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Founder of FranceWebAsso, draws on his own experience in advocating the win-win partnerships that can come from big and small companies working together.

It’s the new way of working that evertually every large company will embrace,’ says David Butler, Coca-Cola’s Vice President of Innovation, when speaking about collaboration between big and small businesses. In the past two years, giant corporations such as Coca-Cola, MetLife, General Electric, IBM, Mondelez International, Cisco and Tyco International have started taking cues from the start-up and venture capital world (Fortune, 2015). GE, for example, has enlisted 500 coaches to train executives to embrace concepts like risk taking and learning from failure. It seems that the saying ‘innovation belongs to the small and nimble’ no longer holds true in an economy where large and small businesses are combining forces to create a smarter ecosystem surrounded by fast and disrupted strategies.

Business Problem

Database marketing, Cross-selling, Up-selling, Profit Optimization, Assignment Problems, Constrained Optimization. Response Models.

The database marketing community has changed significantly over the last several years. In the past, database marketers applied business rules to target customers directly. Examples include; targeting customers solely on their product gaps or on marketers’ business intuition. Marketers have also applied RFM type analysis where general recency, frequency, and monetary measurements as well as product gaps are used to target customers for specific offers. The current approach, which has widespread use, relies on predictive response models to target customers for offers. These models accurately estimate the probability that a customer will respond to a specific offer and can significantly increase the response rate to a product offering. However, simply knowing a customer’s probability of responding to a particular offer is not enough when a company has several products to promote and other business constraints   to consider in its marketing planning.

Marketing departments also face the problem of knowing which product to offer to a customer, not just which customer to offer a product. In practice, many ad hoc rules are used. Prioritization rules based on response rates or estimated expected profitability measures have been used; business rules to prioritize products that can be marketed are sometimes used; and product response models to select customers for a particular campaign are also used. One approach that is easily implemented but, for reasons outlined later, may not produce optimal customer contact plans relies on a measure of expected offer profitability (the estimated probability of response multiplied by the profit given customer response less direct costs) to choose which products to offer customers. However, a shortcoming of this approach is its inability to effectively handle complex constraints on the customer contact plan.

Business Constraints

Database marketing departments face several types of business constraints. Typically, there are

• restrictions on the minimum and maximum number of product offers that can be made in a campaign,
• requirements   on   minimum   expected   profit   from product offers,
• limits on channel capacity,
• limits on funding available for the campaign, and
• campaign return-on-investment hurdle rates that must be met.

These are a sample of the constraints that marketing departments must meet when executing a campaign. Ad hoc approaches are also typically used in an attempt to meet these constraints.

The opportunity costs of the business constraints are generally not known. Constraints are usually negotiated between marketing, product lines and delivery channel management. If the cost of a constraint was known, then the company could choose to tighten or to relax the constraint by removing or adding more resources. For example, channel capacity could be increased if it were known that there was a significant return on the investment by doing so. Knowledge of the opportunity costs could help evaluate these management decisions. Applications of this will be discussed in the “STRATEGIC USAGES” section of this article.

Ultimately, the database marketer needs a concrete framework to effectively act on “the right product to the right customer at the right time” mantra. The approach we take is to transform the database-marketing problem into an optimization problem that is designed to generate the maximum incremental profit from a limited amount of resources subject to the necessary business constraints. This paper will describe an actionable framework that will satisfy this business problem.

Shaun Doyle

Shaun Doyle is President & CEO of Cognitive Box – a global team of business and technical consultants who advise organizations on how to gain sustainable and competitive advantage through the exploitation of their customer information.

He has extensive experience in the design, development and implementation of customer-focused database marketing systems across various industries and has built more then 200 marketing solutions for blue chip enterprises in the financial services, retail, mail order, utilities, charity, media, gaming and telecommunications sectors. His global client list spans the US, Europe, Asia and Australia.

Prior to founding Cognitive Box in 2002, Shaun was the VP of Intelligent Marketing Solutions at SAS responsible for developing business-oriented marketing solutions throughout the SAS organization. Areas of concentration included SAS Marketing Automation Solution and SAS industry specific solutions for Telco and Retail Banking. Before joining SAS, Doyle was Founder & Chairman of Intrinsic, a campaign management vendor acquired by SAS in March 2001. Earlier in his career, he co-founded Berry Consulting, a firm specializing in database marketing.

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