Is data a strategic asset and not just another commodity in the world of fast-paced business today? The answer is that the ability to collect, analyse and derive insights from data is now more important than ever for any company hoping for an edge over its competitors. Data analytics play a big role in creating profitability due to its informational value. Data analytics helps boost revenue streams while minimising costs so that businesses can earn more. This article will explore how data analytics can transform a business in terms of supporting its decision-making process.

Improving Customer Experiences

Data analytics provides useful insight for businesses to achieve service excellence by providing them guidance on how to meet consumer expectations and demands. It cannot be denied for a fact that customer-centricity nowadays is the key to success for many businesses. Data analytics enables companies to gain deeper insights into customer behaviours such as what they like or dislike, what drives their dissatisfaction, and more. 

By employing professionals who have completed Graduate Certificate in Business Analytics and similar qualifications who are well-versed in tools to analyse customer segmentation, sentiment analysis and predictive modelling among different customer demographics, companies can customise unique experiences for each of the target market segments based on their personal needs at each stage of the customer lifecycle.

For example, an online store might send personalised suggestions through email after studying purchase history and the feedback provided by clients. The understanding in this matter not only helps the company to increase sales but also to increase the satisfaction levels of each of its buyers. In another case, Airbnb, a web-based platform that specialises in offering short-term property for travellers uses big data analytics technology platforms such as Airbnb coupled with machine learning algorithms to reach across multiple touchpoints to create personalised guest experiences, thereby driving loyalty through repeat purchases. 

Supporting Product Development

Data analytics help businesses answer pressing questions: “What should their company do when they want to create a successful product? What kind of information will be useful for businesses at different stages during the production of goods or services? How can organisations get the necessary information to prevent risks?” 

Fundamentally, data analytics allows companies not just to create innovative products but also to adjust their current performance indicators using up-to-the-minute feedback from customers. The application of these actions is made through a thorough process of using structured input information based on various methods for improving existing products or developing a new product. Due to the benefits of data analytics, companies can move quickly from laboratory tests into the marketplace while raising adoption rates and increasing competitive advantages, knowing that their products are up-to-date and meet market demand. 

Enhance the Decision-Making Process

In today’s complicated business world, good decision-making skills are necessary to sustain and build any business. But how do you make an effective business decision? Most of the time, when there is too much data available, decision-makers may find themselves overloaded with information. This makes it difficult for them to conclude anything due to certain facts presented from within their organisation or from outside market analysts. At that point, data analytics can provide business owners with insights they can act on as well as suggest what should be done based on accurate figures.

Advanced data analytical methods such as machine learning algorithms and Natural Language Processing (NLP) can provide business owners with highly reliable information to support their decision-making. For instance, sales managers may need to use predictive analysis techniques to forecast market demand. Predictive data analytics helps the sales manager analyse historical sales, combined with prevailing environmental conditions, to predict how much sales the company can achieve in a period.

On the other hand, analytics can run multiple testing scenarios to prevent future risks. Starting by making hypothetical scenarios of those scenarios that have not yet happened, comparing results received from different samples, and finally suggesting the most suitable solution for companies. The financial industry is one of those industries that greatly benefit from the applications of data analytics for risk management. Data analytics help to avoid any loss by suggesting preventive regulations for those factors that can potentially impact the financial system’s well-being. 

Data analytics make it possible for organisations to make real-time decisions. In fact, in big-size companies, data tends to come from different sources, so, gaining the ability to receive and understand the information from all sources to make immediate decisions is a top priority. For instance, a retail chain may employ real-time analytics to optimise pricing strategies depending on factors like competitor pricing, demand elasticity and inventory levels. Equally, through predictive analytics, a manufacturing company can predict machinery failures and schedule maintenance proactively to reduce downtime and operational costs.

CONCLUSION

Translating information into action is the biggest game changer for gaining profits. By using data analytics to improve the customer experience, support product development, and simplify decision-making processes, organisations will be able to open new revenue channels and cut down on their costs while increasing efficiency. As the future success of the business lies in everything from a data perspective, it is crucial to keep in mind that businesses may soon need to leverage data analytics insights to prepare better for future difficulties and remain financially stable without being impacted by the economic environment. 

Mortgage rates are not directly set by the government but rather by lenders influenced from factors such as the Bank of England base rate of interest and general Inflation. Events such as elections can impact interest rates due to the uncertainty.

Data from Mojo Mortgages helps us to understand the changes in mortgage rates since the announcement of the election 2024.

• The average swap rate for a two year fixed deal rose form 4.5% to 4.7%, this has fallen back to 4.5% now.
• The average fixed mortgage rate across five of the biggest lender rose from 5% to 5.1%.
• Certain lenders such as, HSBC, TSB and Barclays have increased rates on selected mortgage products.

The chance of Mortgage rates falling

Anyone trying to buy a home for the first time, those renewing their mortgage or anyone with a standard rate mortgage will be hoping for a decrease in mortgage rates after the election, but what are the chances of this happening?

The Bank of England have remained consistent with their interest rates at 5.25% since last August due to the high inflation. However, with inflation having fallen to 2.3% recently many have believed there is a high chance for a reduction.

The Bank of England will next announce their new base rate on the 20^th June.

They must feel the inflation rate is steady and that uncertain election matter won’t drastically change anything for a reduction to occur on this next announcement.

With wage growth remaining strong and an uncertain outcome for the election the come, the Bank of England could very well hold off on any reductions.

How mortgage rates affect the election

Mortgage rates are one of the top factors which impact voters choices. When political parties include policies to bring mortgage rates down when they have been soaring in the past parliament this could persuade votes.

Since the conservatives have been in parliament since 2010, Mojo Mortgage reveals that house prices have increased 65%.

House prices affected by the election

New sales tend to stall from the announcement of an election as the feeling of uncertainty for the housing market persuades buyers and sellers to press pause and wait for the outcome.

The average age of a first time home buyer has increased over the years with the challenge of getting on the property market increasing with prices. If mortgage rates do go down then buyers could gain confidence boosting the market.

With both Labour and Conservative parties being fairly vague on what will happen to the housing market there has been a smaller impact than in previous election years, Zoopla have found.

