Intelligent, Connected Banking through SAP Solutions

Process automation is helping all industries transform. Banking institutions in the experience economy are no exception and are anticipated to exceed expectations. As technology becomes more prominent, change is seen in the way banks deal with each other and their customers.

We will be exploring the benefits of using the intelligent bank with SAP and what that means for the future.

Intelligent Technologies…

The next generation of Enterprise Computing will be defined by intelligent technologies, like AI, Machine learning, Big Data, IoT and Blockchain. We will see highly automated processes, massive connectivity and a combination of these technologies working together. Most importantly we will have a lot of data driven insights since data intelligence is the main purpose of digital transformation.

As we take a deeper look into banks shifting from product centric to customer centric, aiming on becoming a “digital bank”. Falk Rieker who is the Global IBU Head for Banking, SAP, stated in a recent interview, that “It all comes down to Intelligent Connective Banking”.

Banks and Technology…

Banks and other financial institutions have the responsibility to take initiatives in bringing financial services to under-served customers, while creating opportunities for individuals, businesses and economies. Banks have to become a platform for digital services. In a whitepaper recently issued by SAP, it stated that “By 2025 the role and revenue streams of banks will fundamentally change. A significant portion of bank revenue will come from non-banking services. Banks will act as platforms for digital services”. The act of shifting daily tasks from humans to automated business systems enabled by machine learning is part of the process in building intelligent banking. Banks will become intelligent enterprises.

SAP for Banking…

The digital economy is changing the way we live, work and function as a global society. The goal of every bank is to become a customer value-oriented digital organization that extends beyond traditional banking. An agile end-to-end, digitized business process for your bank to provide superior customer experience. SAP for Banking & Financial services is very useful for mobile banking, digital banking and other banking technologies. It allows your institution to connect the dots in real-time and convert insights into meaningful action. With SAP for Banking & Financial services you can:

  1. Be customer centric
  2. Manage finance & risk compliance
  3. Reduce cost & complexity

SAP has specifically designed a portfolio for core banking and banking analytics to help your institution attract acquire, grow, manage, report and analyse better than ever. They helped banks reach unbanked customer segments and offer ways to open accounts in non-branch environments.

SAP provides the latest innovations in deployment models offering a set of fully integrated cloud-based business service.

Your Path to Intelligent Banking…

At TYCONZ (SAP gold partners), we are ready to provide you with an end-to-end plan for your banking institution to become an intelligent organization. With proven best practices and deployment options we will optimize your business for continuous innovation guaranteeing intelligent outcomes.

Discover the journey with Us:

  1. Plan – for simplified and innovative SAP intelligent Enterprise Framework.
  2. Build – after years of experience, we have the best proven practices.
  3. Run – on all deployment models and end-to-end on premise support.
  4. Optimize – for continuation and innovation.

Reach more customers and new levels of trust with a real-time intelligent enterprise and integrated financial insight and risk control using SAP solutions for banking. Start running better and winning bigger today!

Get in touch today

SAP SuccessFactors Workforce Analytics and Planning

SAP SuccessFactors Workforce Analytics and Planning

Nowadays, organizations that are expanding to different regions of the world and hiring a diverse workforce at different career levels are operating in conditions of great uncertainty, complexity, and risk. Adopting an evidence-based approach to HR in light of these circumstances, is crucial to business success since it helps provide quick and more informed business and people decisions. Using Workforce Analytics and planning (WFAP) tools help leverage advanced embedded intelligence and insight into all HR processes from recruiting to retention to make smart decisions that support business strategy. In addition to WFAP tools, companies have to use workforce data as an enterprise-wide asset in order to effectively and efficiently drive the strategy and growth of the business through its talent.

Using WFAP solution, you can benefit from the technology and knowledge in people analytics and workforce intelligence solutions. This solution helps you accelerate your organization’s understanding of Big Data in HR and use workforce intelligence strategically to drive business impact. In order to simplify things and better navigate the solution, it is divided into two major solutions: workforce planning and workforce analytics.

