Optimized Strategies for Customer Acquisition

by Theresa Nguyen 6 min read December 19, 2023

While today’s consumers expect a smooth, frictionless digital experience, many financial institutions still rely on outdated technology and manual reviews to acquire new customers. These old processes can prevent lenders from making accurate and timely credit decisions, leading to lost opportunities, revenue, and goodwill.

By optimizing their customer acquisition strategies, financial institutions can allocate their resources effectively and say yes to consumers faster. This guide will walk you through the current challenges facing customer acquisition and how robust optimization strategies can help.

Current challenges in customer acquisition

To stay competitive and engage high-value customers, you’ll need an efficient customer acquisition process that weeds out both fraudulent actors and risky consumers. However, achieving this balancing act comes with a unique set of challenges.

Because today’s consumers can access goods and services almost anywhere online at any time, more than 54 percent of customers expect a heightened digital and frictionless experience. Failing to meet this expectation can lead to huge losses for lenders.

Some of the most common challenges in customer acquisition include:

  • Although 52 percent of consumers prefer digital banking options over visiting branches in person, many lenders still rely on paper documents, which can add weeks to the onboarding process.
  • Requiring consumers to provide substantial information about themselves during an application process can lead to abandoned applications. 67 percent of consumers will leave an application if they experience complications.
  • Verifying consumer identities is growing increasingly important. In fact, about 35 percent of customers drop out of digital onboarding because their identity can’t be confirmed.
  • Poorly defined campaign planning can cause businesses to market to the wrong population segments, resulting in wasted time and resources.

What is optimization for customer acquisition? 

Customer acquisition optimization is the process of implementing new methods and solutions to make acquiring new customers more efficient and cost-effective. For lenders, this means streamlining steps in the credit decisioning process to focus on the right prospects and reduce friction.

What types of processes can be optimized for customer acquisition? 

You might be surprised just how many processes can be optimized for customer acquisition. Here are just a few examples:

  • Having a holistic view of consumers allows you to take the guesswork out of targeting so you can better identify and engage high-potential customers.
  • Utilizing predictive and lifestyle data enables you to pinpoint a more precisely segmented audience for marketing.
  • Digital application solutions that reach across multiple channels, allowing applicants to leave one channel and pick up right where they left off in another.
  • Real-time identity verification and fraud detection during onboarding and after, helping expedite approvals and mitigate risks.
  • Utilizing API integration to leverage multiple metrics beyond credit scores when screening applicants’ financial situation.
  • Building custom risk models that pair to your existing data so you can say yes to more customers and better manage portfolio risk.

Benefits of customer acquisition optimization

Optimization can bring numerous benefits to your business, providing a faster return on investment. Here are some examples.

  • By better pinpointing your marketing through predictive and lifestyle data, you can achieve increased conversions.
  • Faster onboarding with less friction helps retain more customers.
  • Real-time fraud detection and identity verification reduce customer roadblocks, allowing you to realize significant growth.
  • Custom risk models and decisioning platforms can pair your data with additional data elements, providing more than just a credit score rating for your applicants. This can help you say yes to more customers.
  • Using AI and machine learning tools will reduce the need for manual reviews and thus increase booking rates and applications.

A real-life example of these benefits can be found with the Michigan State University Federal Credit Union (MSUFCU.) With over $7.2 billion in assets and 330,000 members, the client was manually reviewing all its applications. Experian reviewed the client’s risk levels and approvals, comparing their risk and bankruptcy scores to determine which were most predictive. This analysis led Experian to recommend a new decisioning platform (Experian Decisioning) for instant credit decisions, an alternative data score tool, and Experian Advisory Services for risk-based pricing. After implementing these optimization solutions, MSUFCU saw a 55 percent increase in average monthly automations, four times improved online application response time and began competing more effectively in the marketplace.

How Experian can help

Experian offers a number of customer acquisition tools, allowing companies to be more responsive in an increasingly competitive market, while still reducing fraud risk. These tools include:

Acquisition optimization marketing

Experian offers a web-based platform that lets clients manage their marketing efforts all in the same place. You can upload and enhance client files, identify lookalike prospects, and use firmographic and credit data to get a holistic view of your clients and your prospects.

Data-driven acquisition and decisioning engine

Experian Decisioning is a data-driven decisioning engine that accepts applications from multiple channels, automates data collection and verification and proactively monitors decision results. It’s flexible enough to reach across multiple channels, letting customers set aside their application in one digital channel and resume where they left off in another. It also provides businesses with access to comprehensive data assets, proactive monitoring and streamlined development with minimal coding.

Enhanced fraud detection and identity verification

Experian’s Precise ID® is a risk-based fraud detection and prevention platform that provides analytics to accurately verify customers and mitigate fraud loss behind the scenes, ensuring a smoother onboarding process.

Robust consumer attributes for better customized models

Experian gives clients access to a wider berth of consumer attributes, helping you better screen applicants beyond just looking at credit scores.

