What happens when you combine big data, statistical modeling, and marketing analytics? If you're Williams-Sonoma, you gain the ability to process 50 million rows of data daily, enabling you to tailor marketing to individual consumers at huge scale.
Mohan Namboodiri, vice president of customer analytics at Williams-Sonoma, a public company with nearly $4 billion in revenue and 30,000 employees, sought to improve the retailer's marketing analytics. The company suspected that online ads and emails were more effective than catalogs for certain customers. And it wanted to find a way to achieve marketing attribution at scale -- that is, to understand the effect of each campaign that led to a sale for each customer. This in turn could enable marketing campaign budgets to be reallocated to target individual customers, not just segments of customers.
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Marketing analytics used to be simple enough: Trace your campaign code to your buyer, and you understand your campaign's effectiveness. The marketing campaign data was relatively manageable, relating to catalogs and other direct mailings, email blasts, ads, telemarketing, and not much more.
But the marketing tools, data sources, and data size kept growing. Today there are mobile messages, banner ads, online search campaigns, in-store promotions, loyalty cards, recordings of every mouse click, and more. Augmented data sources keep expanding, with third-party data covering customer demographics, credit scores, and so on. Plus, there are factors beyond marketing's control, such as seasonal buying habits and customer buying histories. Finally, there are the challenges of managing the marketing systems; many marketing strategies rely on disparate applications or organizations.
The latest marketing systems make it easier to tailor offers to customers for a new product, discount promotion, or loyalty points. However, to be most effective, marketers need to thoroughly understand what offers drive which customers, while being careful not to fatigue their customers -- for example, with constant email blasts. All this makes it difficult to have both one view of the customer's activity and the ability to act on it in the most precisely effective way.
The problem is multifold. Due to the volume, velocity, and variety, it's a big data problem. It's a data silo problem due to the variety of disconnected systems, and trying to understand the effectiveness of marketing campaigns on individual consumers for given factors is a big statistical analytics problem. For instance, some customers might react to an email campaign only during the Christmas season, while others might click on email campaigns and engage to buy throughout the year.
Moreover, in order to get a clear view of the information, you need intuitive dashboards that show the relevancy of campaigns on purchasing behavior.