We are all looking forward to the day where we can live life with some sense of normalcy. That would mean COVID-19 numbers are significantly down, everyone who is eligible is vaccinated, and we can experience life the way we did pre-pandemic (for the most part). Depending on where you are in the world, there are variations of this new-normal. As banks get to that time of year where next year targets are being planned, there are a few pressing questions for regional banks:
- 1. Can we increase the value of our customer relationship in a post-pandemic environment?
- 2. Can we service a large customer base with a small team while still meeting targets?
- 3. Can we leverage historical and future data to recommend a better way to do business with our customers?
The answer is yes, Yes and YES.
We hear the terms AI, Machine-Learning and Big-Data being thrown around and used every day, yet have some difficulty in applying it to traditional businesses like FX. Some firms may have access to resources such as data scientists, quants and developers that can help them analyze data, but in the world of cost-cutting while still raising revenue targets, most firms do not. If however, you have the luxury of hiring someone, do you hire someone to analyze customer data or engage customers directly to generate sales? Most firms will opt for the latter.
But things are changing. AI and Machine Learning is becoming more and more accessible for banks and their business units.
A Bain study of 400 companies in the world with revenue over USD 1 billion found that companies who successfully utilized analytics were:
- Double their chances of being in the top quartile of their respective industries from a financial perspective
- The likelihood if executing decisions as intended was 3x
- They were 5x more likely to make quicker decisions
There is a broader theme of democratization happening which looks to level the playing field in everything imaginable and not necessarily in the extreme scenario of bringing back Blockbuster Video or Gamestop to your local mall or plaza. In its simplest form, we are moving towards a curated world where our user experience is decided for us based on historical and peer performance. Our smartphone apps tell us what music we should be listening to, and if you watched the “Crown” and “Stranger Things”, Netflix will tell you that you will also like “Tiger King”. The truth is, we no longer want to spend time “figuring out what we like. We want to know what we need to know, and we want to know it NOW.
So how are we at TickTrade answering the 3 questions mentioned at the beginning of this article? TickTrade’s new AI and Analytics solution uses historical, real-time and (eventually) actionable outcomes to meet this demand while keeping true to our ethos of sophistication coupled with an intuitive user experience. As a former bank FX salesperson, our head of product for AI & Analytics chose to drive the user experience by enabling bank sales teams to focus on key customers that need attention based on events driven by our TickTrade Notification Engine.
When covering customers, sales teams want to know about:
• Customers that are trending higher (volume/revenue)
• Customers that are trending lower and possible detractors
• Changes in customer behaviour that need immediate attention (ie. Significantly high volumes, changed directional bias, new currency pairs being requested)
• Opportunities to upsell/cross-sell
• Run rate compared to previous time periods
Our real-time analytics dashboard hits all of the above.
How do we do it?
Highlight High-Value Clients Customers – We use historical and real-time information that runs through our proprietary machine learning algorithms that then highlight business-impacting signals to the sales teams. While our TickTrade Real-Time Sales Dashboard displays all the analysis and information, the user experience is driven through our TickTrade Notification Engine which breaks out notifications into 3 categories:
- First Time Behaviour – notifies change in dealing direction, pricing requests, product requests (spot, fwd, swaps), new currency pair
- Benchmark Behaviour – changes based on the customers’ average trade size, currency pair traded, frequency of trading.
- Periodic Analysis – provides notification about significant changes with key customers vs. previous time periods.
The salesperson can then do a deeper dive using our Customer Overview or Customer Details tabs in our dashboard. Users can view historical and real-time information in these tabs on various industry metrics that relate to their business, (revenue, fill ratios, trade frequency data, etc…). Having a centralized dashboard to view important customer information enables you to make decisions quicker and enables you to effectively manage a large customer base with a small team. The Result – a higher quality interaction with your customer combined with increased volumes and revenue.
