3 steps to filtering "big data"

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One of the biggest challenges merchants face is understanding who their customers are so they can interact with them in a way that will drive higher conversions and stronger loyalty. Years of marketing studies coupled with technology innovation and social media adoption means the industry has amassed so many data points on consumers that marketers are swimming in them. Just because you have the data, however, doesn't mean they are all applicable to your business. Knowing what information you need (and what you don't) to meet your KPIs is a vital but daunting part of any marketer's job.

I've simplified this process into three steps:

  • Assess the scope of your customer data
  • Outline your KPIs and business goals
  • Map the data to the KPIs and goals so you understand what will be the most actionable data for your business

Many marketers do not have a grasp on all the different types of data on customers they can access, and this is a missed opportunity. There are a variety of sources for this information. Below I've identified the four main graphs of customer data that marketers can use to gather intelligence:

The social graph represents the influences people have on each other. Companies can most commonly gather this information from social media sites including Facebook, LinkedIn, and Twitter.

The knowledge graph includes tools that consumers use to gather information that helps them make decisions, such as general web search engines, product review sites, and news articles. For example, Google's Knowledge Graph is designed to augment search results with data from other sources to improve relevance.

The interest graph focuses on the things, products, or topics a consumer cares about, which can be used to personalize advertising, discount offers, and other deals. It reveals what products appeal to consumers or merely pique their curiosity based on the messages they broadcast publicly (brands they follow on Twitter, images they pin on Pinterest, etc.).

The payment graph aggregates information related to purchasing history. This includes data on the payment method (cash, credit/debit card, check, wire transfer); the customer's financial institutions (bank, credit card company, PayPal); tools they use (Google Wallet/NFC, Visa's V.me digital wallet); and where the transaction took place (offline store, online retailer).

All of this information can quickly turn into data overload. To make use of the insight in a way that is most effective and scalable for your business, you need to determine how each graph maps back to your marketing objectives and then prioritize the graphs accordingly.