2019 is an exciting time for banking and financial services industry as a veritable smorgasbord of latest technologies like big data and AI are set to disrupt the industry.
A whitepaper by Global Transaction Banking mentions that 62% of banks agree that big data is critical to their success.
With majority of banking and financial institutions voting for Big Data as the success mantra, it definitely speaks volumes in the banking and finance sector. Here’s how financial marketers are listening to it to get better and drive the 4 P’s of Marketing - Preference, Prediction, Personalization, and Promotion.
Preference helps Build Better Customer Experience
Putting the customer in the centre is the success mantra and banks are better able to deliver the type individualized preferences and engagements the customers are expecting by tapping into the potential of Big Data. With 72% of internet users on Social Media , the opportunities for social media marketing across the banking and financial sector are huge. Banks are using big data and AI to build a better customer experience by changing the way banks communicate with their customers. Big data is changing the way in which financial institutions track marketing campaigns, trends, hashtags, brand mentions, and customer complaints. Leveraging the power of Big Data and AI, financial institutions can understand any significant potential threats and work on a pre-emptive respond when possible or desired. By combining the insights with advanced analytics and AI, banks are becoming intuitive in the way they understand customer preferences and desires. Every customer has their own economic activity and with the power of AI based analytic tools banks are offering personalized and preferred products for greater customer satisfaction and experience.
For instance, the CBA (Commonwealth Bank of Australia) depends on an AI based decisioning layer to render “next best conversations” to its customers across 18 different channels. Whenever a customer engages with the bank, the Customer Engagement Engine determines what is the “nest best conversation” for a particular customer, and renders a response in less than 160 milliseconds’. This AI based decisioning approach has seen a 10 times increase in their home lending lead generation.
One more financial institution that has been using the so-called enhanced social listening to drive preference is Barclays. The bank uses sentiment analysis to glean meaningful insights based on the activity of its users on social media. Initially, when the company launched its mobile app people were unhappy and dissatisfied with the product as anybody below the age of 18 could send or receive money. Many customers voiced their opinions of disappointment on social media. As soon as Barclays identified the problem through social listening tools, the problem was fixed and people only above the age of 16 could get complete access to the apps functionalities.
AI based Chatbots are also proving to be highly effective for Banks in considering customer preferences. Bank of America uses Erica, a chatbot messaging service to provide financial guidance to its 45+ million customers. Customers prefer to reply a “Hello” by a chatbot instead of going through the tedious IVR process of calling a bank or exchanging emails in case of any queries.
Prediction – The Gateway to Cross-Selling Opportunity
Cross selling to existing customers is less expensive and easier rather than acquiring new customers, this holds good for the financial market as well. Financial institutions leverage predictive analytics and build models to assign scores to its existing customers. This provides them with a probability of the customer looking to buy another financial product and thus, increasing the revenue per customer for the bank. Prediction helps banks with targeted marketing so that they can convey the right message to the right customer at the right time.
For example, First Tennessee Bank is already making an excellent use of predictive analytics to optimize its marketing strategy. The bank follows a highly systematic and targeted approach that begins with a granular understanding of each customer’s banking requirements drawn from the pool of customer data points. Using predictive analytic models the bank assigns a score to each customer based on the probability of the customer purchasing each banking product available within the bank’s portfolio. This helps the financial marketers to identify product clusters that can form a “sweet spot” for cross-selling other banking products. This has resulted in a whopping 600% ROI through highly targeted offers within the high-value customer segments.
Personalization – The Key to WOWSOME Customer Experience
We live in an era where neither the financial marketers want to settle for mediocre response rates nor customers want to be bombarded with irrelevant offers. Customers expect personalized services; banking and financial services industry is no different. Though the complete potential of AI is yet to be explored, customers already expect that their banks know everything about them, understand them and send them offers on a highly personalized basis. AI based banking applications can work wonders for personalized financial planning. For instance, if a customer wants to buy a new house, the AI based app guide the customer depending on the budget and other details of based the customers income, expenditure, and other banking activities.
Financial institutions are already making the best use of AI processed behavioural big data to recommend its customers appropriate credit and savings products based on their assets. Mexico’s Banorte is creating personalized interactions for its 13+ million banking customers to increase trust and loyalty, cut down costs, boost its overall profitability. Banorte uses big data, machine learning, and artificial intelligence to get into the banking behaviours of specific customers. This helps bank employees know which banking products best suit a particular customer creating a personal experience for the customer within the banking apps and as well on direct conversations with the bank personnel.
Promotion - Targeting the Right Customer through the Right Channel
Customers interact with banking products through multiple channels - mobile apps, websites, kiosks, ATM’s, social media, physical branches, and more. With so many channels of interaction, deciding manually which is the most preferred channel to target a customer is very challenging. The 360 degree view of a customer profile helps the banks gather information on the preferred channels so that they can develop targeted messages on those channels. Big data analytics can help banks learn what kind of content has an increased probability to go viral and what SEO keywords will give them most profitable outcomes. Deeper data-driven insights help banks create more engaging and personalized marketing campaigns leading to higher conversions and increased revenues.
Citizens Financial Groups, a $160 billion regional bank targets specific customers or a customer segment across multiple marketing channels. It leverages big data to ensure that they are connecting the dots by following them in their digital experiences and promote banking products wisely.
It’s Time for Banks to Get on the Big Data Bus or Get Left in the Dust
Doing things the old and traditional way is just too risky nowadays. Any bank not riding on the big data bandwagon, is going to be left behind. Today, most financial marketers who use Big data and AI are seeing higher ROI across the 4 P’s (Preference, Promotion, Personalization, and Prediction) of marketing. In the years to come, AI has become an integral part of multichannel marketing campaigns, medial planning and execution, personalized content and offers, and highly contextual advertisements. Big data and AI is helping marketers design broader, wider and deeper customer journey solution across all aspects of marketing. Big Data and AI are now driving more meaningful conversations between the financial institutions thus driving both trust and sales. Use of big data and AI are no longer optional but a necessity for financial marketers in 2019 and beyond.