Why Last Year’s Data Doesn’t Convert: Big Data Recency for Conversion Targeting

The New York Stock Exchange captures 1 terabyte of data during each active daily trading session.  Modern vehicles are equipped with approximately 100 sensors, all of which quietly work behind-the-scenes to capture an abundance of data points on vehicle performance. Every click consumers make online adds to a massive pool of insights on consumer behavior, demographics and more. With such an abundance of big data resources available, why are marketers trying to convert customers off insights that may be months or even years old? How can the Data Exchange Platform (DXP) improve this?


The average American spends over 11 hours each day interacting with electronic media devices, which typically includes over an hour on a PC and nearly 1.5 hours on a personal smartphone device. Despite the potential for tapping into an abundance of fresh, real-time insights, many marketers aren’t using sufficiently recent data on their prospects to drive the results they want. Nearly 40% of modern marketers at major brands complain that their data resources are updated too infrequently or not “real-time” enough for true insights. Timeliness is a major pain point for marketers facing pressure to incorporate big data into their strategies, as well as increasingly high conversion targets for online advertising campaigns.


The culprit behind marketers’ struggle to drive ROI through advertising could be the timeliness of data resources, in addition to data quality issues. Marketers are nearly twice as likely to be actively using customer survey data that could be months or years old as they are to incorporate mobile device insights (19% utilization) or social media data (35% adoption). In an era where consumers are likely to perform self-guided product research and purchase decisions via computers or mobile devices, data that reflects recent behaviors is critical to achieving a timely response. Even the most sophisticated data-driven marketers, who source behavioral insights via a data management platform (DMP), may not be working with sufficiently fresh data resources.


Does Your DMP Include Timely Data?

The use of a DMP to merge organizational insights with purchased or rented 3rd-party data sources is a common means of targeting banner ads and customer discovery at marketing organizations. However, a lack of efficacy, results or consumer understanding can still result, even with the effective use of DMP. Why? With digital activities growing in “every sphere,” human behavior is changing more rapidly now than perhaps at any other time in history, according to McKinsey. Regardless of how quickly consumers are selecting tablets and mobile devices over desktop computers, consumers have always tended to make purchases within a predictable period of time.

Data on online consumer behavior collected a year ago simply has limited value to brands. Traditional DMPs offer limited quality guarantees necessary for brands to achieve sufficient conversions in highly competitive market spaces. Data quality matters, and marketers need impartial measurement of conversions before committing to a purchase of third-party insights. Just because a data vendor claims their resources claim real-time insights doesn’t mean these insights have an effective track-record of appropriately high conversion rates. Increased transparency is critical around the data purchase and integration process in order for marketers to drive successful results through big data.


Why Real-Time Data Exchange Matters

All too often, the most sophisticated marketers will attempt to drive banner conversions via data that’s simply too old to be effective, or data that offers limited insight into consumers. Exceptional marketers are able to build robust, data-driven profiles of their ideal customers. These profiles should include demographic and firmographic insights, in addition to behavioral insights. Data quality and comprehensiveness matters every bit as much as recency in optimizing for conversions.


Today’s marketers need the ability to combine real-time behavioral insights with equally-fresh data on consumer’s demographics. You can’t optimize to convert a prospect who’s completed their purchase a year ago, and you won’t win a customer who can no longer afford your product due to changes in income level. In order to affect behavior, data must be comprehensive, recent, and accurate. Anything else can lead to disappointing results.


To learn more about the benefits of the DXP, check out our recent blog DXP vs DMP: Why Real-Time Data Exchange is Critical to Customer Understanding


image credit: kamiphuc via flickr/cc


DXP vs DMP: Why Real-Time Data Exchange is Critical to Customer Understanding

Is there truth behind the statement that we’re all “drowning in data”? Perhaps. However, few marketers or ad agencies would claim they’re drowning in actionable insights. Even fewer would argue that their first and third party data insights have led to an overwhelming excess of banner advertisement conversions, because their insights are so remarkably fresh and accurate.