  1. Workforce Planning

Is a well-rounded cloud solution that provides strategic workforce and operational headcount planning, it specifically helps with hiring and retaining the right people for upcoming years. In addition, it identifies risks and skill gaps, build what-if scenarios and cost models, optimize headcount plans, and develop strategies to hire and retain the right talent in turn aligning a company’s human resource planning with its business goals.

With Strategic Workforce Planning you can view, assess, and design your workforce to support your business strategies, in addition to avoiding any possible gaps in critical job roles. Moreover, you will be able to forecast potential vacancies in your workforce which allows you to identify and analyze risks early enough to find the suitable strategy for the situation.

On the other hand, using Operational Headcount Planning creates a continuous headcount planning process by allowing you to respond to changing business conditions. Moreover, it creates a smooth flow of data throughout the organization which offers adequate visibility that will help align the headcount guidelines with available budgets.

2. Workforce Analytics

Is a WFAP solution that helps you enhance your understanding of Big Data in HR, in addition this solution provides you with the tools to use workforce intelligence strategically in order to drive business impact. Using this cloud-based solution allows you to gather and validate workforce information, leverage an extensive library of HR metrics and benchmarks, and use collaborative analysis and compelling visualizations to answer key questions about your total workforce. Moreover, with workforce analytics it will be possible to turn data into intelligence that will measure HR strategies, predict outcomes, and recommend courses of action.

Three key features of workforce analytics are:

As a Summary

SAP SuccessFactors Workforce Analytics and Planning solution promotes the ability to organize and analyze workforce data into business intelligence that will in turn lead to business growth. With this solution, you will be able to create targeted action plans based on factual evidence and not intuition, in addition to the ability to answer key questions through manipulation of data using workforce analytics.

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6 Ways Your Sales Representative can benefit from SAP Sales Cloud in Their Daily Tasks

SAP Sales Cloud has been placed as a leader in the 2019 Magic Quadrant for Sales Force Automation (SFA) by Gartner Inc.

The recent acquisition of CallidusCloud makes the solution even more attractive with products covering areas like: Configure Price Quote (SAP CPQ), Sales Performance Management (SAP Commissions), Producer Pro, and Workflow Administration.

This article will show how a sales representative can benefit from this cutting-edge cloud solution in their daily tasks. A typical B2B process will be demonstrated which we will reveal parts of the SAP Sales Cloud solution. There are many powerful functions worth deep diving into such as business analysis, machine learning, and integration. 

On the way to work…

Since SAP Sales Cloud provides a universal experience on both PC and mobile, the sales representative often starts his workday before he even enters the office. While having his morning coffee, the sales rep opens the app on his iPad. He starts with the home screen; looks at upcoming activities, recent tasks, and key performance indicators. These tiles were customized and personalized according to his business needs. In addition, the data can be refreshed instantly, keeping the data up to date.

Inspect leads in the morning…

The marketing department usually creates and manages leads. However, it is the sales reps responsibility to convert leads. As the sales rep browses through the leads, he notices a potential deal marked as Hot.  He opens the lead to check feasibility and decides to convert this lead to an opportunity. However, due to his busy schedule he decides to follow up on this opportunity later. He creates a task to remind himself to contact the business in the afternoon. Because Sap Sales Cloud automatically synchronizes with Microsoft Outlook, he will be notified on his mobile device.

Follow opportunities before lunch

This morning, the sales representative notices in his pipeline that one of his opportunities shows new progress. A client shows great interest in a new type of product, after having received the sales brochure. The sales representative decides to visit the customer to collect detailed requirements. He involves his colleague from the IT department by adding him into the opportunity. He then posts a feed to remind him.  The sales representative estimates the expected deal size and adjusts his forecast.

