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HOW DIRTY DATA DIMINISHES YOUR BOTTOM LINE

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How Dirty Data Diminishes Your Bottom Line

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The importance of data quality cannot be overstated. Only 16 percent of companies characterize the data they’re using as “very good,” says ChiefMarketer. But, according to The Freeman® Data Benchmark Study, 70 percent of marketers leverage data to impact wider marketing and business goals, including informing business intelligence, improving the customer experience, and assisting product development. In today’s landscape, successful companies cannot afford to use dirty data in their operations.

“Poor customer data costs U.S. businesses $3 trillion annually.”

– IBM

What Is Dirty Data?Dirty data is contact data that’s not completely correct and 100 percent up to date.

From every typo on a form fill, every contact you lost touch with, to the duplicate leads that ended up in your system because of a sync issue with your MAP and CRM, it’s inaccurate, incomplete, outdated, or duplicated data that sits in your contact database.

40% of contacts are invalid, incomplete, or duplicated

– DEMANDGEN REPORT

Why Should I Care?According to Experian, poor data quality can cost an organization 20 percent of their revenue. It’s a curse on conversion rates and makes personalized campaigns near impossible. Dirty data kicks off a domino effect throughout your marketing efforts: Inaccurate contact data means incorrect lead scoring, lackluster lead data means ineffective segmentation and audience personalization, leading to meager campaign metrics, low ROI, and ultimately sub-optimal revenue.

Worse, bad data inhibits both success and progress. Not only is your company incapable of reaching its full potential if your database is riddled with errors, you’re impeding the ability to ‘future-proof’ your business model and embrace innovation. Just consider the rise of artificial intelligence:

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How Dirty Data Diminishes Your Bottom Line

“Marketers understand the problem [of dirty data], but they don’t always truly respect the cost bad data imposes on the organization,” said Justin Gray, CEO of LeadMD. “Predictive marketing and artificial intelligence sound sexy. But if the data is so bad, you’re constantly adding new flat records that make it hard to understand who a prospective buyer is. That’s where the costs start to amplify.”

Dirty data puts your current and future success — in sales, marketing, finance and customer relationships—in jeopardy. Data maintenance can’t be an option – it needs to be a regular and frequent practice in your operations. Accurate data leads to more closed business, better informed decisions, and stronger ROI.

Businesses lose as much as 20% of revenue due to poor data quality.

– KISSMETRICS

The Source of Dirty DataDirty data is the result of a shifting B2B landscape. Decay happens when people change jobs, companies go out of business, and mergers happen. According to Introhive, 66 percent of people change companies or jobs annually. You may be looking for data to keep up with these changes, however, how your data is sourced is the biggest indicator of its trustworthiness and reliability.

Many B2B marketers rely on third-party data vendors to complement their existing lead generation strategies. But when it comes to net-new contacts and contact acquisition, those data vendors rely on sourcing methods that backfire on you down the road.

Bad List BuysBuying from a list-purchasing company to provide a quick boost to your contacts might seem too good to be true — because it is.

SiriusDecisions reports that, on average, purchased contact lists are 15 months old at the point of sale, at best—and they continue to deteriorate over time at the rate of about 25 percent per year. While buying email lists is tempting, long-term consequences like poor deliverability and a bad reputation aren’t worth the short-term fix.

As Hubspot blogger Corey Eridon writes, “One customer’s ill-gotten email address list can poison the deliverability of the other customers on that shared IP address.”

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Compiled DataWhen looking to buy from a list broker, a key consideration must be where they source their data. Data that comes from the original source — i.e. self-reported information from the actual contact — is ideal. However, few data providers offer data that comes straight from the source. Instead, the vendor will take large lists of contact data from myriad sources and combine them into their database. Accuracy and precision are ignored, and the name of the game is quantity over quality.

Crowd-Sourced DataIf it’s not compiled, it’s likely crowd-sourced, meaning the data informant is a secondhand source reporting information about the actual contact. In crowdsourcing, that secondhand source declares contact records either accurate or inaccurate based on information that may or may not be closer to the original source (the contact). Typically, there are rewards and incentives to encourage customers to correct records when they’re wrong. Data accuracy is not a democracy. Just because someone says that a record is accurate does not mean it is.

Natural DecayToday’s workforce is more transient than ever. Between contacts changing jobs or companies, and organizational changes at the company level, data is a fluid asset rather than a fixed one. Contacts decay and expire as time passes. Seventy-six people change jobs every minute—about 30 percent per year—according to ZoomInfo.

That may seem like a high percentage, but it’s worth considering that, according to Dun & Bradstreet, in the next hour, 211 businesses will move, 429 telephone numbers will be changed or disconnected, 743 new businesses will open, and 13 companies will change their name.