There’s been a real surge in interest in investing over the past decade, although data from GFLEC suggests that the proportion of younger people who are financially literate is actually under 50%. This means a lot of would-be Warren Buffets are making bad decisions with their money on a daily basis.

Accurate data can flip this issue on its head – but first, you need to appreciate why it’s necessary, and what can be done to harness it. So let’s unpack both of these things in quick succession.

The Basics

Put simply, accurate data is the bedrock of any successful investment strategy. Without precise information, investors risk making misguided decisions that could lead to significant financial losses. And based on data from an NYU researcher, there’s a 25% chance of seeing your investment portfolio in any given year, even if the long-term trend is for growth.

So with that said, here are several reasons why accurate data is indispensable for investing:

• Risk Management: Accurate data enables investors to assess risks effectively. Knowing historical performance, volatility metrics, and economic indicators helps in identifying potential threats and avoiding bad investments.
• Market Analysis: Understanding market trends requires up-to-date information on stock prices, trading volumes, and other relevant metrics. Investors can forecast market movements more reliably when they base their analysis on accurate data.
• Valuation Accuracy: To determine whether a security is overvalued or undervalued, one needs precise earnings reports, balance sheets, and cash flow statements. Accurate valuation prevents overpaying for assets.

Overcoming the Data Accuracy Conundrum

We’ve established that you need accurate info to make investment decisions – but accessing and wrangling this all-important data is easier said than done, or at least it used to be. And since there’s over $100 trillion invested in stock exchanges globally, with the largest of the bunch representing $28 trillion in assets alone, it might seem like looking for a needle in a haystack.

Luckily, advanced solutions for gathering, cleaning, and demystifying extensive datasets have simplified investment strategizing significantly in recent years. Here are a couple of examples of how this is done:

Web Scraping Techniques

Web scraping allows investors to extract large volumes of data from various online sources automatically. This method can pull financial reports, stock prices, news articles, and social media sentiment in real-time. Benefits include:

• Automation: Tools like Python's Beautiful Soup or Scrapy can automate the extraction process. Automated scripts reduce manual effort and increase accuracy by minimizing human error. Meanwhile, solutions like ZenRows are capable of sidestepping anti-scraping measures put in place by many web-based data sources, allowing up-to-the-minute info to be harvested without disruption.
• Customization: Tailoring scraping algorithms to target specific data points ensures that investors receive exactly what they need without unnecessary clutter.
• Scalability: Web scrapers handle vast amounts of data quickly. They allow for real-time updates which are crucial in fast-paced markets where delays can lead to missed opportunities.

API Usage

APIs (Application Programming Interfaces) provide structured access to precise data offered by financial institutions, stock exchanges, or market analysts. The advantages are as follows:

• Reliability: APIs from reputable sources ensure that the information is accurate and up-to-date. Services like Alpha Vantage or Quandl deliver high-quality datasets essential for informed decision-making. Whether you’re into online investments or more traditional avenues for growing wealth, this will serve you well.
• Ease of Integration: Most APIs come with detailed documentation making them easy to integrate into existing systems or analytics platforms. This seamless integration speeds up the analysis process.
• Versatility: With APIs, users retrieve a wide range of data types - from historical price trends to economic indicators - all through standardized requests.

Wrapping Up

Dealing with data as part of putting together solid investment strategies is understandably intimidating, but as we’ve shown it’s not only essential in this context but also easier to achieve with the right tools. As more people are informed and empowered by modern tech, investment mistakes should be less commonplace.

In today's era, technology plays a significant role in streamlining operations and enhancing security measures across different industries. The increasing use of identification (ID) scanning apps offers businesses a way to verify customer information. However, businesses need to address considerations to ensure adherence to regulations and safeguard both their customers and themselves.

Privacy Consent

When incorporating an ID scanning app like MicroBlink, businesses must consider the privacy laws applicable to their area. These laws regulate data collection, utilization, storage, and sharing. Depending on the business's location, there might be requirements dictating the use of customer information.

Obtaining consent from customers before scanning their IDs is an aspect to address. Businesses should clearly outline the reasons for collecting data and how it will be utilized. This transparency fosters trust with customers and ensures compliance with legal mandates.

Data Protection Measures

Implementing ID scanning apps involves handling and processing data by default. To comply with regulations and uphold customer confidence, businesses need to establish data security protocols.

Encryption stands out as an element of data security for these apps. Encoding customer data into formats that can only be deciphered by authorized individuals ensures customer data protection. Businesses should regularly assess their security measures to pinpoint any weaknesses or areas needing enhancement.

Data Preservation Constraints

Keeping customer data longer than necessary poses a threat to both businesses and the people involved. This increases the risk of access or misuse of details. Thus, it's vital for businesses utilizing ID scanning applications to establish guidelines on how long data should be retained.

Maintaining customer data for as long as required showcases responsible management practices while meeting legal obligations. By removing information from their systems, businesses reduce the potential risks of storing sensitive data.

Age Verification Adherence

ID scanning applications are commonly employed for age verification purposes in sectors like alcohol, tobacco, or age-restricted events. These apps aid businesses in verifying the ages of individuals they serve.

Nevertheless, when using ID scanning apps for age verification, ensuring compliance with age restrictions is crucial. Businesses need to verify that their scanning methods are precise and aligned with laws and regulations. Inaccurate verifications can result in repercussions, harming both the business's image and customer confidence.

Anti Discrimination Policies

Although ID scanning applications are useful, businesses need to be cautious when verifying customer identities to avoid discrimination based on protected characteristics.

It is crucial to treat all customers according to their race, gender, religion, or other protected attributes. Providing staff training programs can help reduce the risk of bias during the verification process and promote treatment for everyone.

Continuous Compliance Oversight

To maintain compliance with laws and regulations, businesses that use ID scanning apps should regularly monitor and assess their procedures. As technology advances, so do the legal requirements related to data protection and security. Therefore, businesses need to stay updated on any modifications that could affect their operations.

Regular evaluations of data management practices can pinpoint areas that may require enhancement or modification. Taking a stance ensures that any potential compliance issues are dealt with promptly before they escalate into challenges.