Complete activities in the afternoon…

The Sales Manager previously set a list of best-practice activities for his sales team to follow on each opportunity. An activity is commonly used to record an interaction with other parties like meetings, visits, appointments, tasks, or more.  The task created by the sales representative in the morning, pops up with a notification that he is supposed to contact his client at 1:00 pm. On the other hand, the sales representative remembers that he will visit another client with his colleague at 3:00 pm. After having a pleasant call with the first client he gets on his way to visit the second one. With the power of speech to text in the mobile app, he quickly adds his notes to the task by voicing input. At 4:00 pm, the sales rep’s visit to the client leads to a good result with all doubts cleared. He puts an end to the appointment with a summary on his way back. The appointments document flow shows the complete route of his sales process.

Propose quote on the way back…

A quote stands for a formal proposal. It contains the products, pricing details, payment terms, delivery methods, and more. A quote is also the carrier for synchronizing with ERP orders. Though an order can also be placed directly within SAP Sales Cloud, the quote is usually the last object that a sales rep is working on. On the way back, the sales rep decides to issue an initial version to his client so that he can hold the opportunity proactively. He opens the opportunity and configures the products for the client. SAP CPQ supports completely customizable capabilities defining steps, dependencies, and restriction rules to fulfilling various business requirements. Additionally, it calculates the price in real time by considering multiple factors like corporate rebates, volume discounts, ongoing marketing campaigns, and more. The result of SAP CPQ includes a structured product list, pricing procedures, and commission information to name a few of the things that are visible for the sales rep. The sales rep re-confirms the output and instantly mails out the document to his customer. Doing so may involve getting a special approval from the sales manager. If special rules have been set for discounts, rebates, and other, note that these topics will be covered in another article. From within SAP Sales Cloud, the sales rep sends out an e-mail with a PDF attachment of the quote. There may be some back and forth if the customer changes his mind. However, SAP CPQ makes the sales representative’s job much easier. He doesn’t need to manage plenty of files or check the dependent rules again and again.

Review performance in the evening…

SAP Commissions is the industry-leading solution for managing incentives and compensation programs. Commissions are a significant reward to a sales rep’s due diligence. The sales rep enjoys viewing his commissions after a hard-working day. He can also check his year to date earnings, and his current achievements using the standard Commissions dashboard.  The sales rep knows what he can earn when configuring the products in each quote. SAP Sales Cloud automatically estimates the commission amount in real-time. Helpful incentivized tips will be shown to encourage sales rep to sell more.

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SAP Concur | What it Can Do For You

SAP Concur | What it Can Do For You

SAP has acquired several companies along the years. It has obtained Concur enterprise back in 2014 for 8.3 billion dollars, making it SAP’s biggest purchase ever! Concur enterprise, now referred to as SAP Concur, is the world’s leader in travel, expense management solutions and invoice solutions. It is a cloud-based solution that helps employees handle their expenses comfortably and simplifies the spend management process in organizations of all industries. Thus, it helps companies save both time and money.

SAP Concur started based on the idea that “There must be a better way”. There must be a better process for employees to handle their expenses, for organizations to analyze those expenses and for companies to reimburse their employees faster. Gone are the days where employees must manually enter expenses into a spreadsheet, print all the required receipts and wait to get reimbursed.

Now, with SAP Concur, You Can…

These are few of the features that Concur offers.

Concur aims to preserve efficiency and reduce cost in an organization. This is due to integrated solutions that are customizable to meet the needs of the customer within the Concur platform.

Learn how automating your expense, travel, and accounts payable helps you thrive and grow.

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Predictive Analysis – What Can Predictive Analytics Offer?

What is Predictive Analytics?

Predictive analytics encloses a diverse set of statistical methods from data mining, modeling, and machine learning that analyze present and past information to make reliable predictions about upcoming or otherwise undetermined events. In business, predictive models take advantage of patterns discovered in previous and transactional information to analyze risks and opportunities. Models use links between many data elements to grant assessment of risk or potential accompanying a particular set of circumstances, guiding decision making for user transactions.

Why is Predictive Analytics Important?

