Focusing on the customer for better results

Rather than choosing the best customers for each campaign, IBM SPSS predictive analytics chooses the best campaign for each customer. It accomplishes this by helping answer four crucial questions:

• Who should we contact?
• What should we offer?
• When should we make the offer?
• How should we make the offer?

IBM SPSS predictive analytics allows business managers and business analysts more input into which inbound interactions are the best candidates for an up-sell, cross-sell or retention offer. These decisions are based on customer data such as transactions, purchases, call histories and web visits. A business analyst, who understands business needs and goals, analyzes the data in real time and provides an offer the customer is most likely to accept. The results of the interaction are then fed back into the database, where they can be used to refine future offers and achieve progressively better results.

For example, if a high-value customer calls or visits your company’s website to complain about a product or service, IBM SPSS predictive analytics can analyze previous behavior and tell you how likely it is that this individual may stop doing business with your firm. This information, combined with the customer’s history, can be used to create a customized retention offer on the spot. This offer can be modified, depending on how the customer behaves or responds; and this new information can be used to refine your customer’s profile and improve future interactions.

When you know how to reach the right customer with the right offer at the right time, through the right channel, your organization has the key to truly successful direct marketing campaigns.

IBM SPSS predictive analytics combines historical and real-time customer data to create predictions about future behavior, preferences and needs.

IBM SPSS predictive analytics gives business development the analytical power that was previously available only to statisticians. By incorporating predictive analytics and business expertise into daily campaign processes, business managers and analysts are able to understand and anticipate their customers’ needs to an unprecedented degree, resulting in more effective campaigns and significant increases in revenue.

Creating, optimizing and executing campaigns

The conventional campaign approach

The conventional approach to direct marketing has been to launch between 10 and 50 large, calendar-driven campaigns each year and add even more campaigns during ideal times. This approach primarily focused on internal company processes, rather than the needs and preferences of its customers. Too many campaigns ran the serious risk of alienating customers by overloading them with offers and inundating them with ineffective information.

Traditionally, analysts chose customers for their campaigns by using a few basic selections or exclusions. For example, they may have selected groups from within certain geographic boundaries or excluded customers who already owned the product. Response to conventional campaigns has often been less than one or two percent.

The IBM SPSS predictive analytics approach

IBM SPSS predictive analytics focuses on your customers’ needs and preferences, providing the business analyst with the right customer, channel, timing and offer to create a compelling and profitable marketing campaign.

Select the right customer

First, the business analyst selects a predictive model and uses it to determine which customer segments to target. IBM SPSS predictive models often significantly reduce the number of customers contacted, thus reducing campaign costs by 25 to 40 percent while maintaining and even increasing response rates.

Select the right channel

IBM SPSS software enables business development to optimize outbound campaigns by using each customer’s preferred channel of communication and balancing it with the expected constraints, costs, responses and profits.

Select the right time

Consumers have many choices, making it critical for your company to reach its customers at the right time: when their behavior indicates an unmet need, or a risk of churn or attrition. IBM SPSS predictive analytics continually scans your customer databases for just such events, and triggers specific campaigns when a need or risk is detected. An event-marketing approach can result in as much as double the typical response rate.

Select the right offer

IBM SPSS predictive analytics optimizes your campaign by making the customer – not the campaign – the focus. It evaluates all of the available campaigns and selects the one that best balances the customer’s predicted response with the profit potential of the campaign. It also takes into account any suppressions or contact restrictions, such as “contact only once a month.” Focusing on the customer rather than the campaign has enabled companies to report a profit increase of between 25 and 50 percent.

How to optimize campaigns with IBM SPSS predictive analytics

IBM SPSS predictive analytics adds new and essential campaign modeling and optimization capabilities to the results of your company’s existing models; this enables you to use your predictive models and methodologies to greater advantage. The following steps outlines the process that answers the “who, what, when and how” of a successful marketing campaign.

Step 1: The predictive models are created to efficiently find appropriate customers and discover the best timing, channel, and message for each customer.

Step 2: Business information is added, such as contact restrictions, campaign budget, projected size and cost, as well as campaign objectives and predicted response and revenue.