Third-Party Vendor Accountability

When utilising ID scanning apps provided by third-party vendors, businesses must ensure they adhere to the same legal standards and data protection measures. Businesses should thoroughly vet their vendors to confirm they comply with all relevant laws and regulations and have robust security protocols in place.

Drafting clear contracts that outline the vendor's responsibilities and obligations regarding data protection, privacy, and compliance is crucial. Regular audits and reviews of the vendor's practices can help ensure ongoing adherence to legal requirements, protecting both the business and its customers from potential legal and security risks.

Wrapping Up

ID scanning apps bring advantages in terms of streamlining operations and bolstering security for businesses across sectors. Nonetheless, ensuring adherence to privacy laws and other legal considerations is crucial throughout their deployment. By ensuring that customers consent and follow privacy regulations, companies can successfully handle the data gathered through these applications. Additionally, abiding by data retention practices, adhering to age limitations when necessary, avoiding bias in verification processes, and conducting compliance checks play key roles in effectively deploying ID scanning apps in accordance with legal requirements.

Data plays a pivotal role in making informed business decisions, particularly in business purchasing and acquisition. Applying data analytics to various facets of a business can unlock vital insights, negate risk, streamline operations, and maximize profits.

This article offers a detailed rundown on the power of data-driven decisions in business purchasing and how analytics can be leveraged in business acquisition, procurement, and more. 

The Benefits of Data-Driven Business Purchasing

When you leverage data, you can improve forecasting accuracy, which aids in budget planning and reduces financial risk. Data can also provide insights into supplier performance and quality, allowing you to make informed decisions and negotiate better terms.

Additionally, identifying patterns in your procurement data can uncover opportunities for consolidation, leading to cost savings. Data-driven procurement can enhance your financial stability and improve cost management.

Using Data Analytics for Business Acquisition

Whether you're looking for a restaurant for sale within the US or businesses for sale in Calgary, data analytics can help analyze various aspects of the business. These include:

Thorough Business Evaluation

When buying an existing business, use data analytics to conduct a detailed assessment. This could involve studying the business's past performance, profits, client base, market share, and return on investment. 

Financial Data Analysis

With data analytics, buyers can evaluate the financial health of a business. Important elements to be analyzed include revenue, expenses, profit margins, cash flow, and debt level. Besides, forecasted financials can also be scrutinized to predict future performance.

Customer Analysis

It's very important to understand the customer demographics of the business. Analyzing customer data like customer behaviour, preferences, spending habits, churn rate, and customer lifetime value can help in understanding the business's customer base better.

Market Trend Analysis

Data analytics can help to find out market trends and patterns. By assessing the industry's growth rate, market size, and competitor’s performance, you can understand where the business stands in the market and its growth potential.

Vendor Analysis

Evaluate the business's relationship with its suppliers and vendors. Vendor reliability, quality of products, pricing, and delivery timeframes all play a key role in business operations.

Employee Retention

Data analysis can also indicate the turnover rate within the business and provide valuable information regarding employee satisfaction and potential staffing issues.

Risk Assessment

Analyze data to identify potential risks related to the business. These could be operational risks, market risks, financial risks, and compliance risks. This helps you prepare and strategize accordingly.

The Power of Procurement Analytics

Leveraging procurement analytics provides actionable insights into your company's purchasing activities that can aid in decision-making and contribute to the bottom line.

The benefits are substantial, from improved forecasting for budgeting and better risk management to pinpointed opportunities for consolidation.

The types of analysis involved include descriptive, diagnostic, predictive, and prescriptive analytics. Each type offers a different perspective on your procurement process, helping you understand what happened, why it happened, what might happen in the future, and what actions you should take.

Here are some practical applications:

●      Spend Analysis: Unveiling your spending patterns enables cost-saving opportunities.

●      Supplier Evaluation: Analytics can provide insights into supplier performance, aiding in better partner selection.

●      Risk Management: Predictive analytics can help identify potential risks, allowing you to take preemptive action.

●      Contract Management: Data can highlight contract inefficiencies, guiding you to negotiate better terms.

●      Demand Forecasting: Predictive analysis can help anticipate demand, ensuring you're always adequately stocked.

The Role of Tools in Data Handling

You need these tools to extract, clean, and process the enormous amounts of data that come with procurement.

●      Data Extraction Tools: These pull data from various sources into a single platform for easy access and analysis.

●      Data Cleaning Tools: These ensure the data's accuracy by removing errors and inconsistencies.

●      Data Processing Tools: These convert raw data into a more understandable format for analysis.

●      Business Intelligence Tools: These provide visualizations and reports to help you interpret the data.

●      Predictive Analytics Tools: These use historical data to forecast future trends, helping you make informed decisions.

Exploring Savings Lifecycle Analytics

This powerful tool can track and analyze the entire savings process in business purchasing, from identification to implementation. This method allows you to understand not just the 'what' and 'how' of your savings, but also the 'when'.

Savings lifecycle analytics enables you to:

● Identify potential savings early in the procurement process.

● Track the progress of these savings through each stage.

● Analyze how effectively these savings are being implemented.

● Understand the temporal distribution of your savings.

● Make data-driven decisions for future procurement strategies.

This approach ensures that every dollar counts, optimizing your procurement process and maximizing your financial efficiency.

Implementing Analytics in Your Business

You need to identify the key metrics that matter to your business. Then, invest in an analytics tool that can track these metrics and generate insightful reports.

Consider these steps:

● Define your business objectives and identify key performance indicators (KPIs).

● Choose an analytics software that suits your needs.

● Train your team on how to use the analytics tool efficiently.

● Regularly review the data and adjust your strategies accordingly.

With a data-centric approach to business purchasing, businesses not only streamline their procurement process but also achieve a robust growth trajectory.

In today's fast-paced financial landscape, data reigns supreme. Financial institutions are inundated with vast amounts of data ranging from customer transactions and market trends to regulatory compliance requirements. Amidst this data deluge, harnessing actionable insights has become a strategic imperative for staying competitive. Enter the data warehouse – a cornerstone technology empowering finance professionals to extract, transform, and analyze data for informed decision-making. Alongside data warehousing, financial software development plays a crucial role in creating specialized tools and platforms tailored to the unique needs of the finance industry, further enhancing the efficiency and effectiveness of data-driven decision-making processes.