Predictive analytics is unique due to its ability to foretell a predefined pattern of behavior at an individual level. Companies can define specific conditions, which when met would let an analyst determine an individual’s behavior such as a customer’s probability to visit a store again, or the chance of a voter to be persuaded into electing a particular candidate. A predictive score can be generated to each person as to the actions that might be important for an organization to predict. These actions may improve drive operations for a business or simply offer smart insights on upcoming events.

Predictive analytics is not the same as traditional business intelligence frameworks in that it follows a proactive approach to collected information. Traditional business intelligence frameworks utilize data to learn about a customer or to find trends in businesses. Predictive analytics determines how a customer will act in the future and how that customer may behave in response to various touchpoints. The difference stands in the capability to directly identify patterns in data that showcase conflicts and pinpoint opportunities. Predictive analytics strengthens companies to organize for the future, which can transform uncertainty into a usable action with high probability.

The capability to forecast and impact the future is a lucrative opportunity and organizations like IBM and SAP are good examples of companies that rely on this initiative. IBM utilizes predictive analytics software to raise profits, obstruct fraud, and even calculate the social media impact of marketing campaigns. SAP grants customers the ability to act on big data and provide insights into new opportunities and any hidden risks. Predictive analytics also extends beyond these two companies and to many industries.

What are Some Use Cases of Predictive Analytics?

The following are brief examples of some ways that predictive analytics could be used. More detailed examples will be covered in future articles.

Predictive Analytics in Marketing…

The main goal of almost every marketing campaign is to maximize the returns. Predictive analytics has made it possible to acquire real-time information from several customer touch-points, both static and dynamic, to improve the effectiveness of upcoming marketing projects. Predictive techniques can be used to gain insights into most efficient ways to assign budgets to a media mix or understand the likely effectiveness of a potential campaign. Highly sophisticated market strategies, market segmentation, real-time pricing, and contactless conversions have all been possible due to predictive analytics in recent years.

Predictive Analytics in Healthcare…

The healthcare industry has gone through a massive change ever since this sector got introduced to information technologies. Predicting plausible diseases and patients who are at high risk is a main benefit that machine learning and big data have offered, making patient-care a joint task between the healthcare providers and the patients. Professionals can use predictive analytics to analyze a patient’s information and forecast the potential for illness. Healthcare is more about preventing illnesses than about treatment, and the more the healthcare industry works with patients the more stable the sector will be to stop several potential sicknesses in the future.

Predictive Analytics to Identify Fraud…

Fraudulent events cost both organizations and customers billions of dollars every year. To add to the issue, trying to demonstrate that claims are fraudulent can, in turn, further increase expenses incurred. For this reason, many companies have been relying on machine learning and predictive models to identify fraud situations. This helps showcase more claims that should be researched by human auditors. The method doesn’t just reduce the costs of human hours but also increases the opportunity to reclaim stolen dollars from fraudulent claims. Once the algorithm becomes fine-tuned, the accuracy and rate at which a team processes fraudulent claims will dramatically increase.


Predictive Analytics gives an opportunity to assume future trends and allows organizations to act beforehand. Better decision making leads to success. Previously, decisions were based on intuition. As data has become more available, making completely intuitive decisions has become rare. As a result, data-driven decision making has become more prevalent to ensure a reasonable path for success.

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Digitalist Flash Briefing: Transforming Corporate Banking With Intelligent Technologies

Bonnie D. Graham

Blog source:

Today’s briefing looks at how intelligent technologies can help banks survive challenges from customers and competitors.

  • Amazon Echo or Dot: Enable the “Digitalist” flash briefing skill, and ask Alexa to “play my flash briefings” on every business day.
  • Alexa on a mobile device:
    • Download the Amazon Alexa app: Select Skills, and search “Digitalist”. Then, select Digitalist, and click on the Enable button.
    • Download the Amazon app: Click on the microphone icon and say “Play my flash briefing.”
  • Other apps:  Alexa | iTunes |  Google Play |  TuneIn |  Stitcher |  VoiceAmerica

Find and listen to previous Flash Briefings on

About Bonnie D. Graham

Bonnie D. Graham is the creator, producer, and host/moderator of Game-Changers Radio series presented by SAP, bringing technology and business strategy discussions to a global audience. Listen to the series flagship, Coffee Break with Game-Changers.

