Quantzig, a global analytics services provider, has recently completed their latest marketing mix optimization solution for a retail banking firm. The retail banking sector is under tremendous pressure to develop digital and data processing capabilities in order to simplify their business processes. Additionally, the companies in retail banking also need to cater to the growing needs of the customer about transactional accounts, personal loans, credit and debit cards, and mortgages.
“The marketing mix optimization solution offered by Quantzig assisted the retail banking client to accurately monitor, forecast, and periodically assess the impact of marketing campaigns on their market performance.” says an industry expert from Quantzig.
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The marketing mix optimization solution focused on quantifying the impact of marketing decisions of the past and forecasting future sales. By factoring in the impact of various media channels, the client was able to allocate marketing resources to form an optimal marketing mix optimally.
Additional Benefits of the Marketing Mix Optimization Solution
- Evaluate the impact of different components of marketing plans
- Understand the trends in the specific industry and pricing differences across sales regions
- Develop robust marketing models to improve top-line revenue and ROI continuously
- To know more, request a free proposal
To know more about how our marketing mix optimization solution helped the retail banking client
The banking industry is by far one of the largest industries in the world. In the US, insurance, real estate, and financial industry account for 20% of the total GDP. In order to keep the economy smoothly flowing, it is essential that banking industry operates seamlessly. However, the truth is that the banking sector is far from stable, as banks face numerous risks which threaten not only their profits but also the economic balance as a whole. As a result, it is essential that banks perform proper risk analysis and mitigate such perils for smooth operations. Keeping risks unchecked can lead the world towards financial meltdown as witnessed in the 2008 global crisis. So what are the kind of risks faced by the banks that needs to be regularly monitored?
Credit risk can be defined as a risk that a borrower or the counterparty will fail to meet their obligations by agreed terms. Such risks occur due to borrowers inability to pay back loans arising out of interbank transactions, trade financing, foreign exchange transactions, swaps, bonds, financial futures, options, guarantees, and the settlement of transactions. To simplify the matter, a $100 borrowed and not paid back will result in banks taking the loss in full. Additionally, banks will have to redress the money from their lenders who can be government, other banks, or the general public. Such losses in large amount can cause a serious dent in the economy. The banking industry usually declares the high rate of interest for borrowers who are associated with high credit risk. Banks need to perform timely risk analysis at an individual level to protect its wealth.
“Mutual fund investments are subject to market risks.” You may have heard this statement a thousand times over in the banking industry. So what is a market risk? It is the risk that causes losses in the bank’s trading books due to changes in interest rates, credit spreads, equity prices, foreign-exchange rates, commodity prices, and other indicators. However, this type of risks only troubles players who are into investment banking space since they are active in the capital markets. Market risks are hard to assess as some factors are highly volatile like commodity prices, whereas some are stable, but small deviations can cause big consequences like interest rates. Proper risk analysis can be carried out by dividing it as per their potential cause, i.e., interest rate risk, equity risk, currency risk, and commodity risk.
Losses that could arise from failed or inadequate internal processes, people, and systems or from external events is termed as operational risk. It also includes legal risk but does not incorporate strategic or reputation risk. Humans are prone to making errors and mistakes, and such errors can occur in the banking industry due to improper operational risk analysis. Filling incorrect information while clearing a financial instrument can cause loss of time to rectify that error and in some cases loss of money due to improper crediting of balance. Apart from human risk, operational risk can also occur due to system risk or process risk.
Liquidity risk arises when banks perform inadequate risk analysis relating to marketability of an investment which cannot be sold quickly enough to prevent a loss. In simple terms, it is a risk that disables a bank from carrying out their day-to-day cash transactions. Even though it may seem like a theoretical example, it happened in Northen England when one of the bank was taken over by the government due to its inability to repay the investors during the 2008 global crisis.
Businesses in the banking industry may be unable to meet its anticipated profit targets due to various reasons. Sometimes they may even incur a loss in place of making a profit. In case of banks and financial institution, missing the target can have severe implications as banks will have to shuffle their investment and public money. Business risk arises due to the failure of bank’s long-term strategy and errors in estimation and forecasting of profit metrics. A proper business risk management strategy can ensure sustainability even in the harshest economic environment. Conducting thorough risk analysis by guaranteeing flexibility and adaptability to the market condition can help banks avoid business risk.
