APRIL 2017

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ONE TOUGHQUESTION

Is Poor Data Quality Derailing Your Campaign’s Results?What steps should marketers take to be sure that first-party data is not only reliable, non-duplicative, and up-to-date, but that all of the probabilistic data needed to enrich it is as well?

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ONE TOUGH QUESTION | IS POOR DATA QUALITY DERAILING YOUR CAMPAIGN’S RESULTS?

There are great solutions on the market now that can help brands leverage huge quantities of consumer data to create ever-more-granular audience segments along

with ever-increasing personalized messaging. But what’s the benefit if the data is of poor quality?

No surprises here. There is no such benefit. In fact, using bad data to shape your marketing program may very well have the opposite effect: It will likely create false characterizations of who your customers are and how they prefer to engage, as well as how — even what — they purchase.

As noted by Edward Hunter, lead data engineer at Clutch and one of the participants in this eBook, “When you do not validate customer information, you lose the opportunity to build a relationship past the first engagement.”

Marketers are well aware of the importance of having reliable data. A recent Ascend2 survey, Marketing Data Quality Trends, states that 62% of marketers agree that improving data quality is the most important objective of a successful marketing strategy.

To gain insights on what actions marketers should employ to be sure the data they gather is worth their time, money, and effort, we asked nine industry thought leaders on the front lines of the data-quality challenge — experienced pros from AuthO, Bazaarvoice, ContentSquare, Datorama, Infogroup, MediaMath, Oracle, and White Ops, as well as Clutch — the following question:

What steps should marketers take to be sure that first-party data is not only reliable, non-duplicative, and up-to-date, but that all of the probabilistic data needed to enrich it is as well?

Their answers to that poser provide a roadmap demonstrat-ing how you can be sure that bad data quality doesn’t cause you to miss the opportunity to build a solid and remunerative — and self-fulfilling — relationship with your customers.

Kim Davisexecutive

editorDMN

Data quality is more important than ever

Illustrations by Thinkstock

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ONE TOUGH QUESTION | IS POOR DATA QUALITY DERAILING YOUR CAMPAIGN’S RESULTS?

5 Edward Hunter lead data engineer, Clutch

10 Tamer Hassan CTO and cofounder, White Ops

10 Gayatri Bhalla VP and general manager, Infogroup audience solutions

6 Martin Gontovnikas VP of marketing and analytics, Auth0

11 Philipp Tsipman director of product commercialization, MediaMath

9 Jonathan Cherki CEO, ContentSquare

8 Brett Sanderson product marketing and advertising manager, Bazaarvoice

7 Cory Treffiletti VP, marketing and partner solutions, Oracle Data Cloud

11 Leah Pope CMO, Datorama

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ONE TOUGH QUESTION | IS POOR DATA QUALITY DERAILING YOUR CAMPAIGN’S RESULTS?

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ONE TOUGH QUESTION | IS POOR DATA QUALITY DERAILING YOUR CAMPAIGN’S RESULTS?

Bad data is wasted money, so I’m constantly amazed at the number of companies that do little to no authentication up front. When you do not validate customer information, you lose the opportunity to build a relationship past the first engagement. That is, quite often, where unreliable data starts.

To gain better information from your cus-tomers, create incentives based on what they want and need — and use those incentives to build a data-capture strategy focused on unique identifiers. Cellphone numbers make great unique identifiers, creating opportunities for real-time verification.

Many consumers have become accustomed to entering fake addresses or emails when signing up for rewards cards or discounts, but by using text messages to validate consumer information, retailers can gain authentic information immedi-ately and then build out other data fields asso-ciated with the original purchase over time.

For example, if a customer buys online in response to a promotion sent via SMS, you have then verified that mobile number and captured at least one more reliable data point — a valid shipping address.

Marketers can continue to build out profiles for customers by developing routines that grad-ually merge new fields of data around the initial proven identifier.

Not knowing whether the data you have is legitimate is a slippery slope. While it is possible to retroactively sift through a database to identify what information is authentic and which is not, it can be a sophisticated process.

Marketers would be wise to find out where they are validating data and where they are not. From there, they can look to establish engagements that confirm customer information at each step, lead-ing to better, more reliable data.

In this way, marketers can be sure that they are building trustworthy customer relationships. ■

To gain better information from customers, create incentives based on what they want and need — and use those incentives to build a data-capture strategy focused on unique identifiers

Edward Hunterlead data engineer, Clutch @ClutchSuccess

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ONE TOUGH QUESTION | IS POOR DATA QUALITY DERAILING YOUR CAMPAIGN’S RESULTS?

Data quality is one of the biggest challenges for marketers today. To prevent poor data quality, marketers must be diligent and invest time in maintaining data integrity.

I recommend a four-step process for making sure that marketing data is of the highest quality and without duplicates or inaccuracies.

First, assess first-party data for any data leaks. If there are leaks, determine the cause and plug them. For example, we found we had 20% more user signups in the back end than signup events from Google Analytics. Why? Many of our customers were using a plugin that blocked our client analytics tool. We had to figure out a way to adjust analytics to account for this discrepancy. We wound up building a workaround.

