Data analytics is one of the biggest topics currently being discussed by banking groups and many other providers on the market. However, some financial institutions are starting to back away from this trend. Why? In many cases it comes at high costs, produces results that are difficult to understand, delivers modest successes, and might pose data privacy problems. Nevertheless, the potential benefits of data analytics are simply too great to not make use of it at all. So how can banks use it to achieve the success they need? Our answer: BE Digital.
Sales in banking typically means one of the following options: “Aggressive” product sales, regular customer consultations, and preselections of the customer base to promote certain products. The customer orientation leaves a lot to be desired, and this in turn negatively impacts revenues.
The data from a recent Simon-Kucher study shows just how much room for improvement there in this regard. At many of the banks surveyed in this study, an average of just 10 to 15 percent of calls made to their call center resulted in an appointment being made. On average, less than 30 percent of these customer appointments ended with the customer buying a product, and when they did, they usually bought just one product.
At the same time, there is clearly enormous potential. If a company can successfully increase these three metrics – rate of making appointments, rate of closing deals, and number of products sold simultaneously – it can quickly generate added profits of over 10,000 euro per advisor per year.
So how do banks get more out of their resources and provide their customers with real added value instead of irrelevant content?
Data analytics – Challenges and potential
When it comes to addressing the right customers at the right time with the right topic, so that these institutes increase the chances of closing a deal and stopping customers looking elsewhere, data analytics algorithms may be one solution. These algorithms use customer data regarding demographics, product usage, deal closure rates, cashflows, and more to recommend next-best actions concerning the issues that are most relevant at that time to whether a deal is reached. The basic idea is that the more data is fed into the system, the better and more precise approaches it produces. This allows banks to migrate from the stagnant narrow customer base to a broader approach targeting customers who express some sort of need.
There are now many providers and solutions on the market, but the road to successfully implementing data analytics approaches doesn’t always go smoothly. Some of the obstacles affect:
- The products: underdeveloped algorithms or excessive costs per lead
- The advisors: Nudges from data analytics are sometimes difficult to understand, meaning the issue of why an advisor should recommend one product over another to their customers often isn’t resolved. There are internal conflicts between the resulting nudge and the advisor’s own instincts, or even explicit conflicts between the nudge and the bank’s objectives.
- The customers: Mental obstacles prevent customers from completing purchases, even when the nudges are right. The customer’s question of what they get out of purchasing a particular product is often left unanswered.
The use of data analytics also raises fundamental questions: What customer data can be used? What kind of consent do customers have to give for this data to be used? And how can the bank obtain the customer’s consent for the data analytics algorithms to be used?
Effectively obtaining customer consent has become a hugely important element in banks’ terms and conditions since the German Federal Court of Justice’s ruling on assumed consent, and the process therefore needs to be optimized.
So how could data analytics be put to better use? The key is a combination of data analytics, behavioral economics (BE), and smart tools, or “BE Digital” for short. This research-based approach shifts the bank’s focus toward optimizing conversion, customer interaction, and customer retention through the following essential steps:
The main challenge is in aligning the customers’ interest with the campaign’s objective. Customers have to want to buy a product if they are going to give the bank some of their attention in this fast-paced world.
Nudges have proven effective at moving customers in that direction. Among these motivational mechanisms is the incompleteness effect (the aim of completing something creates an incentive to actually do the task), which is particularly effective in combination with the goal gradient effect (the closer someone is to a goal, the more effort they put into attaining it). When embedded in a bonus system that gives customers incentives to do certain things, these mechanisms help breakdown the first obstacle to the customer making a purchase, regardless of the product: unnecessary hesitation.
Data analytics is only useful if you can grab the customers’ attention in the first place. To do this, it is important to consider the following:
- Timing: The customer must be contacted while they still consider the topic top of mind.
- Ease of understanding: Both the marketing messaging and the advisor’s efforts at outreach must address the customer in a way that makes the topic being discussed most relevant to them. For example, if someone who enjoys traveling is interested in credit cards, the messaging to them should differ depending on whether they are looking to save on the benefits provided by a card or on insurance products.
- Long-term, far-sighted approaches: It is crucial to sell the product that will have the greatest effect in the long term and not simply the first one that fits the bill.
- Holistic approaches: The algorithm supports the advisor with determining not just the most important topic, but any related topics that could be discussed together with it.
- Learning, evaluating systems: An open-system procedure may help the bank identify the best algorithm out of all those that have been developed. Which algorithm used in which situation actually resulted in a sale being made? The system incorporates and evaluates various approaches and then prioritizes the best algorithm for the respective scenario. At the end of the process, an individual combination of the most successful algorithms can be developed.
- Integrated churn prevention: The customer data can also be used to determine the risk of the customer bailing and therefore the need for targeted contact.
- Expanded usage for pricing and marketing engines: The data can also be used to ascertain customers’ willingness to pay for a bank’s products, and in turn to help position products at the right price together with the right messages for the customer. This is especially important now as we see interest rate hikes.
Trigger action and nudge
The ultimate goal is to convert advisor contacts into actual deals. However, if the customer finds it too difficult or too time-consuming to reach a deal, entering the data analytics nudges into the regular nudge management systems will only have a limited benefit.
Both the customer and the advisor need a simple method with few obstacles to ensure that a product is sold. That is why it is essential to optimize the customer and sales journeys, including by incorporating them into digital sales tools.
The focus should be on creating the right incentives, actively approaching customers, and presenting suitable products to take customers from a personalized recommendation all the way to the final purchase and allow them to sign a contract when they are ready (online) if they are unsure.
Optimize the sales journey
The sales journey should be subject to continuous optimization using data. This could also increase the probability of identifying products that suit the customer. Some important measures include regular A/B testing and tracking the customers’ usage of digital tools. Evaluations of active digital applications optimized based on behavioral economics and used by a significant number of banks show that more than 40 percent of users bookmark products that are interesting to them based on the “wish list principle.” However, this step isn’t always immediately followed by an appointment or sale. It’s therefore the advisor’s job to identify the needs of the customers and convert them into sales.