A few years ago, the customer experience was how a customer was treated when they were at a store, a restaurant or a hotel. It perhaps began when the customer entered and ended when they left the premises. In that context, enterprises, especially in consumer goods, placed a lot of emphasis on in-store experiences right from point of sale material, shelf display and packaging. Service brands laid emphasis on people-driven customer service.

Procter & Gamble, coined the phrase ‘First Moment of Truth’ to an in-store experience, especially when the customer is interacting with the packaging or POS material. It is believed that it occurs within the first 3-7 seconds of a consumer encountering the product and it is during this time that marketers have the capability of turning a browser into a buyer. The actual product experience was the ‘second moment of truth’ – as it was about the product living up to the claim. As the world became more digital, enterprises realized that the web played a big role in influencing brand choice even before they entered a retail store or went to an e-commerce portal. Google called it Zero Moment of Truth – as the web, especially search results, expert & customer reviews influenced the consideration set before the final brand purchase. So what the consumer saw in the digital world influenced the sale. In a way, it paved the way for Customer Experience – heavily influenced by digital, as we know it today.

Customer Experience (CX) today goes beyond the product or service a customer is looking to buy (or has already bought). Technology has made communication quick and easy, and customer experience today is a continuous, on-going process. When Amazon, for example, receives an order, they follow a process to ensure the customer is happy and informed throughout the journey. Constant email and message communications inform the customer where their product is, and when it will arrive. Once it is delivered, they ask for feedback and offer a return-back window. They have a 24×7 service line that customers can use to express issues. They send periodic emails about other products that a customer could be interested in. All of this culminates in ‘Customer Experience’. It may begin with a web search and include an app experience, interactions with customer service or the website. CX today is associated not just with the product, service, store or salesman, but with the entire brand that is delivering the experience.

In many cases today, the digital experience is perhaps the most dominant factor in customer experience. [click to tweet]

However, this is not to say that a great customer experience in digital can be a substitute for a poor product or service experience. It is said that good advertising can kill a bad product faster. Similarly, today’s savvy consumers will see through ‘all style and no substance’.

Why Customer Experience is Important

The sheer variety of options that are available to consumers today, makes CX the primary differentiating factor. Consider the e-wallet market, for example. Customers looking to perform the simple task of making quick transactions have over 100 options, as listed in this blog alone. In terms of functionality, there’s not much these apps can do differently. The only factor setting them apart is how customer-centric the brand is, and that encapsulates the app, the support team, the customer-facing representatives, etc.

 13% of customers will share with 15 or more people about a bad experience with a company, and 72% of consumers will tell 6 or more people about a happy experience.

It’s quite evident that in 2020 and for the foreseeable future, customer experience is going to play a vital role in the growth of sales and customer retention. Here are 3 factors that sum up the importance of CX:

1. Customer Experience Enforces Customer Retention

More often than not, customers return to a brand because of their experience than only due to the product or service itself. ‘Build it and they will come’ is simply not true. A product or service with great functionality needs the expertise of customer experience design too. In today’s world of product parity, this is even more important. There is very little that separates one bank’s services from another and hence the primary interface – in many cases, the mobile app becomes crucial for customer retention. According to Forbes, 96% of customers pledge loyalty to a brand based on customer experience. Acquiring new customers is important, but retaining customers and getting repeat orders can propel a business.

2. Consumers Decide Based on the Experience, Not the Product

Yes, having a great product is important, but it does not ensure a sale. Research shows that CX plays an important role in consumer decision making. In fact, consumers who have a good experience with a brand tend to spend more, return and refer. According to GrooveHQ, a customer that is highly engaged is 6 times more likely to return to try a new product or service. The report also states that customers who have had a pleasant experience with a brand spend 140% more compared to the customers who have had a bad experience in the past and 69% of these happy users would recommend the brand to others.

Here’s how McDonald’s was able to scale their CX with a user-friendly mobile app. McDonald’s India had a vision to make its mobile app the preferred medium for ordering. The goal was to bring the emotions of joy and delight from the in-store experience to the new McDelivery app. By analyzing the pain points in their current user journey and crafting a strong intent for the desired experience, McDonald’s was able to simplify the process of discovery, personalization, decision making and create delightful interactions. With the best UI in place, they increased orders by 103% with the new app, redirecting traffic from web-based ordering.

3. CX Gives Brands the Information Needed to Make Their Product or Service Customer-Centric

Customer experience involves customer engagement, which gives businesses the data needed to improve products, services or the experience itself. According to this article on Hubspot, 70% of dissatisfied customers are willing to shop with the same brand again if their issues are promptly resolved by customer care. Enabling customer feedback channels through store forms, online chat windows, email, and telephone is a great way to initiate communication between the brand and a customer, and also seek feedback that allows brands to improve.

However, customers do not think about these things as much as enterprises do. They do not segregate an ‘offline’ and an ‘online’ experience in their minds. They simply want to get things done irrespective of the channel or technology.