Gartner reports that sales via a data management platform (DMP) or data exchange (DXP) is the third most-common driver of revenue for marketing organizations. Despite the increasing prevalence of data purchase, Yesmail and Gleanster have found that 80% of consumer-facing companies lack an understanding of their clients beyond “basic demographics and purchase history.” In many cases, linking third and first-party (self-collected) insights within a DMP environment can lead to mixed results. Programmers have long used the term “garbage in, garbage out” (GIGO) to describe the results one can obtain from custom code. This concept holds remarkable truth when it comes to the results obtained from many DMP environments.

While some 36% of brands are using behavioral and attitudinal data, and 41% are using browsing history and online behavioral insights, why is there such a major gap in consumer understanding? The Yesmail report indicates that well over three quarters of marketers believe their ability to effectively segment customers is limited by their data insights. It’s clear that marketers today have data issues that aren’t necessarily related to volume of data available within their data management platforms or the volume available for purchase via traditional vendors. Their issues are likely more related to the quality and comprehensiveness of their data insights.


Data Quality Matters

In a traditional DMP environment, both first and third-party data insights may offer limited value and veracity issues. Just because a consumer was searching for a mortgage six months ago doesn’t mean they’ll convert on targeted banner ads today. If they were well-qualified for traditional home funding sources, they’ve likely received it and closed on a new residence. Additionally, just one piece of behavioral-based data on a customer isn’t enough to develop a robust understanding of customer segments. Traditional DMPs build segments based on a single piece of behavioral data. In a DXP, buyers are able to develop highly-qualified segments by merging data points. You may choose to sort targets based on a combination of qualifications and current activity. Perhaps your target is someone who is searching for a mortgage, but also meets certain income guidelines, drives a luxury vehicle, and lives in a specific region of the country.

Consumer behavior and needs change quickly, and marketers should beware data vendors who aren’t offering comprehensive data insights. Consumers may be lumped into segments based on outdated online behaviors or interests in making purchases that have long been completed.  Without the ability to merge and sort based on both propensity and current activity, you’ll struggle to target the right consumers. BDEX’s data exchange platform is the first to offer truly full-spectrum data exchange. By offering a wealth of seller opportunities, buyers are able to select the right vendor offering insights within the right segments, at a price the seller and buyer determine.


Impartial Data Quality Scoring

While every third-party data vendor will claim their insights represent the best and freshest consumer knowledge available, few offer any genuinely impartial information to back up these claims. There is a necessity for impartial data scoring based on results, not the seller’s claims of recency or efficacy. Without impartial measurement, buyers may find themselves saddled with data sets that are out-of-date, or offer poor fit of consumer behavioral or attitudinal insights within segments. With the help of truly fresh insights, Clorox was recently able to unlock an entirely new series of “micro targets,” or sub-segments within their massive customer base. A recent case study revealed this allowed the brand to realize that their customer perception was decades out of date, which allowed total revision of their online marketing efforts.

The DXP offers a first-of-its-kind algorithm to score data based on buyer’s results. Before you make a data purchase, you’re able to track other buyer’s conversion rates based on the same seller’s offerings. In a world where both consumer behaviors and individual consumer needs can change on a day-to-day basis, being able to obtain unbiased measurement of quality is crucial. Fresh insights can yield improved targeting, conversions, and allow marketers to finally unlock genuine customer understanding.


How Else Does the DXP Differ from the DMP?

Marketing organizations utilizing the DXP can appreciate a number of additional benefits not seen in traditional DMP environments. In addition to the quality measurement and transparency features stored above, BDEX’s DMP offers the benefit of buyer and seller-controlled pricing, instead of pricing controlled entirely by the data broker.

Eight points of comparison between traditional DMP environments and the new DXP are detailed below. Buyer’s can also take advantage of unbiased, built-in tools to determine optimal purchase and selling price points to maximize their existing data resources and marketing budgets.

For an in-depth exploration of how the DXP improves upon the DMP on a features basis, check out BDEX’s recent blog, DMP 2.0 – Introduction of the DXP



image credit: r2hox via flickr/cc


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