Big Data For Marketing
Big data can help understand customers on a more personal basis and achieve the strategic marketing goal of delivering relevant, personalized and contextual communications and offers. When you send communications that resonate, as opposed to blind communications or yet more irrelevant credit card offers, consumers believe you actually know them and are doing your part to contribute to a real relationship. They also take advantage of those offers more frequently thereby boosting conversion rates, customer share and lifetime value.
Consumers interact with financial services companies through multiple channels — websites, mobile, social media, kiosks, physical branches, ATMs, e-commerce sites and more. Deeper, data-driven customer insights enable more personalized campaigns which achieve higher conversions and increased revenues. Customer insights can also predict defections, giving FSIs the ability to intervene with steps to retain customers.
Consider these examples. What if you could:
- Increase customer segmentation specificity by analyzing customer activities, transactions and behavior patterns across all channels?
- Enrich your understanding of customer behaviors by integrating multichannel data—including physical, online, mobile, social media and third-party data—to understand which channels each customers prefers for various types of engagements?
- Optimize customer interactions by knowing where a customer is and delivering relevant real-time offers based on that location?
- Predict consumer purchase behavior and offer relevant, enticing products to influence customers to expand their portfolio?
The benefits of leveraging big data with marketing campaigns and promotion offers include:
- Increased offer conversion and response rates
- Improved up-sell and cross-sell offers of higher margin products for deeper product penetration
- More marketing occasions to deliver relevant offers
- Higher asset and portfolio values and increased customer share and advocacy
- Identify high value customers for specialized offers and predict response rates
Here's some examples.
Geo, mobile and proximity marketing are proving to be very high conversion big data marketing techniques. For example, banks may detect a consumer's location via the consumer's use of an ATM, smart phone app or by capturing credit card and merchant data at the point of sale. If that customer has opted in to mobile communications, the bank may send an offer for a relevant solution which benefits from face to face consultation, and further identify the closest bank to the consumer's real-time location. This is essentially adding location to the Next Best Offer recommendation engine and focusing on offers which are best redeemed in bank branches. A regional bank in the Northeast is using this technique and realizing 14 percent conversion rates. Banks know that getting people into the branches can be difficult so delivering offers when they are already in the vicinity removes a big obstacle.
Another high conversion marketing technique is mass product customization. Many types of customers prefer custom products, however, financial product proliferation can quickly become difficult to manage. Mass customization creates a standard product which is 90 percent or so complete, but permits customers to select final options based on their preferences. These options are essentially variants of the standardized product. The concept attempts to bridge mass product efficiencies with the sales uplift garnered from customization.
Retargeting is a big data marketing method which uses paid search and social media to follow customers and non-customers who have visited your website in their continued web browsing. For example, if an anonymous visitor checks out your home loans website page, but leaves before identifying themselves, retargeting will display your home loan offers or content on other sites visited by the anonymous visitor. Retargeting conversions are typically in the range of 7 to 10 percent, which is more than double the normal website conversion rate.
Retargeting on social networks can increase the conversion rate even further. In this big data scenario, your offers follow known or anonymous visitors to their social networks. I piloted this technique using the Facebook Exchange (FBX) with an offer targeting high net worth individuals. After some initial A/B tests and optimization, the campaign achieved an 11.5 percent conversion rate.
Understanding what your customers buy from you is important, but knowing what they buy from other FSIs provides an opportunity to convert that business and increase your customer share. Third party data acquired from data aggregators can show what financial products your customers are buying from competitors, and give you an informed basis to create highly relevant bundles which encourage customers to reassign that business to you.
Big data applied to multi-variate offer testing can systemically isolate components and exercise diverse mixes of customer behaviors, product attributes and offer elements in order to identify the combinations that generate the highest conversions. When further using this learning to dynamically update customer segments or customer profiles this intelligence can also be applied to real-time engagement techniques such as Next Best Offer (NBO) recommendations during financial advisor sessions, teller transactions and e-commerce activity. Applying more types and volumes of data can create customer intelligence that aids marketers in their quest to improve targeting and deliver the right message to the right customer through the right channel at the right time.
The consumerization of retail banking has extended to wealth management, private banking, insurance, capital markets and more. Each of these financial service sectors must now meet customers where those customers choose to communicate if they expect to engage. Beyond the engagement, FSIs also leverage the volumes of increased data that can be used to display customer journeys and points of failure where customers exit a purchase process. Understanding where customers depart before making an online or physical purchase enables FSIs to review and improve their business processes.
Big data enables FSIs to expand their existing customer information in order to get a more complete picture. In addition to the many FSI benefits, the customer wants to know that his or her financial services provider knows who they are and understands their individual needs and preferences. Customers want to be treated as individuals and want to be valued.
The Point is This
The transition to data-driven segmentation, behavior based dynamic targeting, contextual communications and predictive offer management is the future of financial services marketing.
Marketers are using Big Data to better forecast what products to sell to what customers and when, and how to bundle products to increase sales of high margin solutions. Marketers are sifting through external data to determine what products correlate to different customer segments for increased sales conversions. They are also using big data such as competitor price lists and market acceptance rates to calculate pricing elasticity for their products and services. Marketers are using social graph analysis to implement influencer marketing campaigns with some big results. In this scenario, social media data is mined to show which customers or even non-customers have the most influence over others inside social circles. This helps marketers leverage those influencers in unique ways and with results that generally cost less and perform much better than traditional advertising methods.
FSIs that mine data volumes and effectively extract the signals from the noise can create new products and services that are more enthusiastically consumed by customers, market to customers with more relevant and personalized messaging, and apply better information to support customers in the many post-purchase service scenarios. Big data is a complex undertaking, but the impact to revenues and profits is substantial and sustainable.
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