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The global financial crisis has brought the viability of investment banking business model under scrutiny, resulting in banks struggling to redefine their roles. Therefore, evolution and innovation have become the need of the hour in the banking sector. This means that the investment banking companies that once comprised of private partnerships and focused only on specific markets and financial products have to reinvent themselves now. Technology disruption, talent wars, new business models and structures, and emerging fintech startups are creating a perfect storm in investment banking.
Earlier, the portfolio offered by investment banks were similar to a big fat menu card of restaurants. Companies in the banking sector were focused on cross-selling and up-selling financial products and services to the clients irrespective of the client’s actual needs. There is a gradual shift in this mindset in the investment banking sector towards a more personal and specialized approach. With increased competition and several emerging smaller fintech companies, large investment banks can no longer survive by becoming the jack of all trades. Today, many investment banks are keen on focusing their efforts and resources in areas where they can make an impact. Specialization has become the key to understanding the client’s needs in the banking sector.
Digitization has already paved the way for itself in the banking sector. Big data, AI, interactive platforms, the blockchain, and mobile technologies are changing the way of business in investment banking. Technology will help investment banks and customers to eliminate unwanted processes and intermediaries making things easier for both the parties.
Mobility and Security
Mobility refers to the easy access to information anywhere, anytime, and from any device. However, as far as the financial industry is concerned, they deal with confidential user information, so security of information is a significant concern. Additionally, there are several stringent regulations and legal policies that require investment banking companies to keep data security at the top of their checklist.
Investment banking is becoming more inclined to the thought that “Employee satisfaction is the key to customer delight.” Especially after the emergence of platforms like Glassdoor and social media, where the negative work experiences can be made public, the players in investment banking have realized that their reputation is at stake here.
Sustaining a business in the service industry has always been challenging. The customers are more demanding, forcing companies to fight hard to sort out operations, improve products and services, and maintain profitability. Such a phenomenon holds true for the banking sector as well. In a quest to provide minimal interest rates and extended services, banks are fighting to remain profitable. To solve such issues, the banking and financial industry is turning towards predictive analytics to predict consumer behavior and maximize revenues from each customer. Analyzing factors such as customer loyalty, spending patterns, purchase frequency, and other buying behavior helps banks and financial institutions adjust their services and promotions to build their revenue base.
Cross Selling and Upselling Opportunities
The competition in the credit card business has increased at such a phenomenal rate that banks have started providing credit at 0% interest rate, extended credit period, and offer higher bonus points on purchases made through cards. Amongst all these services, one would be perplexed as to how banks remain profitable. Well, that is because they use customer data to cross-sell and upsell their other products like housing loans, auto loans, locker services, or a platinum credit card. Analyzing behavioral data of the consumers can paint a picture for the banks as to whom should they offer a specific product to and at what rate. This, in turn, increases the bank’s share in customer’s wallet and builds brand loyalty.
A generally agreed upon adage in the service industry states that acquiring new customers is ten times more expensive than retaining existing ones. As a result, banks are focusing their energy on retaining their customer base and lowering attrition rate. Since banks deal with thousands of customers on a daily basis, it is almost impossible to identify dissatisfied customers. Additionally, they would not know if the customer they are about to lose is profitable or not. Adopting predictive analytics by analyzing customer’s historical data, spending patterns, and other behavioral data can help identify customers who are likely to churn. Predictive models can accurately identify such sets of customers, and automated systems can be built to send out lucrative promotions to retain such customers.
Enhanced Customer Screening
Banks and financial institutions have embraced advanced analytics solutions, which help them assess customers on various parameters such as creditworthiness and credit score. Banks can now generate every single detail about the customer including spending pattern, monthly billing, and spends across different shops. This way predictive models can be built to trace their spending pattern. Such screening can be helpful in multiple ways. For instance, if their card is stolen and misused by others to make a significant purchase, banks can verify the purchase by calling the customer. Additionally, predictive analytics can also help them identify a customer who might default from their payments so that timely measures could be put in place to increase collection.
