It’s estimated that bad data costs companies between 15 and 25 percent of their total revenue. With clean data, companies could earn up to 25 percent more each year. On a per-unit basis, bad data costs anywhere between $10 - $100 a record. Most CRMs have over 100,000 records...that’s up to $10m in lost income. A less obvious but just as significant expense is time. Bad data wastes 50 percent of workers’ time in finding, correcting, and confirming information. Analysts spend even more time - 60 percent - cleaning and organizing faulty information. Essentially, productivity could be doubled if CRM managers didn’t have to spend time cleaning their database. Even reps suffer. Inside sales reps waste 27 percent of their time dealing with inaccurate records, which totals 546 hours per year, per rep. That’s valuable time that could be spent selling. The reasons for cleaning databases are obvious. More money, more time, more effective selling. But fixing bad data isn’t really the solution. In fact, these statistics are related to the cost of fixing it, not just the bad data itself.
What if there was a way to not just clean your CRM but keep it clean?
CRM databases grow at a rate of 40 percent per year, with 20 percent of the database being dirty. That means inaccurate information grows each year, making it more expensive to clean and confirm. Most solutions are centered around enrichment and cleansing. But as your CRM grows, the cost to continually update your data increases with the growth of your database. Instead, there needs to be a solution that builds policies and rules that govern data and prevent bad data from entering and staying in your CRM. The most important thing you can do for your CRM is to build rules and processes that prevent bad data from entering and stagnating in the first place. Within CRM, we have the ability to build logic, rules, and required fields to stop errors like account duplication, missing fields, and retaining dead leads. Treating our CRM as a breathing exchange of information incentivizes us to not let data rot away as teams spend hours updating it. Instead, we bring fresh, accurate information in and expel old information out in real-time, saving ourselves from the expensive time and effort of fixing errors. Let’s talk about what information is stored in your CRM and how we can think about creating processes and policies to keep it clean.
Accounts and Contacts
Accounts are the cornerstone of your CRM database and your go-to-market strategy. The number of accounts and their relationship with each other defines how you allocate coverage, territory, and ownership. To manage account data, create data rules that give you consistent definitions. Examples:
- How to define an account and its minimum data requirements?
- What are the lifecycle statuses of accounts in relation to your company?
- How do you deal with duplicate accounts?
These policies define how you treat an account, who creates it, and the minimum data requirements for a valid account. Policies define the buying behavior of an account (i.e. prospect, customer, attrited) and reduce account duplication. You need a similar approach when creating and managing contacts. Contacts change roles and leave organizations, requiring almost constant updating. Build policies to define and govern contact lifecycles that match your buying cycle.
Account Hierarchy and Industry Taxonomy
Integrating third-party data sources enables you to have accurate information when building account hierarchies and industry taxonomy. However, each data provider has its own taxonomy and account hierarchy structures that differ from your go-to-market plan. Invest in creating a custom hierarchy and taxonomy model to match your go-to-market plan. This allows you to segment accounts accurately and provides a coverage plan to support your go-to-market plan. Addressing these CRM policies enables more actionable data in your CRM investment and creates processes for keeping records current and accurate. Rather than fix bad data on the backend, CRM policies reduce errors that enter and stagnate in CRM. Changing how we think about database management will ultimately solve our bad data problem. We must go from reactive cleaning to proactive maintenance to save our companies the growing expense of wasted time and money.