Leveraging Predictive Analytics in Marketing Campaigns
How well do you know your customers? How often do they buy? What motivates them to make multiple purchases? How can you ensure long-term loyalty? How can you attract and retain new customers? And most importantly, how can you cost effectively align your marketing campaign to ensure that you are sending the most relevant message to each customer segment at the time they are most likely to buy?
The number one asset of a company is its customers and following closely is the information about those customers gained through operational customer relationship management (CRM) systems. Leading marketers have taken advantage of the powerful benefits of sales force automation, call center software and other CRM systems to identify customer demographics, track purchases, monitor shopping habits and identify product preferences. As a result, they have been able to maximize the interaction between company and customers, increase sales and build a loyal customer base. Managing this wealth of valuable customer information as a strategic asset, however, is what makes the difference between simply tracking customer behavior and capitalizing on that information to understand and optimize the financial value of each customer.
Predicting customer product preferences and purchasing habits - and crafting the most relevant marketing messages around this information - requires a carefully orchestrated mix of intuition and an analytical framework that supports fact-based decision making. Without an analytical structure in place, even the savviest marketer will have difficulty manually analyzing all of the complex information they may be gathering on customers. And, while still a powerful resource, an operational CRM system alone will struggle to provide the deeper customer understanding required to add value to every interaction with each customer.
Many analytical CRM products, such as online analytical processing (OLAP), provide historical analysis, summarizing what has happened in the past. In other words, historical analysis can reveal who the best customers were last month and who they are this month. This kind of traditional analysis is important, but it doesn't anticipate what will happen in the future. Predictive analytics, such as data mining, are needed to provide a clear picture of what is going to happen in order to take the most effective action. The predictive analytic process discovers the meaningful patterns and relationships in data - separating signals from noise - and provides decision-making information about the future. For example, which customers will be buying what next or which customers are likely to defect. By supporting CRM with predictive analytics, companies of all sizes can begin to manage customer information as a strategic asset when developing marketing campaigns, resulting in better decisions on what message to send and to whom and when to send it.
Predictive analytics provide the most beneficial ways for marketers to:
Understand Customers - Using typical data-driven segmentation approaches, marketers can easily uncover literally thousands of attributes that define customer behaviors. However, with so much data it becomes too difficult and time-consuming to manually process the information for efficient fact-based decision making. Predictive analytics that support the operational CRM system automatically scan the data and "crunches" it quickly so that marketers can go in to query the results and get specific answers. With the results of the multidimensional customer profiles applied to current marketing campaigns, the interaction with the customer is optimized to be more relevant, more appropriate and targeted for increase response frequency.
Develop Targeted Offers - Once marketers gain a deeper understanding of their customers, they can more easily target specific offers to their most profitable customers and promising prospects. Applying predictive analytics to determine customer propensities toward certain product categories enables better decision making in selecting the right products to promote. Moreover, predictive analytics can help marketers analyze more accurately the results of targeted campaigns more accurately, revealing patterns in customer behaviors and preferences that can be leveraged for unique product offers in the future.
Execute Campaigns in Real Time - With specific messages and marketing channels in place for specific customers, a CRM system enhanced with predictive analytics can achieve real-time customer recommendations. Individual customer predictions, or a model that assigns scores based on customer behaviors, help marketers match the most relevant product offers based not only on the typical factors of recency and frequency, but on the complete range of demographic and purchasing behavior data available for each customer. Because the scoring process evaluates past data to forecast the probability of future customer behavior, marketers can tailor their CRM systems to respond with specific offers for specific customers - a strategy proven to increase response rates and optimize the value of each customer.
Match a Specific Offer to a Specific Individual - Predictive analytics facilitates propensity modeling, which enables marketers to fine-tune specific messages to specific customers within each marketing channel - e-mail, direct mail, Web site, call center - and determine what approach elicits the best response. By employing propensity modeling using predictive analytics, marketers can quickly isolate different customer segments and replace a "one-size-fits-all" campaign with an individualized, highly relevant message tailored to the customer's profile that results in a higher response rate.
Monitor Campaign Results - With predictive analytics in place, the entire CRM process can be monitored to determine if the current marketing campaign is generating the expected results. Customer metrics can be easily tracked and evaluated on an ongoing basis, providing instant insight into current customer behavior as well as statistically sound calculations to help marketers predict future activity. By keeping a close eye on customer metrics such as sales, retention rate, and churn propensity (the likelihood that current customers may be lost to competitors), marketers can revise marketing campaigns to respond to the customer's actual behavior at any given time and continue to monitor the success or failure of marketing efforts.
Satisfying customers in today's highly competitive global marketplace has never been more challenging. Having a deeper insight into customer expectations and future behaviors is the key to successful marketing campaigns. Predictive analytics enable marketers to understand the key factors that drive customer value and loyalty, and attract more customers. As they measure and monitor the effects of marketing campaigns in light of the impact on customer profitability, marketers can manage their organizations around the goal of improving the value of their customer base.
For more information on related topics visit the following related portals...
Data Mining and
Colin Shearer, vice president, customer analytics at SPSS Inc., a global provider of predictive analytics, is responsible for the product management and marketing of products and solutions related to the analysis of customer data. He was a pioneer of data mining in the early 1990s ? as a founder of Integral Solutions Ltd.(ISL) and the architect of ISL's award-winning Clementine system, the first data mining tool aimed at non-technologist end users. In December 1998, ISL was acquired by SPSS Inc. Shearer can be reached at email@example.com.
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