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The best banks don’t stop at accepting change, they embrace it. Grand Savings Bank knew they needed a new website to embody their approach to banking. Now, Grand Savings Bank shows how Life is Grand through an immersive customer experience built on a framework that can grow and evolve in this ever-changing industry.

Texana Bank

How does a 100+-year-old community bank reinvent itself to grow? Through dedication, hard work and collaboration. Texana Bank’s ownership embodies “marketing partner” through their approach to rebranding and reinvention.

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Radical growth is possible in banking. Renasant Bank dedicated itself to intelligent expansion through organic growth, smart acquisitions, and timely de novo expansion. Mabus Agency has been there to support and enhance these efforts at every turn.

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Getting personal: How banks can win with consumers

Key takeaways.

• Despite significant investment in AI, only 8 percent of banks are able to apply predictive insights from their machine-learning (ML) models to inform campaigns.
• Although banks know that time to insight matters, just 16 percent have standard protocols for algorithm development.
• By codifying, unifying, and centralizing key analytics and supporting processes, these organizations generate 5 to 15 percent higher revenue from their campaigns and launch them two-to-four times faster.

A large retail bank invested in ML technology to take its personalization initiatives to the next level. The goal was greater predictive power and efficiency in designing and automating customer campaigns. But two years later, the bank was still managing its personalization program much as it always had: manually and in silos. Although it had acquired a sophisticated analytics engine, the bank had overlooked the elements needed to turn that engine into a smoothly functioning “brain.” The result was a perpetual cycle of subscale efforts.

About the authors

This article is a collaborative effort by Jorge Ferreira, Lars Fiedler , Carlo Giovine , Louise Herring , and Megha Kansal , representing views from McKinsey’s Growth, Marketing & Sales Practice.

This bank is not alone. We see many well-intentioned personalization programs stumble from a mix of inconsistent data gathering, sluggish production models, and bespoke algorithms that are hard to update and share.

There are organizational issues as well. Too many businesses continue to view personalization as either a marketing initiative or an analytics initiative, when it needs to be managed as a joint initiative across the business. In the bank’s case, the marketing team was tasked with managing the program, but marketers had only sporadic access to analytics resources, forcing them to fall back on basic heuristics that were easier to manage but less effective at personalization. The bank’s aspiration also proved narrow, with a heavy focus on boosting click rates and conversions rather than on long-term drivers of customer value. As a result, the institution struggled to meet its retention and satisfaction targets for key segments.

There is a way to crack these challenges, and it starts by reframing them. We’ve seen organizations across the banking sector test a model that puts customer value at the center of personalization efforts. The outputs are individualized, but the inputs and algorithms that produce them are codified, unified, and centralized. Companies that have employed this model have generated 5 to 15 percent higher revenues from their enhanced campaigns and have halved or quartered their time to market. The bank referenced above, for example, learned to shift from equating value with mortgages sold and accounts opened to measuring it based on customer outcomes, such as retention and willingness to recommend. Instead of sales messaging such as “Try our zero percent introductory rate,” they developed journey-based communications, such as “Here’s how to make your holiday stress free.” Instead of monthly or quarterly campaign releases, they shifted to a daily or weekly tempo. Other companies can do the same.

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Rather than creating an engine, most banks focus on parts.

Often banks claim to already work in an agile way or to have the analytical tools in place to drive personalization. But without effective mechanisms to coordinate and amplify customer initiatives across the organization, many end up with one-off use cases, hard-to-replicate models, and limited knowledge sharing—all of which run counter to scale. We’ve observed the following six common challenges that banks across segments face/have faced (Exhibit 1):

• Sporadic and inconsistent customer data: Our research suggests that only about 28 percent of banks today have the ability to rapidly integrate internal structured customer data into their AI models. 1 “ The state of AI in 2020: McKinsey Global Survey results ,” McKinsey, November 17, 2020. For many, a key struggle is that customer information is housed in different ways across the business and stove piped within functions, preventing the bank from gaining a complete view of the customer. To get around these constraints, some banks overindex on third-party analytics and unstructured data. But these can add cost and complexity while ignoring valuable customer data within a bank’s own systems.
• Narrow scope of machine-learning models: Only about 9 percent of banks have a full suite of ML models capable of driving personalized engagement at every touchpoint. Most are trained on isolated moments with short-term, product-driven aims, such as boosting mortgage applications or account openings, rather than on identifying drivers of customer lifetime value (CLV) and shaping their customer interactions based on those insights. As a result, they spend time, attention, and budget on interactions with only marginal returns and underspend in areas with higher potential.
• Subscale analytics development: Just 16 percent of data-science teams follow a standard protocol to develop AI tools. In the majority of cases, analytics are developed on a campaign basis, which slows build times and limits the number of personalization initiatives that can be launched.
• Poor campaign integration and tracking: ML models and campaign-management systems often lack feedback loops to connect them, with the result that a mere 8 percent of banks are able to apply predictive insights from their ML models to inform campaign execution and decision making.
• Inadequate AI risk management: Only 14 percent of banks have a specific AI governance framework. Instead, many AI risk-management practices are driven by traditional-model risk-management conventions such as those developed for know-your-customer and credit risk processes. But these conventions are poorly adapted to the iterative nature of the AI environment, which requires a more dynamic risk-management approach.

From hand crank to flywheel

At-scale personalization needs more than great analytics. It needs an integrated infrastructure with clear mission alignment around high-priority opportunities, use cases that cover the whole customer value lifecycle, an asset library equipped with ready-to-deploy case code, and a uniform set of practices to guide teaming and execution. We have outlined five core elements that make up this flywheel (Exhibit 2). Using this structure, initiatives flow across functions, forcing silos to collapse. Insights, tools, and practices are folded into playbooks that are deployed on successive campaigns, reducing launch times and continually improving outcomes. Equally important, the focus is on CLV rather than on purely transactional gains. Companies that have adopted this model have seen substantially improved customer outcomes.

• Identify high-value opportunities: Starting with one or two simple, high-impact journey use cases can help organizations roll out personalization initiatives faster while delivering value as they go. Those use cases should be determined based on customer and commercial impact and feasibility—taking into account the needs of high-value segments and microsegments, operational complexity, and the amount of new data or skills required. For example, a retail bank that was just starting out focused on the “deposits churn journey” within its affluent segment, then extended that to the “investments churn journey” for the affluent segment, and moved from there to the other segments. Once they were further up the maturity curve, the bank challenged its personalization teams to identify mass segment customers whose near-term value might be low but whose potential lifetime value was double or triple the average, making them good candidates to be upgraded to the affluent segment. Teams then developed campaigns targeting these new segments and developed relationship metrics to gauge whether the shift in segmentation strategies was effective.

The analytics factory within the personalization model

Understanding the factors that drive CLV requires massive amounts of historical data on multiple areas of customer behavior, from transaction histories to online footprints to demographic and psychographic characteristics. And it has to cover a sufficiently long period of time to enable pattern analysis. These needs alone explain why personalization programs cannot operate within discrete functions. Instead, organizations have to establish mechanisms to extract that data from their finance, marketing, channel, and product teams.

Even then, it can take months of trial and error to test thousands of different data combinations to arrive at a reliable algorithmic model. Once done, companies must figure out what actions they could take to drive CLV higher. Orchestrating these steps for a single major customer campaign can feel like a remarkable accomplishment, never mind doing it repeatedly and across all campaigns.

This is where the asset library comes into play. The library sits at the heart of a high-performing personalization model, centralizing, standardizing, and modularizing data and analytics and integrating decisioning and execution. It consists of four layers (exhibit).

The data layer takes inputs, known as data fields, from a company’s customer data and transforms them into a set of variables or features, such as a customer’s debt-to-asset ratios over a two-year timespan. These features are standardized with appropriate formatting, labeling, and categorization, and made available to serve as inputs to the machine-learning algorithms.