Trended 3DTM attributes let you uncover unique patterns in consumers’ behavior over time, allowing you to manage portfolio risk, build better models and determine the next best actions.

Premier AttributesSM aggregates credit data at the most granular and meaningful levels to provide clear insights into consumer credit behavior. It encompasses more than 2,100 attributes across 51 industries to help you develop highly predictive custom models.

Enterprise-wide credit decisioning engine

Experian’s enterprise-wide credit decision platform lets you combine machine learning with proprietary data to return optimized decisions and quickly respond to requests. Robust credit decisioning software lets you convert data into meaningful actions and strategies.

With Experian’s machine learning decisioning options, companies are realizing a 25 percent reduction in manual reviews, a 25 percent increase in loan and credit applications and a 26 percent increase in booking rates.

Highly predictive custom models

Experian’s Ascend Intelligence ServicesTM can help you create highly predictive custom models that create sophisticated decisioning strategies, allowing you to accurately predict risk and make the best decisions fast. This end-to-end suite of solutions lets you achieve a more granular view of every application and grow portfolios while still minimizing risk.

Experian can help optimize your customer acquisition

Experian provides a suite of decisioning engines, consumer attributes and customized modeling to help you optimize your customer acquisition process. These tools allow businesses to better target their marketing efforts, streamline their onboarding with less friction and improve their fraud detection and mitigation efforts. The combination can deliver a powerful ROI.

Learn more about Experian’s customer acquisition solutions.

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The average credit union manages between 100-300 vendor relationships, and nearly a third have no dedicated staff managing them. What was supposed to drive efficiency is stunting growth and compromising the member experience. Application volume is continuing to rise, and consumers expect a loan decision at digital speed. But for credit unions, meeting that expectation isn't the only goal. They must also continue to make the right lending decisions while also prioritizing risk management. The institutions pulling ahead aren't the ones with the most tools. They're the ones that have turned credit, fraud and decisioning into a single, coordinated system. A single loan decision touches: Credit data Identity verification Fraud signals Decisioning logic Reporting Rarely does all of that live in one place. More often, it’s stitched together across separate platforms, each with its own contract and its own support line. When a credit view and a fraud view are generated separately, they must be reconciled instead of read together in the same instant. Every handoff is a place where a decision slows down and a signal gets missed. It's why a fraud pattern that would be obvious next to the credit file often surfaces only after the funds are already gone. Fragmentation also makes it harder to optimize lending strategies over time. When data, analytics and decisioning are disconnected, credit unions have less visibility into what's working, what's changing and where strategies need to be adjusted. An integrated decisioning ecosystem closes those seams by design, with credit, fraud and lending strategy running on one platform and accessed through a single Application Programming Interface (API). Identity and compliance capabilities can be an integrated decisioning flow. The data doesn't need to be reconciled after the fact because it was never separated in the first place. Stop overengineering, start removing friction When decisioning underperforms, the instinct is to build something more custom: a proprietary model, tighter segmentation, more configuration.  Custom modeling has its place for institutions with the scale and data to justify it. But often, for small or mid-sized credit unions, the bigger opportunity is a flexible decisioning strategy that uses a strong, continuously refreshed model. This shouldn't be either/or. The right approach gives credit unions a strong decisioning strategy today, with the ability to introduce more customized analytics and modeling as their needs evolve.  The same principle applies to strategy management: credit unions can gain greater control through self-service capabilities as they build a more sophisticated data science team.   That kind of flexibility is important as credit unions modernize their decisioning infrastructure. Research surveying more than 2,000 senior credit risk and fraud decision-makers across 11 countries found that organizations investing in unified platforms and modern infrastructure are moving faster and realizing the greatest Return on Investment (ROI).   As their needs evolve, credit unions can adjust decisioning strategies without introducing another layer of technology or relying on third parties to make every change. Until self-service is the right fit, choose a partner that understands what makes your credit union’s membership and risk appetite different from others.  Same member, same afternoon, faster answer Consider a credit union growing its auto and personal loan originations.  A member applies for an auto loan on Saturday afternoon. Credit data, identity and fraud signals are read together in a single real-time pass instead of separate reviews:  Most applications resolve immediately, approved or declined based on a more comprehensive view of the applicant. A smaller number are flagged for quick step-up verification and cleared within minutes. Only genuinely ambiguous cases reach an underwriter, who is working from one complete picture instead of reconciling multiple signals. The member has an answer before the weekend is over. The credit union books the loan before competitors even open on Monday. Built to scale This kind of process holds up today. However, the better test is whether it holds up eighteen months from now when application volume has grown, new products are live and the data flowing through the system looks nothing like it does right now. Future-proofing isn't only about handling more volume. It's about having room to be nimbler and make adjustments as quickly as the market changes. A credit union that relies on standard models within a decisioning strategy may want more customized analytics and greater control over its strategies tomorrow. 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