Customer Experience Strategies That Brands Should Adopt to Scale Acquisition, Engagement & Retention

1. Collect Data & Infer Results

Collecting customer feedback is quite easy with numerous options available. A simple NPS survey or customer effort score (CES) survey can give brands valuable information about their product/service and CX. Advanced technological solutions like AI and Machine Learning enabled bots that can interpret a customer’s mood through Natural Language Processing allows brands to take proactive measures in solving a customer’s problem. There are many solutions at a brand’s disposal, and it is important to collect consumer experience data to derive useful insights.

2. Find a Balance Between Automation and Human Intervention 

Anyone who has dealt with customer service calls (and the hold timings) can say how frustrating it can be. Chatbots, powered by Artificial Intelligence was meant to be the solution to resolve customer issues. However, we can safely surmise that neither of the routes have reached perfection. It’s vital for brands to implement a smart mix of technology and human intervention. The first step is to identify roles that are best addressed by humans. AI can play the role of enhancement by analyzing interactions, the context of queries and automating several processes. An often neglected aspect of human intervention is the role of strategy and the role of design thinking prior to crafting or solving a customer experience issue.

3. Implement Immersive Technologies

During the launch of their SUV XC90, Nissan rolled out a VR enabled application that allows customers to take a test drive, virtually. Advertising and marketing methods have changed with the changing technological landscape. Technology like AR, VR, 3D Images/Videos, etc present customers with an immersive shopping experience that helps them better understand the product, in turn improving customer experience and satisfaction.

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4. Deliver Hyper-Personal Services

An article by Accenture stated that 75% of customers are more likely to purchase from a brand that knows their name, purchase history and/or recommends products based on past purchases. This forms a part of several design thinking principles that can help in customer retention and help prevent churn. With smart feedback systems and CTAs in place at customer exit locations (in-store or on a website/app), it’s not difficult to gather relevant customer information. Brands should then segment their marketing campaigns based on customers’ data, which increases the chances of engagement and purchase.

5. Have a Relevant Omnichannel Presence

Customers will not articulate that they choose a brand because of its omnichannel presence. But a relevant usage of platforms and experiences adds to a brand’s aura and helps in creating a ‘big brand’ feel. It can lead to brand affinity – a key differentiator in a parity world.  The modes of communication consumers prefer, depend on demographics, location, age, gender, lifestyle etc, making it vital for a brand to be active and reachable on multiple channels today.

An omnichannel service does not simply mean having a token presence across channels of communication, but a one that is relevant and adds value to the consumer [Click to Tweet]

Success Stories Resulting from Exceptional Customer Experience (CX)

Hewlett Packard’s Virtual Agent for Customer Service

HP Inc sells over 50 million PCs every year, and with every PC they acquire a new customer who will in some time in their journey be in need of service and support. In fact, HP Inc handles over 600 million technical support contacts every year. Working with Microsoft, HP rolled out an AI-enabled virtual assistant that interacts with customers and helps them solve issues they face with a product. It guides users through a troubleshooting process and provides solutions. If a solution isn’t available, it transfers the customer to a human representative. Here’s a brief overview of the key benefits:

• Prior to implementing the virtual assistant, HP would address 15 % – 20% issues digitally. Now, they address close to 80% issues successfully.
• The bot helps customers navigate over 50,000 pages of product information by understanding their needs.
• All of this resulted in increased customer satisfaction.

Mercedes-Benz Leveraged Predictive Analysis and AI to Better Serve Customers

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Mercedes-Benz was looking for a solution to better serve its customers in Brazil, which has the second-largest plant followed by Germany. They invested in a Microsoft Azure cloud solution coupled with Power BI and Cortana Intelligence to map their sales processes, and analyze decades worth of data like license plate records, macroeconomic indicators, regulations, sales information, and statistics by each region of the country. Through this change in data analysis and interpretation model, Mercedes-Benz was able to:

• Provide 180 service locations around Brazil with consistent, actionable information to ensure each location could provide accurate proposals to their clients.
• Assist sales reps to engage with consumers proactively before a need is even expressed with the help of predictive analysis.
• Support employees in engaging and serving customers better, and in improving overall customer experience and satisfaction.

Thomas Cook’s Nurture Program

Thomas Cook was looking to establish a better and direct relationship with their existing customers, and prospects. They wanted to influence customers to be able to recommend their travel packages. Thomas Cook ran a CX improvement program a few years ago and collated data to study their customers’ interests and ran campaigns to request for personal recommendations based on past interactions and through display re-targeting. Through these hyper-personal campaigns, they were able to:

• Receive over 15,000 leads.
• Increased ROI, as high as 7.5 : 1 in a span of 3 months.

Hotel THE PIG Revamped their Online CX Experience to Boost Revenue

Kitchen garden hotel THE PIG wanted to increase their customer acquisition through online channels. They revamped their online booking process to make it more customer-centric and easy to use. In collaboration with Etch and Micros, the hotel implemented Opera Reservation System (ORS) in order to put in place a customized booking system that is customer-centric. By simplifying the customer’s booking journey, they achieved a 250% growth in online revenues over a two year period.