What is a Data Warehouse?

A data warehouse is a centralized repository that aggregates and organizes data from disparate sources within an organization. Unlike traditional databases designed for transaction processing, data warehouses are optimized for analytical queries and reporting. They integrate data from various operational systems, such as customer relationship management (CRM), enterprise resource planning (ERP), and trading platforms, to provide a unified view of the organization's performance. Many organizations rely on data warehouse consulting expertise to effectively design, implement, and maintain these critical systems, ensuring they meet the unique requirements and objectives of the business.

Business Benefits of Data Warehousing in Finance:

1.    Enhanced Decision-Making: By consolidating diverse datasets, data warehouses enable finance professionals to gain comprehensive insights into financial performance, customer behaviour, and market trends. This facilitates informed decision-making across functions such as risk management, investment strategy, and product development.

2.    Improved Regulatory Compliance: Regulatory requirements in the financial industry are stringent and constantly evolving. Data warehouses streamline compliance efforts by providing a centralized platform for storing and analyzing regulatory data. This ensures adherence to reporting standards and mitigates the risk of non-compliance.

3.    Operational Efficiency: Traditional data silos impede collaboration and hinder efficiency. Data warehouses break down these silos by providing a single source of truth accessible to stakeholders across the organization. This fosters collaboration, accelerates reporting cycles, and enhances operational efficiency.

4.    Personalized Customer Experiences: In an era of heightened competition, delivering personalized experiences is critical for customer retention. Data warehouses enable finance companies to analyze customer data in real time, allowing for targeted marketing campaigns, personalized product recommendations, and proactive customer service.

Use Cases of Data Warehousing in Finance

Risk Management

Financial institutions rely on data warehouses to assess and mitigate various forms of risk, including credit risk, market risk, and operational risk. By analyzing historical data and market trends, data warehouses help identify potential risks and develop proactive risk mitigation strategies.

Financial Reporting and Analysis

Data warehouses play a pivotal role in financial reporting and analysis, enabling organizations to generate accurate and timely reports for stakeholders, regulators, and investors. By consolidating financial data from disparate sources, data warehouses facilitate comprehensive financial analysis and forecasting.

Customer Segmentation and Targeting

In the fiercely competitive financial services industry, understanding customer preferences and behaviour is paramount. Data warehouses enable segmentation and targeting based on demographic, behavioural, and transactional data, allowing organizations to tailor products and services to specific customer segments.

Implementing a Data Warehouse in Financial Business in 5 Easy Steps

To implement a data warehouse effectively within a financial business, a structured approach is essential.

Step 1: Define Objectives and Requirements

Begin by clearly defining the objectives of the data warehouse implementation. Identify key business requirements, such as regulatory compliance, risk management, financial reporting, and customer analytics, to determine the scope of the project.

Step 2: Assess Data Sources and Quality

Conduct a comprehensive assessment of existing data sources, including transactional systems, CRM databases, trading platforms, and external data feeds. Evaluate the quality, consistency, and completeness of the data to ensure accuracy and reliability in the data warehouse.

Step 3: Design Data Model and Architecture

Develop a robust data model and architecture that aligns with the organization's goals and requirements. Determine the structure of the data warehouse, including dimensions, facts, and hierarchies, and design an architecture that supports scalability, performance, and security.

Step 4: Data Integration and ETL Processes

Implement data integration processes to extract, transform, and load (ETL) data from disparate sources into the data warehouse. Develop ETL workflows to cleanse, standardize, and enrich the data to ensure consistency and accuracy.

Step 5: Implement Data Governance and Security

Establish data governance policies and procedures to ensure data quality, integrity, and security throughout the data warehouse lifecycle. Implement access controls, encryption, and auditing mechanisms to protect sensitive financial data from unauthorized access and breaches.

By following these five main steps and adopting a systematic approach, financial businesses can successfully implement a data warehouse that empowers them to unlock valuable insights, drive informed decision-making, and achieve their business objectives effectively.

Empowering Finance Through Data Warehousing

In the ever-evolving landscape of finance, data warehousing stands as a beacon of innovation and efficiency. As financial institutions navigate through intricate regulatory frameworks, volatile markets, and evolving customer expectations, the role of data warehouses becomes increasingly indispensable. By consolidating diverse datasets and providing a unified view of organizational performance, data warehouses enable finance professionals to make informed decisions, mitigate risks, and seize opportunities with confidence.

Moreover, data warehousing fosters a culture of collaboration and efficiency by breaking down traditional data silos and providing stakeholders across the organization with access to a single source of truth. This not only accelerates reporting cycles and enhances operational efficiency but also facilitates personalized customer experiences through targeted marketing campaigns, product recommendations, and proactive service delivery.

In essence, data warehousing has transcended its role as a mere technology platform; it has become a strategic enabler for finance companies to thrive in a data-driven world. By harnessing the power of data, financial institutions can unlock valuable insights, drive innovation, and ultimately, deliver superior value to customers and stakeholders alike. As the finance industry continues to evolve, the transformative potential of data warehousing remains steadfast, guiding organizations towards success in an increasingly competitive landscape.

The availability of vast amounts of data presents an incredible opportunity to gain valuable insights into customer behavior, market trends, and competitor strategies. By tapping into this data goldmine, businesses can make informed decisions, tailor their sales and marketing efforts, and ultimately drive revenue growth. It's time to recognize the immense power that lies within data and harness it to unlock unprecedented success.

Driving Precision: Leveraging Data for Targeted Campaigns

Gone are the days of generic mass marketing campaigns. With the power of data, sales, and marketing teams can now achieve precision like never before. Outlets, and information access such Enigma's business data provide are proof of this. By analyzing customer data, businesses can gain a deep understanding of their target audience, their preferences, and purchasing patterns. This knowledge allows for the creation of highly personalized and targeted campaigns that resonate with customers on an individual level. Whether it's through segmentation, predictive analytics, or customer profiling, data-driven marketing ensures that every message hits the mark and maximizes the return on investment.