Next, compare different third-party data. For example, we cross-reference data from FullContact, ClearBit, DiscoverOrg, and Data.com to be sure our data has the best chance of being correct.

Cross-verify third-party data with actual data from the lead (the prospect). Once we know the lead’s actual data, we can assess which third-party service is right. Doing this helps us rank third-party data services based on accuracy.

Lastly, scrub and cleanse the data based on what you found in steps one to three before using it to make decisions

To be truly useful, this process must be auto-mated and conducted on an ongoing basis. ■

We found we had 20% more user signups in the back end than signup events from Google Analytics. Why? Many customers were using a plugin that blocked our client analytics tool

Martin GontovnikasVP of marketing and analytics, Auth0 @mgonto

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ONE TOUGH QUESTION | IS POOR DATA QUALITY DERAILING YOUR CAMPAIGN’S RESULTS?

Writer Nathaniel Hawthorne once said that “accuracy is the twin brother of honesty,” which is particularly true in databased marketing. A hundred com panies will try to sell you the earth and the stars, claiming unprecedented segmenta-tion, massive scale, and unsurpassed match rates. Don’t be-lieve the hype.

Your data partner should help you understand the nuance and tradeoffs involved in building an audience for your campaign. In having those conversations, here are four areas on which you should focus.

First, your best data is often your own data, so identify a partner who can maximize the impact of your CRM file. In onboarding that data, you should ask about metrics like match rates to offline IDs (names and postal addresses), ac-tive cookies (less than 30 days old), and media partners (usually your DSP or DMP). Your data partner should also assess the accuracy of your cookie audience once the campaign is live so you can be sure the matches are correct.

Next, dig into your potential partner’s cross- device graph to ensure you can reach your target audience across all their connected devices. In

that assessment, ask your partner about their data sources and how they tie them together. Do they connect profiles through algorithms (for instance, by manual programming?) or via automatic ma-chine learning? How do they measure the accu-racy of their ID graph and how does it compare with others?

Third, pinpoint your purchasers, as past purchases are often the best predictors of future behavior. Ask your partner about the data on in-store and online sales. If such data is available, is it on the category, brand, or SKU level? You should also ask about modeling to learn how a partner identifies groups of customers similar to the ones they know.

Finally, find a balance between precision tar-geting and broad reach. The ideal partner will offer to help you design a best-of-both-worlds approach of “relevant reach” that offers target audiences at scale while using data to prioritize potential buyers.

In summary, the best way to build the right au-dience is to identify the best partner to help you understand and navigate the complicated issues of reach, accuracy, and effectiveness. ■

A hundred companies will try to sell you the earth and the stars, claiming unprecedented segmentation, massive scale, and unsurpassed match rates. Don’t believe the hype

Cory TreffilettiVP, marketing and partner solutions, Oracle Data Cloud @ctreff @OracleDataCloud

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ONE TOUGH QUESTION | IS POOR DATA QUALITY DERAILING YOUR CAMPAIGN’S RESULTS?

In order to determine if your audience data is reliable and up-to-date, it is first crucial to under-stand the source and strength of the data signals being used, as not all signals are equal.

For example, if a shopper reads a DIY article or looks at a toolset once, this alone does not necessarily warrant their being placed in a “home improvement enthusiast” segment. However, a shopper who has looked at five toolsets in the past 30 days, thoroughly researched those toolsets by reading multiple product reviews, and then ultimately purchased a toolset is much likelier to be successfully influenced by well-targeted home- improvement product ads.

The second key factor is data recency and freshness, as using stale data has the potential to perform as poorly as using no data at all. If

a shopper is placed into an audience segment, but does not continue to display related intent signals, that shopper should be removed in a timely manner.

As more time passes with no additional signals, users are much less likely to remain interested or relevant to that particular segment.

There is no universal rule to determine how long is too long, but marketers should devise busi-ness rules for keeping their audiences up-to-date. This can depend on such factors as the consider-ation level and purchase lifecycle for the various products they sell. For example, a $15 screw-driver may have a short — let’s say seven-day — consideration window, while a much-higher- consideration product such as a $1,200 band saw may demand a 45-day consideration window. ■

There is no universal rule to determine when marketers should devise rules for keeping audiences up-to-date because it depends on consideration levels and purchase lifecycles

Brett Sandersonproduct marketing and advertising manager, Bazaarvoice@bsands5 @Bazaarvoice

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ONE TOUGH QUESTION | IS POOR DATA QUALITY DERAILING YOUR CAMPAIGN’S RESULTS?

The term “data-driven” has become an all- encompassing buzzword that marketers use when defining strategies and making decisions. However, the quality of strategies and decisions fully depends on the quality of data. Therefore, it’s essential for teams to understand just what data they have at hand and, equally important, what data they’re missing.