Improving customer experience is a seemingly simple two-step process: understand the needs of the customers, and implement effective methods to satisfy those needs. However, it is a lot more complex to accomplish and calls for collaboration with experienced partners who understand customer journeys, the role of digital experiences and the technologies that help craft them. There are also a plethora of tools available that help to collate consumer feedback, analyze customer satisfaction levels, and serve customers better. With the right strategy and tools in place, brands will be able to scale their customer experience alongside brand reputation, sales, and customer retention.

Personalization has inherently been associated with digital commerce where technology and data often go hand-in-hand. But owing to the proliferation of new-age tech inside brick-and-mortar stores, the trend is now seeping into retail.

In the past, in-store personalization was mainly attributed to a sales assistant interacting with customers and asking them relevant questions to map their shopping behaviors and needs. Modern-day technologies have been able to capture and scale this behavior towards better avenues of segmentation, customization, and contextualization. Armed with a plethora of solutions, retailers are now able to combine historical cross-platform knowledge of every visitor with a real-time context. A study from Accenture reiterates the importance of such a strategy. According to it, 75% of the customers are more likely to buy from a retailer that leverages personalization elements such as their name, personal recommendations, and preferences.

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The same research also reveals that most of the consumers comfortably share their data with businesses that are transparent about how they use their data and lets them control how the data is used.

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Even after all this, there is a knowledge gap between the expectations of the market and the response from retailers. For instance, research by Retail Week in the UK revealed that only 14% of retailers consider personalization as an essential consumer experience strategy.

Key Technologies Personalizing Retail

To successfully leverage personalization, it requires a prompt understanding of the customers and their needs in a way that collects, analyzes, and streamlines data. Here’s a look at the various technologies are helping retail companies in this regard:

1. Artificial Intelligence and Machine Learning

Retailers are leveraging AI and Machine Learning to Automate workflows and drive better decisions. With AI, they are now able to track the buying behaviors and lifestyle choices of customers, automate redundant tasks, and improving the productivity of employees. Emerging AI trends in retail include:

• Customer Insights and Journey Mapping: Natural Language Processing and Machine Learning can sift through copious amounts of customer data to understand them better and accordingly personalizing product recommendations, designs, and end-to-end shopping experiences.

• Virtual Personal Assistants (VPAs): VPAs such as conversational chatbots can now address customer queries in real-time and at scale. Digital retailers can cater to thousands of customer queries simultaneously, boosting response time and query resolution.

• Predictive Analytics: By analyzing data about historical sales, marketing campaigns, website interactions, and customer support, prescriptive and predictive modeling is being used in marketing, merchandising, and even hiring.

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Case Study: Sephora is equipping its employees with hand-held devices that can scan the faces of customers, capture their exact skin tone, and recommend products by matching it with relevant shades. The AI-powered device even creates a four-digit code that is known as the Color IQ of the customer. All associated data with every ID is then securely stored to personalize future shopping experiences.

2. Voice Recognition Technology

With numerous voice-powered devices such as Amazon Alexa, Apple Home Pod, and Google Home being launched by tech giants, use cases of voice technology in retail have been on the rise. It is revolutionizing shopping by making it more accessible and conversational. This has presented retailers unique opportunities to target more customers by optimizing their digital listings for voice searches. Marketing opportunities that retailers can leverage in this regard include:

• SEO Optimization: By targeting long-tail keywords that are more oriented towards conversational searches, retailers can optimize their SEO scores and increase their voice search rankings on SERPs.

• Platform Listings: Product listings on e-commerce websites such as Amazon and Flipkart can also be optimized to improve the chances of being recommended by voice search assistants such as Alexa.

• Content Marketing: By creating niche content that is specifically targeted for voice searches, retailers can increase the organic footfall on their websites and shift their marketing stance towards inbound.

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Case Study: Coffee giant Starbucks has embraced voice recognition by going a step ahead and including the capability in the My Starbucks Barista app. Customers can directly place their orders with voice commands before they visit their local Starbucks and find their orders waiting for them.

3. Mobile Point-of-Sale Terminals

Research suggests that in the United States alone, more than 50% of all digital shopping would be executed via mobile devices. Retailers are catching on with this trend and leveraging Mobile POS (mPOS) devices inside their stores that are blending the boundaries between digital and in-store shopping.

A mPOS is a smartphone, tablet, or a wireless device that functions as an electronic point of sale terminal. It enables sales associates to conduct financial transactions. Various benefits of this technology include:

• Reduced queuing and idle time of customers, especially during times of peak footfall.

• The system can easily collect and store personal information of customers such as their email IDs. Feeding the data in the ERP system and connecting it with marketing campaigns, such as a loyalty program, can then help to retarget these customers in the future.

• Since the mPOS devices run on a high-caliber point of sale software, they include an added level of security that is backed by reliable servers.