The analytics layer houses predictive intelligence. Algorithms (built with features from the data layer) test the strength and causality of different CLV factors and solve for customer questions, such as the risk of churn or propensity to buy. They are designed in a standardized, modular way and rigorously tested before being placed into the library to catch errors when they can be more easily addressed and enable greater sharing and reuse. Reuse, in turn, drives better campaign outcomes, since machine-learning analytics become “smarter” the more they are deployed in the field. For example, the retail bank mentioned earlier developed a sophisticated CLV forecast model in about a week by invoking routines that had been developed in their cross-selling, upselling, and value-reduction models. The model delivered better-than-expected accuracy in projecting a customer’s CLV growth because the algorithmic components had the benefit of prior rounds of field testing.

The decisioning layer tests which actions to take based on the predictive intelligence. But where heuristics and A/B testing may only be able to accommodate a handful of business rules, ML engines can test billions of parameters, enabling prescriptive interventions to be dialed in for a specific individual. The retail bank’s decision layer, for example, crunched through 50 billion parameters in different combinations, testing for the optimal channel, day, and time to send communications to specific high-value customers. The effort led to a 20 percent increase in the bank’s commission revenue across campaigns within the first eight months after implementation.

The execution-and-measurement layer takes these “decisions” and integrates them with relevant third-party tools to activate the campaign. Performance management is baked into this layer, creating a continuous information loop that feeds data from the field directly into the ML engine to refine subsequent analytics. Measurement and feedback requirements can vary significantly from one campaign to another, and even over time. So the modularity given by libraries is key to helping teams reuse previous components and speed delivery. After completing its first customer use case, for example, a bank had 1,500 features. While some elements were hyper-specific to the campaign, many others could be applied to new use cases—and because the algorithms underlying the features and data models are continually tested in the field, they became ever more predictive. Reusable models and interfaces can then be deployed to different parts of the business.

• Enable rapid activation: The asset library plays a vital role in helping personalization teams scale quickly (see sidebar, “The analytics factory within the personalization model”). The ready-to-deploy algorithms and automated decisioning and execution this structure enables make it easier and quicker to test new ideas, ingest data from the field, and issue ever-more-refined outputs. In addition to the advanced machine-learning “brain,” rich customer-data set, and marketing automation tools, organizations need a robust set of performance metrics. An Asian bank, for example, introduced a battery of AI protocols, processes and tooling that enabled them to cut implementation time by 50 percent for more than 150 analytics use cases. Creating playbooks of best practices can also shorten the learning curve. What goes into these guides can vary, but at minimum they should include campaign catalogs that document effective lead management and other tactics, triggers and performance metrics that have proven particularly predictive, and agile rituals such as how to break large projects into smaller cycles. They should also include recommendations on the ideal team size and composition and protocols for autonomous decision making. Organizations have to be willing to invest in building momentum around the activation effort and sustaining it over time. A European bank, for example, was committed to driving its personalization agenda forward aggressively. They brought in or trained several hundred data scientists to help with the effort. These experts huddled with counterparts from marketing and the product-and-channel organization to create granular profiles of their customers’ financial lives and isolate drivers of CLV. Initial use cases focused on adding rich media imagery to campaigns, with the selection and messaging tailored to customer profile data. Subsequent use cases sought to allow customers to stop and start an application with different devices. Over time, the bank developed more than 200 use cases, adding them to a common asset library and sharing playbooks with lessons learned. Since launching its factory model, the bank has improved conversion rates ninefold.
• Invest in fit-for-purpose martech enablement: An integrated technology stack is essential to orchestrate the insights loop that connects enterprise data to the ML models, feeding the resulting inputs into campaigns, then running the field data back through the engine until desired customer outcomes are achieved. Figuring out the best way to build this stack can get complicated quickly. Many companies fall into the trap of “throwing technology at the problem,” but instead of an integrated tech stack, they end up with a bloated one that adds cost and complexity. Best-in-class organizations let value dictate their spend. They align martech resources around their highest-priority use cases and tease apart the data, design, decisioning, distribution, and measurement dimensions they’ll need to meet their customer goals. This approach creates a martech road map based on value capture, which is more effective and sustainable. Given the expertise required to navigate the often-fluid martech environment, some companies find it helpful to collaborate with specialists, particularly when first building out their stack.
• Commit to creating a truly agile operating model: Many organizations apply agile development in pockets, but only a few manage their personalization efforts in a way that cross-cuts through the whole business. To help make that leap, some banks find it helpful to build integrated teams comprised of multiple agile pods, each tasked with a specific use case. One company, for example, broke campaign-related teams into “customer pods” and “enabling pods.” The customer pods served as the ideation center, responsible for capturing CLV insights and generating new ideas to test. The enabling pods took those insights and turned them into reusable assets and approaches. Both employed rapid test-and-learn practices and flexible teaming—pulling diverse talent together into specific project teams, then expanding, collapsing, and regrouping them as projects reached different milestones.
• Invest in talent and capability building: Leading banks take a data-driven approach to building their teams. They identify the skills needed to support personalization at scale within their business and train and hire for specific acumen, be it in advanced analytics, digital banking, digital marketing, and other areas. Given the high demand for talent, they also invest in developing knowledge champions who can carry lessons from team to team and raise the overall skill level. This train-and-scale approach helped the bank referenced earlier roll out its personalization model to six divisions, ultimately delivering over $120 million of value.

Breaking through data-architecture gridlock to scale AI

Leadership must also be in alignment.

Given the cross-cutting nature of the flywheel model, organizations will need to secure strong senior-leadership alignment and define a large enough scope for campaigns to have a material performance impact. Some have found it helpful to focus on an individual business line to start, then expand across the enterprise. One retail bank set up its personalization model within its personal-banking division. That domain was small enough to help teams manage the learning curve, but large enough to contain multiple customer segments and product categories around which to design customer outcomes and campaigns.

Senior leadership must also be open to leading the case for change. We recommend establishing a steering committee comprised of individuals from analytics, marketing, and major product divisions who together have joint accountability for delivering on agreed customer outcomes. Authorized delegates can manage the committee’s day-to-day responsibilities, but it’s important that these individuals have sufficient organizational clout and decision-making authority to ensure that needed changes take effect.

Tailoring to markets of one requires a bank to function as a collective rather than as a collection of functions. By focusing on the right building blocks and prioritizing integration and alignment, financial institutions can create the machinery to personalize at scale, gaining the breakthrough value that they have long sought.

Jorge Ferreira is an associate partner in McKinsey’s Madrid office, Lars Fiedler is a partner in the Hamburg office, Carlo Giovine , Louise Herring , and Megha Kansal are partners in the London office.

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Detailed Marketing Strategy Of HDFC Bank | IIDE

By Aditya Shastri

The following case studies include the marketing mix, competitive analysis, the presence of social media, and their marketing campaigns. Also, be sure to check out our other blogs for more case studies like this one.

About HDFC Bank

Founded in 1978 and actively involved in finance and supporting financial activities across the country for companies and services in the various sectors, Housing Development Finance Corporation, which is generally known as HDFC , in India. HDFC became popular as a private bank in 1994, during a time of financial revamping of India and privatization and liberalization of the entire structure to improve its financial framework. Ever since then, it has evolved and matured as a bank, to such an extent that it has now become one of the most trusted customer banks in the nation. The reasons HDFC is one of the top 2 banks in India are reasonable marketing, the introduction of attractive financial products, the aggressive expansion of services, and above all superb service.

Marketing Mix of HDFC Bank 

1. product strategy of hdfc bank.

One of India’s largest banks is HDFC Bank. In its marketing mix strategy, HDFC offers a wide range of products, in particular for individuals and businesses. The following are summarised in the numerous services provided by HDFC Bank. Savings accounts and deposits, salary and current accounts, deposits, safe deposit lockers, rural accounts, and pension accounts. accounts and deposits HDFC Bank provides loans to satisfy the various demands and covers personal loans, automobile loans, company loans, etc. HDFC offers credit cards, debit cards, prepayment cards, credit card award programs, and credit card loans.

