Would any additional resources or the re-alignment of existing ones be needed?.

If it is favorable, can it be implemented within realistic timeframes?.

Is the cost/benefit projection favorable?.

What would be the margins impact if dynamic pricing were implemented? In almost all cases, the margin-per-product would diminish, which would have to be weighed against the lift in sales.

What is the estimated cost of implementing data infrastructure (e.g., a data management platform or advanced personalization engine) to create composite customer profiles (combining purchases, refunds, web analytics, customer service, etc.)?.

What is the best-case lift in sales given the total number of customers and average purchase frequency?

Assume that catalog pricing was 100% dynamic and based on each individual customer’s data.

This would entail the following considerations:

Take the example above: suppose that this analysis is needed for implementing dynamic pricing. Individual optimization options must be further elaborated and subjected to cost/benefit analysis. Marketing, product management, finance, customer service, and supply chain have all contributed, resulting in a bloated agenda. Say, you have over 50 possible analytical options that might deliver on the larger business objective. Conduct a high-level cost/benefit analysis of options This inclusive approach not only allows the capturing of potential optimization options from all parts of the business, but also helps keep key stakeholders engaged. Managers can frame problem statements by analyzing how the larger objective relates to every department and by identifying each department’s specific optimizations. Every possible interpretation above will have different data requirements, modeling approaches, risks, and cost/benefit profiles, but these would be impossible to assess without dividing the larger objective into smaller data problems. This step involves breaking down one high-level business objective into specific problem statements that make sense from a data perspective. What about online merchandising? Could a different merchandising scheme be used to increase sales from an existing inventory and product catalog?.What about product pricing? Can we maximize sales by offering more dynamic pricing for specific customer segments?.What if we focus more on retention rather than acquisition marketing? Acquisition costs would go down and we may generate more sales over the customer lifecycle.Can we focus on a particular segment of customers in order to maximize overall sales?.How can we use our marketing budgets more efficiently? Are we spending the optimal amount on each channel? Non-optimized spending can quickly deplete marketing budgets, especially in a highly competitive niche.How can we utilize our customers’ data to ensure that they stay with us? Clearly, keeping a customer active for longer will reduce acquisition costs while increasing lifetime sales.

So, what does “improve sales while reducing acquisition costs” mean to someone other than the CEO or shareholder? This is a rather loaded remit, but what could it mean tactically? We’ve broken this down into smaller problem statements: Translate strategic objectives into specific problem statements Utilizing this four-step approach will enable companies to make informed decisions about committing time and resources to data mining and analytics projects by using comprehensive cost/benefit analysis. This article details a high-level framework for streamlining your BI project plan. The most compelling of these use cases can then be added to the implementation queue. However, to prepare even these basic inputs, companies should follow a structured approach to developing the business cases, which senior management can then evaluate. Project managers then work within these constraints to deliver the final project. Successful implementations require that expected outcomes be clearly defined, resources earmarked, ballpark delivery estimates published, and risks captured in advance.

While companies employ several methodologies for implementing business intelligence projects, the same rigor is rarely applied when planning them. Completing these projects is no simple task but the one requiring the company to define the specific business problem, allocate resources, evaluate the outcomes from a business perspective, and operationalize the modeling process recommendations. However, companies still struggle when implementing BI solutions. Business intelligence, or the art of utilizing data to find hidden patterns and insights, has become integral to strategy development for leading organizations, including Fortune 500 companies, government agencies, and academic institutions.

Business intelligence strategy example