Case Study: Apple has been successful in eliminating cash registers from its stores around the world with the implementation of mPOS. Every store associate is equipped with a mobile device that includes the information of customers and multiple digital payment capabilities. This has helped them to eliminate checkout lines and improve customer experience in a manner that fares well with their brand strategy.

4. Computer Vision and Sensor Fusion

Powered by computer vision, deep learning, heavy-surveillance and sensors, retail giants like Amazon have been working towards the implementation of a next-gen shopping ecosystem. This is being executed with the aim of eliminating redundant steps associated with purchases, checkout, supply chain, and inventory management.

Interestingly, the result amalgamation of these technologies has been dubbed as ‘Just Walk Out’ and works similar to that of a self-driving car. In order to remove human cashiers from the workflow of such a store, it leverages a high level of surveillance on all shoppers. Although there is no intervention of facial recognition technology, the store would use hundreds of cameras to monitor the activities of shoppers.   Powered by computer vision, the core system would be able to identify every object in front of it. As soon as customers pick up products and place them in their shopping carts, the same would be placed in their virtual carts as well. It can even remove the item from this digital cart if a product is put back on the shelf.

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Case Study: Amazon Go stores are leveraging these technologies to extend a cashless and instant shopping experience to their customers. Powered by deep learning and a plethora of smart algorithms, the system is able to identify customers with the help of advanced pattern recognition that draws conclusions from vast datasets. As a result, customers are automatically charged from their credit cards or digital wallets as soon as they walk out of the stores.

5. Robotic Process Automation (RPA)

RPA in the retail industry is paving the way for better productivity by catering to administrative processes such as product scanning, data analytics, and inventory management. This results in better customer relationship management, easier and quicker auditing, reduction in administrative costs, and more. The various use cases of the technology in retail include:

• Returns Processing: With the ability to make automated changes to the inventory database and billing details, RPA can drive redundant tasks like product returns.

• Workflow Management: Management of shifts, measurement of time and attendance, auditing of sales activities, performance monitoring, and payroll management can be easily executed.

• Customer Support Management: Sending real-time updates to customers, extending round the clock customer support, and capturing feedback from customers after the sale.

• ERP Integration: Tracking price changes, generating reports, monitoring employee vacancies, managing billing, and more.

• Supply Chain Management: Automation of emails among customers, suppliers, and distributors along with monitoring inventory levels and tracking shipments.

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Case Study: Walmart is leveraging RPA along with smart robots to patrol stores and automatically scan shelves for missing items. This will allow them to identify the products that need to be restocked while also identifying pricing details and misplaced items.

6. Indoor Positioning Systems (IPS) and Geofencing

IPS is another leading technology that is overhauling the retail industry by empowering brands to stitch together digital and physical shopping experiences. With IPS, retailers can:

• Track the movement and location of customers inside their brick-and-mortar stores.
• Help customers by empowering them to find their way around it and locate the products.
• Send personalized discounts and offers to customers based on their shopping needs and preferences.
• Boost the conversion rates of customers by making physical shopping more targeted and personalized.

On the other hand, Geofencing creates a virtual boundary around a physical location. The boundary then acts as a trigger system when customers cross it. For instance, as soon as a customer walks into the store of a particular brand, the app on his/her phone can identify this and automatically switch to the in-store mode. A navigation map can then pop up that displays the exact location of all product types inside the store.

Case Study: Aswaaq, a leading supermarket chain in Dubai, has equipped its stores with a connected lighting system that communicates with its customers. It enables them to find items that are on their shopping lists with an accuracy of 30 cms through the brand’s app.

The Verdict

Successfully mapping the present technological backdrop in retail and planning for future disruptions requires meticulous knowledge about consumer behaviors. Retailers that are able to use data as a precursor to change management while adopting these technologies would be able to foster a sustainable technological ecosystem.

Retaining customers and building customer lifetime value is a crucial aspect to increase conversions. Digital disruption with the advent of Artificial Intelligence and Machine Learning has resulted in a unique challenge for marketers. Though it allows them to provide customers with experiences like never before, it also opens a myriad of options for consumers to choose from. This may have made customer retention a tricky task for organizations. After all, repeat purchases from existing customers is a sign of a healthy brand, since acquiring new customers can be expensive and may often impact the bottom line. While expanding the value and building lasting loyalty cannot be created overnight, disruptive technologies can help.

Let’s understand how organizations around the world are deploying cognitive technologies such as AI to create brand loyalty and adopt agile strategies using their existing data.

Current Scenario

Most organizations understand the importance of collecting data and do it through several digital marketing strategies. But analyzing and implementing this data still remains a distant dream. At best, companies analyze data in silos for a specific campaign. The result is that these organizations only influence a few touchpoints of the buyer’s journey while focusing on specific data related to online conversion rates. To combat this problem, businesses have slowly started embracing AI and ditching data processing through traditional methods.

