What banks can learn from leading retailers
What banks can learn from leading retailers
Personalised experiences, mobile apps, and catering for the non-linear journey through technology are just some tactics banks should adopt from their retail peers.
INTRODUCTION
Retailers are learning that highly personalised experiences, underpinned by millions of proprietary data points, are difficult for competitors to replicate.
Executed well, retailers have shown a top-line revenue uplift of 2 or 3 points.
Equally, personalisation can create positive spikes in customer satisfaction, loyalty, and employee experience. The latter improves recruitment and retention, which builds on the former.
Banks are high-street retailers – they have the vast data sets and customer bases and know their customers.
The sector has also some experience in using artificial intelligence with machine learning (AI/ML) platforms.
Yet, in contrast to both traditional and online retailers, the banking sector has focused on its use cases in other areas of the business, such as security and fraud detection, but have not extended this capability to drive sales through personalisation.
Retail banks are in a unique market situation where they have expansive customer bases across numerous segments and possess vast data sets.
Clicks and Bricks
A decade ago, retail faced similar challenges. Preeminent brick-and-mortar brands shut their doors as online retailers emerged.
The survivors identified data and analytics as an equaliser between buyers and sellers. Many also deployed mobile apps and set out strategies to harmonise traditional and digital channels to better understand customers and their non-linear buying journeys.
Some brands have had notable success:
Users of the Starbucks mobile app will find 400,000 variants of hyper-personalised messages. External feeds take in the day’s weather, time of year, location and buying history to promote unique offers for each specific member. Its mobile app (order, pay and delivery) currently accounts for 72% of total sales volume.
Over 40% of sales conversions from Amazon are powered by personalised recommendations embedded across the entire online buying experience and touchpoints such as discovery and checkout.
Europe’s largest electronics retailer, Media Markt, generated a 14% uplift in revenue per user by merging all customer data, both online and in-store. It uses this repository to provide personalised content and recommendations. The company can also understand buying journeys and calculate customer lifetime value (CLV) and churn ratios holistically.
Many other household brands from Adidas, IKEA, H&M, Sephora, to Walmart and Wayfarer report varying degrees of market success with personalisation.
The next four billion customers
Like consumer retailers, banks engage customers through digital channels including mobile apps capable of collecting similar data sets: transaction history, location, time, search and browsing analytics that can be enriched with a range of external data feeds.
Banks are also at the frontline witnessing the same digital revolution that accelerated during COVID. On the horizon sits Gen-Z and Millennial segments.
These groups are approximately half of the world’s population – they are also the fastest-growing segment. Millennials are born in the cloud, mobile-first in mindset, and considered digital natives.
A report from McKinsey predicts these two cohorts will make up half of the APAC consumers in 2025.
These buyers are a new breed who will select and engage brands of their terms, typically through low touch digital channels. They are least likely to ever visit a branch and may not even have or see a need for having a primary banking institution.
How can banks take it to the next level?
personalisation sells
When PAYTM deployed a managed AI/ML service with AWS, it saw an increase of conversion rates 3X the success legacy banners ads.
Banks can learn from retailers and other banks, too.
Any trials and POCs should focus on high-value segments. Retailers have shown that loyal customers yield returns two to three times higher than mass market events. Hard-fought tech investments need to show the quick wins and ROI.
build a playbook
Most banks are likely to have data management and governance capabilities. Once segmentation, analysis and buying triggers are identified, banks should build vast libraries of playbooks to correspond with buying behaviours.
The right mix of campaign triggers can result in click-through rates that outperform traditional mass marketing.
Retailers that have perfected personalisation can reduce sales and marketing costs by 20 to 30 per cent.
keep the human touch
While AI/M-based technologies and digital tools can surface leads, and drive individualised offerings to the masses, an effective omni-channel strategy in banking will also need an effective handover to skilled agents.
While digital often supplants voice traffic, consumers will continue to place high value on voice calls for specialised engagements, such as wealth management, commercial loans and advising on the best mortgage options.
Digital tools will evolve to better understand context and handover at the right moments in-session, while agents in turn will evolve to support advisory.
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