Big data continues to capture the imaginations—and budgets—of brand marketers across industries, but should it? Certainly the ability to harness huge data sets, collect vast ranges of data types in real-time, and mine that data for insights can yield profound results. But running to big data is often a lot more involved and expensive than what many marketing teams actually require.
While big data projects carry prestige and new-toy appeal, they require significant (and almost-always new) investments into databases, analytic tools, people, and storage infrastructure. Tapping into insights from big data means sipping from a firehouse of unstructured data to gain digestible, actionable information—and that’s not cheap or easy.
By contrast, maximizing existing investments into tried-and-true analytic methods and existing sources of structured marketing data lacks the reputational sizzle of big data, but it can still prove more effective while conserving cash. Before you drop the big dollars on the hottest new thing, ask yourself a few questions about how effectively you’re using the small data right in front of you:
- Are you overlaying customer data from multiple digital touch points like web visits, advertising interactions, social media, and email?
- Have you created an optimization council (or leadership role) focused on creating and applying a measurement framework for customer experience?
- Do you have formal, regular processes to bring customer data and insights related to those touchpoints and experiences back to business groups?
“Small data” is really just the data ready for the taking in your website, ad servers, email platform, CRM files, social listening tool, content management system and others. It has such potential because it too often goes underutilized—but has gems of marketing strategy wisdom.
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The most important goal of marketing data analysis is learning how to provide better experiences that attract, win over, and ultimately keep customers coming back. Marketers may anticipate big data as providing revolutionary new insights which would otherwise be unavailable. But, in reality, big data practices (such as collecting every social media mention of a brand) still require considerable processing to refine into actionable insights – and may not necessarily represent opportunities as big as the size of the data involved.
Given this reality, most marketers ought to pursue the more efficient strategy of increasing their use of existing marketing data. Taking a deeper and more rigorous dive into data that a brand already has on hand can certainly drive the development of new and more effective techniques – the kind that marketers seek when they think of big data.
For B2C brands, more robust small-data analysis allows marketers to better target the optimal moment and channel for presenting that ideal offer to each individual customer, improving customer experiences, customer loyalty, and sales. Data analysis offers similar advances in B2B settings, by accelerating long sales cycles and decreasing the cost-per-lead and the cost of providing account services.
Developing the small-data analysis capabilities that yield insights requires both dedication and the right technological support. This can mean investing in platforms for marketing automation, CMS, sales and CRM, social media listening and management, customer service and more. Producing actionable insights from these various small data sources often requires achieving a single view of the customer with the use of a business intelligence tool like Tableau or Power BI. These data aggregator tools are essential to compiling data from disparate sources and enabling marketers to slice and dice data around a more holistic view of their customers.