Harnessing the Power of AI

There are several ways in which organizations can harness the power of AI to empower decision making. The low hanging fruits such as sales forecasting and cross-sell/up-sell can help companies showcase proof-of-concept while understanding how vital it is to change the company culture for it to be successful. A mindset shift is required if teams want to adopt agile strategies by acting on crucial insights and create longer customer lifetime value. Some of the ways that AI can be incorporated into effective decision making include:

Sales forecasting

Traditionally considered a nightmare by management teams, AI algorithms can predict revenues for the upcoming quarter with higher accuracy. This helps the sales team and leadership to take a data-driven decision and achieve operational excellence.

Cross-sell/up-sell

Cross-selling and or up-selling is the easiest way to increase marketing ROI. To determine which customer segments will have a bigger value, use AI to identify the clients that are likely to upgrade or broaden their purchases across various products/services.

Price optimization

AI-enabled campaigns can help you identify what type of promotions incite buyers to make a purchase.

Up the Ante on Digital Marketing

2019 is the year to up the game in terms of digital marketing. Customers are no longer going to just click targeted ads based on their demographic information. As such, several organizations have started using AI to detect how consumers are engaging with content across different touchpoints. It also identifies marketing messages that may boost audience engagement. These insights are instrumental in refining marketing campaigns and boost overall ROI.

By mapping out the factors that affect customer lifetime value, both marketing and sales teams can create customer loyalty and advocacy programs targeting their most valued customers. AI is no longer being used to only improve the value, it is also forcing marketers to use it as a major KPI metric. Since AI is vital to understand the impact of several aspects of customer behavior, it also helps the team fine-tune their strategies and make metric-driven decisions for improved user experience.

How AI Increases Customer Lifetime Value

Create Personalised Experiences

AI’s ability to analyze data makes it possible for marketers to deliver personalized experiences to customers and make them feel special. Armed with insights from AI algorithms, teams can deliver relevant content that can further strengthen the relationship and build customer loyalty. Not so long ago, Netflix and Spotify started using AI-driven insights to provide recommendations to its customers, and today, all other brands are trying to provide the same level of personalized recommendations to its customers.

More Sales

AI can transform the way organizations engage customers with their products. By taking advantage of data, AI can be used to create a holistic and hyper-targeted approach that compels customers to purchase more. E-commerce giant, Amazon does a great job by suggesting other products, based on the historical preferences of the customer. As teams gain a deeper understanding of personal preferences, they can deliver tailor-made offers for their customers. In other words, AI equips organizations with tools to create trust and change short-term sales pitches into long-term loyalty.

Renew Faster

When customers are made to feel more valued and understood, they tend to stick around and become advocates of the brand. And as an AI-empowered marketer, the insights will predict what your customers might need next. By incentivizing longer-term commitments, businesses can grow their customer lifetime value at a fraction of the cost when acquiring a new customer.

Visual Search

Customers are gradually moving away from text-based search to more visual searches thanks to advances in AI image recognition and analysis. Platforms like Pinterest and technology such as Google Lens are making it possible for brands to improve merchandising and provide a personalized shopping experience. US retail giant Target’s partnership with Pinterest in 2017 shows their determination to integrate visual search in their e-commerce experience. The partnership allows users to take a picture of an item and find similar products on Target’s website.

Evangelize Eagerly

Implementing AI demands marketers to focus more on lifetime value. When this shift happens, customers see brands as an indispensable entity and start loving it. And with AI’s help in calculating lifetime customer value, organizations can determine the reward for their best customers.

Summary

Traditional marketing channels have considered all customers to be equal. But today, things have changed. Customers demand personalized experiences to make them feel special and build better brand affinity. For this purpose, organizations are embracing AI to make full use of their data, collected at various touchpoints of the customer’s journey. Primarily because not all metric-driven decisions make it easy for everyone to collaborate and create a customer-centric business model.

AI-driven marketing empowers teams to deliver better ROI (Return on Investment) from their marketing investment. It also helps businesses and companies make data-driven decisions and see what works for their brand and what doesn’t by forecasting sales, up-selling/cross-selling, and renewing clients’ contracts. Thereby, accelerating customers ‘ purchasing decisions and help organizations better tailor their content strategies.

The financial services industry is at a crossroad of digital disruption in Fintech and legacy systems. The wave of digital transformation which has impacted several industries including retail, media, and transport is making great strides in the financial services industry with several non-banking innovators providing both clients facing and back office technologies.

The last few years have also seen several acquisitions of finance technology software providers by banks and holding companies. These acquisitions have the potential to define the norms in the industry and the way in which software is deployed and utilized by money management firms including wealth management firms, RIAs, hedge funds or pension funds, fund managers, and large banks.

As these money management companies embrace technology, their bet is on offering their clients great solutions that deliver fully-integrated workflows. Even though this sounds compelling, there are several emerging industry trends that currently pose a challenge to this ostensible emerging paradigm.

This article will explore some of the challenges and opportunities provided by Fintech in the banking and financial services industry.