Data Management Challenges For Financial Services

Karsten Egetoft

Blog source:

Part 1 of the “Data Management For Financial Services” series

Unrelenting pressure from non-traditional players is driving financial services organizations to digitally transform themselves. To become data-driven enterprises, banks and insurance companies need to address three key data management trends: data volume, ubiquity, and user demands.

Mobile apps and devices generate massive volumes of data from new sources, such as images, audio, and video. Combined with new business models and actors in the value chain that increase the digitalization of financial services, this new data provides enterprises with opportunities to gain additional insight and value.

Today, data is everywhere. And financial services companies need to capture it all: customer information, financial transactions, product and service purchase histories, customer journeys, marketing campaigns, service inquiries, market feeds, social media streams, Internet of Things (IoT) streams, software logs, and text messages (including emails and SMS), plus those newer sources.

User demand for this data is rising. In today’s financial services enterprise, it’s important to recognize that every employee is really an analytics user who needs:

  • Decision support, allowing users to base decisions on empirical evidence rather than gut feelings
  • Trust in the security and accuracy of data
  • The ability to proactively anticipate and influence business outcomes by paying attention to new and increasingly forward-looking signals
  • Self-service access to data and easily usable analytics tools
  • Speed and intelligent information equivalent to what users experience with personal consumer technology

Impact of rising complexity

These expanding data sources and volumes create a new challenge: an increasingly complex enterprise data management landscape comprising hundreds of silos. It’s not unusual for firms to deploy multiple data lakes, data warehouses, operational applications, mobile apps, online apps, call centers, IoT sensors, and analytics solutions. Data can be located in hybrid environments, on-premises, and in the cloud.

To reduce complexity, companies need to combine their existing and new data into a single data universe. Universal data helps firms enhance visibility, delivering insights that can improve efficiency, automation, and growth. By converting data into insights, organizations can become intelligent enterprises.

For many financial services enterprises, however, a single data universe is still an aspiration. More often, data resides in multiple siloed environments (see Figure 1). Because data is not meaningfully connected across these silos, it has become less accessible – compromising insight into customers, partners, products, sales channels, and financial performance.

Figure 1. Siloed data environments

Worse yet, data silos often are reinforced by organizational silos. For example, the group managing the Hadoop data lakes are not the same people who manage the cloud storage. And too often, teams use different tools and rarely interact with one another.

To overcome the challenge of multiple data silos, financial services companies tend to build large enterprise data warehouses. The reality of rapidly changing, growing data sources means that traditional enterprise data warehouses can no longer keep up with the analytics needs of the business. Here are a few reasons why:

  • Solutions often cannot deliver real-time insights, as data capture and production of analytics are processed in batch.
  • Data typically is replicated across multiple data marts built for specific reporting purposes, reducing transparency and requiring time-consuming reconciliation efforts.
  • Solutions often cannot handle the growth of new data types.
  • Data linage is challenging when users have no insight into data origins or any transformations applied, including data replication and consolidation across multiple data marts.
  • Responding to business needs is slow and costly, especially considering the growing number of internal customers demanding new analytics and insights.

These challenges are further complicated by the increasing number of data consumption endpoints, the business processes and analytics solutions that require real-time data access to support decision-making.

A critical missing link

When considering data management challenges, financial services companies need to address two types of data:

  • Enterprise data – High-quality, structured data with clear governance, security concepts, and lifecycle management practices. It is typically captured in relational database management systems. Examples include customer information, contractual agreements, and financial transactions.
  • Big Data – Characterized by high volumes of semi-structured or unstructured data, such as text files (including emails, social media streams, or SMS messages) as well as image, audio, and video files. It is typically captured in data lakes such as Hadoop or cloud object storage systems, which are substantially cheaper than traditional database management systems. However, these platforms typically lack comparable enterprise governance, security, and lifecycle management.

