In the fight for higher profits, battles with competitors and industry forces may not be the only obstacles facing companies today. Internal tugs-of-war between departments and business goals could be working against the best-laid profit-building plans. For example, in a typical conflict scenario, a customer acquisition department might be working overtime to attract new customers. At the same time, a collections department is working hard to collect outstanding debt but, in the process, is creating a certain level of customer attrition. Another common example of conflict fallout is the attrition that occurs when the marketing department’s carefully crafted retention promos are pitched to every customer. Some customers will welcome the upsell and cross-sell offers, while other customers will become annoyed by what they view as “sales pressure.”
In fact, customer contact centers are often ground zero for many of today’s most common internal push-pull conflicts. The good news is that intelligent, proactive customer analytics tools can bridge the divide between opposing forces. These tools can help contact centers achieve four dual-purposed goals:
- Execute effective retention programs and lower resource costs;
- Maintain customer contact strategies and optimize workforce schedules;
- Target new customers in non-prime markets and lower the risk of these acquisitions; and
- Achieve both marketing’s customer retention objectives and the collections department’s collections objectives.
Retention Objectives Versus Resource Costs
Does your company wish to retain more high-value customers, but at the same time struggles to achieve this goal due to budget limitations? Every company today knows that retaining existing customers is more profitable than spending resources to acquire new customers. Yet, in the name of cost savings, many organizations forego proactive retention programs. This is a high-risk decision that can result in missed opportunities to keep your best customers loyal long into the future.
Instead of compromising their customer retention objectives to save money, companies can launch proactive retention programs and maintain their budgets — using intelligent predictive analytics. The secret to achieving this ideal situation is to perform proactive retention activities on high-value customers only. This strategy saves companies the high cost of indiscriminately executing retention actions with every customer, including those who offer no long-term loyalty or value. What’s more, it focuses precious energy and resources where they will make the biggest impact — on customers who will continue to contribute to the bottom line long into the future, by repurchasing their current products and additional product lines.
Contact centers can easily focus their retention efforts on their high-value customers by deploying predictive analytic applications specifically designed for this mission. The secret lies in the applications’ ability to use existing customer data to both pinpoint each customer’s long-term value to the company and discover how each customer will respond to specific retention efforts.
For example, will an upsell offer to high-value customer Bob increase his customer loyalty or aggravate him? Should you let high-value customer Susan complete her inbound self-service over the phone to check her bank balance, or should you pre-empt the call and make an upsell offer? With the right answers to critical retention questions, contact centers can take the right action at the right time with every customer. This practice will give companies a fighting chance against aggressive competitors, volatile market changes, fickle customers and their own internal struggles.
Contact Strategies Versus Workforce Constraints
What’s the first sign that a call center’s agents aren’t busy? They start popping their heads above their cubicles, which call center managers call “groundhogging.” At this point, the managers scramble to find something cost-producing to fill the agents’ valuable time, fully aware that the clock is ticking and profitability is dropping every second.
The problem of keeping agents’ workload optimized is so pervasive that entire software programs are employed to overcome the problem. However, these workforce management applications often compound the problem by analyzing call centers’ historic workloads to estimate their future work requirements. This back-to-front workforce projection frequently requires call centers to forcefit their staffs to the workload estimates. The result can be less than optimal.
Predictive analytics solutions approach the problem from a different angle. Instead of creating impractical theoretical agent schedules, these solutions work with each day’s actual available staffing levels — not just in one call center, but also in multiple call centers. The applications then create practical schedules in real-time, achieving the ideal balance of workload to actual agent resource across an entire business day. By working with the staff at hand, contact centers no longer have to throw their carefully planned customer contact strategies out the window when agent resources are low, because predictive analytics will make sure high impact contacts are made first.
Good Versus Bad Account Acquisition
Is your company one of the many businesses focusing attention on customer acquisition in today’s non-prime markets? If so, you already know that the current state of the unsecured credit industry is not a pretty picture. But competitive pressures are heating up, forcing companies to make a move into these uncharted waters in the hope of achieving two fundamental business goals: attracting new customers and building the balances of the new accounts, hoping they won’t become delinquent.