Challenges faced by the current financial services sector

Legacy Systems

To begin with, the likes of computing power, extensive connectivity, large data storage, and advanced analytical solutions provide a feasible digital alternative to both financial institutions and customers. It also poses several challenges for these money management firms. Even though this was inevitable, banks are finding it difficult to strike a balance between its legacy operations and the emerging technologies.

For decades, financial institutions have been dependent upon localized ‘on-site’ computing technologies. Therefore, the rate of adoption of disruptive Fintech solutions has been restricted by various factors, including a lack of better understanding of the technology in financial services.

Shrinking Fee

As money management companies are witnessing fee compression; the financial services industry is shifting its focus towards cost containment. This has seen several physical manifestations of this including the rising number of merges in the asset management space. This poses a unique challenge to the bank and software solution provider model as their acquisitions will mean that the companies can only achieve their ROI by significantly cross-selling to their client bases.

Think of it this way, as a money management firm facing razor-thin fees, what will be your priority? Not boosting your spend on technology in Fintech. On the contrary, investing in software to provide comprehensive solutions to the client doesn’t seem a great idea when there is a constant pressure of reducing costs. Such money management firms will then need to either charge extra up front or charge more on the back end.

Lack of Specialization

The myth that software solutions increase efficiency falls flat particularly in the financial services industry since most software solutions that are known for doing everything generally often lack specialization. In other words, most technology in Fintech cannot handle one particular functionality if it is known for having a great feature in another area.

This poses a great risk to the money management firms since investing in a software solution may help them at a given point in time, but not in the longer run. As the requirements of companies continuously evolve and as the industry’s priorities change, the technology that might appear ahead of the curve today may probably be behind the curve in months to come.

Innovation

Innovation is another challenge facing the large bank and software provider model. Technology in financial services has been changing as fast as the blink of an eye. So, when the vendor sells their solution, it is usually nearing the end of its innovation life cycle. The seller usually sells the software to the larger banks in the hopes of being able to continuously innovate and keep abreast with the latest technology to maintain its growing market share.

But there is a fundamental glitch: most of these large banks that use these software solutions serve a huge client base, who operate on different software versions. This makes technology updates even more difficult. With digital transformation in Fintech, the future of software is moving towards artificial intelligence, big data, advanced analytics, machine learning, and cloud computing. However, in reality, the technology currently being used by the clients mostly do not align with this model.

New Business Models

The rise in technology companies has forced several large money management firms to acquire these start-ups in order to remain relevant. However, there are emerging business models as well, which pose a challenge of inconsistencies within the industry. The digital disruption is causing banks and solutions provider to rethink their business models that align with their goals.

For instance, one way of operating is when the bank continues to be the face for customer interaction android offer products in a segment where they do not have the capabilities. While this is great for the customers, who will benefit from more product choices, it will also allow the banks to generate fee-based revenues from the software provider. This is one such model, and it is up to the money management firm to decide whether they want to acquire, partner or outsource Fintech provider. But these different models eventually create differences within the industry and provide inconsistent experience to the customers.

The rise of digital banks is another shift that the current banking industry is witnessing. According to a study, visits to bank branches are expected to drop 36 percent between 2017 and 2022, while mobile transactions are expected to grow 121 percent in the same period.

One such digital bank Revolut provides a finance app that enables currency and cryptocurrency exchange. Users can also control their finances by setting up a budget and tracking their daily spends. In 2018, Revolut has more than 2 million users. Another such mobile bank N26 allows its customers to open a bank account and manage money using their mobile devices.

Advantages of Fintech in the Banking and Financial Services Industry

The digital transformation in Fintech provides a great opportunity to large banks who can automate much of their manual tasks. It can help banks provide comprehensive solutions to deliver services directly to their customer’s mobile devices. Such technologies can also help banks to send sensitive communications with the help of encrypted Internet transmissions and also broadens the horizon for the banks to use cloud computing and save cost spent on data centers.

As rightly quoted by Jamie Dimon, Chairman & CEO of JP Morgan Chase “Hundreds of startups with a lot of brains and money are working on various alternatives to traditional banking.”

In addition to this, several Fintech companies like OpenLink, TransferWise and Poynt also provide point solutions in areas such as remittances, payments, savings and investments, trade and invoice finance, lending, and insurance. There are robust models being created for Anti-Money Laundering-Know Your Customer (AML-KYC) compliance, underwriting and risk management, credit scoring, collections and recovery, customer service, capital markets activities, etc.

Traditional banks are also collaborating with the new-age FinTech to create a mutually beneficial partnership.

For instance- Goldman Sachs works with Symphony, which develops a secure way for sell-side and buy-side firms to collaborate keeping compliance in place. Blend Labs works with banks and financial institutions to develop an efficient underlying technology for mortgage lending. BBVA Compass has teamed up with OnDeck to offer loans to small businesses that would not otherwise qualify for bank credit.

