How predictive marketing helps banks connect with businesses and consumers during COVID and beyond.
What is predictive marketing?
Simply put, predictive marketing uses predictive analytics to forecast future needs through an analysis of current and historical data. Predictive marketing employs advanced analytical techniques (like data modeling, statistical algorithms, and machine learning) to discover real-time insights that can be applied to improve marketing outcomes and ROI. Financial marketers can use predictive analytics for a wide range of insights – for example – which customers will leave, which products/services a customer or prospect will need, or which prospects are likely to become new customers and what their lifetime value will be.
Why predictive marketing is crucial to banking companies RIGHT NOW.
The COVID crisis has not hit all businesses or consumers equally, making it harder than ever to gauge how to serve their needs and understand which offers and messages will resonate with existing and prospective customers. Personalization has always impacted marketing performance, but now, it’s crucial – the message you communicate to a small business owner who is struggling because they had to cease operations for two months is not the same one you would offer to a business account that hasn’t experienced a large impact on operations. Similarly, for consumer-focused products, some households have been furloughed or out of work, while others have not had income disruptions and may in fact be in good financial health because staying home has decreased their spending.
On top of the unexpected challenges from COVID-19, the financial services industry was already facing massive industry changes – with new competition from tech companies and peer-to-peer lending, as well as changing generational behaviors and expectations creating new demands for digital experiences. These pressures mean retail banking companies must adopt innovative marketing and product strategies to stay ahead. Banks can leverage predictive analytics as a cost-effective method to prioritize acquisition targets, understand which product and services their customers need now, and identify new opportunities in a shifting landscape.
How banks can use predictive marketing to mitigate risk and improve performance during COVID and beyond:
When it comes to advanced analytics, banks need to ensure they have the right data to generate predictive insights. Predictive analytics draws on a clean set of datapoints to deliver accurate insights – usually requiring both the bank’s own data on their clients and prospects as well as third-party data to create comprehensive profiles. In addition, with COVID causing rapid changes to consumer needs and business solvency, banking brands need real-time, accurate data to understand the needs of consumers and businesses alike.
Example: Consumer banking
Combine consumer and business data to identify customer needs
A retail bank that wanted to predict how to best serve the needs of their consumer accounts in the current economic climate might enhance their own customer files with business data. A database like Data Axle’s B2C Link – which provides a unified consumer profile made up of a person’s consumer and business information – could help the bank get insight into the type of products their current and prospective customers need right now, based on this unique blended profile. Using this data set could help the bank identify which customers work in industries that might be well-positioned during this crisis and which may need more proactive outreach or products/services to help them ease financial woes.
For example, consumers working in certain jobs or industries (e.g., wait staff, bartenders, event planners, etc.) have likely experienced some form of income disruption in the past few months and could benefit from knowing about services or features that can help them. To tailor their messaging to consumers who will be most interested in knowing about flexible payment plan options, American Express could personalize the email below and target consumers in specific industries or job functions.
Every marketer knows that it’s much more expensive to acquire new customers than it is to keep them, particularly in industries with high customer acquisition costs (CAC), like finance and insurance. Predictive marketing can help banks reduce CAC by identifying the best prospects to target – accurately predicting the consumers or businesses that are most likely to be in market for banking services, as well as helping prioritize targets based on credit risk and pre-screen data.
Example: Business banking
Use data to understand business status, assess risk, and prioritize prospects
COVID-19 has had an undeniable impact on the business banking sector. Business accounts with disruptions to cashflow are relying on their bank to provide temporary funding and guidance on new loan programs. Meanwhile, banks are seeing their customer acquisition efforts being thrown into disarray due to business closures.
To keep up with COVID’s impact on businesses, banks can access reliable, real-time data on business status (out of business, suspended operations, open for business) as well as up-to-date information on credit risk to help guide new account acquisition and mitigate risks when making loan decisions. Armed with this data as well as intent data – that predicts which businesses are actively interested in their services and products – banks can accurately prioritize prospect accounts.
In addition, if the offices of key target accounts are temporarily closed, banks can still help sales staff reach business decision makers at home by using the same type of blended profile we mentioned above.
Predictive marketing can be used to generate greater value from existing accounts and improve customer loyalty by increasing a bank’s ability to predict the individual needs of their customers and business accounts and personalize offers based on those needs. For example, a bank can identify a correlation between specific customer attributes and the products they use to predict which customer segments are likely to need a certain product or service.
Example: Recommendation engines
Banking brands can take a page from the playbook of retailers like Amazon and employ the same technologies and analytics used in product recommendations to boost profitability. Using a form of predictive analytics called “Next Best Offer” (a.k.a. “Next Best Action”), banks can forecast which services a customer may be interested in based on their purchase history and/or the behaviors of look-alike customers.
Capital One uses predictive marketing to provide personalized, highly relevant offers and services to their customers.i Nitzan Mekel-Bobrov, Capital One’s artificial intelligence and machine learning chief says Capital One is “pushing the cutting edge” and going beyond Amazon and Netflix’s recommendation approach. He explains, “We admire a lot of what they do, but they are selling a single product; we are not in the business of selling a product, we are in the business of relationships, and we need to understand the customer deeply and continuously.” Mekel-Bobrov explains that Capital One is continuously analyzing data across many touchpoints, types and formats (phone calls, emails, digital marketing, etc.) to understand their customers and predict which offers and benefits hold the most relevance for each individual.
Banks can use predictive marketing to understand which messages resonate with various prospect and customer segments and offer the right content to help them make decisions. Strong customer segmentation is always the foundation for accurate personalized messaging. Using predictive marketing and machine learning to improve their segmentation strategies and inform their content, banks can more accurately deliver the right message to different customer segments.
Example: Personalized visuals and content
In a campaign for their robo-advising platform, Intuitive Investor, Wells Fargo created personalized landing pages and multichannel campaigns with different versions designed to attract multiple customer segments. The bank analyzed consumer data to segment consumers based on their lifestyle, values, and retirement goals and then provided dynamically populated imagery and content to serve those audiences – for example, planning for an active or relaxed retirement.iv
A recent report from The Aberdeen Group found that businesses using predictive analytics are twice as likely to identify high-value customers and reach them with the right offer.v To thrive in our current economic environment, financial marketers should tap into this crucial strategy to improve marketing effectiveness and reduce the costs associated with finding (and keeping) the best customers for their institution.
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Elyse DeVries is a Content Specialist at Data Axle where she is responsible for developing content to educate and inspire marketers. For the past decade, she has been sharing her passion for marketing technology as a digital marketer in the B2B software and services industry. When she isn’t creating content, Elyse enjoys exploring the forests, mountains, and seaside towns of the Pacific Northwest and traveling overseas with her husband and daughter. A proud SciFi & Fantasy nerd, Elyse spends her free time gaming, reading geeky novels, and seeing each and every Marvel movie on opening day.