Insurance for many alternatives such as life, health insurance, motor insurance, travel, house, two-wheelers, and trips to students – Suraksha. Forex covers travel solutions, transfer items, other assistance, and purchasing forex services. HDFC Bank gives solutions for online payment, for instance, payment payments, online bills and shops, transfers, bills, and tax payments. Direct equities, common funds, fixed income products, insurance, private equity funds, structured products, and estate planning are the main possibilities

2. Pricing Strategy of HDFC Bank

HDFC has a reputation for its substantial market shares in the Indian banking sector, which is justified in the degree that every company needs to survive inflation and face market difficulties, due to its fair yet profit-inducing price structure for its services. Bank HDFC is priced competitively at premium prices. The price is an insurance premium in comparison to the national and PSU bank, as the minimum amount needed to create an account is large. 

At the same time, however, there are various laws, such as home load, which are competitive by RBI recommendations. Prices for these products are therefore controlled by the market rather than the company. It offers acceptable credits to both old and new clients for maximum payment terms and at an equivalent rate. In addition to standard fees, nothing for different or linked tasks, including replacement of checks, advance repayment of loans, takeover, etc. deserves a great deal. HDFC is hence pricey in certain regions, while in others it is priced equally according to the competition.

3. Place & Distribution Strategy of HDFC Bank

Mumbai, India is the HDFC headquarters. However, through its banking locations and ATMs, financial services companies have maintained and maintained a considerable presence across India. In India, especially in rural areas, it is constantly growing its network. At the most recent fiscal period, 5,103 banks and 13,168 ATMs in 2748 cities and towns were on the physical network of the HDFC (Annual Report 2018-19). More than half of the company’s banking centers are in rural and semi-urban areas. In the 25th year of its inception, HDFC serves directly or indirectly over 4,9 crore consumers. The firm added 316 banking outlets over the last fiscal period. 

4. Promotion & Advertising Strategy of HDFC Bank

HDFC also employs digital technology and other modern tools and ways to promote its brand and services, in addition to traditional marketing and promotion channels. The enterprise promotes its services via its website, including credit cards, credit, and other services. As a significant financial services organization, it has achieved a large market share in India throughout time. HDFC has strong brand shareholdings, which are based on a continual focus on client comfort. It was able, through service, to gain the trust of clients.

HDFC is one of India’s most advanced digital suppliers of banking services. In addition to banking on your mobile device, you can expect highly secure transactions and more innovative services in comparison with other suppliers. It has always focused on greater customer comfort and more security of financial information from clients, which has led to a stronger word of mouth and competition in comparison with its top competitors including government banks. Digital services, particularly the mobile app, are far more advanced and allow it to smoothly deliver services to different types of clients.

Marketing Strategy of HDFC Bank

What is a strategy for marketing? This is a long-term plan of what should be done to build a market-wishing and competitive product/service. We will discuss their different marketing techniques in this section of the HDFC Bank case study.

HSFC BANK aspires to become a World Class Indian bank, benchmarking itself in the fields of product offerings, technology, service levels, risk management, and audit & conformity in line with international standards and best practices. It is aimed at building solid client franchises throughout the various companies to be a preferred banking provider for target retail and wholesale client groups and at achieving a healthy profitability growth that is in line with the Bank’s risk appetite. The Bank is devoted to that while ensuring the highest standards of ethical quality, professional integrity, governance, and compliance with regulations. The Bank’s major business goal is to continue developing new products and technologies. The fundamental and primary purpose of the bank is to maintain excellent relations with clients.

Brand Ambassador of HDFC

Not only business offers but the creation of brand awareness. In the simplest words, brand identity and knowledge are part of the company. Without that, another company would survive in an overloaded market. A company would not. Brand ambassadors are the friends here who can help a brand reach the next level with a healthy awareness of the brand and its consumer engagements.

HDFC’s brand ambassador is Nawazuddin Siddiqui

Competitive Analysis of HDFC

With the support of technological media such as smartphones and tabs, the HDFC Bank offers an extensive range of financial products including personal banking, SME loans, Agri loans, NRI services, and wholesale banking, which not only helps consumers to consume services more conveniently, it also lowers the distribution costs of the bank. To contact the thousand-year-old client bank, the HDFC Bank OnChat has been used to carry out e-commerce transactions via the FB messenger backed by the Techbins solutions company, Pvt. Ltd.

Banks are competitive with banks and banks like ICICI Bank, Axis Banks, PSU Banks like PNB, SBI, Canara Bank, NBFC such as Indiabulls, Murugappa Group, etc.

Marketing & Advertising Campaigns of HDFC

1. “mooh bandh rakho”.

This March, HDFC Bank has successfully held the 1.000th Workshop on the “Mooh Bandh Rakho” campaign about secure banking practices. In November 2020, the bank initiated a 360-level effort to educate individuals on cyber fraud and safe banking to fight online fraud. In addition to 7 crore individuals, it is used for print and internet media. The general public loved the campaign’s internet leg, allowing them to preach the message through the bank’s filters of false reality.

https://youtu.be/MIGFWUql8sM

2. “Summer Treats”

The private sector HDFC Bank started “Summer Treats.” The bank launched a specific campaign to ease lock-out limitations to provide both traders and salaried and self-employed customers with offers. The bank will not give an EMI fee and no down payment on major appliances under the Summer Treats campaign. It also offers discounts and cashback on several brands, together with an additional 50% online reward points on the credit card.

https://youtu.be/1efrpMVoluE

3. “Bonus Back”

HDFC Life introduces its ‘Bounce Back’ brand campaign to show how financially protecting the future facilitates the development of a new successful path. The campaign highlights the spirit of the human race “never giving up” which arose amid the pandemic crisis more than ever before.

https://youtu.be/S6pWCUuW714

Social Media Presence of HDFC

Social media are increasing rapidly and consumer interests are playing an important role in this. The bank reaches a broader section through social media, which helps them to develop a huge sense of different programs and offers. After that the presence of the bank on social media.

Here, we can see that the bank has a large number of adherents on various social media platforms, which will increase the reach of the bank in the long term.

This brings us to the conclusion of the case study:

The Indian banking sector is flourishing and is expanding. Indian banks contribute 1.7% of the world. As previously noted, the objective of the HDFC Bank is to be the Indian World Class Bank. The aim is to create a better customer franchise for different companies to become the chosen banking provider for target customer segments and the retail market, and to generate a healthy profitability growth in line with the bank’s stomach for risk. Over the years HDFC Bank’s target customer franchises have been effectively gaining a market share while preserving healthy profitability and asset quality.

Has our work been liked? Would you like to learn more? Check out more on our website . You could also check out our Free Digital Marketing Masterclass by Karan Shah if you’re interested in digital marketing.

Author's Note: My name is Aditya Shastri and I have written this case study with the help of my students from IIDE's online digital marketing courses in India . Practical assignments, case studies & simulations helped the students from this course present this analysis. Building on this practical approach, we are now introducing a new dimension for our online digital marketing course learners - the Campus Immersion Experience. If you found this case study helpful, please feel free to leave a comment below.

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Leads the Learning & Development segment at IIDE. He is a Content Marketing Expert and has trained 6000+ students and working professionals on various topics of Digital Marketing. He has been a guest speaker at prominent colleges in India including IIMs...... [Read full bio]

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Trustmark Bank Full Funnel Marketing

Financial services marketing case study.

Discover how KORTX Implemented a full-funnel strategy to drive conversions across multiple lines of business in this bank case study. Keep reading to learn how the use of Axon Audience Manager uncovered new audiences to drive leads.

