Contact Center Solutions Featured Article

Best Contact Center Practices with Predictive Analytics

January 05, 2009

Contact center interactions management has typically been rearward-facing: examining past transactions to form judgments on the value of customers today, whose results may or may not be accurate. While in today’s economy that may not be a bad approach: many excellent customers in the past may return to that status when things pick up. On the other hand many previous great buyers may no longer or will return to be just that.
 
That is where predictive analytics software and solutions can help: in driving customer value and maximizing returns from customer relationship management (CRM) systems. SPSS, which provides predictive analytics tools, says this technology breaks enables organizations to acquire, retain and grow customers, combat fraud and address risk management concerns.
 
“By anticipating changes in customers’ attitudes, preferences and actions, organizations gain access to key insight that allows them to create forward-looking solutions that not only maximize the value of their customer relationships, but also drive higher levels of revenues and profits,” explains Colin Shearer Senior Vice President, Strategic Analytics.
 
SPSS (News - Alert) offers these five best practices used by leading commercial, public sector and academic organizations to maximize customer value.
 
1.         Start Your Customer Strategy with Predictive Profiles
 
Detailed, accurate predictive profiles are the essential foundation of any customer strategy and CRM initiative. To better understand customers, organizations should use predictive analytics to create customer segments, which are the grouping of people or organizations with similar demographic profiles, attitudes, purchasing patterns, buying behaviors or other attributes. It should then formulate predictive profiles from each of those segments. These profiles, when deployed across the enterprise, enable an entire organization to focus on one-to-one customer interactions that are most likely to generate the highest returns.
 
Identify Key Customer Segments
Organizations can define customer segments based on behavioral information drawn from operational systems and on attitudinal information obtained through market research or data collection surveys. The two approaches complement each other, providing a more accurate customer understanding and development of more effective strategies for each customer segment.
 
Create Predictive Profiles of Each Segment
Organizations that combine predictive modeling within each segment are able to generate additional insight needed to more effectively and efficiently acquire, grow and retain customers. The more information known about the most and least profitable customers, the more intimate and valuable customer relationships become. The end result is a better understanding of what products and services customers are likely to want next.
 
2.         Target Customer Centricity — Build One-to-One Relationships
CRM is designed to manage interactions with customers and improve contact with them. These approaches often center on campaign management and operational processes and only ensure that interactions do happen and are consistent, efficient and recorded. But that’s where CRM ends.
 
What’s missing is the ability to identify the appropriate interaction for a particular customer: moving well beyond the one-to-many level and into one-to-one customer experiences. Predictive analytics enables the realtime creation of those one-to-one interactions, taking into consideration all types of data (transactional, behavioral, attitudinal and descriptive). It recommends actions that need to be taken at a specific time (when, where, how and what).
 
Data mining and text mining software create the predictive analytics foundation that allows organizations to capture and understand both structured and unstructured customer information. With this insight, organizations can properly address and resolve the most pressing business issues, including attracting and retaining customers, increasing revenues and identifying fraud/minimizing risk.
 
3.         Gather Customer Feedback at All Interactions
 
Customers have never had higher expectations and options to choose from. It’s crucial to be able to continually listen and gather as much information as possible on their opinions, attitudes and preferences.
 
Predictive analytics provides that ability with the introduction of enterprise feedback management (EFM) to the data collection process. EFM enhances the predictive models that drive customer centricity and makes it possible to get to the core of the ‘voice of the customer’. Whether it’s well-positioned surveys, online chat or contact center interactions, predictive analytics captures information across the touch points. It then provides a clearer understanding of who the customers are and what they really want.
 
EFM is not an occasional survey. Rather, it’s a centralized process for continually collecting, managing and hearing the voice of the customer throughout an organization. It’s the ability to fully engage with current or prospective customers through targeted feedback programs or simply by asking questions during naturally occurring events. Only with this added layer of data can an organization properly address customers’ preferences, motivations and intentions.
 
4.         Predict the Best Way to Acquire, Retain and Grow the Right Customers
 
Studies have shown customer acquisition can cost 5 to 12 times more than retention and that improving customer retention rates by just 5 percent can increase an organization’s profitability more than 25 percent. Improving customer retention can obviously have a big impact on profits. Customer attrition is particularly challenging for online retailers and companies in financial services, telecommunications and other industries where customers can change vendors relatively easily.
 
With predictive analytics technology, organizations minimize costs by directing outreach toward customers who are most likely to respond to an offer, as well as those who likely will be the most profitable. Predictive analytics also improves marketing programs by identifying the key attributes customers will most likely to respond to at a specific time.
 
Create a Prediction-based Customer Attraction and Retention Strategy
Organizations should use predictive analytics to create profiles that determine what types of customers to attract and then create a cost-effective attraction and retention strategy that includes separate plans for each customer segment. Most organizations will want to focus their efforts on winning over and retaining prospects that will become their most profitable customers.
 
Optimize Customer Attraction Strategy with Response Modeling
Organizations can fine-tune their customer attraction plans by using response modeling to predict which marketing programs will generate the highest response. This delivers benefits twofold: attain the results wanted, while avoiding the high costs associated with unproductive marketing efforts.
 
5.         Automate Decisions to Make the Right Offer at the Right Time
 
By deploying the results of predictive analytics to every customer touch point, from branch offices to call centers to websites, organizations can achieve greater effectiveness and profitability. With predictive results built into a company’s Web site, visitors will be automatically presented with offers most likely to produce sales in a consistent, relevant and timely basis.
 
It begins with consistency — embedding a common predictive analytics approach (philosophy and process) into key business processes through the organization — and repeating that approach over and over again. It continues with relevancy, the ability to deliver analytical results/recommendations to the business areas where a decision needs to be made at a particular time. It also means that the decision is most appropriate for that given situation or condition and that the data being used is continuously enriched with new information for added accuracy and efficiency.
 
Timeliness is equally key and it’s important to differentiate between “real time” and “right time.” Sometimes the two intersect, but not always and the right action at the wrong time is the wrong action.
 
By incorporating these five best practices for predictive analytics software and solutions in daily business practices, organizations are able to leverage and enhance their existing CRM investments. Predictive analytics unlocks the value of existing enterprise data to identify which customers to target, how to reach them, when to make contact and what messages should be communicated. By automating their data-driven decision making within daily business processes, organizations become more effective and profitable.

Brendan B. Read is TMCnet�s Senior Contributing Editor. To read more of Brendan�s articles, please visit his columnist page.

Edited by Tim Gray

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