Uncovering Opportunities: Spotting Trends and Market Insights

Data holds the key to unlocking hidden opportunities and staying ahead of the competition. By analyzing market data, businesses can identify emerging trends, spot gaps in the market, and capitalize on new opportunities. Through data-driven insights, sales and marketing teams can understand customer needs and preferences, adapt their strategies, and develop innovative products or services that meet evolving demands. Data-driven decision-making empowers businesses to be proactive rather than reactive, positioning them as industry leaders and driving sustainable growth.

Streamlining the Sales Process: Data-Backed Sales Strategies

Sales teams can significantly benefit from data-driven strategies to enhance their performance and close deals effectively. By leveraging customer data and sales analytics, sales professionals can prioritize leads, identify cross-selling or upselling opportunities, and tailor their sales pitches to individual prospects. Data-driven insights provide valuable guidance on customer objections, pain points, and preferences, enabling sales teams to craft compelling arguments that resonate with their audience. With data as their ally, sales professionals can streamline their processes, increase efficiency, and achieve higher conversion rates.

Measuring Success: Tracking Metrics and Evaluating ROI

Data plays a vital role in measuring the success of sales and marketing efforts. By defining key performance indicators (KPIs) and tracking relevant metrics, businesses can gauge the effectiveness of their campaigns, monitor sales performance, and evaluate return on investment (ROI). Data-driven analytics provide real-time visibility into campaign outcomes, customer engagement, and revenue generation, allowing businesses to fine-tune their strategies and optimize their resources. With data-driven measurement and analysis, sales and marketing teams can continuously improve their performance and ensure that every effort contributes to the bottom line.

Conclusion

Data has emerged as a powerful asset for sales and marketing professionals, offering unprecedented opportunities for success. By leveraging data to drive targeted campaigns, uncover market insights, streamline sales processes, and measure performance, businesses can stay ahead in a competitive landscape. Embracing the power of data is no longer optional; it's essential for businesses that aim to thrive and achieve remarkable results in today's data-driven world. So, unlock the full potential of your business data and witness the transformative impact it can have on your sales and marketing endeavors.

Fashion, social causes like prevention of cruelty to animals, ecological concerns, and minimalism are currently core elements. But they may change as you read this page. That’s how quickly consumers’ minds are changing and businesses are trying hard to keep up with it. 

Some businesses are no longer predicting consumer preferences but influencing them to choose their future products. Thereby a market is created and catered to while competing brands are still assessing what the consumer wants in the future. 

Using data for insights

The only way companies can create a niche space in the constantly changing comer dynamics is by grasping the perspective of the present and future consumers. Businesses can make intelligent use of the insight community platform to take proactive decisions that will improve the process and eliminate the wastage of resources. 

For instance, if a company is manufacturing an everyday necessity product like budget sofas, then it pays to understand if consumers are willing to pay more for durability or aesthetics. Matching the requirements, running it against the product line available from competitors, and drawing up its strengths, will allow the company to manufacture a product line that can please the crowd. A well-made product is sold out with very little effort in this age of information, where consumption is not far away even though the consumer is at a distance. 

The insight community is a valuable tool

To understand how insight communities can be used advantageously, a business needs to know what comprises one. Think of an insight community as an online forum or town hall, where all the stakeholders who are connected to the business and may hold even a passing interest meet, share ideas, and news, collaborate on tasks, resolve issues that concern, and voice their opinions. 

Few debates are healthy, and few can lead to a social-media brawl, but every event will give the moderators and insights community in charge team members important takeaways. These pointers act as those neon stickers on a dark road that guide the first wanderers. Without the points that the content and insight-community management team picks, from what seems like social-media banter, feeling the real pulse of the consumer who is connected more to the smartphone than to the real world is difficult. 

Need to optimize data from the insight community

Often it is seen that though companies initiate and maintain an insight community platform, they are not using it effectively. Data collected and unused will become worthless as it comes with a shelf life, and old data should be archived but often does not hold much value. 

Companies should not just come up with an in-house insight community platform that acts as a bridge between the enablers and the end-users of their products but also come up with strategies to make it a happening hub of information and ideas. 

Here are a few pointers to maximize the data output and its uses from an insight community platform:

• Targeted content

An insight community should aim at including all kinds of consumers who may have used the product and services in the past. It should also include people who are interested in availing the brand services in the future. Even developers who are interested in the technology and like to brainstorm about open-source codes for the technology should be attracted to the platform. So the entry should be free and fair with the only barrier being the interest in the product to target the right category of audience. In this manner, the related content will also be targeted, and efforts taken to avoid derailing from the core topic through group moderation. 

• Product development

The idea of unleashing the power of an insights community platform is to help in ideation, which is inclusive of consumer insights to develop future-ready products. And in line with the perception of customers. Using effective analytics and consistent filtering from the platform teams can predict and discover new concepts. Understanding the current process, improvising or maintaining the standard, and forecasting future outcomes become easier when the insight community is vibrant and active. 

• Operational efficiency

An insights community acts like a mind reader. The team that is managing an insights community needs to scan the content regularly so that important data projecting consumer preferences do not go unnoticed. 

Regular exercises to collect relevant and related data points should be conducted to assess the pulse of the consumer. 

Organizations can channel and streamline their resources to come up with saleable products that have been designed keeping consumer preferences in mind. Identifying new opportunities and working towards maximizing products is possible when one listens to consumers. 

• Improve customer engagement

When participants of an insight community are engaged proactively and the discussions prove to be productive more people join the discussion. There will be long trails of messages along the same lines and the brand with help of engaged consumers and stakeholders becomes a trending topic. 

In this manner, the brand gains popularity without any hardcore advertising and invokes the interest of more people who will be interested to join the insight community. Being a member of a few insight communities is regarded as a cool and woke thing for most Gen-Z people. 

Summing-up:

Community platforms offer crucial insights that can act as game changers if the team can understand how to utilize the information. Deciphering between the lines is as important as collecting data and insights when it comes to data sourcing and business intelligence. 