Trustmark Bank, a financial services company, was challenged to drive qualified leads in new branch locations while simultaneously growing existing locations. The brand was trying to balance a marketing strategy and budget across multiple lines of business including retail, mortgage, advantage, commercial insurance, personal insurance, wealth management and commercial banking. Each of these service lines had specific goals tied to them, and Trustmark required a partner that could create a streamlined strategy to ultimately solidify their existing footprint and expand growth markets across various service offerings.

KORTX was originally one of three partners working with Trustmark. After continuously driving strong campaign results, our team was selected as the brand’s sole integrated digital marketing partner. Here’s what we did:

• Incorporated Axon Audience Manager to collect and analyze millions of Trustmark Bank data points.
• Utilized First-Party analysis to uncover interests and behaviors of the brand’s digital audience that they were previously unaware of, such as interests in college sports and travel.
• Implemented a full-funnel strategy across Display, Audio, Video, and Connected TV to target the identified audience.
• Included Captivate Rich Media uniting the strategy, with map functionality to drive users to new and existing branch locations.
• KORTX’s local marketing program helped accommodate the complexity of Trustmark’s campaign structure and target many specific locations with lower budgets.

Image: After incorporating Axon Audience Manager, KORTX uncovered interests and behaviors of Trustmark Bank’s digital audience that they were previously unaware of.

As Trustmark’s integrated digital marketing partner, KORTX drove strong results across multiple conversion points. We took a complex advertising strategy and simplified it utilizing Axon Audience Manager which allowed us to uncover new audiences, drive leads to existing locations and drive awareness of new locations. Additionally, our tag management strategy provided Trustmark with a better understanding of its customer experience and highlighted opportunities for improvement both online and offline.

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Motion has acquired RMC of Cincinnati.

• Health and wellness
• Durable goods
• Consumer brands
• Building products
• Perspectives

Leading B2B agency 2.718 is now a part of Motion.

MB Financial Bank / Case Study

We know why you work.

• Advertising

MB Financial Bank, a regional business bank, wanted to become Chicago’s premier business bank and asked Motion to help with an integrated branding effort to reach business owners.

The Solution

Motion created the “MB Means Business” tagline and a campaign that celebrated the varied reasons business owners run a business, creating a campaign that was powerfully differentiated from the communications of other banks, giving MB a real advantage in the market.

To reach the business owner audience, the campaign included targeted print ads in Chicago institutions like Ravinia and Steppenwolf, business radio sponsorships, television spots on news and sports programming and interactive banners offering unique and desirable content.

Positioning Process

We have an insight-driven process that leads to powerful and successful brands. Through it, we assess a brand’s situation, explore alternative creative directions, get feedback from the marketplace and seek guidance on how to best connect. Here’s how it works:

We start with a perspective of that brand in the marketplace: a Brand Communications Assessment. For that Assessment, we identify not only the weaknesses and threats to a brand, but also its strengths and opportunities. This prompts us to examine a brand in a balanced way.

Positioning Strategies are made of three elements:

1. Insights into the audience, 2. Competitive positions, and 3. The truth about our offering.

We look at each of these using primary data, secondary data, customer information, online offerings and more to help develop and consider Positioning Alternatives.

Positioning Strategies consider the six key ingredients of successful creative:

COMPETITIVE SET

Area banks that have a focus on mid-sized businesses.

TARGET DEMOGRAPHICS

Owners of mid-sized businesses. Averaging 45 – 65 years old. Skewing male.

THEIR WORLD

They have built the businesses they run. While they have become financially successful, that is not why they work. The reasons are more personal and intrinsic: about creating something for which they are proud and responsible.

ATTITUDINAL BARRIERS

All banks are very self-focused.

EMOTIONAL BENEFIT

You’ll feel that you’ve found a bank that “gets you.”

REASON TO BELIEVE

MB Financial Bank’s experience with business owners helps us better understand your needs.

We will write as many of these Strategic Directions as we believe are viable and logical. Refining and reducing until we have the directions that the data says are worth exploring.

No customer or prospect ever sees any of the tools we use to communicate with each other about a brand’s direction. Yet a Strategy needs audience feedback. Motion uses alternative Concepts to understand the power of each Strategy so we translate the Strategy into a commonly formatted Concept using a simple visual and user-friendly language. We share these Concepts through one-on-ones, dyads, triads, focus groups, and other qualitative tools. And quantitatively through inexpensive online surveys.

From the feedback to Concepts, Motion will work to draft a final positioning statement for the brand.

MB Financial Bank Brand Position

For mid-sized business owners whose focus is more than just the bottom line, MB Financial Bank is the financial partner that understands them better than any other bank so they are better able to meet their goals.

The feedback from the Concepts and other data available would drive the development of creative that best expresses the brand while also providing guidance on how to deliver that message.

Related Work:

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TymeBank Case Study: The Customer Impact of Inclusive Digital Banking

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This publication is also available in French  and Spanish .

Executive Summary

This case study presents insights from customer research with TymeBank clients that bolsters CGAP’s hypotheses around how digital banks can support the mission of financial inclusion. As a fully digital South African bank that disproportionately serves low-income rural customers, TymeBank has created a suite of basic products that cater to the essential financial needs of those customers, namely a low-cost transactional account and a high-yield savings account. Judging from product uptake and client testimonials, these products add to a compelling value proposition that not only resonates with customers but improves their lives.

TymeBank’s distribution network, which is based on its partnerships with the nationwide Boxer and Pick n Pay (PnP) grocery store chains, helps to keep operational costs low and passes cost savings onto customers in the form of more affordable services. A clear majority of the bank’s customers cite affordability as a key source of value and the reason they opened a TymeBank account. The distribution network also extends the bank’s reach to areas that are underserved by traditional players. The affordability and accessibility likely explain why underserved segments, such as low-income women and rural customers, are over-represented in TymeBank’s (active) customer base as compared to the overall banked population in South Africa.

Despite having access to other banking options, TymeBank customers overwhelmingly see no compelling alternatives in the market. Crucially, the value customers see in the bank appears to be inversely related to income, with poorer customers reporting higher levels of satisfaction.  

In today’s high-tech financial services landscape, which is often dominated by headlines about fintech startups and tech giants, it is easy to overlook the role banks can play in advancing financial inclusion. The high cost of running brick-and-mortar branch networks has traditionally inhibited banks from serving less profitable client segments, including the low-income groups that are the focus of financial inclusion. Banks have also been slow to adapt the digital innovations that have helped some newcomers reach these segments at lower cost. It is no surprise that some observers have questioned whether banks are even relevant to financial inclusion.

However, there are reasons to believe that banks can play an important role in financial inclusion if they overcome the challenges of their legacy systems and processes and digitize operations. In fact, banks have advantages over other types of financial services providers (FSPs) that may allow them to have an outsized impact on financial inclusion – if they are willing to expand down- market. Most importantly, banks do not face the same regulatory constraints as other providers. Whereas mobile money providers and fintechs generally cannot provide a wide array of financial products (ranging from savings to credit), banks can. License to intermediate retail deposits further plays to a bank’s advantage in the arena of digital credit. Banks can fund their lending portfolios with retail deposits that are typically cheaper than the other funding sources pure lenders use, which further reduces the cost of reaching low-income customers with credit.

CGAP previously presented three emerging business models in banking that we consider to be particularly promising for financial inclusion (Jeník and Zetterli 2020). These models are fully digital retail banks, marketplace banks, and Banking-as-a-Service (BaaS) (see Box 1). We conclude that they have the potential to deepen financial inclusion by:

• Lowering the cost of financial services; 
• Improving access to a greater variety of services;
• Creating services that better meet the needs of various customer segments; and 
• Improving the customer experience. 1

We analyzed several fully digital retail banks in a series of detailed case studies (Jeník, Flaming, and Salman 2020). One of these cases focused on TymeBank in South Africa. TymeBank is a fully digital retail bank founded with financial inclusion as a core business objective. Since its 2018 launch, the bank has onboarded over 4 million customers.

