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Personalization Can Be Quite Dynamic

  • Tom Hannigan, Christina Palendrano
  • October 01 2002, 1:00am EDT
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Does your grandmother get offers in the mail from "twenty-something" dating services? The days of mass marketing are over. Today, smart marketing organizations are using personalized communications that speak to the individual needs, interests and preferences of consumers. Customers expect you to apply the information you know about them and to continue to learn from the relationship. They don't want their time wasted with irrelevant or inappropriate offers, and neither do you.

Personalization is all about delivering the appropriate offer to the proper customer through the right channel(s). As a cornerstone of customer relationship management (CRM), it can lead to stronger and more profitable relationships with customers. By understanding and predicting customer behavior, companies can create a competitive advantage – but that can be highly dependent on the personalization technique used.

Dynamic personalization technology allows companies to provide their customers with an entirely new experience of personalized offers and content. The process requires the collection, analysis and distribution of information about the customer. This technology enables a more precise matching of offers to customer needs, which can improve customer relationships and build loyalty. The real-time data mining capabilities of dynamic personalization provide greater accuracy and the flexibility to adapt to changing trends.

To realize the true benefits of personalization, a company must be able to effectively learn from each customer interaction and record the results of that learning. By gaining a better understanding of each customer's needs and preferences, the company can determine how to best service each customer over his or her lifetime. Historically, personalization required marketing professionals to update the customer data analysis and IT professionals to implement those updates every three to six months to remain current. This approach is a never-ending process that is extremely time-consuming and expensive.

There have been three significant generations of personalization, each building upon the previous. All three processes are still in use today, although their capabilities and limitations are quite varied. These three processes are summarized as follows:

Rules Based: First generation personalization efforts were dependent on companies performing a great deal of up-front analysis of customer and sales data. Once this analysis was complete, the project team designed a set of complex business rules that controlled the presentation of personalized content. This approach was effective, but it forced companies to keep business rules simple and relatively static.

Profiling: Second generation personalization efforts became more sophisticated by allowing companies to "bucket" or profile customers into a small number of distinct segments. Business rules are written to personalize content based on a visitor's profile. This approach allowed companies to increase the complexity of the business rules, but was still limited.

Dynamic: The latest trend in personalization technology is dynamic personalization, a process that replaces fixed business rules with the capabilities of a real-time data mining engine. This data mining engine uses learned information to determine which content to present to customers, a process called "arbitration." As each offer is presented, the self-learning analytics of the software will register each customer's response to the offer and will develop an ideal profile of the customer most likely to accept each offer. Each time an offer is accepted or rejected by a customer, the ideal profile is further refined.

The dynamic personalization process begins with the marketing team selecting a set of key customer attributes. These are the attributes that the marketing team believes will have the greatest influence on a customer's propensity to accept an offer. The marketing team can deploy multiple offers, allowing the real-time data miners to arbitrate the best offer for each customer.

To demonstrate, consider three scenarios for a fictitious sporting goods company, TSC Outfitters.

Scenario 1. The TSC Outfitters marketing team reviews historical sales data and identifies a correlation between ski equipment and high-end mountain bikes. Because TSC wants to increase sales of mountain bikes, the company creates a marketing campaign targeting customers who spend more than $100 on ski equipment. Using a rules-based approach to personalization, the marketing team works with the IT department to define the campaign and business rules, test the campaign and deploy it on the TSC Web site. When online customers make a purchase of more than $100 of ski equipment, they are presented with the mountain bike promotion in place of a generic promotion.

Unfortunately for the marketing team, analyzing, coding and testing each campaign is time and labor intensive, as is the additional effort required to monitor and fine-tune each campaign based on results. The required systems work for this effort only provides personalization benefits for the company's Web site. Should other sales channels be added, additional work is required to design, code and test each of those campaigns.

In this scenario, the company successfully provides customers with personalized content by matching an offer to their needs, but this personalization process cannot last. As the company attempts to deliver more targeted content for multiple products and channels, the number and complexity of the rules grow exponentially. Like most companies, TSC Outfitters finds maintaining this complex set of rules cost prohibitive.