The bottom line is, dirty data is inevitable. When you understand the source of your dirty data you can be more strategic and diligent in your data management plan.

30% of email addresses change annually.

– CONVINCE & CONVERT

The Financial Impact of Dirty DataThomas C. Redman, otherwise known as the Data Doc, says, “The reason bad data costs so much is that decision makers, managers, knowledge workers, data scientists, and others must accommodate it in their everyday work. And doing so is both time-consuming and expensive.” Dirty data affects almost every aspect of a business. Leaders rely on it to make decisions, and without

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How Dirty Data Diminishes Your Bottom Line

quality data their budgets, goals and strategies are flawed. It is an overhead cost that is rarely exposed on a balance sheet or income statement.

Productivity suffers with dirty data. According to Forrester, “nearly one third of analysts spend more than 40% of their time vetting and validating their analytics data before it can be used for strategic decision-making.” When employees must take time to correct errors, efficiency decreases, while operational costs increase.

Inaccurate data can also lead to major customer dissatisfaction. Missing invoices, lost deliveries, or incorrect orders not only cause the business headaches, but lead to frustrated customers—and as a result, mistrust in the business and the loss of customers.

Many B2B marketers struggle to keep their contact data clean and useful. As a result, they’re not making the most of their marketing automation investment, nor are they gaining the revenue they could. Campaigns are ineffective and fail to provide quality leads; the sales team wastes time chasing the wrong opportunities.

The cost of bad data is immeasurable. From poorly segmented, undelivered marketing campaigns to customer service issues, there are many ways that bad customer data can affect your company. Successful businesses aren’t built on unreliable data. It’s their most important asset.

Organizations believe poor data quality to be responsible for an average of $15 million per year in losses.

– GARTNER

5 Best Practices for Data CleaningIgnoring data quality issues risks the success of routine operations and leads your company down the wrong path. Identifying where dirty data originates is a step in the right direction. Resolve schema conflicts to ensure successful data cleaning, then implement these best data cleansing practices for continued health.

1. Develop a Data Quality PlanKnowing where most data quality errors occur and identifying incorrect data Interactive Quick Guide to Data Cleansing will help you better assess the root problem and develop a project plan. A comprehensive data quality plan will impact many departments so keep communication open and emphasize that better intelligence will save everyone time, money, and energy.

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2. Standardize Contact Data at the Point of EntryCheck important data at the point of entry and standardize the method across all departments. This ensures that all information is uniform when it enters your database and makes it easier to catch duplicates.

3. Validate the Accuracy of Your DataValidate the accuracy of your data in real time or by cleaning your existing database regularly to ensure it is complete and up to date. Do your research and invest in data tools that can clean information like list imports or provide address verification software. Effective marketing occurs when high-quality data and cutting-edge technology are used in tandem to seamlessly merge various data sets.

4. Identify DuplicatesSave your team time and implement a data hygiene tool that can effectively identify duplicates. The less manual work, the better.

5. Append DataAfter your data has been standardized, validated, and scrubbed for duplicates, use a third-party vendor to append it. Reliable third-party sources can capture information directly from first-party sites, then clean and compile the data to provide more complete information for business intelligence and analytics. This will help you develop and strengthen your customer segmentation and send more targeted information to customers and prospects.

One in two businesses say they have data integration plans

– EXPERIAN

What Successful Data Cleansing Looks LikeBefore implementing data cleansing, it’s important to look at the big picture. What are your goals and expectations? How do you plan to execute successfully? As you get started, keep the following in mind.

When done correctly, successful data cleaning follows these three key practices:

• Detects and removes major errors and inconsistencies in single data sources and when combining multiple sources.

• Utilizes tools to reduce manual inspection and programming efforts.

• Works in conjunction with schema-related data transformations and specific mapping functions, not solo

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CONTACT US

Contact Synthio today to learn how we can eliminate your dirty data woes. 1-888-530-6723.

The cost of bad data is immeasurable. From poorly segmented, undelivered marketing campaigns, to customer service issues, there are many ways that bad data can affect your company. There’s no need to watch this money go out the door. Prioritize data hygiene and once you adjust to regularly cleansing and appending with a data management plan, all other efforts become more efficient and successful.

An organization can generate up to 70% more revenue based solely on clean data.

– SIRIUSDECISIONS

15% of leads contain duplicated data

– INTEGRATE

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ABOUT SYNTHIOLeading global brands improve their prospecting performance and sales conversion rates by using Synthio’s contact data management platform. With our data health analysis and self-service functionalities, marketers can build, refresh, cleanse, and append data sets on the fly. The data are then continuously refined to ensure integrity. Even more, they appreciate that we follow the person, not the company. With Synthio, you know you’re constantly in contact with the right person, allowing you to reach your desired audience with the most effective message.