TymeBank offers low-income customers simple products at low prices, such as checking accounts, savings accounts, and debit cards – all through a distribution network that combines online and offline customer interaction based on partnerships with grocery store chains Boxer and PnP. In the area of credit products, TymeBank only offers a “buy now, pay later” option called MoreTyme. This case study provides a compelling example of how challenger banks can leverage digital technology to reach excluded customer segments with more affordable and useful products.

This paper builds on the TymeBank case study by examining the impact the bank’s services have had on low-income customers. By combining a quantitative analysis of TymeBank customer data with a phone-based survey of a randomly selected sample of low-income customers, the paper addresses the following questions:

• Does TymeBank serve low-income customers?
• Are its products relevant to low-income customers?
• What impact do the bank’s products have on low-income customers’ lives, in their own words?

The aim of this research is to shed light on the potential of digital banks to deepen financial inclusion in a way that improves the lives of low-income customers. CGAP is conducting additional research with other providers to better understand the impact of new financial services business models on customers. 2

TymeBank’s main value proposition consists of (i) simple, affordable, and accessible products; (ii) fast and automated onboarding; and (iii) incentive programs that appeal to target segments (e.g., the SmartShopper loyalty program). These are the qualities we would expect customers to point out when talking about the benefits of using TymeBank.

They are also important features that respond to three frequently cited barriers to financial inclusion: (i) expensive services, (ii) limited access points, and (iii) prohibitive know-your-customer (KYC) requirements. 3

Product affordability relies on TymeBank’s ability to maintain low operational costs and proportionally reduce them further as the bank grows. Current cost efficiency is due to the bank’s technology and microservice architecture (Flaming and Jeník 2020), its branchless model, and digitally facilitated onboarding. TymeBank onboards approximately 110,000 customers per month: about 93,500 through kiosks at an estimated cost of US$3 per customer, and about 16,500 via web at approximately US$0.60 per customer. 4

SOUTH AFRICA 5

South Africa enjoys relatively high levels of financial inclusion, including a banked adult population of approximately 85 percent in a market dominated by the country’s well-established commercial banks. However, many customers only use their bank account to receive government benefits; other use cases lag. There is little to no use of non-bank mobile money wallets.

Across demographic, socioeconomic, and geographic factors, financial inclusion levels positively corelate with higher age (people aged 18–29 are among those least included), urban areas, income level and regularity. Only 38 percent of individuals who reported having no income are banked, while 31 percent are entirely excluded.

METHODOLOGY

For the qualitative analysis based on customer interviews, 1,162 customers were screened from an overall sample of 10,000. The aim was to reach those TymeBank customers living in poverty (i.e., 70 percent or more likely to be living on less than US$5.50). Ultimately, 278 customers were identified for in-depth interviews. The screener surveys were conducted partly through interactive voice response (IVR) surveys and partly through live phone calls.

The quantitative analysis used customer data from TymeBank to assess the potential impact of the bank’s offering on its customer base, particularly individuals from groups that generally exhibit lower levels of financial inclusion. The data examined spanned a nine-month period from July 2020 to March 2021. The analyzed data correlated to active EveryDay account customers, defined as those who had performed a transaction within the past 30 days. Various sets of proxies were applied to estimate income level (e.g., onboarding location, outstanding balance, frequency of transactions, average size of transactions).

The analysis considered several important caveats:

a) We recognize that TymeBank is not representative of all fully digital retail banks in South Africa or elsewhere. The findings presented in this paper should not be interpreted as automatically applicable to other digital banks without careful consideration.

b) The research was conducted during the COVID-19 pandemic; some findings were or could be affected (e.g., as customer behavior changes in response to the pandemic).

c) Despite our best efforts to exclusively focus the analysis on low-income segments, we were unable to identify customers based on their stated income levels since TymeBank does not collect that information. Customer segmentation was performed through the previously mentioned set of proxies for the customer data analysis and through the screening questionnaire for the customer interviews. 6

d) The quantitative analysis focused on active customers with at least one transaction performed over the past 30 days, unless otherwise noted.

e) Where customers stated they had been financially excluded before opening a TymeBank account, we did not identify the underlying cause(s) of financial exclusion.

Key Findings

Does tymebank serve low-income customers.

Our research showed that TymeBank serves a higher proportion of low-income customers than the typical bank in South Africa, and a significantly higher portion of the most financially excluded segment.

Low-income customers in South Africa are relatively highly banked, although they are under-represented. South Africans earning US$200 per month or less constitute 47 percent of the population but only 41 percent of the banked population. 7 However, we estimate that this segment represents 48 percent of TymeBank’s active user base. 8

Among the three-quarters of TymeBank customers for whom data are available, 58 percent live in metropolitan areas and 42 percent in rural areas. This compares to South Africa’s rural population of 35 percent (as of 2016); we estimated this share to be even lower in 2021 (approximately 30 percent). 9 Hence, rural customers appeared to be noticeably overrepresented in the TymeBank user base.

Young, rural, low-income women comprise the most financially excluded and underserved segment in South Africa. This group forms 2.3 percent of South Africa’s banked population but 7 percent of TymeBank’s active base – nearly three times as much. 10 Finally, 13 percent of TymeBank’s active customers are first-time bank customers. 11

From a more general perspective, women in the low-income segment represent a higher-than- average share of the bank’s overall customer base sample (65 percent versus 57 percent),12 which suggests that low-income women particularly benefit from TymeBank’s services.

These findings lead us to conclude that TymeBank customers disproportionately seem to come from traditionally unbanked and underserved segments. In fact, the evidence suggests that the bank’s customer base may particularly skew toward the most underserved segments.

DOES TYMEBANK OFFER PRODUCTS THAT ARE RELEVANT TO LOW-INCOME CUSTOMERS?

Customers find TymeBank’s products useful and act upon features designed to promote certain behaviors.

The bank’s customers particularly value the low cost of its services and the convenience of access and usage. The lower their income, the more value customers seem to derive from its services. While the vast majority of TymeBank customers have previously held bank accounts, 67 percent say they see no good alternative to TymeBank (Figure 4). This response is despite the fact that, as of the time the research was conducted, the bank still only had a relatively modest payments and savings offering and had yet to launch credit products. (TymeBank has since launched MoreTyme, a “buy now, pay later” consumer credit product.) Customer endorsement seems driven by the strength of the bank’s value proposition and the low cost of its services. When asked, customers specifically appreciate the low fees (48 percent) and the high-yield savings account (38 percent).

Importantly, women make up a larger share of the total number of GoalSave (savings account) users compared to their representation in the overall customer base (3 percentage points higher). This finding suggests that female customers find value in the product, although they had slightly lower savings per user than men (US$58 versus US$59). The number of their deposits exceeds the number of withdrawals.

We did not find any significant differences in usage and product lifecycle patterns across income groups (aside from the frequency and size of transactions that correlate with income level), which suggests that TymeBank covers its customers’ essential needs across segments. The similarities in lifecycle (behavior patterns across products, such as most frequently performed type of transaction and their change over time) indicate that customers across income levels increase their engagement as they grow confident with the products.

However, important nuances do exist. For instance, the most excluded segment uses till machines for cash-in and cash-out transactions that are free-of-charge (and perhaps more accessible in certain areas), compared to the ATMs other segments prefer. This may be explained by price sensitivity that drives the preference for free till point withdrawals compared to ATM withdrawals, which are charged at US$0.61 per part of US$70.

The value generated for low(er) income customers will hopefully further expand as TymeBank expands its product offering (e.g., insurance and diverse credit products).

WHAT IMPACT DOES TYMEBANK HAVE ON CUSTOMERS’ LIVES?

Most customers report positive life changes due to their use of TymeBank. Importantly, levels of customer satisfaction increase as customer income decreases. This suggests that the TymeBank value proposition tailored to lower-income customers resonates well.