Scenario 2. Using a profiling approach, TSC Outfitters performs more detailed analysis of historical sales data and identifies six segments of customers based on previous purchase value, geography and selected demographic data. Each segment is matched with a different combination of product and promotional offers. The marketing team works with IT to define the attributes of each of the six customer profiles and develops the business rules to dynamically assign a profile to each Web site visitor. This approach allows the marketing team to be more flexible with their marketing efforts. However, they are limited by the number of defined profiles, and they depend on IT to implement and modify business rules. As with a rules-based approach, profiling requires a major development effort as new sales channels are added.

In both of these scenarios, the effectiveness of the marketing analysis will begin to erode almost immediately. In the first scenario, warm winters in certain geographic regions depress ski sales. Based on the analysis driving the personalization effort, some high-potential customers will never see the mountain bike offer and sales will be lost. In the second scenario, there is no flexibility to allow customers to easily move between segments as their needs change. This static profiling leaves room for error and will begin to lose effectiveness over time. As new analysis of data is performed, customers may shift from one segment to another and will experience a sudden change in the stream of communications and offers.

Scenario 3. In this final scenario, TSC Outfitters implements a dynamic personalization model using customer geography, past purchase history, income level and the customer's specified sporting interests as attributes to feed into the real-time analytics engine. The analytics engine will use these attributes to determine which content and offers the customer is most likely to accept. When customers visit the company Web site or speak with a representative in the contact center, the real-time data mining engine will arbitrate the best offer for each customer based on these attributes. Individually, the results of each contact are reflected in the customer profile, refining future personalization of content and offers. On an aggregated basis, large-scale shifts in customer behavior due to emerging trends or changing market conditions will lead to automatic adjustments in the arbitration process. The biggest benefit for the marketing department is that in-depth analysis and the development time of IT resources are not required. This flexibility allows the marketing team to react more quickly to the needs of their customers. TSC Outfitters is now positioned to sell more mountain bikes during warm winters instead of losing sales, without extensive analysis and IT efforts.

Figure 1: Dynamic Personalization Technology

Dynamic personalization has additional benefits as a strategic tool as well. The real-time personalization analytics are designed to increase sales and build relationships within single or integrated marketing channels. This analytic process can be used strategically to test new offers or content and to build target market profiles that can be carried over into other marketing channels that lack interactive capabilities, such as mass media advertising.

With their initial success using dynamic personalization, TSC Outfitters modifies their marketing process to the following pattern:

  1. Offer – The company introduces new offers and content to a broad target audience through an interactive channel such as their Web site.
  2. Learn – They apply dynamic personalization techniques to allow real-time analytics to arbitrate the offers and identify the right target segments.
  3. Profile – They establish profiles of the ideal target segments for each offer.
  4. Target – Those profiles are used for targeting in marketing channels where real-time personalization is not possible. The company targets their advertising more effectively and segments the offers within their direct mail campaigns.

Most companies doing business today understand the importance of a strong relationship with their customers. Over the past decade, many companies have sought to improve that relationship by implementing the processes and technology of CRM. Personalization is becoming a critical component for any comprehensive CRM initiative, but historically it has been the most challenging to implement. It is an enterprise-level initiative and needs to be implemented across all sales and service channels of the organization to achieve the greatest benefit. Customers should be presented with personalized content whether they call the company's contact center, visit the company's Web site or are a recipient of an e-mail or a direct mail campaign.
When companies include a personalization strategy in their CRM initiative, they stand a better chance of delivering on the promise of CRM. Personalization provides companies with the ability to recognize customers in real time and vary the content or services to those customers based on previous interactions. Rules-based personalization is too restrictive for all but the simplest scenarios. Taking a more dynamic approach to personalization offers clear benefits – increased customer loyalty and more profitable marketing.

Your grandmother will no longer be tempted to join the "twenty-something" dating service; but perhaps with the right offer, for the right age group...

For more CRM personalization information, download the white paper "CRM Initiatives: Taking IT Personal – 7 Key Steps for Personalization Success" at

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