Real-Time Customer Data will shape the finance industry’s future

14 May 2020

In recent years, finance institutions have been facing a challenging market situation. Low interest rates, the requirement for increased monetary reserves and stricter regulations have been putting pressure on profit potentials. Besides, the advent of new rivals in the industry has toughened the fight for market shares. In a 2020 Digital Trends report, Adobe ranked the competition with digital native companies as the most significant challenge on the market.

Digital Trends in the Finance Industry

According to this report, there are at least 58 venture capital-backed fintech and insurtech unicorns operating by the end of 2019.

In an ever-connected world, customers are highly informed and expect the best experience in their own journey. It comes then as no surprise that respondents of the Adobe survey, indicated customer journey management, personalization and customer data management as the top 3 digital initiatives to scale up for the future.
Exceptional Call for Personalized Customer Service

To top it all, the Coronavirus crisis has been putting an additional strain on the industry, demanding even more financial flexibility and putting additional load on the support side of customer journeys. The Adobe Digital Trends Report even points out that 9 out of 10 customers are likely to shop with a brand that is handling a crisis well. It seems that quality customer support and service will be a key differentiator in the coming period.

Now, how can financial institutions make the difference? While browsing online, customers expect effortless engagement – they seek easy access to services and quick answers to their questions. And in this process, they would rather avoid repeating information that has already been given. Unsurprisingly, studies point out that more than 90% of customers expect personalized experience and that more than 80% of them are willing to share data to achieve this.
Self-service and Chatbots Improving Efficiency

To efficiently handle the increasing amount of customer requests during the crisis, a lot of companies are investing in self-service and chatbots. Any organization that receives questions from its customers wants to be able to answer them all in a timely manner but, the overload of repetitive questions often hinders an optimal result.

Requests for deferral of payment conditions for example, could peak in times of uncertainty. In order to handle all questions within this category quickly, NLP driven chatbots (based on Natural Language Processing) can be used to automatically browse through databases and suggest corresponding answers. Smart bots assist customers in narrowing down the questions and indicate when to switch to human expertise. This is where the real-time data proves its importance.

The Importance of Real-Time Customer Data

To achieve effortless experience for customers, service agents should not only have a good 360° client overview at hand, they should also be fully aware of what customers were doing online before initiating the contact. When provided with such information, agents can accelerate the response process and customers do not have to go through the hassle of reexplaining for the umpteenth time what they have been searching for. In other words, this enables both a better personalized customer experience and an increased call handling efficiency.

Another aspect to consider is the use of several channels to engage with financial institutions. Customers could well send an inquiry via the chatbot on a bank’s website in the middle of the day and be willing to follow up the answer on their mobile app in the evening. Therefore, customer profile data ought to be properly centralized to allow exploitation across all engagement channels, to ensure a consistent experience.

In this way, real-time customer profiles combined with service automation initiatives such as chatbots are perfect tools to provide effortless service. By adopting these tools, organizations can give their customers the necessary information much faster, while maintaining a personalized experienced through service agents.

Anticipation of Future Friction

Effortless engagement is not only about efficiently handling customer questions, it is also about anticipating and especially avoiding future friction. Think about what banks could do for example, in terms of improving their offline journey. Mobile apps could, for example, use a customer’s geolocation as well as his agenda to recommend ATM’s close to his favourite lunch bar.

Or it could proactively notify users that their usual go-to ATM is out of order that day and redirect them to another one nearby; It could recommend shops with cashless pay options, also based on the customer lunch and payment preferences. This leverages again customer data in real time to provide relevant recommendations.

Real-Time Data Driving Results Throughout the Customer Journey

Next to improving support and service, real-time customer data also drives results across the entire customer journey. Imagine that a customer is exploring a bank’s mortgage options: the bank’s online tool should be prefilled with information on the person’s online context, salary, expenses, savings etc, so that simulations and alternatives can be presented quickly. This will avoid losing customers to a too lengthy, repetitive or cumbersome process.

Analyzing your customer’s journey will let you know what you can do to influence them at each stage. By identifying the exact point in which the customer abandons the process, you can determine what the next best action for that customer should be and follow up on those hot leads. Coming back to our previous example, if you detect in real-time that a customer is checking out mortgage options on your website, you might want to proactively reach out offering assistance, and making sure any potential questions are answered instantly.

You can also suggest a chat, a call with the contact center, or an online appointment with an advisor, before your customer abandons the online simulation process… Omnichannel engagement based on such canvas has the potential to not only drive sales, but also customer loyalty.

Enabling Customer Conversations

While personalization is becoming the new normal, customer conversations are the future. For example, offline customer data suggest that a customer could be interested in travel insurance, but real-time digital data points out that this same customer is exploring e.g. car finance options. Starting a conversation on this second topic is much more relevant.

Only real-time customer data will allow you to address situations like this. It is the fuel for digital decision-making, a crucial capability in enabling customer conversations. In this scenario, the real-time customer profile will mention both his interest on car financing as a digital experience event and as well as the suggestion for travel insurance.

However, digital decisioning logic will combine this profile information with business priority rules to suggest the most relevant offer, which in this case makes sense to both the customer and the organization. This is how companies balance recommendations based on commercial driven objectives and customer intent. Real-time data, digital decisioning, combined with analytics are the future to unlock personalized customer conversations.

The Action Plan

As finance institutions are challenged with short term crisis factors, they also need to face more underlying industry trends. Therefore, key recommendations include:

Implementing solutions that quickly allow the management of additional support (like self-service options and chatbots)
Designing with a real-time customer data mindset, to reduce customer effort and anticipate future friction
Using real-time data to drive result across the entire journey
Enabling customer conversations based on real-time decisioning and profile data.

Good action plans start with clear priorities in mind and asking yourself the right questions– Where can I make the biggest impact? What would the best improvement be for the largest group of customers, or for the largest product categories? Which request types could be optimized? How to avoid the biggest risk, or find the biggest company gain? (…) Answering these questions will help you determine the first steps to your strategy. This will guide you to identify opportunities to improve service, personalization better conversations and results through different channels.

Whatever you would like to improve, make sure you keep it consistent across channels. Then, identify the data you need to support personalized experience. What customer profile data would you need to realize your current and future goals, avoid customer friction and make personalized recommendations? And what knowledge base, offer descriptions, metadata and content would you need? You should also determine how you would you like to measure the success of your new experience tactics (improved handling time, improved satisfaction, number of personalized offers presented vs offers sold).

Finally, check that your customer engagement technology capabilities support your strategy. Review these capabilities to capture digital signals of customer intent, and how it can combine this with offline data in a complete and real time customer profile. Verify then, that this profile is available across all channels and departments. Based on this, customer journeys will be atomically generated, offering decision-led or machine learning driven product recommendations. This will enable a balance between service interactions and commercial interactions.