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Hottest information about 9th Annual Customer Experience Management in Banking Summit

25 FEBRUARY 2020

9 steps to successful Digital Customer Onboarding

The fight for a new customer has never been more difficult or costly. Research reveals a global increase in overall customer willingness to change banks or insurance companies. This is particularly true for the Millennial segment, who have only known a digital world. They understand how easy it is to switch loyalties and don’t see any reason they should treat your institution differently than another service provider.

The competition for new customers is fierce. That customer wants, in analogy to his retail experience, to be up and running in a matter of minutes. As soon as he is confronted, even online, with tedious interfaces, exhaustive question lists, time-consuming authentication processes and, yes, the request to finally meet a representative in person to literally fill in the paperwork before his account is active, it shouldn’t come as a surprise that he is quicker out than in. Result? A potential customer is lost!

So, if all this is known then why are there still so many organizations that rely on the classical methods of customer onboarding?

Read on in the whitepaper below to discover the 9 steps of a successful Digital Customer Onboarding process:


20 FEBRUARY 2020


Head of Division Group Corporates
Erste Group IT Int. GmbH

Questions For Davor GAŠPARAC

What do you see as the biggest challenge in banking customer experience nowadays?

A decade or two ago, a typical customer came to a bank to open a giro and savings account, ask for a loan and then visited a branch a few times when in need for banking services. On the other hand, banks usually spammed customers with promo materials about new products and services hoping something might be interesting. These days, under pressure of FinTech and inspired by companies like Apple, banks are competing in providing “amazing customer experience” with their apps and competing who will offer more “digital products”. However, although things improved in last years, customers still have difficulties to understand financial products and are still overwhelmed with “digital offerings” they do not necessarily need. So, analysing clients’ needs and offering meaningful and useful assistance when a customer really needs it would be the biggest challenge I see.

How can banks improve customer engagement?

I am not sure banks should focus that much on customer engagement. Banks first need to understand customer needs better and be there when customer needs support – but nothing more. All industries are fighting for “more engagement” from clients, but I am not sure it is what clients need, and I am sure it is not what clients want.

What makes a digital transformation successful?

Digitalisation is usually an endeavour which aims to build “new ecosystems and platforms” as expensive consultants are teaching us, but it usually ends only in “digitalising” products and services – at best. Putting PDF forms on your new web page is not digitalisation. Digitalisation shall focus on finding new ways of solving clients’ problems, by integrating (sometimes existing) products and services into new offerings. Only when this mindset change is done, we can consider that digitalisation has started. If you manage to solve at least one client’s problem like that – you can consider yourself successful. But, do not aim on “total” digital transformation. Transformation shall not be a goal. It is just a tool.

What technology do you consider to have the biggest impact on the banking industry in the near future?

No single technology will have a big impact on banking by itself. One single technology does not change a lot and we should not search for a silver bullet technology. The key is in finding new ways of helping people and companies doing their finances. We can do miracles with existing technologies already, but we are too busy digitally transforming our products and services instead of trying to be more useful.



Global Head of Marketing
UniCredit CIB

Questions For Marcello Calabrò

Which are the key enablers to become more customer-centric?

I believe it is a combination of two soft factors: the corporate culture and the individual attitude. The first one is instrumental to align all the value chains, the functions and the processes to deliver the best customer experience. Very often there is the right tension in the front line but it is not easy to spread the same tension across the whole organisation, especially in big corporates. About the individual attitude, I have always worked in people businesses, and there, without the full and deep buyout of everybody, you cannot excel. I also think that these two factors are strictly correlated: a customer-centric corporate culture can foster the individual contribution.

Why is it important to focus on customer insights and data analytics?

Well, they are the tools. It is like sailing: you need the passion and the spirit for adventure. But then you also need the technical equipment to navigate safe and fast. Customer insights and data analytics are the tools that tell us what clients are looking for. The ultimate goal is to tailor-make the offer and possibly also to anticipate the needs. Being able to surprise a client is a major achievement which can be a game changer when it comes to loyalty.

How to manage customer data in the most efficient way?

