Your CRM has messy data.
Your sales teams are getting inaccurate and irrelevant prospects.
Your marketing team is making embarrassing mistakes.
Your reports keep giving incorrect insights .
You introduce more processes. You invest in a costlier, more advanced CRM platform. More marketing budget is allocated. More resources are added - yet, you're far from making sense of your data.
Sounds all too familiar? You’re not alone.
In a Forrester survey, it was found that for, “every 21 cents of every media dollar spent by their organization in the last year was wasted due to poor data quality, which translates to a $1.2 million and $16.5 million average annual loss for the midsize and enterprise organizations.”
Organizations are struggling to deal with bad data. For many, this translates to poor marketing performance & sales intelligence.
Here’s everything you need to know about bad data, why it happens, and what steps you can take to fix it.
What is Considered as Bad Data?
Marketing and sales have to deal with the very dynamic nature of data. For example, customer information keeps changing. Their emails, professional designations, addresses, phone numbers etc. change frequently, but this is seldom recorded in your database.
Managing changes in information is just one part of a data problem. The bigger challenge is poorly structured, incomplete, inaccurate, inconsistent, duplicated data that marketing teams do not have the capacity to handle.
Take a look at the image given below. Now go back to your CRM and take a look at the information there. Have similar problems?
Bad data, therefore, is any data that suffers from:
- Inaccurate information: Wrong email or location addresses
- Incomplete information: Missing zip codes
- Unstructured data: Formatting issues, mixed numbers and letters, abbreviations etc.
- Outdated data: Changing customer records not updated
- Disparate sources: Too many platforms recording the same information in multiple ways.
The efficiency, productivity, morale & success of your marketing campaigns need clean data. Everything you do – whether it’s purchasing a new CRM, updating to cloud technology, integrating more apps or tools, you will need to have clean, reliable data.
Why Does Bad Data Happen?
Raw data gathered from any data source is inherently flawed. Data acquired from second or third-party sources such as social media information, web forms, surveys, etc will always be flawed.
Humans create data - and humans are always prone to errors. A misspelled name, a wrong variation of a name, an incomplete email address, a fake name, or a fake email address are all instances of data that has been provided, handled, or recorded by humans. You cannot prevent bad data from entering the database, but you can stop it from affecting your operations.
To get clean data, it must be put through a cleansing process that weeds out duplicates, restructures formats, and correct spelling names. But data cleansing is in itself a time-consuming task and one that has been restricted to the IT domain. Business leaders are not aware of the tools and processes required to clean data, so they rely on IT to perform basic ETL (Extract, Transform, Load) operations to clean this data.
During this process, IT runs algorithms to remove duplicates and clean structural issues, but it doesn’t make the data valuable enough to be put to its intended use.
Here's an interesting excerpt from Thomas C.Redman's article, 'Data's Credibility Problem', as he eloquently sums up the struggle :
Fifty years after the expression “garbage in, garbage out” was coined, we still struggle with data quality. But I believe that fixing the problem is not as hard as many might think. The solution is not better technology: It’s better communication between the creators of data and the data users; a focus on looking forward; and, above all, a shift in responsibility for data quality away from IT folks, who don’t own the business processes that create the data, and into the hands of managers, who are highly invested in getting the data right.
Can We Prevent It?
The bad news: No. Because bad data is inherent, it’s inevitable. No matter how strong a data governance or data control strategy you
put in place, raw data will always be dirty.
put in place, raw data will always be dirty.
The good news: Companies are finally waking up to the problems with bad data. There's a rise in the demand for data analysts. Commercial data cleansing solutions designed for business users are gaining attention. Data quality experts (or doctors) are offering consultancies to help companies identify issues with their data. The market is ready for data quality and this is the right time for you to take the necessary steps to fix your data.
How Do You Solve the Problem of Bad Data?
To say that it’s easy or simple, would be a lie.
To say that advanced data cleaning tools are the answer, would be false selling.
To say that you need to hire an experienced data analyst, would be unfair to their profession.
The solution to bad data is not more advanced tools or more expensive resources. It's culture, training & developing a data quality standard for everyone to follow.
Here's a step-by-step process you can follow.
1. Start by Assessing Your CRM Data Hygiene
A thorough diagnosis of your CRM data will help you understand the degree of cleaning required. You can also decide whether you need to hire a full-time data analyst or use a data cleaning tool to get the job done.
For instance, after you promote a landing page, take a look at all the names, phone numbers and email addresses generated. It's probable more than half of that data is fake, invalid, incomplete or inaccurate. If you don't have a data cleaning or sorting mechanism in place, this dirty data will go straight into your CRM. You'll end up with flawed analytics, high email bounce rates (because of the fake data) and a very frustrated team.