Current data management landscapes often fail to create a link between the enterprise data and Big Data worlds. This makes it difficult to operationalize data science and derive the valuable benefits of data-driven analytics. As a result, users may struggle to search massive haystacks of data to find the hidden needles of actionable insights. This missing link also prevents organizations from delivering data-driven innovations, which are a core ingredient of digital transformation.

How can financial services organizations address this missing link and what should they look for in a modern data management platform? Read my next blog and the rest of the series to learn more.

Karsten Egetoft

About Karsten Egetoft

Karsten Egetoft is a senior solution architect of the Financial Services Industry Unit at SAP and a senior-level financial services professional and SAP veteran with over 20 years’ experience. He is globally responsible for driving the success of SAP data management solutions for financial services with a focus on the go-to-market and solution strategy. Karsten is an expert in data management technology and analytics use cases in financial services. He is based in SAP headquarters in Walldorf, Germany. Follow Karsten on Twitter @KarstenEgetoft and LinkedIn.

Data-Driven Analytics

Data-Driven Analytics: Practical Use Cases For Financial Services

Karsten Egetoft

Blog source:

Part 3 of the “Data Management For Financial Services” series

By capturing and leveraging massive volumes of data, financial services companies can capitalize on new data-driven business opportunities. As discussed in my last blog, the first step toward realizing this goal is to create a solid data management foundation that supports the analysis of both enterprise data and Big Data.

Once this foundation is established, you can begin implementing machine learning algorithms to support automated decision-making and data-driven process optimization – helping you generate insights that create better customer experiences, improve operational efficiency, and drive sales (see Figure 1).

Figure 1. Preparing for data-driven analytics use cases

These insights can help you identify the best use cases for data-driven analytics within your business. Following are some of the most effective use cases deployed by financial services industry leaders.

Improve the customer experience and drive growth

Machine learning algorithms can enable the following customer-facing use cases:

  • Deliver personalized services based on customer profiles, using data on customer satisfaction, preferences, buying history, demographics, and behavior to better understand their needs. These insights can help you tailor products and services and deliver highly targeted, personalized offers that improve customer satisfaction and retention.
  • Recommend the next-best product to buy using deep insights to accurately cluster customers and prospects into segments according to their profiles and probable needs. Use these insights to develop cross-sell and up-sell opportunities, which can be triggered at the right time through the right channel.
  • Provide robo-advisor services to help customers with investment decisions by offering a peer-to-peer comparison or customer-specific portfolio advice. A robo-advisor can manage portfolios without human influence, basing investment decisions on algorithms developed from customer risk profiles.
  • Automate personal finance management, which gives customers a holistic view of their finances and provides forward-looking advice. Identify investment opportunities based on customer risk profiles and available funds, propose remortgaging a house loan, or use previous spending data to understand trends and encourage better customer savings habits.
  • Offer chatbots that address customer needs and inquiries, walk customers through process steps, provide predictive messages and behavior insights, and automate tasks such as money transfers or balance inquiries. Over time, chatbots collect behavioral data on users and learn the appropriate replies to user requests.

Optimize risk controls and business outcomes

The following use cases demonstrate how machine learning algorithms can help protect your business:

  • Provide early warning predictions using liability analysis to identify potential exposures prior to default. You can also work proactively with customers to manage their liabilities and limit bank exposure.
  • Predict risk of loan delinquency and recommend proactive maintenance strategies by segmenting delinquent borrowers and identifying self-cure customers. With this insight, banks can better tailor collection strategies and improve on-time payment rates.
  • Improve collection and recovery rates. To minimize delinquencies, credit card issuers can use account pattern-recognition technologies and develop contact guidelines and strategies for delinquent accounts.
  • Predict risk of churn for individual customers and recommend proactive retention strategies to improve customer loyalty. Identify at-risk customers and act quickly to retain them.
  • Detect financial crime such as fraud, money laundering, or counter-terrorism financing activities by identifying transaction anomalies or suspicious activities using transactional, customer, black-list, and geospatial data.