The platform can be used to mitigate future risks like needing to increase the price to keep a decent range of profit margins. Businesses instead of introducing higher prices can first let consumers understand the need to increase the same like raw material cost and the entity’s perseverance not to cut back on the quality as primary reasons for a small increase in the price. If used intelligently, insight communities are a powerhouse of information and decision-enabler tools. 

But as with any emerging technology standard, progress is littered with both milestones and speed bumps. Below I will outline some of my key observations from working with leading players in this space.

Open Banking will reshape the global financial services system

It is no longer a question of if Open Banking will continue to evolve, but a question of how quickly it will accelerate. As Open Banking’s remit continues to expand, it will fundamentally change how we use financial products.

Open Banking can be used to assess a consumer’s creditworthiness, for example, by opening the doors to novel products aimed at supporting financial health and inclusion. 

The complex world of credit scores will be simplified through the transparency Open Banking provides. Authorised Open Banking fintechs can securely access a customer’s bank account to see incoming and outgoing transactions, providing a foundation from which to accurately assess users’ credit scores and personalise services accordingly. 

Personal Financial Management platforms (PFMs) like Money Dashboard are leveraging Open Banking technology to provide their clients with insights into transaction behaviour. Its retailer clients, such as supermarket chains, benefit from a better understanding of what their customers spend their money on when they are shopping at other stores. Its investor clients, meanwhile, use the data to predict how companies are operating in order to decide whether to invest in a stock. 

Another example of a company paving the way forward is Bud – which is demonstrating what is possible through Open Banking-powered personalisation and AI automation. Banks use Bud’s products to automate lending decisions and perform more accurate affordability checks – improving risk assessment while delivering more tailored services to their customers. 

From Open Banking to Open Finance

In the future, Open Banking will evolve into Open Finance, meaning that data-sharing will not be limited to transactional bank account data only. Other types of (financial) information will become accessible to authorised third parties, creating a more interconnected financial ecosystem.

Crypto wallets, pensions, insurances, mortgages, stock trading and other wealth management accounts – will all become accessible to facilitate easier exchanges of data, helping providers to establish a comprehensive digital overview of a customer’s financial position and encourage continued innovation. 

These benefits will not be limited to retail customers. Another important area of expansion will be to use Open Banking solutions in the B2B space. Highlighting the potential use-cases, McKinsey estimates that merchants collectively spend $100 billion annually on transaction fees. Through account-to-account (A2A) payments, Open Banking players are already enabling the direct transfer of money between accounts without relying on third-party intermediaries or payment cards – offering a real-time and cost-effective solution to the problem. 

Overcoming the biggest challenges

There are three main obstacles on the road to Open Finance:

1. Access to data

How do we make it easy for providers to access data from a broad range of financial institutions? Technological integrations (APIs) must be built to support the efficient flow of data, but building integrations that work with each financial institution is a tedious and fragmented process. To facilitate this, data and API standardisation needs to be implemented in order to make the task of providing access to data across the whole ecosystem simpler.

On the other hand, the reluctance of institutions to share highly valuable customer data will restrict access. This means regulators will need to step in – as they did for Open Banking – to create a legal environment that opens financial data for third parties to access through standardised APIs. 

2. Analysing the data

Making sense of huge volumes of data is already a gargantuan task, even when it “only” covers Open Banking data. This becomes even harder if data from a wider set of financial products is considered. Fintechs will need advanced categorisation engines and other analytical tools to structure and analyse the information they receive.

Fintechs and companies can have access to all the Open Banking data in the world, but if they cannot create a way to analyse it, they will struggle to draw out any valuable insights. Leading providers like Money Dashboard have already done the legwork when it comes to data analysis – its Open Banking categorisation engine has been trained on over 10 years of data, which allows it to accurately classify consumer transactions. Other providers must follow suit if they haven’t already. 

3. Compliance 

Whenever personal information is shared, it is crucial to have a stringent compliance framework in place, to prevent any breaches or misuses of data. This, however, is not the challenge – the real challenge is ensuring that regulation protects the consumer, without stifling innovation. 

In order to achieve this delicate balance, regulators will need to have open and constructive dialogues with Open Finance providers, and together create an environment that nurtures innovation without threatening data privacy. 

An M&A outlook

Open Banking is still a relatively early-stage technology, so we will continue to see a lot of investor activity in this space, with the market expected to grow to $43 billion by 2026.

Companies with an innovative product and state-of-the-art tech will have no problems raising funds. For instance, UK-based Bud raised $80m in June to continue to scale its AI-based Open Banking platform and expand internationally. 

In the M&A space, we expect to see an increase in activity as small, unprofitable companies (who have developed good technology) might decide to look for a buyer as Venture Capital funding becomes harder to access. Some of the industry’s largest players could also merge in order to consolidate the market, create synergies and expand their reach. 

Notably, Apple’s recent acquisition of Credit Kudos, which develops software that uses consumers’ banking data to make more informed credit checks on loan applications and is a challenger to the big credit reporting agencies (Equifax, Experian and TransUnion)., signals interest from further afield. With more and more businesses making inroads into financial services, M&A activity in this space is heating up.  

Having advised on a number of M&A and fundraising transactions in the Open Banking space, Royal Park Partners have seen first-hand the impressive leaps companies are making to transform Open Banking and increasingly Open Finance into a positive and productive tool for customers and businesses. In the future, Open Finance will provide the infrastructure to connect all financial products that consumers and businesses use, while also providing access to innovative new solutions.

The digital imperative for financial services firms cannot be understated. In order to ensure their (and their products’) relevance in the future, they will have to embrace Open Banking and Open Finance technology.

About the author: Ricardo Falter is Fintech M&A Associate at Royal Park Partners.

The advent of HMRC’s Making Tax Digital (MTD) initiative has changed the way in which we process and arrange our tax affairs irrevocably. Whilst previously only businesses with a taxable turnover above £85,000 had to comply, since April of this year, all VAT-registered businesses have been subject to mandatory online MTD submissions. Soon, similar regulations will apply to Corporation Tax (CT). But what does this mean for how we submit our returns and are most companies ready?

Disconnected data

Traditionally, VAT and CT, with their widely varied deadlines, have not been connected for reporting purposes. However, this is set to change when MTD for CT arrives, as the new quarterly CT submissions will have to be sent to HMRC within days of their equivalent VAT filings. This means that it makes sense for organisations to align their VAT and CT processes more closely.

