Desperate for the Right Insights: How Data Exchange Can Solve Your Procurement Issues

Big data procurement is a pain point for analytical marketers. Chief Marketer reports that “getting a 360-degree view of the customer” is a primary struggle for today’s marketing leadership. While it’s clear that integrating a wide array of data insights is the right solution, many marketers are simply unable to obtain the right big data assets via traditional procurement methods, such as data management platforms (DMPs), internal assets or leading third-party vendors.

 

The most sophisticated marketers understand there’s more to customers insights than “RFM – recency, frequency and monetary value.” To effectively maximize conversion potential, subject matter expert Karl Wirth recommends including insights on relationships, persona and intent. You must understand how your prospects are researching across platforms, individual motivational factors and pain points, and the context that surrounds each of these qualifiers. It’s abundantly clear that big data means big opportunities for marketers, but only if they’re able to procure sufficiently recent and comprehensive insights.

 

What Comprehensive Big Data Procurement Looks Like

While analytics experts have multiple ways of categorizing the types of data that provide marketers with comprehensive understanding of their target customers, marketer Jim Robert’s definitions are among the most intuitive:

  1. Identity
    A consumer’s identity includes basic demographic characteristics, such as age, gender, and race. It also includes geographic details on the area of residence, and insights such as employment, job title, and income.
  2. Quantitative
    Quantitative data is most likely to be first-party insights stored within an organization’s DMP based on their interactions with a customer, but can also be sourced via data exchange with third-party vendors. This includes data on transaction history and communications with the brand. It will also include online activities across desktop and mobile devices, including historical engagement with branded content or company’s sales teams.
  3. Descriptive
    Descriptive data offers a more comprehensive view of an individual’s life than pure identity data. It can include parenthood status, including the number of children and whether an individual owns pets. It can detail whether someone owns or rents their home, their education level, and work history.
  4. Qualitative
    Many marketers are familiar with the concept of “attitudinal data,” but qualitative insights actually encompass much more. A consumer’s opinions, brand preferences, and motivations may be included among these insights. Qualitative profiling can also lead to an understanding of brand preferences, consumer pain points, and individual priorities.

 

While you can gain a basic understanding of customers by procuring just identity and quantitative insights, it won’t be a truly comprehensive understanding of how your customers operate. You won’t understand why they make the decisions they do, or how they’re most likely to research based on education level.

 

Each additional type of data insight can change a consumer’s goodness-of-fit with a marketer’s target market. While consumer identity factors may dictate that they can afford to purchase a product, descriptive and qualitative factors may affect their priorities or reveal that budgets are most likely directed elsewhere.

 

DXP: Simple Procurement of Comprehensive Insights

For marketers struggling to build comprehensive profiles and filter targeted advertising opportunities towards the most qualified customers, the Data Exchange Platform can represent the solution. Instead of relying on limited or aging insights in a DMP environment, marketers can procure big data via a wide range of third-party resources all in one place via the DXP. No other platform can give marketers the breadth of data availability like the DXP due to it’s inherent access to so many data providers at once and it’s ability to merge data points from multiple sources across a single data taxonomy. This facilitates the first steps towards a true, 360-degree understanding of who brands are trying to connect with.

 

It goes without saying that better understanding leads to better conversions and sales. By ensuring their messaging lands in front of genuinely qualified prospects exactly when they’re motivated to buy and actively searching, conversion rates can finally exceed organizational targets. Instead of struggling to drive sales with data that reveals only part of the picture, marketers are given the opportunity to finally achieve the understanding they need.

 

For more insights on customer understanding through big data analytics, we recommend our blog: Re-Imagining the Consumer Needs Through Data.

 

image credit: nec corp via flickr/cc

 

 

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