Step 3: The approved campaigns are executed. Afterward, IBM SPSS predictive analytics compares projections to the actual results and incorporates this information to improve the effectiveness of future campaigns.

Building and using IBM SPSS predictive models

IBM SPSS predictive models make your marketing campaigns more effective and profitable. Our software segments your customer information to include only those most likely to accept a particular offer or respond to a certain message that has been launched to accomplish a specific goal.

IBM SPSS predictive analytics creates a range of models, including:

• Acquisition model: predicts the possibility of a prospect converting, or buying your company’s product or service
• Cross-sell model: predicts the possibility of an existing customer buying an additional product or service
• Up-sell model: predicts the possibility of an existing customer making an additional investment in a product or service upgrade
• Attrition model: predicts the possibility of a customer no longer wanting to purchase your company’s products or services

IBM SPSS predictive analytics can also create the following customer models:

• Value model: predicts a customer’s expected lifetime value, or the expected value generated if the customer buys a specific product
• Tone-of-voice model: predicts which message is best for each customer, as messages resonate differently with different customers
• Risk model: predicts possible fraudulent activity or loan defaults in order to exclude high-risk customers

IBM SPSS predictive analytics incorporated with your business expertise will result in immediate improvements in customer response and satisfaction, as well as increased revenue.

Creating predictive models efficiently

A streamlined predictive model-building process is imperative to the success of your company’s marketing campaigns, and to accomplish this you no longer need a team of statisticians. IBM SPSS predictive analytics allows business analysts to quickly create, validate, and assess models using its intuitive model-building environment. What used to take days now takes just a few hours by following four simple steps:

Step 1: Set up the analysis

As illustrated in below, the business analyst sets the campaign objective (Create Cross-sell model), then selects the product or service to offer (Europe Investment Fund) and the channel to use (Branches).

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Step 2: Create the model

IBM SPSS predictive analytics creates a predictive model designed to optimize the Europe Investment Fund cross-selling campaign for branch offices and then finds the customer segments that are most likely to respond to this campaign.

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Step 3: Assess the business impact

IBM SPSS predictive analytics provides group size and response probability to give the business analyst an understanding of the business impact of the model. Our software then uses business parameters to assess the profit margin for each segment by comparing the cost of the campaign with the profitability of individual customers and products.

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IBM SPSS predictive analytics provides an overview of each campaign’s projected costs, revenue, and profit, versus its size (see Figure 1). This chart helps your company keep campaign sizes in line with marketing and business objectives.

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Step 4: Apply business knowledge

After determining the impact of the models, managers and analysts apply their business knowledge to further refine the models. They can interactively change rules to reflect best practices, incorporate specific knowledge (such as excluding a segment that has responded poorly to recent campaigns), and immediately assess each campaign’s impact on response probability and profitability.

Managing models

IBM SPSS software stores all complete and in-progress models in a central repository (see Figure 2) for easy access. Business analysts can use our software’s model management capabilities to:

• Define the status of each model, such as in development, testing, in production, or obsolete
• Define the type of model, such as cross-sell or attrition
• Restrict access to certain models, such as those defined as in development or obsolete
• Apply versions, so that old models can be reused or compared to current versions
• Test models for quality and performance

[Download PDF to see Image]

IBM SPSS predictive analytics enables your organization to run more effective campaigns at every stage (see Figure 3).

[Download PDF to see Table]

Assessing the impact of campaign decisions

IBM SPSS predictive analytics eliminates the guesswork of determining which campaigns to run. Business analysts can view detailed, interactive charts that show the projected revenue impact of each campaign (see Figure 4). This helps analysts know in advance which campaigns are likely to be the most successful in reaching specific goals and achieving positive financial results.

[Download PDF to see Image]

Monitoring and improving campaigns

Successful campaign optimization is the result of monitoring and feedback. This enables business analysts to adjust an in-progress campaign if the initial results are not as positive as expected, as well as measure the final results of a campaign.