Here's an example of deeply ingrained data quality issues.
You might want to use the service of a data quality consultant. Sometimes, data quality issues are at a much deeper level. Your data may, 'seem' fine, but it may be duplicated, it may have more fake email addresses than you realize and it may also have invalid values (such as writing a customer age as 204 instead of 24). The consultant will review a sample data size based on various taxonomies and data quality benchmarks (such as the validity of phone data) designed to identify these issues and will guide you on the next step forward.
2. Begin a Data Quality Training Session for Your Team
Your sales and marketing team will need to be trained on the basics of data quality. Every time they run a campaign, record data or use data for analysis or reporting, they must make sure they have accurate data to work with.
You'll need to initiate a series of data quality training that should cover topics like:
- Identifying bad data
- Identifying the sources of bad data
- Data hygiene best practices
- Data quality benchmarks
- Following standards
- Using data cleaning tools
- Ensuring transparency
Training your team will help drive a data-conscious approach to customer data. Once your teams know the consequences of dirty data in downstream applications or processes, they'll be more careful with data handling. That's one way to prevent bad data from affecting your sales intelligence reports and analytical data.
3. Create CRM Management Rules
A CRM is the central source of truth of an organization. It is also a platform that has multiple users accessing it every day for information. If there are no user rules set in place, your CRM may end up being a big mess.
For instance, a sales manager might add a new Contact in Future field. The marketing manager wants to add a Google Analytics tracking feature. The Account manager wants to add a Product Interested in field. These wants and needs from multiple users eventually make your CRM a horrible maze where there are dozens of unused fields that lead to bad data.
To counter this, start implementing CRM management rules. Authorize only one person to be able to create fields, that too after getting approvals. Create fields and modules based on user roles and job functions. For instance, the Account Manager need not see marketing information if it's not relevant to his/her role. Or the Sales Manager need not see Analytics if it has nothing to do with him/her.
Controling the customization and accessibility of the CRM (without compromising on insights) is the most important step you can take to preventing bad data.
4. Choosing a Self-Service Tool
Your first reaction to a data quality crisis would probably be to hire a data analyst. While that may serve your purpose of introducing a data quality framework, it won't really help your marketing and sales team.
Here's why.
The marketing and sales reps are data users. They need that data to carry out targeted marketing and advertising campaigns, so it makes more sense if they were the ones to handle that data. Usually, companies take the data away from the actual users and hand it over to the fixers (in this case the data analyst). Some companies have the IT department handling the whole data quality and CRM process. This is at best, inefficient. If your marketing team is using a CRM to handle customer data, why can't they be the ones to handle issues with bad quality?
Hence, it's better to equip your team with a compatible, self-service data preparation tool. These are designed for business users to clean, match, dedupe and consolidate their CRM data without relying on an external team member or third-party resource.
So for instance, if you want to clean your Salesforce CRM data, you no longer need to extract files and run formulas on Excel to clean them up. All you need to do is integrate Salesforce into the tool and your team can start cleaning and transforming data with ease.
It's necessary to mention that a tool is only as smart as its user; so if you think your team members do not have the time or the capacity to use a tool, you can consider hiring a data analyst. As a rule of thumb, always ask the potential hire their experience with data quality and how they can help resolve your problems. Many enterprise-level organizations have dedicated Data Quality Managers for their marketing and sales teams.
5. Establishing Standards, Policies & a New Data-Driven Culture
Now that you've got a clear idea of the problem, spoke to experts, hired analysts, procured tools, the next step is to implement standards and policies.
Reward employees who implement the training sessions and improve quality. Train employees who still have to get there. Make data quality a standard operating procedure. Create processes and policies.
For instance, after every campaign, the data collected must be passed through a cleansing routine. Only when the data reaches the quality benchmark (meaning it's complete, accurate, updated and validated), will it be used for its intended purpose. This process, infused in the overall operational process of your organization will save you from costly mistakes resulting from the lack of attention to bad data.
Keep these procedures up-to-date. When you hire new staff, make the CRM data management training a mandatory part of onboarding. Look for other ways to make data and data cleanliness an essential part of your company culture.
To Conclude
The impact of bad data on marketing performance and sales intelligence can be devastating, but it doesn’t have to be this way. You can make
the right choices, bring in the right people & help your department lead
the organization into a new age of data-driven profitability. By proactively rooting out bad data and implementing business processes, quality frameworks and operational workflows, you can maximize the use of a CRM and ramp up your marketing and sales performance.
the right choices, bring in the right people & help your department lead
the organization into a new age of data-driven profitability. By proactively rooting out bad data and implementing business processes, quality frameworks and operational workflows, you can maximize the use of a CRM and ramp up your marketing and sales performance.