Clean Your Bad CRM Data & Boost Efficiencies: Best Practices
At a Glance
Sales teams around the world rely on Customer Relationship Management (CRM) systems to drive sales force automation. Bad data or “dirty data” prevents companies from gaining the benefits of a CRM system. It causes revenue losses while risking damage to your brand reputation through incorrect messaging and lack of targeted marketing.
Today’s Customer Relationship Management (CRM) solutions are a lot more than just salesforce automation or contact management tools. CRM systems are being leveraged to run Sales, Field Service, Customer Service, Project Service Automation, Marketing, internet of things (IoT), call records, emails and more.
CRM is now at the core of every aspiring company. They can capture and present a 360 degree view of customer interactions in real-time. Different departments within your organization can benefit from this single source of truth. Embedded Analytics tools assimilate data from different data sources such as ERP, Big Data, Social Media, Email interactions and aid in building this 360 degree view. It is imperative to plan for and include Data Quality Management (DQM) processes and tools while managing such critical data.
One of our customers decided to migrate to Microsoft Dynamics CRM. They immediately saw their data quality was bad and required extreme manual intervention. The clean-up initiative was a nightmare lasting several weeks requiring 10 full-time people to clean three years worth of dirty data.
Among the questions this client asked were: “Are there any tools that can help automate this clean-up? Is this some kind of prehistoric CRM?” This is more common than you might think.
Only 15% of all companies are confident in the quality of their CRM data. This customer was using a good CRM solution equipped with powerful data-cleanup tools. So why weren’t these tools used? According to the customer, they were.
A major reason for the dirty data was some technology-averse users recorded data inconsistently and used fields for incorrect purposes. Enforcing good data capture processes would have eliminated these issues.
This customer’s database includes more than 30,000 leads in the system. About 10% are duplicates. Roughly 20% of the remainder have problems such as:
All of this dirty data delayed marketing campaigns for key events, costing revenue. Current leads are at best “unknown” causing missed sales and opportunities and adding stress to the sales and marketing teams.
This customer isn’t the first to have these problems. Many of our customers suffered similar ordeals resulting in lost opportunities and revenue earning potential.
How did data get this dirty, and most importantly, what are the lessons learned?
A few of the many ways that a CRM system can go from a super valuable tool to an expensive headache are:
These recurring issues can be addressed proactively by active management.
The success of an organization relies on a constant flow of smart data-driven decisions. This creates the question: “How do you make the right decisions to ensure organizational growth?”
Among the environmental challenges facing chemical and pharmaceutical firms are setting and meeting public targets for greenhouse gas (GHG) emissions in-line with the 2018 Intergovernmental Panel on Climate Change (IPCC) report.
High-quality information and strategy are the pillars of any objective decision taken within an organization. Imagine the impact of incomplete or unreliable CRM data—a significant element used by sales and marketing departments—on marketing campaigns? Lead conversions and other metrics cannot be calculated because of bad data.
A CRM solution with unreliable data is similar to buildings without foundations. Just as a building can’t stand without its foundations, a customer relationship management system can’t deliver intended value to businesses without up-to-date, high-quality, reliable information.
Poor data quality costs organizations an average of $9.7 million per year.
$3.1
Trillion amount of loss being incurred by US-based businesses annually due to poor quality data.
Source: IBM
Dirty data or bad data refers to information with incorrect facts or assumptions. It can exist anywhere —in your CRM, ERP, shared documents, reports, etc.— and it’s usually a specific type of error or simple mistake.
According to Techopedia: misleading data, incorrect data, inaccurate data, duplicate data, non-integrated data, misspelled data, non-formatted data and incomplete data fall within the category of dirty data.
Some definitions are:
Many companies overestimate their quality of data and at times even ridicule any chance of dirty data attack to their business. This often causes significant losses in business.
Bad data costs U.S. companies $3 trillion a year. Most organizations lose $14.2 million a year.
Bad data cost businesses a minimum of of their revenue, which reached as high as 25% in some cases.