IBM SPSS predictive analytics stores all your campaign interaction information, such as the offer made, the campaign used to make the offer, and the models used in the campaign. This enables your company to monitor:

• Campaign-level performance, such as predicted response versus actual response
• Customer performance, such as profitability, cross-sell ratios, and attrition risk
• Channel performance, such as effectiveness and expected load versus planned load per channel
• Predictive model performance, to assess results and revise, refine or discontinue

IBM SPSS predictive analytics tracks the performance of models and campaigns and creates a “feedback loop” of information, enabling business analysts to refine and create even more effective campaigns and achieve progressively better results.

Increasing marketing productivity

IBM SPSS predictive analytics not only helps your organization increase response and profits, it enables business development to be more productive. The productivity enhancements result from two important capabilities:

First, streamlining the process of campaign creation and execution reduces preparation time from a few days to just a few hours. This leaves business analysts time to launch more campaigns and to create new campaigns without increasing personnel or resources.

Second, allowing business analysts to optimize campaigns without help from statisticians eliminates the potential for a bottleneck in the statistics department. IBM SPSS predictive analytics puts the tools and capabilities business analysts need literally at their fingertips, so they can achieve significant marketing campaign results in less time.

Adhering to internal and external restrictions

As important as it is to reach your interested customers with compelling offers, it’s just as important to respect your customers’ contact and channel preferences. IBM SPSS predictive analytics enables you to incorporate critical preference information such as internal opt-out lists and external do-not-call lists. In addition, restrictions on the frequency of contact for specific channels and customers are incorporated into the campaign optimization strategy. IBM SPSS technology frees business analysts from manual cross-checking, thus creating more time for them to focus on receptive customers and preferred channels, increasing the likelihood of positive customer response.

Integrating with existing systems

IBM SPSS predictive analytics integrates seamlessly with all major databases and campaign management systems, including legacy systems. Organizations that already have campaign management tools in place use IBM SPSS software to get better results from the campaigns they already produce. Our software leverages your company’s existing databases, systems, and processes to deliver results quickly and to generate measurable business value.

Optimizing interactions across all channels

IBM SPSS predictive analytics optimizes your company’s entire customer interaction process by integrating across channels and processes to predict and effectively respond to customer needs, preferences, and behaviors. For example, you can use our software to turn inbound customer service calls into sales opportunities, creating a new revenue-producing channel. Implementing our software into your organization’s inbound and outbound call centers, direct mail operations, and website, will result in immediate improvements in customer response and satisfaction, and increased revenues across these channels.

Conclusion

The challenges faced by business managers and analysts today are not significantly different than they have been in the past: convert prospects into customers, and retain and increase revenue from existing customers. What has changed is the complexity of the marketing landscape. Customers expect personalized campaigns and insist on communication only through the channels they indicate. Organizations must adhere to any suppression or contact restrictions put in place by customers and by government legislation.

IBM SPSS predictive analytics will handle these complexities for you and at the same time improve your CRM programs. IBM SPSS predictive analytics can help your company convert prospects and cross-sell to existing customers. The campaign optimization capabilities provided by predictive analytics provides you with an unprecedented level of targeting and coordination across all channels.

This unique campaign optimization approach quickly results in decreased costs and increased revenue, as you target the right customers at the right time, through the right channel. IBM SPSS predictive analytics can increase your company’s profit by 25 to 50 percent, making a significant and positive financial impact.

About IBM Business Analytics

IBM Business Analytics software delivers complete, consistent and accurate information that decision-makers trust to improve business performance. A comprehensive portfolio of business intelligence, predictive analytics, financial performance and strategy management, and analytic applications provides clear, immediate and actionable insights into current performance and the ability to predict future outcomes. Combined with rich industry solutions, proven practices and professional services, organizations of every size can drive the highest productivity, confidently automate decisions and deliver better results.

As part of this portfolio, IBM SPSS Predictive Analytics software helps organizations predict future events and proactively act upon that insight to drive better business outcomes. Commercial, government and academic customers worldwide rely on IBM SPSS technology as a competitive advantage in attracting, retaining and growing customers, while reducing fraud and mitigating risk. By incorporating IBM SPSS software into their daily operations, organizations become predictive enterprises – able to direct and automate decisions to meet business goals and achieve measurable competitive advantage. For further information or to reach a representative visit www.ibm.com/spss.