The reality remains, though, that currently most companies are simply not prepared to leverage data across multiple MTD streams. Today’s typical accounting landscape has siloes with specialists dedicated to one specific area – be it VAT or CT – with separate data and separate timescales. Unsurprisingly, this means that tax advisors can be skilled in either CT or VAT but rarely in both. As processes continue to align, this presents a challenge.

Also notable is the fact that CT filing happens twelve months after the end of the CT financial year whereas VAT fillings happen quarterly or monthly, with reporting occurring 30 working days after. Such distinct deadlines don’t have data overlap because CT uses data that has long since been checked and finalised, while VAT-related data can be subject to change during the reporting cycle. Currently, this is no major problem but the arrival of MTD for CT with its new reporting cycles will disrupt the landscape.

Changing reporting cycles

When MTD for CT arrives, there will be additional data to submit on a quarterly basis, bringing VAT and CT tax data closer than ever – with greater interaction between the two. If your company follows calendar quarter, it currently files its VAT returns on May 5^th. Going forward, you will also be submitting CT returns on 30th April, making the time between submissions much shorter. Naturally, this means that data must be aligned across both processes and that the CT team will need visibility of the VAT team’s reporting and vice versa.

So how do we connect these disparate teams more closely? Firstly, we need to revamp the legacy, siloed approach to CT and VAT and instead introduce fully integrated tax teams. This will encourage a holistic, transparent view of both disciplines underpinned by a single source of truth, enabling clarity and seamless processes throughout the tax department.

Secondly, we can look to technology to provide new ways of doing business. Too many companies still depend on Excel and similar software to enable their MTD calculations even though this puts severe constraints on processes. This old-fashioned approach needs continual manual updates, with great potential for human error, risks regulatory compliance and lacks smooth integration with other financial systems. With HMRC recently issuing updated guidance on penalties relating to MTD for VAT non-compliance, the incentive to not make mistakes continues to grow.

New opportunities and added value

The time is right, therefore, for companies to evaluate the new generation of UK-specific VAT and CT applications. These are less time-consuming, integrate seamlessly with other core IT platforms such as ERP, and automatically update according to the latest regulations. Specialist software also has the potential to minimise risk, improve precision and increase control while boosting efficiency. This can help companies of all sizes to eliminate common problems, such as laborious data formatting.

Modern, best-of-breed financial systems and VAT calculation tools can also generate value beyond meeting MTD compliance requirements. They provide more precise, timely and transparent data, which enables smarter decision-making and improved business intelligence.

This consistent access to large volumes of accurate data provides clearer insight into the profit margins in different areas of your business, helping companies identify disparities. This data can also be extracted beyond the tax department to the broader business where additional value can be leveraged.

At the same time, they enable more complex calculations, such as partial exemption, helping companies potentially recover more in VAT, for example. Not only does this ensure faster results, but it also takes the monotonous number crunching out of the hands of skilled professionals who can be redeployed to more high-value tasks.

Introducing the cloud

The traditional approach to on-premise computer platforms was to get tied into lengthy, expensive partnerships with big legacy vendors, requiring significant upfront investment in both hardware and software as well as costly ongoing maintenance. This might well provide access to an extensive solutions portfolio but is not always the best tool for the job at hand.

Today, companies are increasingly turning to the cloud instead, where best-of-breed solutions can be built from an ecosystem of existing components, connected via APIs. This means you can build the specific solution you need in less time and with fewer upfront costs, paying only for what you need when you need it.

A vision for the future

By integrating tax departments across VAT and CT and migrating to new, flexible, constantly updating cloud technologies, companies can futureproof themselves for whatever comes next on the MTD journey. Furthermore, outside of HMRC regulations, many anticipate that wider EU standards will be introduced to address similar issues. With the right solution already in place, companies will be able to comply quickly and with minimal effort.

MTD for CT is set to be introduced in 2026, which may seem like the distant future, however re-evaluating your tax reporting processes, integrating data across tax teams and implementing versatile solutions today will ensure you are well ahead of the competition. Starting to make the necessary changes now means that your team will be fully integrated and efficient – having already ironed out any preliminary issues – ahead of the compliance deadline. Using all the available data in the most connected, transparent and accessible way, will ensure VAT and CT are synchronised for success.

For more information visit https://www.taxsystems.com/.

Fintech companies are on the rise, with more and more people using them to manage their finances. The international fintech market is projected to grow rapidly, reaching a value of about $324 billion by 2026. It will develop with compound annual growth of approximately 25.18 percent between 2022 and 2027.

This skyrocketing growth prediction shows the relevance of fintech companies in the current world. These companies are also under constant pressure to develop new tech and services. And while creating these services, they need to safeguard customers' data.

This article will help you comprehend the reasons behind the increasing need to prioritize privacy in the fintech industry.

What is data privacy?

Data privacy is the degree to which individuals should be allowed to access, possess, use, and share information. For example, you wouldn't mind sharing your name with a stranger while making an introduction, but you would not want to do so until you've gotten to know one another better.

Furthermore, when sensitive data enters the wrong hands, things can go wrong. A data breach at a government office, for example, might result in sensitive information being released publicly. A data security incident at a school might jeopardize students' personal information, which could be used to commit identity theft.

Therefore, the risk of losing data is everywhere, and each sector must take action to eliminate this. If privacy is breached, the company will suffer financially and reputationally. But the good thing is that consumers are more aware of their data privacy rights than ever before and are vocal about when their privacy is violated.

Why do fintech companies need to prioritize privacy?

This is the digital age, and one cannot forget that the number of hacking cases is increasing every year. The attacks on big companies like Equifax have made it clear that no company is safe from cybercrime.

Besides the risk of cybercrime, fintech companies also need to prioritize privacy to protect their users from other dangers. For example, if a customer's data falls into the wrong hands, it can be used for blackmailing or identity theft.

Users themselves show incredible interest in security and privacy-focused options online. They might drop certain services if their operation or track records seem invasive.

