The Cost of Dirty Data
The impact of bad data on your business can range from lost revenue and customer churn, to reputation damage and low-performing campaigns.
And as the volume and complexity of data grows, so do those ramifications.
Studies show bad data can impact a business’s annual revenue by up to $9.7 million. We’re not just talking about large enterprise companies – small businesses can lose up to 6% of their annual revenue to bad data alone.
- 21% of companies admit to experiencing reputation damage due to bad data
- 28% of brands report hits to customer service due to invalid email addresses and an inability to effectively connect with customers
- 93% of consumers receive irrelevant marketing communications, and 90% of those consumers are annoyed by it
- 38% of companies rely on manual, time-consuming duplicate checks using spreadsheets
- 67% of brands report issues with email deliverability and bounce rates as a direct result of poor data quality
Whether one lost account, or catastrophic revenue loss, the takeaway is clear: it’s more expensive to compensate for bad data than it is to take a proactive approach. In fact, one SiriusDecisions study found that companies with “high quality data management” generated 66% more revenue than companies with poor/nonexistent data quality strategies.
Other commonly cited benefits include:
- 25% higher conversion rates between the inquiry and the marketing qualified lead stages
- 85% improvement in timely, relevant customer communications
- 83% improved employee efficiency
- 81% improvement on linking data from different systems
Why does data quality matter?
All strategic sales and marketing initiatives rely on high quality data – without the right data fueling efforts, companies are guaranteed to suffer from the adage, “garbage in, garbage out.”
A recent Dun & Bradstreet (D&B) report painted a grim picture of expectations versus reality when it comes to how businesses view data quality, and how confident they are in their own data. Nearly 90% of respondents reported they believe data quality drives the right B2B sales and marketing campaigns, yet 50% said they are not confident in their own company’s data, resulting in a slower adoption of account-based marketing (ABM) and content personalization. While marketers widely agreed ABM is imperative to growth, only 38% listed ABM as part of their go-to-market strategies due to low confidence in the data required.
On a positive note – survey participants who increased their investments in data quality consistently reported business-wide performance gains.
So what’s preventing companies from investing in data quality? According to D&B, the biggest barriers reported were cost (37%), a lack of understanding (27%), and better ROI with other initiatives (27%).
While investing in data quality demands a certain level of understanding, and can require up front costs (i.e. investing in a data management platform, enriching records to replace outdated data, etc.) it’s important to consider the long term impact and savings of a strong data quality strategy:
- Reduced data storage costs by merging duplicate data
- Faster lead response times and connection rates with improved routing of inbound leads
- Higher conversion on hyper-personalized marketing campaigns based on the right segments
- Increased employee productivity and CRM and MAP adoption
So where is all of this bad data coming from?
The average company relies on four or more sources to collect the bulk of their customer contact data.
Here’s a simple breakdown:
- 73% of companies collect data from their web forms
- 60% collect data from field sales reps
- 54% collect data from inbound sales reps
- 47% of brands collect data from mobile users
Many companies struggle to enforce data quality standards at all entry points, causing them to rely on manual, retroactive clean ups that are error prone and time consuming. In some cases, the dirty data is left untreated entirely.
- Only 38% of businesses use software to check data at entry points
- Only 34% use software to clean data retroactively
- 38% of companies rely on manual checks on Excel spreadsheets
- 26% limit checks to one-off checks for seasonal campaigns.
- Without ongoing data quality at the edge, it’s impossible to maintain a strong database. In its widely circulated study on the cost of data, SiriusDecisions found it’s 10x more cost effective to prevent dirty data from entering a CRM, than to cleanse it after creation, and 100x more effective than taking no action at all.
In other words: an ounce of prevention is worth a pound of cure.
Data entering your system should pass through a series of intelligent processes:
- Identity Resolution
- Duplicate Prevention
- Lead-to-Account Linking
- Account and Territory Based Assignment
- Rule-based Round Robin (assign more leads to your better closers)
The cost of not protecting your database at the edge is startling, and extends far beyond typically cited stats around low campaign performance, brand reputation and email deliverability.
Many businesses report significant hits to employee morale and CRM adoption as a direct result of poor data hygiene. Surveyed sales and marketing professionals estimated 30% of the records they rely on for daily tasks contain serious inaccuracies, causing significant challenges in executing multi-channel marketing or ABM strategies.
The direct impact of bad data on your sales reps
Where does the time go for your reps?
Sales reps spend over 30% of their day outside of the CRM, manually searching for data that should be right at their fingertips, and when they find it – it rarely makes it into your CRM. According to Gartner, <10% of customer interactions are ever entered into the CRM, with most reps opting to maintain customer relationships through email to avoid data entry.
How do we assess where we’re at now?
The first step in assessing data quality is to figure out how your company uses customer data to support business objectives
Meet with managers and reps across your company and ask a few key questions:
- What are your business objectives?
- What customer data is required to support those objectives?
- How are you using that customer data?
- Where is your customer data stored?
Here’s an example of what most businesses find during an analysis:
While each department has a unique set of objectives, many departments rely on the same customer data. Unfortunately, each department tends to have its own process for creating and maintaining that data, resulting in no single source of truth. For example, when discussing customer contact data, many companies find Sales uses Salesforce, Accounting has an intricate system of spreadsheets, and Customer Support barely has any system at all. More times than not, this story repeats itself for all types of data.
You try your best to find two departments that use the same process for creating records. But you’d have more luck finding a vegetarian at a pig roast. You check with several sales managers. One tells you that her team keeps all their customer info in a shared spreadsheet. At the end of every month, her assistant transfers the information to Salesforce. That is, until her assistant quit 3 months ago. Another sales manager told his team not to worry about entering the data into Salesforce (even though it’s company policy!).
With data practices like these, it’s no wonder your reports are riddled with duplicates, incomplete records, and stale data. It turns out that the data genie doesn’t just appear and grant you three wishes. If you don’t have company resources devoted to data quality, chances are your data can be better.
The good news? Recognizing you have a data quality issue is half the battle.
Fortunately there are a number of free data quality assessment tools on the market to help you get started.
Here are the top 3:
Join us on June 25 to learn more about the role data plays in your ABM strategy in a live webinar. If you can’t make that date, register anyway, and we’ll send you the recording and a few “how-to” resources following the event.