Beyond Rules-Based Decisions:
Predictive Analytics Advances Customer Interactions to
New Levels of Precision, Consistency, and Profitability
Despite a wide variety of rules-based decision-support tools and massive customer databases, many companies today are still not making customer-interaction decisions that deliver the highest possible value. However, as pressures to raise their operations to higher levels of profitability intensify, more companies are continuing to look for decision-support tools that deliver precise, consistent, and profit-focused results. A growing number of leading-edge companies have already found the solution—predictive analytics technologies.
The Power of Predictive Analytics
Many forward-thinking companies have realized the limitations of traditional operations and turned to the more sophisticated decision-support capabilities of predictive analytics. A study conducted by IDC comparing predictive to non-predictive applications found that predictive solutions scored a 56 percent higher ROI. According to the IDC, “The main benefits in predictive projects were attributable to business process enhancement.”
The advantages of predictive analytics over rules-based and other decisioning processes can be summed up into three key benefits: greater precision, bigger impact, and higher profits.
Greater Precision. Predictive analytics-based technologies allow companies to replace unreliable guesswork and ad hoc rules-based decision-making with intelligent, scientifically generated action plans that simplify the growing complexity of customer data analysis.
Bigger Impact. Predictive analytics-based technologies allow companies to be proactive instead of reactive in making customer decisions, thereby helping them achieve a better overall impact.
Higher Profits. Conclusions made through predictive analytics-based technologies are accurate, target the highest value actions, and eliminate unproductive actions, thereby consistently delivering higher profitability and faster ROI.
By using predictive analytic decision-support strategies, forward-thinking companies are advancing their outcomes in many business-critical areas including:
- Targeting customers for up-sells and cross-sells
- Identifying accounts at risk of attrition and taking actions to prevent attrition
- Pulling high-value inbound callers out of waiting queues and routing them to the right service levels
Predictive Analytics in Action: Two Examples
The capabilities of predictive analytics in the marketing environment are best understood through two of today’s largest marketing challenges: revenue generation and campaign effectiveness.
Increase Revenue Potential. One of the most sophisticated developments in predictive analytics is the ability to assess not just each customer’s profitability, but also their short-term and long-term revenue potential. For example, predictive analytic models can assess customers’ revenue-generation potential when given a particular action, such as an upsell or cross-sell offer. Using revenue-generation predictive models, companies can target actions with the greatest revenue-generation potential for each customer, thereby creating business-building opportunities that did not exist before.
Maximize Campaign Effectiveness. In an inbound customer contact center, the difficulty in predicting which callers will respond to your marketing campaigns makes it challenging to know which should be routed to an agent or agent queue. The business-as-usual approach can waste valuable agent resources on low value callers while high value callers are in self-service. Predictive analytic applications, on the other hand, can segment callers based on the potential value of the interaction and the relationship, enabling you to know before a call is routed which calls deserve agent attention. By proactively routing your highest value callers to an agent, you are ensuring that your agents are spending their time with the most productive calls, thus increasing campaign effectiveness.
Conclusion
Today, the demands for more precise results and higher profits are intensifying. As the demands grow, customer-facing organizations are forced to evaluate their current processes and search for new ways to improve their contributions to the bottom line.
The science of predictive analytics has evolved to meet companies’ critical needs for more intelligent answers. As a result, companies can add increased sophistication to their customer-facing decision strategies and move into the new world of decision-making. By deploying advanced predictive analytics technologies, companies will gain today’s most powerful way to achieve the highest possible level of decision precision, consistency, and profit-generation.
Austin Logistics' is taking predictive analytics to a new level. The company’s powerful solutions embed predictive analytics into the business process, enabling companies to rapidly predict consumer value and behavior and strategically determine optimal actions. For more information, please download our white paper, “Achieving Both Customer Service And Marketing Objectives In The Customer Service Call Center.”