PayTM one of the largest e-commerce payment system and digital wallet company of India, has partnered with leading banks to offer traditional services augmented by technology. The company has partnered with ICICI Bank, to announce an all-new initiative where users can get interest-free short-term digital credit. Called Paytm-ICICI Bank Postpaid, the new offer lets Paytm customers get instant credit for things like paying for movie tickets, bill payments, flights, as well as physical goods. It has also collaborated with Induslnd Bank to introduce a facility that creates a fixed deposit when the customer balance exceeds $16K (INR 100K) at the end of the day.

Final Thoughts

All the opportunities and challenges posed by Fintech in the financial services industry call for a new model for large banks and conglomerate. Instead of offering monolithic systems to the customers, it is time that these money management companies along with challenger banks look at customized and specialized solutions that have the capability to offer a suite of functionalities integrated with by APIs.

Innovation and competition will play a leading role to shape the future of digital transformation in Fintech so that they change according to their clients’ requirements. Least to mention, that this digital transformation in Fintech will eventually lead to the lowering of total systems cost.

The use of automation to drive tangible business benefits has become a reality today, with nearly all organizations riding the wave of emerging cognitive technologies. The insurance industry is no exception and lies at the crossroads of digital disruption. Traditionally considered as a highly regulated and cautious sector, today it faces a radical shift due to the rising benefits of intelligent technologies.

According to a research, up to 25% of full-time positions in the insurance industry will either be consolidated or reduced in about a decade as a result of the prevalent digital disruption. With such headwinds in the industry, automation can be a game-changer strategy that can help insurance companies increase their profit margins and revolutionize customer experience.

To gain a competitive advantage, several insurers have already deployed an automation strategy in areas like New Business Processing, Claims Processing, & Finance, and more are following suit.

The insurance industry is now aggressively evaluating use cases for cognitive automation to boost efficiency in their current processes and thereby lower operational costs. They are embracing digital solutions to remain profitable amid stringent regulatory norms while handling complex portfolios in a low-interest rate environment.

The good news is that the insurance sector is quite digitally savvy and an early adopter of new technologies. Especially, the players in the short-term insurance sectors (auto, home, health, etc.). Prudent players who have been the disrupters have reaped the benefits of digitization and will continue to do so. According to a Mckinsey report, a large insurer could more than double profits over 5 years by digitizing existing business.

For digitally savvy insurers, most of the routine activities like quote, purchase, policy documents, renewal, claims (processing) etc. are completely digitized and requires least human intervention and offer higher customization. For example:

• LexisNexis Data Prefill solution helps in pre-populating insurance applications using only a few customer data points leading to reduce costs, and faster & and accurate quoting and underwriting process.

• Progressive Insurance, enables customers to “name their price” and choose elements of a policy that fit their budget—the level of deductibles.

• Some insurers offer pay-as-you-go auto insurance whereby drivers are charged by the mile.

• Ladder Insurance has partnered with Fidelity Security Life to offer life insurance without the use of agents directly to consumers online without charging any annual policy fees.

Opportunities in the Insurance Industry

The insurance market has become competitive and more robust over the last few years with the coming of the online P2P insurers, technology, and insuretech players.

Far-sighted insurers acknowledge the looming competition from companies such as Amazon, Google, and Facebook, which can leverage their users’ pool of data to provide customized insurance products. Amazon has already taken in a leap in this area by hiring insurance professionals and is set to transform the insurance market in several European countries.

While digitization in the insurance sector is set to create opportunities, it will also present newer challenges for traditional players. With low-interest rate scenarios, the revenue streams of legacy insurance companies are quickly drying up, as investing customers’ premiums in several financial institutions are not paying the same returns when compared to the last few years. In the future, these challenges may increase, owing to the fast pace of digitization and changing customer preferences.

For instance, in the auto insurance sector, traditional insurers might face challenges as the use of sensors and telematics makes driving less risky and liabilities from autonomous cars go to manufacturers. The McKinsey report suggests, in the future profits for traditional personal lines, auto insurance might fall by 40 percent or more from their peak.

Insurers who have picked the pulse of the digital disruption and have innovated their products and value chain have seen growth. Some of these insurers are Progressive, Direct Line, Geico and more.

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It is only a matter of time when the likes of Facebook jump onto the bandwagon and provide stiff competition to the traditional mortar and brick insurance companies. Therefore, the need to adopt cognitive automation in the insurance industry has never been so pressing. And the call of the hour is to optimize operational costs, enhance accuracy, improve customer experience and get the most returns out of the allocated capital.

Challenges Faced By Insurance Companies In Adopting Automation

• Scattered Data

One of the biggest challenges in the insurance industry is that data is collected both electronically and on paper, making it extremely difficult for cognitive technologies to play their role efficiently. Dealing with mixed data format means that companies need to transfer this data into their machines, which can be costly and susceptible to human errors.

• Legacy Applications

Larger insurance organizations today use several legacy applications and point solutions. This leads to operational inefficiencies and excessive costs used by administrative functions.

• Manual Processes

The insurance companies are infested with several back-end processes which are usually labor-intensive, time-consuming and repetitive in nature such as underwriting, renewing premium and conducting compliance.