We relied on the actual voices of customers from the demand survey to gauge the impact the TymeBank offering had on its users. When asked, 73 percent of customers reported a positive change in quality of life attributable to TymeBank. The change could be associated with multiple factors. For instance, 80 percent of interviewed customers reported a decrease in the amount spent on bank fees, which is crucial for low-income segments that have historically experienced cost as one of the biggest barriers to financial inclusion. Nearly a third (31 percent) of customers who reported life improvement said that their access to financial services had expanded thanks to TymeBank. Customers also reported an improved ability to digitally transact and receive money (51 percent and 55 percent of all interviewees, respectively).

One of the most important findings concerned the ability to save. Seventy-three percent of interviewed customers reported an increase in their savings balance due to TymeBank. Savings likely drove customers’ ability to achieve their financial goals (68 percent) and improve financial resilience (32 percent).

These findings support our overall hypothesis that digital banks are well placed to deepen financial inclusion with cheaper, better products that reach beyond payments and are relevant to improving the lives of low-income customers.

It is critical to note that the high-interest yield on the GoalSave savings account was among the reasons most prominently cited by customers as driving them toward TymeBank. Our finding that female and young TymeBank customers were more likely to save using the bank service compared to what nationwide averages suggest was also important. While the national numbers show a 9 percentage point gap in formal savings between men and women (35 percent versus 26 percent), the gap among TymeBank customers favored women by 10 percentage points (45 percent versus 55 percent).

Our findings also revealed areas for improvement. Perhaps not surprisingly, TymeBank customers have not been spared the surge of fraud in South Africa. Ten percent of customers reported challenges concerning security and protection of funds. Six percent of respondents mentioned delays in service delivery and nearly the same share complained of issues related to digital access. Complaints were related to system downtime, clearing time (TymeBank is planning to offer real-time clearing), and the general concerns first-time users may have about their funds.

When asked about potential improvements, the presence of physical branches scored the highest (11 percent), followed by improved security (9 percent) related to the challenges mentioned in the previous paragraph and improved digital services (5 percent).

While these findings are encouraging, more research is needed before conclusive statements can be made about the broader role of digital banks in advancing financial inclusion. We encourage other experts to undertake similar research and add to the emerging evidence on the impact of digital banks on financial inclusion.

Acknowledgments

This case study features insights from research commissioned by CGAP and conducted by 60 Decibels and Genesis Analytics under the leadership of Ivo Jeník.

The author thanks CGAP colleagues Gayatri Vikram Murthy and Mehmet Kerse for reviewing this paper, and Gcinisizwe Andrew Mdluli for contributions and insights. Peter Zetterli and Xavier Faz oversaw the effort. Andrew Johnson led the editorial work.

This paper would not have been possible without the time and dedication of the team from TymeBank and TymeGlobal.

Flaming, Mark, and Ivo Jeník. 2020. “ How Does Tech Make a Difference in Digital Banking ?” CGAP blog post, 11 November.

Jeník, Ivo, Mark Flaming, and Arisha Salman. 2020. “ Inclusive Digital Banking: Emerging Markets Case Studies .” Working Paper. Washington, D.C.: CGAP.

Jeník, Ivo, and Peter Zetterli. 2020. “ Digital Banks: How Can They Deepen Financial Inclusion? ” Slide deck. Washington, D.C.: CGAP.

Download a PDF of this Case Study >>

1 To assess bank inclusivity, we developed and implemented a four-dimensional framework focused on cost, access, fit, and experience (CAFE). See Jeník and Zetterli (2020), page 42. In a business-to business (B2B) model, BaaS providers have other FSPs as their customers. Thus, their impact on end users is indirect.

2 see collection of cgap research on fintech and new financial services business models: www.cgap.org/fintech, 3 world bank global findex database (2017)., 4 atm-like machines placed in partner grocery stores – mainly pnp and boxer – allow for automated customer onboarding in less than five minutes., 5 this section is based on data from the finmark trust finscope (south africa) 2018 database., 6 the quantitative analysis used the average monthly inflows of customers originated at pnp value stores (us$271) and boxer stores (us$224) to estimate income level. the qualitative analysis estimated that 35 percent of tymebank’s customers live on less than us$5.50 per day, based on the screener survey findings., 7 the finmark trust finscope (south africa) 2018 database., 8 using place of origination (pnp value and boxer stores) as a proxy for low income., 9 south africa gateway .  , 10 the finmark trust finscope (south africa) 2018 database., 11 n = 1,162., 12 comparing screened customers (n = 1,162) and interviewed customers (n = 278)., related resources, inclusive digital banking: emerging markets case studies, digital banks: how can they deepen financial inclusion, related research, 8 billion reasons: inclusive finance as a catalyst for climate action, open finance self-assessment tool and development roadmap, global landscape: data trails of digitally included poor (dip) people.

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REGIONS BANK

The perfect customer journey.

When it came to understanding the ideal combinations of messaging, creative, and channel, however, Regions Bank struggled to find the correct mix to drive the highest return on their marketing investment.

To address this problem, Regions Bank chose Marketing Evolution . Backed by unified marketing measurement capability, Regions Bank was able to effectively measure the impact of offline and online channels and tie those insights to online and offline conversions.

Additionally, the platform helped identify the best messages to deliver to the right consumers, and, ultimately, give them the ability to optimize their marketing while campaigns were live. The result?  A  100% ROI increase  using people-based marketing, and a 100% incremental revenue increase from new checking accounts.

Download the case study to learn how.

Read their story

Bank Marketing Case Study

Team: kelli belcher, nessie tran, adrianne kristianto, mingwei gu, executive summary.

A logistic regression model was used to predict whether or not a customer would subscribe to a long-term bank deposit, recorded as either a success or failure. Since this is a dichotomous classification problem, logistic regression was our first choice for this case study as the model is also able to provide information about the statistical significance of the predictors and their relationship to the outcome variable. In our analysis, we fit two logit models, one using stepwise selection with Akaike Information Criterion and one using Bayesian Information Criterion, and compared accuracy and sensitivity scores to find the best model. Our AIC model yielded a score of 75.6% accuracy on the testing data and 60.8% sensitivity. Although the BIC model had a slightly lower accuracy score of 73.4%, it had a higher sensitivity score of 62% and contained fewer variables, making it our final choice for this classification task.

The Problem

Bank marketing campaigns are a popular way to engage with customers, advertise a new product or service, and enhance business. Direct marketing is the approach used in this case study, which is a promotional method that involves contacting a targeted group of customers directly in order to increase sales of a product of interest. Some examples of direct marketing include text messaging, telemarketing, and email marketing. One of the important features of this strategy is the ability to measure the effectiveness of a marketing campaign by monitoring the feedback and responses from customers.

Using direct marketing campaign data from a Portuguese financial institution, our goal is to predict the likelihood of a customer subscribing to a long-term deposit. In this campaign, customers were contacted through phone calls, both inbound and outbound. Often, more than one contact to the same client was required to determine if the bank deposit would be subscribed to.

The importance of this study is to be able to identify and target customers that are likely to subscribe to long-term deposits using the predicted probabilities from our logistic regression model. This can help the bank more strategically contact clients by calling only those who are in our target group. Additionally, these probabilities can be used to segment customers into different clusters based on other marketing goals, like reaching out to customers who are at risk of leaving or switching financial institutions.

There are twenty attributes in the data set that provide information about the customer’s demographic, social, and economic status. We will explore how these features affect their likelihood to subscribe to a long-term deposit. In the remainder of the case study, we will discuss additional literature on existing methodologies used in this area, followed by a discussion of the techniques we used in this case study, and a brief description of the data set. Finally, we will present our findings from our final model and discuss our recommendations.

Review of Related Literature

Logistic regression is a statistical learning method that has been widely used as a data analysis and inference tool with roots that date back to the 19th century. In 1958, British statistician David R. Cox introduced the logistic function in his paper, “The Regression Analysis of Binary Sequences.” This powerful classification technique can be used to predict the probability of an event occurring in many different application areas, including financial services, marketing, and healthcare.

