Addressing Banking’s Deadly Digital Intelligence Debt – How Finserv Marketing Needs To Evolve In 2017
The financial services landscape has fundamentally changed. Top banks are under attack from niche players and non-banking companies.
In fact, 49 percent of banking executives say the traditional transaction/ branch based banking model will be dead by 2020, according to The Economist‘s 2016 report, “Retail Banking: In tech we trust.”
Consumers increasingly expect highly contextual, personalized experiences across their connected devices. Banks and other financial service providers who can’t deliver are under threat of extinction.
Most banks are not ready for this new demand. Fewer than 20 percent of bank executives feel prepared for the future, according to PWC’s Retail Banking 2020.
In order to compete, banks must demonstrate customer knowledge and understanding beyond what previously has been possible. Existing infrastructure is such that many systems do not speak to one another and the MarTech tools and processes that are in place have limited agility in terms of what they are able to deliver and how.
Banking’s digital intelligence debt is primarily rooted in these disparate data siloes. Without some sort of machine learning- or AI-powered digital intelligence layer, there is simply no way for banks to process the millions of disparate sets of behavioral, profile, and operational data to deliver the promise of 1:1 customer interactions at scale.
Without addressing this fundamental issue, banks are increasingly at risk to competitors outside of the industry. And banking executives know it.
In 2016, The Economist reported that 61 percent of banking executives believe their biggest competition will come from outside of the industry.
So, what do these industry outsiders look like? Well, they look a lot like Mark Zuckerberg, Jeff Bezos and Jack Ma.
Competing for the millennial market with tech disruptors
According to a new 2017 survey, approximately one in three banking and insurance customers globally would consider switching their accounts to Google, Amazon, or Facebook if the Silicon Valley-based companies offered financial services.
Take, for example, Google. They have Android Pay and Google Wallet which allow consumers to send, receive, and request money. Google Finance also offers some online portfolio tools that could eventually expand to displace traditional brokerages.
Meanwhile, Amazon, with more than 250 million users, partnered with Wells Fargo in 2016 to offer discounted student loans to Amazon Prime customers. They’ve been active in direct loans through Amazon Loans and payments via Amazon Payments. In addition, Amazon Web Services (AWS) has played a large role in migrating heavily regulated banks data to cloud infrastructure, which could accelerate further partnerships. And yesterday Amazon announced its new Prime card, a credit card with no fee, ordinary interest and a whopping 5% cash back on Amazon purchases. It will undoubtedly come with other Amazon Prime benefits further strengthening Amazon’s relationship with its customers.
If you’re wondering what expansion by a tech giant into financial services actually looks like, look no further than Chinese tech giant Alibaba. In 2015, Alibaba launched their all-digital bank, MYbank, which has grown to more than 450 million annual active users. The Wall Street Journal reported that 58 percent of online payments in China use the bank’s payment service, Alipay.
Other major Chinese tech companies, Tencent and Baidu, both launched banks of their own over the past two years. Tencent, the company behind the ubiquitous WeChat social media app in China, launched WeBank. Baidu, the online search giant, partnered with a traditional bank to offer both online and offline services.
Alibaba’s capital lending arm is an optimal example of what the future of retail banking could look like. There’s no staff involved. Data algorithms calculate loan amounts and the application and approval process happen online—in minutes.
How CMO’s can address digital intelligence debt in 2017
Banking CMO’s are already adopting technology at a breakneck pace to cross the digital intelligence chasm.
Unfortunately, it’s still a major challenge to create and act upon a unified customer profile. The technology doesn’t scale, segmentation is too macro, processes require too much heavy lifting and the data is still disconnected from the front-line marketers who are tasked with maintaining and strengthening relationships with customers.
While the traditional marketing technology stack places personalization at the end of a complex collection of infrastructure investments in rules-based tools, forward-thinking CMOs should be thinking about investments in technology with core AI capabilities to circumvent much of the resource and integration issues that has plagued marketers for the past five years.
Instead, as CMO’s continually skew toward direct revenue drivers, they need to be evaluating machine learning-based digital intelligence platforms that integrate with their existing stack and data environments. These types of tools should be able to ingest all of their customer data, test 1000’s of messaging permutations, and adaptively optimize every customer experience.
Done right, this will result in significant growth in customer lifetime value and retention. Done poorly, and you’re still dealing with expensive, static marketing infrastructure with no provable ROI across your KPIs.
Business as usual, unfortunately, for many marketers.
But as financial services marketing leadership seeks to bridge the digital intelligence gap in 2017 and compete in a rapidly transforming industry, there’s no more room for business as usual.
By Santo Criscuolo, VP of Strategy & Digital Transformation, Amplero
Santo Criscuolo has extensive experience helping large B2C brands, such as AT&T Wireless, T-Mobile, Nike, Adidas, Weight Watchers, Disney and Microsoft, leverage digital marketing to acquire, engage, and retain their customers.
As VP of Strategy & Digital Transformation at Amplero, he currently empowers Fortune 500 marketers to utilize machine learning and continuous optimization technology to drastically improve customer retention, increase average revenue per user, and deliver personalized 1:1 customer experiences at scale.
Contact him at [email protected]