Marketers must take a step back and ask the following questions when assessing their data (and never stop until they get answers):

Where does the data come from and how is it being collected? At ContentSquare, we collect over 1 trillion user mouse movements, screen touches, and interac-tions to segment visitors based on intention and behavioral patterns. This tactic ensures that our customers are receiving exactly the type of data for which they are utilizing our services — their customers’ purchasing behaviors.

Are you collecting all data, or sampling? Sampling can be very harmful and lead to a multitude of glitches including erroneous results

and a loss of data integrity. Never rush the data- collection process.

What is it that are we cannot capture or filter out? Be cognizant of what your end goal is and realistic about different behaviors or patterns that you may not be able to accurately capture through data that, in turn, may affect your re-sults. Going into it knowing your obstacles puts you one step ahead of the game.

When are we in danger of running duplicate data? Duplicate data can be responsible for crushing a marketing campaign. It’s important that you are working with a partner that provides the granular findings you want, but also keeps up with the quality that your initiative needs.

If these questions are top of mind, you’re al-ready well aware of what data to rely on and what data to ignore in order to make real and accurate data-driven decisions that will improve your company’s ROI. ■

Be cognizant of what your end goal is and realistic about different behaviors or patterns that you may not be able to accurately capture through data that, in turn, may affect your results Jonathan Cherki

CEO, ContentSquare @JonathanCherki@ContentSquare

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ONE TOUGH QUESTION | IS POOR DATA QUALITY DERAILING YOUR CAMPAIGN’S RESULTS?

Tamer HassanCTO and cofounder, White Ops @WhiteOps

Gayatri BhallaVP and GM, Infogroup audience solutions@gbhalla @Infogroup

Every year, more than $7 billion in ad dollars are lost to criminals who write sophis-ticated code. Their code clicks on ads, views videos, puts items in shopping carts, and even has a demographic profile so advertisers can bid on it. These bots go undetected to most measurement technology, since they look and act just like humans, even though there’s no possibility that they will ever buy anything that marketers are selling. Since the ad marketplace happens inside chains of anonymous ad requests, cutting- edge human verification is needed to deter-

mine who, or what, interacts with the page.The impact of inaccurate measurement

yields inconsistencies across all analytics. If your data is wrong, decisions can be skewed, money can be wasted, and campaigns will likely fail to achieve the results they should.

When you’re paying for eyeballs, a human- first approach to measuring and analyzing data is critical. ■

Much like a recipe, if you start with qual-ity ingredients, the outcome will prove all that much better. Regardless of source type (deterministic or probabilistic), vetting the quality of the underlying data is critical.

Ask questions: How often is the data set refreshed? And how does that sync (or not) with how often the onboarded data set is re-freshed? If the data is probabilistic, ask if it is sourced from survey or panel data, and if the latter, how often does the panel turn over?

A well-balanced dish requires that a variety of flavors come together elegantly. Similarly, combining a broad base of data sources can yield the most comprehensive targeting strat-egy for marketers. Once you have established

that the data is based on quality ingredients, determining optimal ingredient proportions is crucial. A/B test splits can help you to determine the best mix. DMPs further refine targeting inputs and give marketers more insight into their data elements. ■

“ If your data is wrong, decisions can be skewed, money wasted, and campaigns will likely fail to achieve results” “ Ask how often the data set is refreshed

and how that syncs with how often the onboarded data set is refreshed”

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ONE TOUGH QUESTION | IS POOR DATA QUALITY DERAILING YOUR CAMPAIGN’S RESULTS?

Philipp Tsipmandirector of product commercialization, MediaMath @ptsi @MediaMath

Leah PopeCMO, Datorama@Leah_Pope @datorama

Insights are only as good as the data utilized to derive them. As measurement issues persist among some of the largest advertisers today, data quality should be of the utmost impor-tance to today’s marketers.

Marketing data is ever-changing and ever- increasing, so the most critical step a mar-keter can take to ensure data reliability is to move away from manual coding to a solution that automates the extract, transform, and load process.

Today’s marketers must embrace what technology can do best — repetitive, tedious data-preparation tasks — while focusing on where human brainpower excels: providing strategic insights.

Making the change to an automated ETL process will not only collapse a marketer’s time to value, but the decision will also pro-

vide marketers with the clean data they need to drive strategic insights, which results in value to a marketer’s bottom line. ■

High-quality data is critical to high-impact direct marketing. Whether clients have a num - ber of existing data vendors or are assessing a new one, we recommend they start with an assessment RFI ensuring that the data vendor is clear about the accuracy and scale of its data and quality practices and that its data is right for the client’s use case. For cross-device solutions, the Data and Marketing Associa-tion’s XDID RFI is a good place to start.

If you have an existing known set of shopper data, we recommend that you also run a live two-part quality assessment pilot. Request your quantitative analytics or data science team to compare the data samples

from various data vendors against each other. Second, run a marketing campaign using data from each vendor to compare performance.

Stepping back to invest in internal data- quality best practices and working with vendors who see data accuracy and quality as key differentiators is well worth the investment. ■

“ The most critical step a marketer can take to ensure data reliability is to move to automate the ETL process”

“ Request your quantitative analytics or data science team to compare the data samples from various data vendors”

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