Data-driven personalization is the key to big gains in customer satisfaction, customer acquisition, and account growth. But the complexities involved in implementing a cross-channel personalization platform can be overwhelming. Here’s a break down of the pieces involved with crafting a personalized omni-channel and cross-channel experience.
By Mark Ryan, Chief Analytics Officer at Extractable
It’s amazing how many parties benefit from personalization. Pioneers in digital personalization such as Amazon, CapitalOne, and Facebook have demonstrated that customers love it, proven by superior results and loyalty. Engineers thrive on the challenge that it represents. Copywriters and designers appreciate the focus that it brings. Marketers and sales teams love the results. Executives take pleasure in using it to jump ahead of the competition. But today very few financial organizations are using personalization and even fewer are coordinating personalization efforts across multiple channels.
In our experience with developing public websites, email campaigns, online banking interfaces, mobile applications and social campaigns, we have found that personalized messaging almost always shows double-digit (and often triple-digit) improvements in engagement and correlating improvements in conversion (i.e., personal loan applications). So why are so few organizations implementing personalization in any channel let alone multiple channels? In short, it’s complex to implement and requires the coordination of many different groups. In the next several paragraphs we’ll look at the complexities involved in implementing single-channel personalization, cross-channel personalization, and omni-channel personalization.
Major Categories & Motives for Personalization
The motive for the personalization often dictates the types of data available and the way it is implemented. Marketing-based personalization does not have the luxury of access to deep information about prospects and is typically focused on behaviors such as product views. Support-based personalization typically has a large amount of data about customers, such as products owned, and is able to leverage that data for very fine-tuned communications.
Here are the three major categories and motives for personalization:
1. Marketing-Focused Personalization. We’ve all seen some form of the “Welcome back Jane” personalization. These techniques are intended to excite visitors by not only demonstrating a willingness to help visitors perform their desired tasks but also impressing them with some technical showmanship. In this case, it’s remembering and using the user’s name.
2. Sales-Focused Personalization. The majority of personalization platforms and techniques are focused on getting customers to the point of purchase and increasing purchase size (“People who liked this item also liked more stuff”).
3. Support-Focused Personalization. Some personalization is focused on helping customers solve problems quickly and effectively. Many Financial Institutions (FIs) have knowledgebase systems that predict support questions and answers based on products for which a customer has just enrolled in.
Data Streams & Platforms
These are the types of platforms that have valuable prospect data and customer data that can be used to personalize messaging and tools:
- Core Platforms – The core platform has valuable customer attribute data such as age, gender, products currently used (or previously used), and affluence.
- Call Center Platforms – Financial institution call centers have amazing data. When call center staff is diligent with logging call details, the call center knows what problems customers have, what problems are easy to solve, what problem perturb customers the most, and often what products a customer is most interested in as well as how close they are to making a decision.
- Web Analytics – Visitor behavior is great for predicting intention and as well as showing how prospects and customers prefer to consume information and utilize tools.
- Chat Tools – These excellent tools not only show visitor intent but also show visitor nomenclature. For instance, in chat logs we can see if an individual customer calls the product a mortgage, home loan, or home financing.
- Customer Relationship Manager – The CRM typically contains great information on what types of information customers need to make decisions on products, services, and commitments as well as interaction preferences that cannot be easily tracked in the digital realm.
- Third-Party Data – Many research and data companies such as Acxiom, Rapleaf, Exact Target, Quantcast, NectarOM, and DemandBase provide excellent data to drive many types of personalization.
So far we’ve looked at types of personalization and the systems that provide data for personalization. Now let’s look at some simple examples of algorithms used to present dynamically personalized messaging and functions.
- New Products – This is such a simple algorithm that very few Financial Institutions use. Promote products that the customer does not already have. Considering 50% to 90% of the traffic to a typical FI site, branch, or call center is from current customers; this algorithm should be employed frequently.
- Life stage – Certain products are just more appealing to different age groups. Prospects aged 18–25 are more interested in auto loans and credit cards than retirement products. While prospects aged 40–70 are more interested in home refinance products and retirement products. The algorithms are also great at predicting how customers want support.
- Affluence – Similar to life stage, algorithms based on a prospects/customers affluence, wealth, or credit worthiness are able to more precisely predict what products a customer wants and has access to.
- Nomenclature – Chat tools, external search reports, internal search reports, phone logs from CRM platforms and call centers, and surveys all provide data to determine a customer’s intent, how they refer to products, the types of problem they want solved, and how they relate to their financial institution.
- Recency/Frequency – For complex financial products such as mortgages, the number of visits and the amount of time in between visits can help deduce the type of information and the amount of information a prospect needs.
( Read More: Banks’ Power to Mine Data Frightens, Intrigues Consumers )
Algorithms & Analysis
These are a few simple examples of algorithms that can be used to dynamically predict how to market to, how to sell to, and how to support customers. The final piece of this puzzle is the channel with which we deliver personalized messages and functionality to prospects and customers.
- Public Website – Typically the most trafficked channel for any FI is the public website. The dynamic nature of a website and its ability to integrate with multiple data sources make this channel ideal for personalized messaging and functionality.
- Online Banking – The majority of time spent in an interaction between a customer and a typical bank, credit union, or brokerage happens within the authenticated area where balances can be reviewed and financial transactions can be executed. These platforms are typically already integrated with multiple platforms that deliver data needed for personalization such as core platforms.
- eMail Campaigns – There is significant research to show that open rates, read rates, and clickthrough rates improve significantly with personalized subjects and personalized email content.
- Display Ads/Remarketing – Personalized remarketing and display ads drive higher levels of engagement, which typically leads to high conversion rates.
- Call Center – An informed call center that is able to personalize phone trees, hold messaging, and person-to-person messaging makes calling for support or product applications enjoyable and helps build loyalty.
- Branches – The first type of personalization that FIs likely ever offered was a friendly personalized branch experience. In-branches tellers, loan officers, and other branch staff connected with systems such as CRM and smartphone identifiers are able to offer friendly personalized greetings, service, and recommendations.
- Mail Campaigns – Possibly the longest running mass personalization in financial institutions today is personalized snail mail with messaging and offers targeted to individuals.
For many banks, credit unions, insurance providers, brokerages, and other financial institutions personalization brings a confident promise to make a better impression on prospects, convert new customers more successfully, grow customers, and more efficiently serve existing customers. The next generation of financial platforms will ease the complex implementation of cross-channel personalization as well as achieve the holy grail of omni-channel personalization, which continue to drive up customer satisfaction and grow accounts.