Let me say that before the data, you first need a vision for your business. Even the most accurate marketing analytics cannot help of “ex-ante” strong product and entrepreneurial ideas. In my view you cannot start form data, and define the offer as a consequence. You would get lost in the volume, variety and velocity of data. Of course, the more information you have, the more you can test and fine-tune your vision, and the more customised and unique the solution is. But there is also a growing concern of data protection which is a very legitimate request to avoid that personal data travel where is not necessary and that can be used for pushed advertising. I think a regulatory framework was necessary. Now we need to avoid overshooting in the opposite direction. We marketers can support this process in order to strike the right balance.

What do you see as the biggest challenge in banking customer experience nowadays?

Banks have faced a revolution in many respects: from iper regulation to negative interest rates, from technology disruption to new entrant in the competitive arena. I think as an industry, banks have done quite a job and have been able to adapt to survive. In other industry radical transitions, we have observed many more casualties, if not the extinction. Now the challenge for our industry is to combine the two bank souls: the advisory based on experience and personal relation on one side, the convenient and fully digital access for many repetitive services on the other. It is like to mix a high profile consultant approach with a FinTech spirit. I wish marketing could play a central role in this evolution.

27 JANUARY 2020


Head of Applied Sciences & Artificial Intelligence
Lloyds Banking Group

What every CEO needs to know about AI.

It’s a mistake to simply delegate AI to your digital team. Here’s how you can use it to transform every aspect of your business

This article was originally published by London Business School.

Few CEOs realise that AI can help them achieve their business objectives and shape P&L. They know how to use organisation redesign, cost-cutting, mergers and acquisitions, business and product launches and geographic expansion, but fail to see that AI is a powerful tool to be added to their toolkit.

Since most CEOs of non-tech companies don’t know how to integrate AI into their business strategies, they tend to delegate the AI work to their digital teams. But these teams inevitably look at it from the technology perspective rather than the P&L perspective. And what I see is digital teams getting lost in the trees at the expense of the forest. The latter needs senior business leaders to fully understand and appreciate how AI can help them.

 Few CEOs realise that AI can help them achieve their business objectives and shape P&L.


Every CEO should be able to reimagine their business and P&L with the help of AI. As a guide, I have identified 17 levers that CEOs can shape with the help of AI to boost their P&L. The framework I have developed (below) can be used both as a barometer for assessing the current state of your teams’ efforts around AI and for spotting new opportunities. The 17 levers show how AI can help drive superior growth, increase return on capital, and manage both desirable and undesirable risks.

This framework will help you have structured and focused discussions with your teams around how to harness the power of AI, looking at questions such as:

  •  What is the potential size of the prize?
  •  At what stage is the market evolution?
  •  What have early adopters achieved?
  •  Which pitfalls to avoid?

Sustain superior growth

Let’s start by looking at growth. Sustaining superior growth is elusive. There is no winning formula. But successful companies seem to get the concoction right of strategy, capital allocation, innovation, market expansion, and excellence in sales and marketing. The last three of these levers have been the focus of the early adopters of AI.

Creating the new

The secret to driving growth is to be bold and develop an entirely new product that wasn’t possible before. Take the example of Amazon’s smart speaker Alexa – now in one in every six American households. It is an innovation that has broken the previous stranglehold that telecoms giants, phone manufacturers and providers of home entertainment systems had on the “voice” coming into the home.

In retail, the online supermarket group Ocado has put AI at the very core of the way it runs its new Customer Fulfilment Centre – something we used to call a warehouse. The company maintains that the time it takes to assemble an order has been slashed from a couple of hours to a few minutes. Algorithms run the warehouse to ensure speed, accuracy and quality – even making sure that a watermelon isn’t put on top of a carton of eggs. And not only does the AI platform allow Ocado to run its own operation efficiently: it has sold the system to other retailers overseas.

Small advances, such as adding some AI to an existing product, might help you stay competitive. But it’s unlikely to turbocharge your revenue or profits unless you are Apple launching iPhoneX with facial recognition feature or you can modularise your product and command price for the AI module.

Reaching a wider universe of clients

Forward-looking companies have started using AI to allow them to serve customers who were previously considered not worth bothering about.

Take the provision of credit. Machine learning can look at data such as an individual’s past shopping habits, the payment of utility bills and rent to build up a picture of their likely credit-worthiness. Armed with this information, a company can then decide whether to grant a loan – even if the person concerned has no past credit history. Globally, about 1.7 billion adults remain unbanked. Meticulous attention needs to be paid, however, to privacy laws and customer consent and preference; the sector is facing a few regulatory headwinds.