To know more about how banks and financial institutions use customer data to carry out predictive analytics along with predictive models, cross-selling, attrition rate, and customer relationship:
In today’s competition-driven environment, companies in the banking sector are relying on marketing techniques to improve campaign effectiveness and enhance returns on their marketing investment. Also, to stay relevant in today’s competitive market scenario, leading investment banking companies are turning to marketing mix modeling solutions to plan their development and improve their marketing campaigns. Additionally, with the increasing cost-cutting pressure, companies are facing the need for marketing mix modeling solutions to optimize advertisement investments and generate sales growth and revenue. With the help of marketing mix modeling, leading investment banking companies can effectively measure the value of their marketing inputs and identify suitable investment opportunities that generate long-term revenues. Furthermore, leveraging marketing mix modeling also helps investment banking companies determine the effect of marketing on companies’ overall sales.
To effectively monitor and assess the impact of marketing campaigns on revenue, renowned investment banking companies are approaching organizations like Quantzig. Quantzig’s marketing mix modeling solution assists companies to gain actionable insights on sales and market share. Moreover, investment banking companies can identify the strengths and weaknesses of their marketing campaigns and understand the impact of these campaigns on the sales performance.
The Business Challenge
A renowned investment banking company with a considerable number of service offerings was facing certain predicaments assessing the effectiveness of marketing campaigns and reallocating their marketing spend. The investment banking firm wanted to revamp the existing marketing plans and meet the business goals to further improve the sales and profit margins. Moreover, with the help of marketing mix modeling, the investment banking company wanted to further determine the business drivers and accordingly allocate company resources.
The primary objective of the engagement was to analyze the market spend for the services rendered in the investment banking companies space.
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Benefits of Marketing Mix Modeling
The marketing mix modeling solution helped the investment banking company assess the impact of past campaigns and business performance and accordingly helped the company reallocate the marketing budget. Moreover, with the help of this engagement, the client was able to answer the most generic questions on the future spending patterns. Also, the marketing mix modeling engagement assisted the client to drive more profitable promotions and enhance the overall business outcome.
Marketing Mix Modeling Predictive Insights:
- Understood the impact of traditional, digital, and social media on sales performance
- Improved the business growth by 20% and achieved better returns on marketing investments
- Optimized investments to drive sales and profits
- Analyzed the impact of brand equity and customer satisfaction on the overall sales revenue
- Assessed the future impact of change on the marketing strategy
Want to know more about our marketing mix modeling solution for the investment banking sector?
In the current market scenario, rising cost pressures and changing customer expectations are playing a pivotal role in enhancing customer satisfaction and profitability across all the industry segments. In the investment banking industry space, firms are moving toward a value-based pricing approach to delivering better transparency without compromising the proficiency of their services. Moreover, to enhance customer satisfaction and reduce churn rates, investment banking firms have started leveraging the use of big data analytics to gain more profound insights into the customer data sets. Additionally, with the help of big data analytics, firms operating in the investment banking industry space can create customized products, facilitate effective loss prevention, and improve pricing accuracy.
With years of expertise in providing a wide range of big data analytics solutions, Quantzig helps investment banking firms devise new strategies and strengthen their foothold in the industry space. These solutions also help clients gain profound insights into the customers’ buying patterns and current market trends, which ultimately helps in maximizing the ROI.
The Business Challenge
A renowned client in the investment banking sector was facing predicaments in gauging information about essential metrics such as their past performance and current sales performance. Additionally, the investment banking client was facing difficulties in gaining a holistic picture of their sales team’s activities. Consequently, they wanted to devise a robust sales strategy to improve product sales and customer experience across platforms.
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Big Data Analytics Solution Benefits
This robust big data analytics engagement helped the investment banking firm gain more profound insights into the customers, their needs, and buying behavior patterns. The big data analytics solution also enabled them to tailor service offerings accordingly and anticipate future behavioral trends of the customers. This would help the client to categorize end-users accordingly and effectively track customers to calculate and predict risks. The solution offered also helped the client enhance customer engagements and develop long-term relationships.
Big Data Analytics Solution Predictive Insights:
- Analyze data sets and draw conclusions about customers to help organizations make informed business decisions
- Automate processes in terms of the compliance checks, data entry, and repetitive tasks
- Enhance customer experience, innovative service offerings, and implement superior risk management strategies
- Increase transparency, improve process quality, optimize resource consumption and performance