We are often interested in predicting which class an observation will belong to. Logistic regression is a parametric approach that estimates the probability of a qualitative response belonging to a particular category, for example, whether an email is spam or not. It can be used both when the response variable is dichotomous, i.e. 0 or 1, yes or no, as well as when the dependent variable takes on multiple outcomes. Logistic regression also provides insights into the relationships between the predictors and the response based on the coefficients of the model. Other popular techniques used in classification problems include k-nearest neighbors, support vector machines, and tree-based methods, like decision trees.

Methodology

In our analysis, we used a logistic regression model to fit the data and performed stepwise model selection using AIC criteria and BIC criteria to find the best subset of predictors. Akaike and Bayesian Information Criterion are two estimates of how well the model fits the data. Both methods involve minimizing the loss of information, with BIC penalizing the model more for its complexity. A lower value of these criteria indicates a better fit.

Prior to fitting the model, the data was split into two subsets, one for training the model and one for testing the performance of the model. The bank marketing data set was randomly split into two sample sets using an 80/20 ratio: training data ( bank_train ) and testing data ( bank_test ). The training data consists of 3,295 observations and the testing data consists of 824 observations.

The full logistic regression model was then fit using the training data set. Next, stepwise model selection was performed with AIC criteria, followed by BIC criteria. This was done in both directions using the Chi-squared test (direction = ‘both’, test = ‘Chisq’). The final model after performing AIC criteria includes seven variables: nr.employed (number of employees), poutcome (outcome of the previous marketing campaign), month (last contact month of year), campaign (number of contacts performed during this campaign and for this client), contact (contact communication type), cons.conf.idx (consumer confidence index), and age . The final model after performing BIC criteria contains only two variables: nr.employed and pdays (the number of days that passed by after the client was last contacted from a previous campaign).

In order to classify the predictions of both the AIC and BIC models, we first converted the probabilities into binary values, 0 or 1. To find the optimal threshold level, we looked at the Sensitivity and Specificity plots for each model and chose the point where the two lines intersected one another. For the AIC model, the intersection occurs at about 0.076.

For the BIC model, this occurs at 0.146 in the graph below.

These values were used as the cutoff level for the probabilities. This means that if the probability is equal to or greater than the threshold level for the specific model, it will classify the prediction as a 1, the customer will subscribe, and if it is lower than that value, the model will classify it as 0, the customer will not subscribe.

Logistic regression models perform under several major assumptions. The first assumption is that there is a linear relationship between the independent variables and the log odds of y. Additionally, the outcome variable should be categorical, either binary or ordinal, with no high correlations (multicollinearity) among the predictors. The final assumption is that the observations are independent of each other. Failure to comply with these assumptions may lead to a poor model fit and inaccurate results.

The bank marketing data set can be found and imported from the UCI Machine Learning Repository. It comprises 4,119 instances with twenty input variables (ten numeric variables and ten categorical variables) and one response variable (variable y ). The classification goal is to predict if the client will subscribe to a long-term bank deposit (yes or no) based on phone calls. The data was relatively clean to start out with. There were no missing values in the data set. Since we will be using a logistic regression model for this case study, we converted all categorical variables into factors at the beginning of our analysis.

The input variables for the logit model are divided into three categories: customer’s demographic data ( age , job , marital , education , default , housing , loan ), data related to the current campaign ( contact , month , day_of_week , campaign , pdays , previous , poutcome ), and data related to social and economic context ( emp.var.rate , cons.price.idx , cons.conf.idx , euribor3m , nr.employed ).

The response variable, y , was converted into a binary outcome, 1 for yes and 0 for no. When the call is made, the variable duration is not known until the call ends and y is known. The duration is only used for benchmarking purpose and is highly correlated to the outcome variable, y . Therefore, we removed the variable duration from the data set.

After checking the structure of the data, we then looked at the distributions of the variables, and there is one histogram that stood out, which is the histogram of pdays .

According to the description of pdays , it is the number of days that passed by after the customer was last contacted from a previous campaign. It is a numeric variable, with the number 999 indicating that the customer was not previously contacted. Looking at the histogram shown above, we think the majority of the samples are clients who were not previously contacted. We did try removing pdays from the data set before fitting the models. However, it did not change the accuracy or sensitivity of either the AIC or BIC model, so we decided to keep this variable in the data set.

After fitting our models using the training data, we then compared the AIC and BIC models’ performance on the test data. The prediction results from the confusion matrix below show that the model using AIC criteria has the highest overall accuracy at 75.6%, with sensitivity at 60.8% and specificity at 77.2%. The model using BIC criteria has an accuracy of 73.4%, with a sensitivity at 62% and specificity at 74.6%.

Our final choice for this case study is the Bayesian Information Criterion model as it has the highest sensitivity level and contains fewer variables, meaning that it is less complex. Below are the results of the BIC model.

Based on the table above, our final model equation is: \(\log\frac{p}{(1-p)} = 56.4726824 - 0.0111319Nr.employed - 0.0014777Pdays\)

Both predictors are negatively associated with our response variable. The odds that target customers are likely to subscribe to long-term deposits change by a factor of \(e^{-0.0111319} = 0.989\) with one unit increase in nr.employed , when all other variables are held constant. This means that a customer is more likely to subscribe when the number of employees is lower. Additionally, the odds that target customers are likely to subscribe to long-term deposits change by a factor of \(e^{-0.0014777} = 0.999\) with one unit increase in pdays , when all other variables are held constant. Thus, the likelihood of a customer subscribing increases when fewer days have passed since the client was last contacted from a previous campaign.

Conclusions

To achieve our goal in this case study of identifying and targeting customers that are likely to subscribe to a long-term bank deposit using the predicted probabilities from the logistic regression model, the model should focus on correctly detecting positive effects. The portion of actual positives that are correctly identified should be the primary importance. Therefore, the model should have higher sensitivity, or true positive rate, compared to the specificity, the true negative rate.

By summarizing the results of the logistic models using Akaike and Bayesian Information Criterion, we recommend the model using Bayesian criteria. Both models have similar accuracy rates but the model using BIC criteria has higher sensitivity, meaning that it predicts fewer false positives. The BIC model also contains less variables, making it less complex and our final choice for this classification task.

27 Case Study Examples Every Marketer Should See

Published: July 22, 2024

Putting together a compelling case study is one of the most powerful strategies for showcasing your product and attracting future customers. But it's not easy to create case studies that your audience can’t wait to read.

In this post, I’ll go over the definition of a case study and the best examples to inspire you.

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Marketing case study examples, digital marketing case study examples.

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A case study is a detailed story of something your company did. It includes a beginning — often discussing a challenge, an explanation of what happened next, and a resolution that explains how the company solved or improved on something.

A case study proves how your product has helped other companies by demonstrating real-life results. Not only that, but marketing case studies with solutions typically contain quotes from the customer.

This means that they’re not just ads where you praise your own product. Rather, other companies are praising your company — and there’s no stronger marketing material than a verbal recommendation or testimonial.

A great case study also has research and stats to back up points made about a project's results.

There are several ways to use case studies in your marketing strategy.

From featuring them on your website to including them in a sales presentation, a case study is a strong, persuasive tool that shows customers why they should work with you — straight from another customer.

Writing one from scratch is hard, though, which is why we’ve created a collection of case study templates for you to get started.

There’s no better way to generate more leads than by writing case studies . However, without case study examples from which to draw inspiration, it can be difficult to write impactful studies that convince visitors to submit a form.

To help you create an attractive and high-converting case study, we've put together a list of some of our favorites. This list includes famous case studies in marketing, technology, and business.

These studies can show you how to frame your company's offers in a way that is useful to your audience. So, look, and let these examples inspire your next brilliant case study design.