Automate business processes

Machine learning streamlines processes in the following use cases:

  • Algorithmic trading based on deep learning, high-performance computing, and geographical positioning can deliver subsecond timing advantages in automated trading.
  • Customer credit risk evaluation uses application and customer data for automated real-time credit decisions based on information such as age, income, address, guarantor, loan size, job experience, rating, and transaction history.
  • Customer complaint management uses data from various interaction channels to understand why customers complain, identify dissatisfied customers, find the root causes of problems, and rapidly respond to affected customers.
  • Inquiry response employs data from customer engagement channels to automatically route and respond to inquiries while spending fewer resources on manual tasks.

Improve operational efficiency

Machine learning can help you predict operational demand based on historic data and future events. With this insight, for example, you can anticipate call center traffic volumes or predict demand for cash at ATMs.

Self-service analytics for everyone

As financial services companies gain value from data-driven analytics, they must embrace self-service capabilities that put data in the hands of employees. Workers across all levels of the organization should be empowered to drill into the data, using self-service analytics to unleash innovation, create organizational enthusiasm for using data insights, and develop new ideas on monetizing existing data assets.

Data-driven analytics are key to the current and future competitiveness of financial service companies. We are just at the beginning of a wave of innovation based on data and powerful analytics, with much more to come.

Organizations that invest boldly in becoming more data-driven – by developing the right data management platform and a clear data analytics strategy − will be winners over the long term.

For more information on today’s data management challenges, read the first blog in this series. To learn more about a modern data management approach for financial services companies, read the second blog in this series.

Something your business needs?

Get in touch

Karsten Egetoft

About Karsten Egetoft

Karsten Egetoft is a senior solution architect of the Financial Services Industry Unit at SAP and a senior-level financial services professional and SAP veteran with over 20 years’ experience. He is globally responsible for driving the success of SAP data management solutions for financial services with a focus on the go-to-market and solution strategy. Karsten is an expert in data management technology and analytics use cases in financial services. He is based in SAP headquarters in Walldorf, Germany. Follow Karsten on Twitter @KarstenEgetoft and LinkedIn.

Unlocked – A New Chapter of Customer Experience

Experiences drive customer expectations, and brand perceptions. They can make or break an organization’s success.

Every organization needs tools to help them better understand the beliefs, emotions and intentions of their customers. However, to achieve breakthrough results, organizations need more than a system of record – they need a system of action designed to intelligently use both X, and O-data to improve customer experiences.

Together, SAP and Qualtrics will deliver a unique end-to-end experience and operational management system to power the economy. Combining Qualtrics’ experience data and insights with unparalleled operational data (O-data) from SAP software will enable customers to manage supply chains, networks, employees and core processes better.

This powerful customer experience platform will offer organizations the tools they need to build better experiences, better understand their customers and adapt to their rapidly changing expectations. Together, this will make it easier than ever for organizations to combine X- and O-Data, gain actionable insights at every step of the customer journey and deliver personalized customer experiences.

That’s why combining Qualtrics and SAP is so powerful. Organizations will now be able to listen, gain insights on what to do, evaluate the impact on the company, and most importantly, act to deliver unparalleled customer experiences.

  • SAP Qualtrics CX for Commerce
    • Organizations can now gather feedback across all digital interactions to provide premium experiences that increase conversion, loyalty and satisfaction.
  • SAP Qualtrics CX for Sales
    • Organizations can now assess the strength of their client relationships, improve the sales productivity of their Account Executives, and provide a premium sales experience.
  • SAP Qualtrics CX for Customer Service
    • Organizations can now design and deliver seamless service experiences to customers across every support channel including phone, chat, in-person, and digital
  • SAP Qualtrics CX for Marketing
    • Marketing teams can now collect feedback to better understand their customer segments, target audiences, and customer behavior.