For one, more privacy-conscious people choose to download VPN apps to minimize their digital footprints. A Virtual Private Network protects data exchanges online by encrypting internet traffic. Thus, users connect to VPNs when making financial transactions online. It gives users peace of mind and more confidence to conduct business online.

To comply with regulations

One of the main reasons fintech needs to prioritize privacy is to comply with regulations. Financial institutions have always been subject to stringent regulation, and fintech companies are no exception. They need to ensure that all customer data is protected and secure. It is particularly crucial considering recent data breaches suffered by Capital One.

To protect customer data

Another reason why fintech needs to prioritize privacy is to protect customer data. As mentioned above, financial institutions are subject to stringent regulations to protect customer data. Fintech companies need to ensure that all customer data is protected from unauthorized access, use, and disclosure.

To protect the company from liability

If a fintech company doesn't take the necessary precautions to protect user data, it could be held liable for the damages or losses suffered as a result. It could include financial losses, loss of business, and damage to reputation.

To build trust with customers

One of the main reasons fintech companies exist is to build trust with their customers. If customers don't trust a company to protect their data, they are unlikely to do business with it. Trust is essential for any company that wants to succeed in the fintech industry.

To compete with other fintech companies

Competition is fierce in the fintech industry, and companies need to do whatever they can to stand out from the crowd. Offering superior levels of privacy and security is one way to do this. Moreover, this can be the unique point of their success story.

To attract new customers

To grow, fintech companies need to attract new customers. One way to do this is by offering superior levels of privacy and security. This will make customers feel more comfortable doing business with them, and they may be more likely to recommend them to others.

To retain current customers

Fintech companies also need to prioritize privacy to retain their current customers. If customer data is mishandled or security breaches, customers can decide to take their business elsewhere.

To prepare for the future

The fintech industry is constantly evolving, and companies need to prepare for the future. In the same way, cybercriminals are also preparing for the future and speeding up the process to stay ahead of time.

So, one way to deal with this is by ensuring that all customer data is protected and secure. This helps build customer confidence and ensures that the company is well-equipped to deal with future challenges.

Conclusion

The fintech industry is rapidly evolving. Companies are putting efforts to do whatever they can to stay ahead of the curve and protect their customers' data. Privacy is essential for companies in the fintech industry, and the reasons for the same are explained briefly above.

Mark Jenkins, Chief Finance Officer At MHR International, explores how digital transformation has fuelled the need for finance teams to move away from outdated software and embrace a more suitable way of processing data.  

A recent MHR survey revealed that over half (51%) of finance leaders depend solely on Excel for their processes – a figure more reflective of the industry’s lack of tech investment than of the usefulness of a software tool now over 30 years old. 

Accordingly, many finance leaders are missing out on opportunities to reshape their role due to being weighed down by time-consuming and tedious manual tasks. This is also using up valuable time which could be better spent feeding into bigger-picture business strategy conversations. Should they continue to be left out in the cold, businesses risk missing out on a wealth of expertise, knowledge, and crucial financial data.  

If finance professionals want to take their rightful place at the strategic table, they must become drivers of tech implementation. 

Stuck with spreadsheets

Excel is still deeply entrenched in the culture of many finance departments. Often seen as a tried-and-tested, ‘safe’ tool, spreadsheets owe their ubiquity to organisations’ traditional reluctance to spend out on innovative software and processes. After all, it is daunting to ditch the only business analytics tool you have ever known in favour of something new, especially when to date your organisation has been completely reliant on it. 

But while Excel is great for rudimentary calculations, its shortcomings in today’s interconnected global finance ecosystem are more obvious than ever. In a world that is increasingly driven by collaboration and information sharing, Excel is simply incapable of providing the multi-user support and complex, real-time data analytics needed for successful financial modelling and forecasting. 

Furthermore, Excel cannot always be relied on to keep data safe and secure. Recent headlines have made this painfully apparent: in 2020, almost 16,000 positive Covid cases vanished from Public Health England’s contact tracing system in a high-profile IT glitch. The reason? Excel had run out of numbers. With almost a third (31%) of finance leaders rating unsaved spreadsheets and lost documents as the greatest risks of their role, such costly and embarrassing errors should spur businesses to prioritise data integrity and move away from outdated spreadsheet tools. 

Leaders or laggards?

Reliance on legacy processes is also hindering the strategic growth of finance leaders and their teams. MHR’s survey found that almost half (44%) of leaders are left out of business strategy conversations, as they find themselves overburdened with cumbersome manual processes. Wasting time copying and pasting data from one spreadsheet to another, talented finance professionals are currently robbed of the chance to participate in long-term scenario planning, leaving them vulnerable to future market changes and missed growth opportunities.  

As a result, technical debt and legacy mindsets are holding finance teams back from flexing in their role and using their expertise to shape important strategic initiatives. This seems thoroughly at odds with the digital transformation happening across all industries. If finance leaders want to be the drivers of the data analytics revolution, they must leave Excel in the past and embrace smarter tools. 

From stagnation to automation

Accelerated digitisation has fuelled the need for finance teams to ditch outdated Excel software and adopt more suitable ways of processing data. By implementing agile and collaborative scenario-planning solutions, finance departments can seamlessly plan and model for the future, enabling them to use their insights to shape long-term business-wide strategy.

Automation is the key to future-proofing finance teams. It removes the need for professionals to reach down and perform tedious, time-consuming manual tasks, thereby freeing them to undertake more high-value endeavours and provide forward-thinking strategic advice at board level. Furthermore, automated processes support teams in boosting their compliance, accuracy, and data security, considerably lightening the load. 

The right integrated corporate performance management solution goes beyond basic financial planning: new market entrants can incorporate extended planning and analysis (xP&A) to put finance leaders back in the driving seat to make more efficient strategic decisions. This enables teams to make considerable time and cost savings, setting themselves and the wider business up for a more productive and profitable future. 

In today’s increasingly challenging and competitive commercial environment, financial data cannot sit siloed with individuals, nor be held in obsolete IT systems. The right tools and solutions will ensure greater data visibility across the wider business to help support long-term decision making. In addition, tech implementation can free finance leaders and their teams from low-value, repetitive manual tasks, securing much higher levels of efficiency, responsiveness, and agility.