all the information they need is readily available.

The importance of context

A key aspect of NexJ’s Integrated Desktop is its ability to integrate enterprise and third party data sources creating a powerful, customized, context-aware system. NexJ CRM services automatically manage all communication and context passing between NexJ and third-party portlets, enabling everything to work together as a single integrated system.

Another important differentiator is our comprehensive customer view that incorporates more than the regular CRM data that our competitors deliver by adding data from other sources such as accounts and holdings. While it may appear that competitors have access to this data, they are integrating at the UI layer alone. This means their solution displays data, but cannot use it for processing, decisioning or analytics. What NexJ does is incorporate that other data into a single, comprehensive customer view, enabling a deeper understanding of the customer.

We provide world-class tools and techniques to best support enterprise integration because that’s what we started out with. Data is the beating heart of our CRM and we will be more than happy to tell other vendors why they should start giving it the kind of importance we always have. They know where to find us.

The post If You Want Your CRM To Fail, Just Make Integration An Afterthought appeared first on NexJ Systems.

 “Vertical CRM will be the preferred CRM Choice in the next three years”¹

 
Why is my adoption low?

This is a great question, and one that pops up often in conversation between advisors at financial services organizations. Low adoption means a weaker push to customers which, in turn, leads to a lower share of wallet. Using the right CRM tailored specifically for your industry is half the battle won, which is what makes that decision so crucial.

Like many enterprises, you are likely using CRM software from one of the big vendors who create horizontal solutions (solutions designed to meet the lowest common denominator across the largest pool of customers) to maximize their reach.  These solutions require expensive customizations to meet the specific needs of your firm or your industry. Customizations are also difficult to maintain and often fail to adapt to a changing demographic.  The end result is CRM that does not do what your advisors or bankers need it to do. They are left with no alternative but to find better easier ways of doing their jobs.
 
This doesn’t make sense because I am using the Financial Services Module

Is your module really right for you, or are you relying on the idea of one size fits all? You need to look deeper than what is stated in the brochures. Forrester places CRM into three categories.

What you may be using are simple templates implemented on top of horizontal CRM.  On the surface, they look like they will meet your needs, but you likely had to customize them further.  You are probably better off than if you were using a purely horizontal CRM but, if your bankers and advisors are still unhappy, you need to question your choices.
 
How do I pick the right solution, given so many options?

You should take a look at deep vertical CRM vendors.  Not only are their solutions specifically designed to meet your needs, their organizations tend to have deep vertical expertise, which makes collaboration easier. It also makes implementations cheaper and ultimately more successful.

@kateleggett’s report lists four questions to help understand if a vendor is a lightweight or deep vertical product. I would add a fifth: Do they understand your sub-vertical? It’s not enough to just have CRM for Financial Services; you must have CRM for Wealth Management or CRM for Private Banking. The specific needs for these two verticals are very different and can only be satisfied with different CRM capabilities.
 
Why should I trust your advice?

You really don’t have to. For proof, you don’t need to look at what I am saying. Look at what other firms are saying instead. “Wells Fargo, for example, chose its CRM solution based on the depth of its vendor’s wealth-management-specific capabilities, integration abilities, and capacity to migrate decades’ worth of data.” ¹

For another example, please read this case study from another one of our customers.

Ultimately, your choice of CRM hinges upon features that work specifically for you and the financial service you offer, be it private banking, wealth management, corporate and investment banking, retail banking or insurance. You wouldn’t try putting a round peg in a square hole, would you?
 

• 1) Forrester, “CRM Goes Vertical In The Age Of The Customer”, February 2018, Kate Leggett

The post Not Getting Results? Stop Using Wealth Management CRM For Corporate And Commercial Banking. appeared first on NexJ Systems.

In terms of technology, there are minor differences between relationship hierarchies for corporate and investment banking and wealth management. The big differentiator is the intent. Corporate and investment banks track multiple hierarchies for the same corporations, such as risk relationships versus legal relationships. This allows bankers to get a holistic view of what makes the most business sense for a client. Wealth management needs a single hierarchy because they focus on single households and extended households.
 
How do relationship hierarchies matter to wealth management?

Wealth management depends upon advisors building trust and making sure their clients have all the information they need to make the right decision to grow their wealth. To do this, advisors need to understand more than their customer; they have to understand the household. CRM for wealth management relies on a relationship hierarchy that gives advisors this single view of the entire household — the financial status of a client, the client’s partner and other family members, their financial health, needs, and implications or events that have or can have an impact on their financial situation.
 