Personalize customer experiences across your business with us!

Get in touch today

Analytics and BI

Why Have Analytics and BI Become Necessary for Competitive Advantage?

Data analytics is the qualitative and quantitative method and procedure practiced to improve efficiency and organizational outcome. Information is collected and organized to determine and study important data models and samples which differ based on the specific requirements of each company.

Nowadays, and especially in highly competitive industries, companies might have no choice other than to eat a piece of the pie from other competitors to enlarge their growth and market share. Business intelligence has already become a multi-billion-dollar industry. In the following years, business analytics will outstretch to almost every potential user, especially as we move deeper into the era of the Internet of Everything. So, what does all the complicated and time-consuming analysis of data have to offer to businesses? Beside the many benefits, the main value acquired from relying on business intelligence is gaining powerful decision making. Making smarter, faster, and well-rounded decisions.

Decrease Cost

BI is a clever investment especially in cases where companies are under the pressure of tight budgets and downturns. With a wider view of the company’s statistics, the opportunity to locate areas of waste is gained. To make the idea simpler, take personal trainers who aim to help their clients at the gym for example. They try to make sure that the client does the workout properly and does not use up energy that would not return the desired outcome. BI tools help identify these areas of waste, so costs are reduced for more important business activities. This includes the cost of the time spent in making decisions, organizing work plans, handling studying the past and present, as well as the financial aspect of the company. By cutting expenses and allocating resources more efficiently, the organization gains the ability to improve the company’s core and manage a more effective team with faster and better choices at the forefront.

Increase Revenue

If used properly, analytical insights can provide a wide array of important information that may allow a company to expand its revenue. The insights range from approaching the right customer at the right time, maximizing client loyalty, building solid personal interactions with users, study customer behavior, plus many other advantages depending on the type of business. Also, another important aspect is customer defection which help understand the reasons for customers preferring competitors. Sales managers rapidly discover the customers that are purchasing and what products are in decline. With BI, it is easy to visualize consumer spending patterns by observing purchases that take place daily, weekly, monthly, or annually.
Observing these insights highlights new sales opportunities, which can also drastically increase the effectiveness of cross-selling complementary products. A store may sell desktop computers and monitors. A customer may need to replace only the computer, but by bundling both products together at an attractive price point, an incentive to purchase the pair is offered. If a distributor finds that only computers are being sold to customers, it may be that other competitors are providing better deals on monitors from another supplier. So, in short, companies can identify sales opportunities better.

Predictive Analytics

Predictive analytics is the use of stored information and statistical algorithms to determine the chance of future results. The main goal is to go beyond knowing what has happened and provide the best assessment of what will happen in the future. If a company decides to go beyond plain analysis and delve into predictive analysis, it means that the organization is serious about its competitive edge. Most executives have solid knowledge of the overall shape of their organization. However, many find that after implementing a BI solution, new information is always discovered. Having all the data allows users to benefit from unidentified opportunities and to handle unrecognized problems before having serious impact. Predictive analysis provides a huge advantage because the company will no longer react to alternating market conditions after occurring but will predict these changes. In this case, revenues can be increased while simultaneously decreasing costs.

To Conclude

A game plan is required while moving towards the future. Whether it involves short-term goals that must be achieved or if it’s a long-term strategy that must be met, reliance on business intelligence can go a long way. Having the ability to view a company’s full information in a well-displayed, easily readable format gives the flexibility to make crucial choices with a lot more confidence than simply looking at old school spreadsheets.
Time is money and relying on business intelligence will lead the road to much quicker decisions and more informed choices. This allows management groups to hit maximum profits, while trimming expenses and acquiring a valuable understanding of the complicated financial dynamics that create an organization.

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