These marketing case studies with solutions show the value proposition of each product. They also show how each company benefited in both the short and long term using quantitative data.

In other words, you don’t get just nice statements, like “this company helped us a lot.” You see actual change within the firm through numbers and figures.

You can put your learnings into action with HubSpot's Free Case Study Templates . Available as custom designs and text-based documents, you can upload these templates to your CMS or send them to prospects as you see fit.

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• Bank of America’s Corporate Culture Crisis: Part 5- A Case Study for Compliance

Compliance professionals constantly seek to understand how systemic issues within corporate hierarchies can lead to severe consequences. The recent revelations about Bank of America’s (BoA) persistent workplace culture problems are a powerful reminder of compliance’s critical role in safeguarding employees and the organization.

This week, I have explored the BoA failure around workplace culture from various perspectives articulated by the Everything Compliance gang, including Karen Woody, Jonathan Armstrong, Matt Kelly, Karen Moore, and Jonathan Marks. This exploration included the failure of internal controls, failures by the Board and senior management, culture failures around highly driven, self-selecting employees, and the cultural miasma that is BoA from a perspective from across the pond. You can check out the full Everything Compliance episode here . We conclude our series with a summary of lessons learned for compliance and how compliance can use those lessons going forward.

The scandal at BoA involving the excessive hours worked by junior employees highlights a profound crisis in corporate culture that has significant implications for compliance professionals. Despite previous promises of reform following similar incidents, BoA’s failure to address these issues effectively reveals systemic problems that transcend mere policy implementation. The tragedy of junior banker Leo Lukenas, who died after working over 100 hours a week for multiple weeks in a row, underscores the urgent need for stronger internal controls, better communication between management levels, and a culture that genuinely prioritizes employee well-being.

This situation at BoA serves as a critical case study for compliance professionals, illustrating the dangers of a disconnect between senior management’s intentions and the actions of middle management. While senior executives may set policies to limit overwork, middle managers often circumvent these rules, perpetuating a toxic work environment. BoA’s manual control system’s failure, ineffective internal audits, and HR oversight further exacerbate the problem. Compliance professionals must ensure that internal controls are implemented, actively monitored, and enforced to prevent similar issues in their organizations.

A key lesson from the BoA crisis is the importance of addressing the role of incentive structures. In high-stakes environments like investment banking, where bonuses and career advancement are tied to deal closures, there is a significant risk of overwork becoming normalized. Compliance officers must advocate for realigning incentives to balance business goals with ethical standards and employee well-being. This involves addressing the symptoms of such crises and tackling the root causes, such as toxic corporate culture and misaligned incentives.

The BoA scandal highlights the critical role of internal controls in maintaining a healthy and sustainable corporate culture. Relying on self-reporting as a key control mechanism in this high-risk environment proved ineffective, as employees were pressured to underreport their hours. Compliance professionals must recognize that self-reporting should be supplemented with independent verification methods, such as automated time tracking and regular audits, to ensure accurate data collected and controls are effective.

A holistic approach to risk management and compliance must be considered. Internal controls must be integrated into a broader framework, including solid ethical leadership, ongoing employee education, and clear channels for reporting concerns. The failure of BoA’s control environment, monitoring, and remediation efforts allowed a culture of overwork to persist, ultimately leading to repeated tragedies. For compliance professionals, this underscores the need for continuous improvement and active management of internal controls.

The role of the board of directors in overseeing corporate culture is crucial. The BoA crisis demonstrates that board members must go beyond surface-level management reports and engage directly with employees to understand workplace challenges. A proactive approach, including regular reports on employee well-being metrics and internal audits focused on workplace culture, can help prevent such crises. Moreover, creating a culture where employees feel safe to voice concerns is essential for identifying and addressing risks before they escalate.

The Bank of America scandal is a stark reminder of the human cost of a toxic work culture and the vital role that compliance professionals play in safeguarding both employees and organizations. The lessons from this tragedy should guide efforts to create healthier, more sustainable work environments. Compliance is not just about preventing legal and regulatory risks but also about fostering a corporate culture that values integrity, transparency, and the well-being of all employees. By aligning business metrics with these values, companies can achieve sustainable success that benefits their bottom line and people.

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Theory and Practice of Assessing the Efficiency of Urban Agglomeration Administration Abroad and in Russia (a Case Study of Chelyabinsk Oblast)

• URBAN STUDIES
• Published: 27 November 2023
• Volume 13 , pages 725–738, ( 2023 )

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• E. Markwart 1 ,
• D. P. Sosnin 2 &
• S. V. Nechaeva 3  

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The efficiency of urban agglomeration administration has so far not been an object of close attention for researchers. The article proposes to evaluate it with respect to the contractual administration model based on three components, i.e., evaluate the overall efficiency of administering the development of the agglomeration as the degree of achievement of the goals of agglomeration interaction, the political efficiency of making and implementing decisions, and the managerial efficiency of implementing agglomeration projects. The approach is theoretically substantied, and the results of a study of the efficiency of agglomeration administration are presented with a case study of the agglomerations of Chelyabinsk oblast. Summarizing the results of the study based on the above three components, the authors conclude the following. First, in a broad sense, the goal of development of an agglomeration (and its administration) is to strengthen the competitiveness of the agglomeration in global, national, or at least large interregional markets and to increase its contribution to development of the economy and society. Second, efficiency (making and implementing decisions) under conditions of the contractual model of agglomeration administration implies a key role of the coordinating body, which consists in finding and achieving a balance of interests of the participants, preparing and agreeing on draft decisions, and monitoring and controlling their implementation. In fact, the efficiency of agglomeration administration is closely related to the efficiency of the coordinating body. Third, the efficiency (more precisely, success) of agglomeration administration by assessing the implementation of agglomeration projects, in turn, depends on the chosen mechanisms and forms for carrying out such projects. Analysis of the Russian practice of urban agglomeration administration, with a case study of Chelyabinsk oblast (the Chelyabinsk agglomeration and Gorny Ural agglomeration), made it possible to test these theoretical conclusions.

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The Relationship Between the European Commission and Local Government Through European Urban Initiatives: Constraints and Solidarities

The Development of Polycentric Agglomeration and the Non-agglomeration Territory in the Economic Space of a Region

European Urban Agenda: The Predicaments of Decentralised Coordinative Action

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For this approach, it is more correct to speak about the effectiveness (or success) of agglomeration administration, not about efficiency in the exact sense of the word. See below for more details.

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Assuming that such goals would generally be set by individual participants in the absence of agglomeration interaction.

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Markwart, E., Sosnin, D.P. & Nechaeva, S.V. Theory and Practice of Assessing the Efficiency of Urban Agglomeration Administration Abroad and in Russia (a Case Study of Chelyabinsk Oblast). Reg. Res. Russ. 13 , 725–738 (2023). https://doi.org/10.1134/S2079970523701083

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• DOI: 10.1134/S2079970523701083
• Corpus ID: 265455871

Theory and Practice of Assessing the Efficiency of Urban Agglomeration Administration Abroad and in Russia (a Case Study of Chelyabinsk Oblast)

• E. Markwart , D. P. Sosnin , S. V. Nechaeva
• Published in Regional Research of Russia 27 November 2023
• Political Science, Geography, Economics

44 References

Features of metropolitan area governance models, largest urban agglomerations in russia at the beginning of the 21st century: status, problems, and approaches to solving them, territorial concentration of the economy and population in european union countries and russia and the role of global cities, agglomeration economies and evolving urban form, effectiveness and efficiency of the activities of regional and municipal governments: purpose and possibility of a correct assessment, structural transformations of the municipal space: substantiation of expediency and evaluation of efficiency, efficiency of public administration: the analisys of methods, characteristics of urban agglomerations in different continents: history, patterns, dynamics, drivers and trends, theoretical foundations for organizing the metropolitan governance system, related papers.

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