How does NexJ CRM manage relationship hierarchies?

NexJ CRM offers wealth management advisors client and organization relationships in a hierarchical tree, allowing them to view details of the parent-child relationships in a household, along with relationships between other members. It lists all products or accounts for which the household has full or partial ownership.

Our CRM offers a host of features engineered to enhance the view of a customer, from an advisor’s point of view. There is a flexible householding functionality, for example, to facilitate policy/account aggregation and modeling of complex relationships including referrals, family relationships, and ad-hoc relationships. A client or an account can be flagged as ‘primary’ to drive CRM workflows or broadcast a specific account context over another.

Advisors can also define their own groups using categories and relationship custom fields, which enable them to view all members of a group using the relationship hierarchy. NexJ CRM can automatically populate or update membership in groups based on information from integrated external systems (i.e., back office or marketing systems) and defined business rules.
 
Details make all the difference

NexJ Interaction Summary provides the ability to roll-up individual client notes to a household grouping level, as well as the ability to maintain notes exclusively at a household level as well. Advisors can instantly view the whole history of their relationship with any selected contact, including scheduled phone calls, meetings, and other interactions since the beginning of the relationship, as well as all documents relevant to the relationship. Our CRM also has extensive experience integrating to third-party systems that are sources of relationship and/or hierarchy data.

To find out more about relationship hierarchies, and why they can empower your wealth management advisors to build trust and offer their clients advice that really works for them, talk to us today.

The post Smart Wealth Managers Service Households, Not Individuals. Here’s How Technology Helps appeared first on NexJ Systems.

 “Vertical CRM will be the preferred CRM Choice in the next three years”¹

Why is my adoption low?

This is a great question, and one that pops up often in conversation between advisors at financial services organizations. Low adoption means a weaker push to customers which, in turn, leads to a lower share of wallet. Using the right CRM tailored specifically for your industry is half the battle won, which is what makes that decision so crucial.

Like many enterprises, you are likely using CRM software from one of the big vendors who create horizontal solutions (solutions designed to meet the lowest common denominator across the largest pool of customers) to maximize their reach.  These solutions require expensive customizations to meet the specific needs of your firm or your industry. Customizations are also difficult to maintain and often fail to adapt to a changing demographic.  The end result is CRM that does not do what your advisors or bankers need it to do. They are left with no alternative but to find better easier ways of doing their jobs.

This doesn’t make sense because I am using the Financial Services Module

Is your module really right for you, or are you relying on the idea of one size fits all? You need to look deeper than what is stated in the brochures. Forrester places CRM into three categories.

What you may be using are simple templates implemented on top of horizontal CRM.  On the surface, they look like they will meet your needs, but you likely had to customize them further.  You are probably better off than if you were using a purely horizontal CRM but, if your bankers and advisors are still unhappy, you need to question your choices.

How do I pick the right solution, given so many options?

You should take a look at deep vertical CRM vendors.  Not only are their solutions specifically designed to meet your needs, their organizations tend to have deep vertical expertise, which makes collaboration easier. It also makes implementations cheaper and ultimately more successful.

@kateleggett’s report lists four questions to help understand if a vendor is a lightweight or deep vertical product. I would add a fifth: Do they understand your sub-vertical?  It’s not enough to just have CRM for Financial Services; you must have CRM for Wealth Management or CRM for Corporate Banking. The specific needs for these two verticals are very different and can only be satisfied with different CRM capabilities.

Why should I trust your advice?

You really don’t have to. For proof, you don’t need to look at what I am saying. Look at what other firms are saying instead. “Wells Fargo, for example, chose its CRM solution based on the depth of its vendor’s wealth-management-specific capabilities, integration abilities, and capacity to migrate decades’ worth of data.” ¹

For another example, please read this case study from another one of our customers.

Ultimately, your choice of CRM hinges upon features that work specifically for you and the financial service you offer, be it wealth management, corporate and investment banking, retail banking or insurance. You wouldn’t try putting a round peg in a square hole, would you?

• 1) Forrester, “CRM Goes Vertical In The Age Of The Customer”, February 2018, Kate Leggett

The post Don’t use the same CRM they use for Hospitality! Use Financial Services CRM appeared first on NexJ Systems.

Continuous Integration lets lots of people work on one project at the same time, while merging their work together in a central place regularly. This maintains the most recent version, so everyone is checking in or out only the latest code.