Higher Quality Client Interaction – We also have our TickTrade Customer Behaviour module which uses AI and Machine-Learning to generate and separate your customers into clusters and segments based on similar traits and characteristics. There might be some similarities about your customers that you might be unaware of that group customers together. Things like seasonality, trade frequency, volumes, price sensitivity, etc… Our algorithms will find them and organize them holistically so you can optimize your customer engagement. Your customers will migrate from one segment to another based on their business growing or shrinking for example, and often banks do not realize that the customer’s business needs have changed. They will not see that the customer is dealing larger volumes, or more frequently, or looking at other currency pairs till it’s too late. In doing so forgo opportunities to price them accordingly or save the relationship.
A big complaint from SME businesses that are growing is that their bank continues to treat them as a “small business” and not educate them on more sophisticated ways to hedge their risk. Our Customer Behaviour module will allow you to visualize your customer groups in our dashboard, identify what caused one segment or a customer within a segment to migrate to another cluster. It could be that their volumes decreased by 30%, RFQ rates increased by 40%, and cancellations increased by 40% – which would tell us that this customer has become more price-sensitive and possibly “shopping around” with competitors. Our product will not only able you to see this analysis graphically, but we will also have a Recommendation Engine with suggestive actions. In the above example, customer communication would have been recommended, in other cases, it could be pricing changes or other products and solutions that would better suit the customer.
Focused Marketing – Don’t we all hate getting marketing that is not relevant to us? Now picture a commercial banking customer that receives marketing by way of trade ideas, products, and solutions that do not apply to them. It shows that the bank does not know what their customer needs. From a marketing standpoint, it allows banks to target customers that are in a particular group more effectively with tailored solutions, therefore optimizing the engagement. There could be particular seasonality for a specific group of customers in a cluster, and a more focused solution could be applied to that group to help them hedge against volatility (ex. Commodity based customer groups). You will be able to identify only the customers that need attention, beyond a threshold even, and directly market solutions to them. This is known in the industry as Hyper-personalization. We also found that your customers are keen to know what their “peers” (by size and/or industry) are doing to manage their business from a best practices perspective; our tools make it easy for your sales team to see this information and share with their customers accordingly.
Actionable Outcomes – The ultimate goal of our program is to have predictive and actionable outcomes based on the analyses and recommendations from our platform. This will mean that sales teams could receive prompts to change pricing to a more competitive template for a cluster of possible detracting customers; push relevant marketing and research material to seasonal customers; and/or proactively push an RFQ rate to the customer via virtual assistant on web or mobile prompting them to trade. If customers are doing quite a bit of price discovery at a certain level, a prompt is sent for them to leave an order or call level with the amount they were requesting. Initially actionable outcomes will prompt the salesperson for acceptance before the machine performs any actions. Eventually, within configured parameters, banks will allow machines to perform the “boring” tasks allowing salespeople to focus on the high-touch/high-value relationships. This is a very important point about AI and Machine Learning, as there are certain nuances of customer interaction that still need human interaction, primarily on the relationship management and structured solution side of the business.
In Conclusion – Sales teams at banks are facing the following challenges:
- Increased competition
- Slimmer margins
- Increased targets
- Shift to cross-asset sales teams from a single asset class
- Tools for quicker decision making
The above means there is less time to sift through data about customers and perform analyses, not to mention being able to respond to demanding customers in a fast paced market. Sales teams are now investing in the tools that inform them, provide analysis to make a decision, and prompt them or the customer to perform an action.
At TickTrade we firmly believe our solution provides your team with the tools to being informed, make decisions and have a higher-quality interaction with your customers.
TickTrade’s AI and Analytics platform allows banks to effectively manage a large customer base with a small team by combining historical and real-time capabilities to recognize and understand your customers’ behaviour. Our future roadmap will also include predictive capabilities which will allow banks to anticipate FX volumes, future customer engagement and manage risk accordingly. (This will be covered in our next article…stay tuned!)
Head of Product, AI and Analytics