But the downside of this risky subprime marketing is steep, because an estimated 50 million Americans have thin or no credit files with the major credit reporting agencies. While many companies are extremely savvy when marketing to traditional prime-market customers, marketing to non-prime customers is a very different situation. To attract prime customers, companies create multilevel marketing strategies that are finely tuned to segment customers with pinpoint precision, allowing the companies to expand business opportunities with “good” customers and limit exposure to “bad” customers. Companies confidently deploy these marketing programs every day knowing that the bottom- line result will be higher profits.
However, these companies’ sophisticated marketing tactics are diminished when marketing into non-prime markets. Here, companies must wait six months or longer to offer new customers business-building offers such as credit limit increases and upselling. The reason for this delay hinges on one archaic business process: today’s sixmonth behavior scoring process. Until a half-year has passed, companies cannot gain the insight they need to accurately gauge a new customer’s risk-and-reward level. While this wait does not compromise business growth in markets where the majority of the customers are good risks, it does significantly jeopardize business opportunities in non-prime markets.
The key to changing this picture from high risk to great opportunity lies in two groundbreaking studies conducted at two of the nation’s largest credit card organizations. Research found that “bad” accounts tended to build their balances to 80 percent of their limit within 30 days, while “good” accounts charged only 20 percent of their available credit in the first month. This new insight leads to one conclusion: the traditional six-month wait for behavior scores is unacceptable. What’s needed? A predictive analytics technology that identifies risk propensity on day one of new account activity.
Marketing Goals Versus Collections Goals
Does the following classic front-end and back-end business struggle sound familiar? The marketing department works hard to gain new customers, plying them with enticing offers and seductive upsell programs. But if those customers ever enter collections, even if it’s only one time, the proverbial honeymoon is over. Your collections department will naturally go after the outstanding debt without discrimination between customer types.
Collection agents are often tasked with the relentless pursuit of outstanding debt without regard for each customer’s individual situation. However, not every customer in collections is a deadbeat. Even good customers can miss a payment now and then. When they do, the last thing you want to do is hassle them for payment. An aggressive collection strategy could quickly turn a high-value customer into an ex-customer.
Though it may sound contrary to the mission of collections, many good customers should not be contacted at all, because they will probably pay the debt. But how do you know which delinquent customers should receive this special collection treatment?
Predictive analytics solutions are able to help collections departments separate typical low-value collections accounts from high-value customers. Plus, these technologies can take the next step to determine if these accounts should be contacted at all and, if so, determine the best course of action. Using a sophisticated collections approach eliminates the cross-purposes of marketing and collections.
Many companies are taking advantage of these collections solutions to finetune and elevate their collections departments into retention support systems. As all collection executives know, even incremental improvements in processes can have an immense impact on companies’ bottom lines
By reducing unnecessary customer contacts and more strategically targeting necessary contacts, companies can expect to lower costs, increase collections and boost customer satisfaction.
Predicting The Future Of Customer Contact
The bottom line on internal conflicts in the customer contact center is that companies can no longer make the most profitable customer decisions without insight that bridges the conflicts and achieves the optimum result under all circumstances. Thanks to intelligent predictive analytics solutions, companies are gaining clearer customer insight and making better decisions. Progressive market players have found that they can make their best decisions — i.e., decisions that ensure the highest profitability from every customer interaction — by adding predictive analytics to their traditional decision support systems. These early adopters are reaping the rewards of predictive analytics, including greater insight, smarter decisions, higher returns and faster payback.
Predictive applications can deliver harmonious results, because they have the unique capacity to simultaneously process new areas of existing corporate and customer data including the company’s strategic business goals, the actual resources available, the needs of the entire account base and the propensity rankings of each individual account. The result is true action optimization — all day, every day.
Robert Tate is the vice president of marketing at Austin Logistics (news - alert) (http://www.austinlogistics.com), a provider of analytic software and solutions. Bob has more than 10 years of experience in call center management and operations, including executive-level positions at call center hardware and software technology firms. He previously served as VP of marketing for Latitude Communications and Vantive. He can be reached at [email protected].
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