Use Cases of Intelligent Automation in the Insurance Industry

• Smart Data Reader

RPA (Robotic Process Automation) can be used to replace the manual paperwork involved during policy issue and claims processes. With the help of RPA, a smart media reader can be developed that can extract relevant information from the scanned documents. This solution has the potential to eliminate the manual extraction of data and its entry into the systems.

The RPA in insurance can use capabilities such as OCR, NLP, and machine learning to read, extract and validate data. This system can be seamlessly integrated with existing operations that deeply rely on extracting information from documents.

• Customer Service With Chatbots

Over the last few years, there has been a growing geographic and functional diversity of chatbots. Whether it is US-based Liberty Mutual Insurance’s “skills” for Alexa or “Nienke,” the “virtual host of Dutch insurer Nationale-Nederlanden or India’s HDFC Life’s chatbot collaboration with Haptik, all these chatbots have been deployed to enhance the customer support services.

Chatbots in the insurance industry can be used to analyze customer needs, modify product offerings and even solve the customer’s questions or provide them useful links.

• Automated Underwriting Solution

Creating a rule-based system for underwriting processes can increase efficiency and productivity. The automated system can identify if a submission or renewal can be handled by a machine or needs human intervention.

In the case of the first scenario, predictive models and machine learning algorithms can easily evaluate and provide a price for the submission. In the latter case, human intervention would be required in addition to the insights provided by the system.

• Smarter Process Analytics

One of the fundamentals to improve processes is by measuring several parameters including the number of requests and transactions processed. In the paper-intensive insurance industry, failing to measure outcomes can result in hefty losses in not just operational efficiency, but also in customer satisfaction.

With automation, organizations can measure not only the number of transactions but also have an audit trail that can be really beneficial for complying with regulatory norms. All this further enhances customer satisfaction, streamline applications, claims, and response of the customer service.

Quick Points to Remember

• Now that you know the use cases of automation in the insurance industry, let us look at some important points to remember before you decide to automate processes.

• Identify the areas to be automated. Trying to automate all complex processes is of no use if they do not provide any substantial savings.

• If you are new to automation, then try to begin with baby steps and then eventually expand. Starting small can help you clearly define objectives, the scope of work and the desired outcomes.

• Understand the extent of automation and avoid going overboard. Using cognitive automation in all areas may not lead to significant savings. Rather than automating all complex processes, insurers should focus on hybrid processes to achieve desired goals.

Future Of Automation In The Insurance Industry

Use of automation in the insurance industry has had a positive impact on customer experience and satisfaction. Several insurance companies have benefited from this, including a UK-based company that used automation to proactively identify customers inflicted by floods. The system then created a waiver plan for the next month’s credit and notified those people that their dues were waived.

With cognitive automation in the insurance sector becoming more and more sophisticated, it will provide a cost-effective way to meet rapidly changing regulatory requirements and help businesses concentrate on strategic long-term issues. Artificial intelligence enabled assistance can lower the documentation time by as much as 80%, thereby making insurance companies more profitable.

Today, companies need to rethink how they want to use human potential and how much they can trust a computer to handle their operations. By doing this, one thing becomes clear that human touch will become premium in the future, especially when robots will do most of the repetitive and mundane work.

Only the work involving a higher degree of human intelligence will not be automated. Positions in operations and administrative supports will likely be prone to layoffs or will be consolidated. However, the extent of the impact of automation will greatly depend upon the market, group, and the potential for automation.

The high-frequency products are digitized and automated to a larger extent. Whereas there could be challenges in converting the legacy policies into this new arena, it is not just technology challenge, but also the product has to evolve to meet current market conditions and tech/regulatory landscape. Insurers will have to re-look at digitization from a broader perspective which is inclusive of product innovation, services, and business models.

Looking Ahead

While the prospect of automation has been there in the insurance industry, the speed of its adoption has been increasing over the last 12 months. As disruptive technologies challenge the traditional ways of operations, organizations need to become “comfortable” with being “uncomfortable”.

The insurance sector can only optimize costs, enhance decision making & productivity, lower costs, optimize the customer experience and alleviate accuracy by embracing smarter technologies such as RPA, artificial intelligence, and machine learning.

As digital technologies continue to disrupt the insurance sector, the industry will have to move from its current – ‘reactive’ mode to ‘predict and prevent’ mode. And, this evolution will have to be at a faster pace than ever before as the stakeholders in the insurance value chain like the brokers, consumers, financial intermediaries, insurers, and suppliers become more adept at using advanced technologies.

Cognitive automation in the insurance sector can help businesses automate key operational processes and maximize returns amid stiff competition. As insurers evolve, the focus will be more towards adding value to key functions by working with intelligent solutions.

All these can only be achieved if organizations strike a balance between automation and human intervention. Businesses who fail to adopt smart solutions will eventually lose to new market entrants, while the ones who do too much too soon to get the early mover advantage will also be at risk.

The key is to have a balance by running test programs to see the capabilities of these technologies and align them to the business’ objectives and expectations. These are extremely interesting times for the insurance sector.