Dirty CRM data is a plague that should be eradicated. Its impact on a single business can be staggering. Drawbacks to having and using outdated, inaccurate or just plain bad data include:
Any one of these effects is bad news for a business. Dirty data can create many of them at once.
Figure: 1Impact of Bad Data in all Areas of the Enterprise
The typical organization invests roughly $150,000 to generate 1,000 leads.Of these leads, only 30% are typically routed to sales and 700 leads are left behind. This translates to the company losing millions of dollars in pipeline opportunities
Data failures can happen to any organization big or small causing serious damage. The cost to repair a company’s reputation can be in the millions. Fines and penalties for legal violations are already steep, and will continue to rise.
For example, the Fleet Bank set out to deploy a $38 million CRM project in 1996. The project failed to meet its objectives for three straight years, leading to a complete failure, TDWI reported.
A global chemical company’s failure to identify and reconcile with global suppliers cost it millions in lost volume procurement discounts. The average cost of fixing and cleaning a single, duplicated data record can range anywhere from $20 – $100.
Most organizations don’t have a data quality management strategy in place. A minimum amount of planning would enable these organizations to design a suitable data quality management (DQM) plan. A DQM plan ensures that the information gathered within a developed long-term business strategy is properly maintained. Such a plan prevents interruptions to day-to-day operations. Among the topics the DMQ looks at are:
Data decays at an average rate of 2% per month or up to 25-30% per year. Losing any of this data often results in lost opportunities. Cleaning this data lets your marketing efforts reach prospects faster, helps target the right audience, increases revenue from campaigns and eliminates wasted time and labor.
So how can a company achieve this? What are some ways to prevent dirty data?
Other methods include:
Setting a data quality policy. Management can hold CRM data input training sessions while using a comprehensive data management policy to guide employees.
Automating data entry to avoid incorrect data insertion and duplication using software that automatically captures and logs information. When manual entry is the only option, check what’s been input for spelling and grammatical errors.
With a little planning and by having the right processes in place to follow, a company can turn its CRM into the organization’s financial heartbeat. Businesses of any size can benefit from keeping clean data.
For example, Schwan Food Company’s parent firm was greatly impacted by invalid customer records leading to delays in delivery. Customer service representative efforts to satisfy complaints were wasted. The company placed a concentrated effort on eliminating duplicate CRM data. This generated a quality data management solution removing duplicate customer records and establishing a 360-degree view of the customer. The result was better customer service.
After a merger, MSC Industrial Direct Co. – a Forbes Platinum 400 company – discovered its business workflow was being affected by duplicate customer record entries, causing compensation issues within sales. To resolve this, the organization implemented a business-insight driven data management solution that removed duplicate customer records while minimizing credit risk exposure.
Microsoft Dynamics customer engagement apps (Dynamics 365 Sales, Dynamics 365 Customer Service, Dynamics 365 Field Service, Dynamics 365 Marketing, and Dynamics 365 Project Service Automation) automatically detect several types of duplicate records. These include: accounts, contacts and leads.
Companies can use Dynamics 365 to clean data by having authorized users—systems administrators and above—create new rules to avoid duplications in other fields. They can create a new duplicate detection rule or edit an unpublished one.
The process starts by selecting a base record type and then a matching record type plus criteria such as “Exact Match.”” The people setting these rules can also exclude inactive matching records, be case sensitive, ignore blank fields and adjust other settings.
These Microsoft D365 apps have a limit of five rules for the same base record type.
Employees of some companies take a casual approach to entering CRM data, which often results in incomplete records. Three years later there are thousands of garbage leads and contacts in your CRM. They lack a quick way to fix the data errors and cannot run campaigns until the mess is sorted.
Keeping a CRM free of dirty data CAN be done with a little work. Having the right processes in place will minimize the amount of dirty data. Your sales and marketing teams can take advantage of an immensely valuable technology.
Cleaning dirty data is admittedly a time-consuming affair: don’t ignore it. The effort you spend on cleaning your CRM is worthwhile. The effort you spend keeping data clean will lead to major growth and a positive change in your day-to-day operations.
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