Continuous Delivery means that when the continuously integrated code is tested and accepted, it gets pushed out to customers. The customers can then choose to manually deploy it or not.

Continuous Deployment means that same continuously integrated code is pushed out after testing and accepting, except that it gets automatically deployed for the customers.

What’s the difference?

Without continuous integration, you can’t have continuous delivery or continuous deployment. Simply put, continuous delivery and continuous deployment provide the product built with continuous integration.

Who cares?

You should! Since continuous integration, delivery, and deployment keep code clean and bug-free, it means you get your features faster, cleaner, and more successfully.

The post Continuous Integration, Delivery, and Deployment: What’s The Difference? appeared first on NexJ Systems.

How does NexJ do it?

For more than 25 years, NexJ’s management team has been delivering customer relationship management software specifically for financial services, allowing us to develop significant domain expertise. Because the financial services industry has unique and specific requirements, NexJ offers best-of-breed features for the sub-verticals within the financial services industry.

The post Vertical CRM: Because When Did One Size Ever Fit All? appeared first on NexJ Systems.

It may sound like a fairly new idea but, conceptually, Continuous Deployment has been around in some form or another for a while now. Before the term came into being sometime around 2002, it was simply about minimizing the lead time between writing a new line of code and the actual use of this code by live users in production. By 2009, the practice was well established as a core element of specialized strategies such as Lean Startup and DevOps.

Delivery, Deployment, and Integration

This may sound confusing at first, but isn’t when one understands what each stage of the Delivery Pipeline is meant to do. First of all, Delivery and Deployment are both associated with DevOps best practices. Secondly, Continuous Deployment is related to the concept of Continuous Delivery. The difference lies in the final deployment step. While Continuous Delivery ensures software is always ready to be deployed into production, Continuous Deployment automates that deployment.

Here’s another thing to keep in mind: A full Continuous Delivery pipeline needs to be in place to reach full DevOps maturity, but Continuous Deployment isn’t always a requirement.

As for Continuous Integration, it performs automated testing of code after every check-in, making sure the entire software codebase works properly with newly committed code. Continuous Deployment is an extension of this, relying on that constant validation for the code to be production-ready. In other words, Continuous Integration is part of the process of Continuous Deployment, making sure an update is safe for deployment and can proceed through phases of automation, culminating in deployment to production. Think of it as one stage slowly facilitating the existence of the other, and you get the picture.

The Benefits of Continuous Deployment

There are a number of reasons why Continuous Deployment matters to businesses. For a start, it constantly improves the quality of a product because small changes are easier to test and easier for users to adopt. It’s great for developers too, because it is more efficient and easier to work on small changes or features than large ones involving hundreds of lines of code. It gives businesses agility by allowing changes to be released multiple times per day.

Other benefits include less overhead, easy identification and faster fixes of bugs, along with an increase in flexibility and experimentation. The ability to constantly test ideas leads to the constant generation of new ideas, which is how CD encourages innovation. Small changes also allow a business to accurately estimate the cost and duration of a large release.

To sum it up, Continuous Deployment offers greater value to end users, minimizes the time between incremental improvements of applications, and leads to more informed decisions based on rapid feedback.

Is Your Business Ready For CD?

If it isn’t, it really should be. NexJ Systems has a few recommended best practices to minimize human error and make sure an organization benefits from the advantages of Continuous Deployment. These focus on the importance of version-control, emphasize the consistency of deployment, and recommend smoke testing to ensure crucial functions of a program will pass basic diagnostics. We also suggest mimicking the production environment as closely as possible and decoupling deployment from release to see how any code functions in a particular environment before it is released to end users.

It is also important to measure success effectively, with metrics and measures that include deployment frequency, the Change Lead Time, Mean Time to Recovery (MTTR), and Change Fail Rate.

Finally, we encourage a long, hard look at company culture, teamwork, and collaboration. Transparency and ownership matters if gaps in processes are to be identified and corrected. This also ensures quick rollbacks in the event of tests failing.

NexJ has long advocated the application of Continuous Delivery methodology. Our award-winning products leverage best practices and tools to meet the delivery expectations of customers, offering to deliver regular updates to both hybrid-cloud and on-premise customer deployments. The intention is to release updates from our framework and application teams at regular intervals and make them available for inclusion into our customers’ individual Continuous Integration pipelines.

What we want to offer, at the end of the day, are solutions that drive productivity and efficiency to increase return on investment. We love talking about this too, every chance we get. If you don’t believe us, get in touch and we can prove it.

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