 Forward-looking companies have started using AI to allow them to serve customers who were previously considered not worth bothering about.


Similarly, a small business can be assessed when it seeks credit: AI can look at information such as sales volumes, customer reviews and data gleaned from places including Facebook, LinkedIn, eBay and PayPal in weighing up whether a company is likely to pay its bills.


Getting more from existing customers

AI is now close to making mass-personalisation possible. It can bring together traditional data with detailed information about a customer’s behaviour gathered from sources such as online browsing, social media and wearables. This then allows the right product to be offered at the right time with right message. McKinsey has estimated that such mass- personalisation can lift sales by 10% or more.

With Amazon, for example, we already see machine learning exploited to make suggestions to existing customers: they are presented with a recommended “next product to buy”. These ideas are generated by looking at your demographic profile, what you have bought in the past and what has been bought by customers with a similar profile. At one point, some 30% of Amazon’s sales were prompted by its recommendation engine.

Business-to-business companies can adopt a similar strategy by mining past ordering patters and comparing them with the sales made to similar customers.

And don’t ignore AI’s potential for reducing churn and holding on to customers. Machine learning can look at such metrics as the frequency with which a customer logs on, their response rate to emails and how often they call service desks. Then it can estimate the likelihood that a customer will defect to a rival, and intervene to try to reduce the number of cancellations.

Take the example of Netflix. Customers are likely to lose interest if it takes them more than 60 to 90 seconds to find something they want to watch. By using personalisation and recommendations, the company reckons it saves more than $1 billion (£757 million) in revenues that it would otherwise have lost.

Getting the right price

There is no law of nature that says every customer should pay the same price for a given product or service – nor, indeed, why a single customer should pay the same price each time she or he buys a given thing.

Uber provides the best-known example of variable pricing: when demand is high in a particular area relative to the number of drivers available, the price of a ride will rise.

The same idea can be applied to a myriad of other industries. Mobile phone operators are experimenting with the use of machine learning to predict demand and gauge the price sensitivity of small cohorts of consumers. They can then use this information to decide the demand-price trade-off to maximise revenues and profitability.

And in the business-to-business arena, companies can use data science to pinpoint clusters of customers with similar buying patterns, identify similar deals and generate information about what prices are being paid. Arming salespeople with this information can help them negotiate the best possible prices without losing business.

Increasing sales productivity

Around two-thirds of a salesperson’s time is taken up with routine jobs such as making contact with potential clients, setting up appointments, taking orders and preparing contracts. Companies have begun automating activities such as these, freeing up salespeople to close deals, nurture relationships and manage deals that are out of the ordinary.

A company that can identify the leads that are most likely to lead to a sale will have an advantage over its competitors. Companies are using AI’s predictive capabilities to pinpoint the most promising leads and routing those to sales people who are best suited to close those deals based on their past sales history. Additionally, companies are recording, transcribing and analysing sales calls, demonstrations and meetings using AI. Looking at how the most successful sales representatives led conversations helps companies to coach other staff to up their game and secure more deals.

Making marketing spend more effective

Personalisation is now at the core of marketing. It can yield huge gains in the returns achieved on marketing spend.

But forward-looking marketers can now go further. Analysing customers’ behaviour is only the start. Companies can now target their marketing in a way that is attuned to a customer’s behaviour, preferences, and sentiment, creating emotionally intelligent personalised content using natural language processing (NLP) technology which could be deployed at scale across all marketing channels. Citi, the global bank, has employed Persado, a marketing language cloud company and has seen a 70% increase in its email “open rate” and a 114% increase in the click-through rate.

Abhijit is bringing machine intelligence to life at the Lloyds Banking Group. His focus is on combining machine and human intelligence with data to fundamentally change the way the Bank does business and create new opportunities for customers and colleagues. Earlier, at McKinsey & Company, Abhijit engaged with CXOs across Europe, Asia, and North America to help them take strategic and investment decisions, build new digital business models, and drive above-market growth. He led a venture with a leading digital corporation to develop analytical offerings for energy and investment sectors. Previously, at HCL Technologies, Abhijit built and led tech-enabled businesses in Europe and Asia. He helped large corporations and governments embrace technology to improve performance. This included setting up technical support infrastructure with over 3,000 people for the UK-wide broadband launch by BT. An engineer by training, Abhijit has an MBA from London Business School with exchange at MIT Sloan.

13 JANUARY 2020

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