Poor-quality data costs US companies $15 million on average annually as it leads to hampered employee productivity and ineffective decision-making. Say, sales reps’ efforts to contact prospects may be fruitless due to incorrect contact info in CRM or sales managers may base their strategic sales plans on reports generated from outdated info.
Looking at the data quality issues through the prism of my experience in managing Salesforce support projects, I believe that minor data quality problems (like duplicate contact and account records) can be addressed with the efforts of an in-house Salesforce administrator. As for severe CRM data issues, like malfunctioning custom reports that provide misleading info, they are better addressed in the course of third-party support. You may see how ScienceSoft provides that within its Salesforce support services. From my experience, the most valued benefits of data cleansing are:
- Reliable sales analytics and accurate forecasting. Sales analytics and forecasting can only be as good as the quality of data stored in CRM. If the data used for analysis is accurate and complete, reliable reports and forecasts will come as a result.
- Increased sales reps’ productivity. Having accurate and complete data in CRM helps sales reps spot leads with higher conversion possibility and make them a focus of their prospecting activities.
- Shorter sales cycle. Leveraging consistent and up-to-date CRM data, sales teams can properly prioritize their sales activities, which paves a way to closing opportunities more quickly.
Best practices of Salesforce data cleansing
Based on the Salesforce support projects I managed, here are the best practices of effective data cleansing:
- Data cleansing should be regular
70% of CRM data becomes obsolete each year, so regular data cleansing should become your routine. The most evident way to maintain data timeliness is to ensure that your in-house administrator or Salesforce support vendor regularly provides data cleansing with dedicated tools. Another measure you can take to fight obsolete data is to enforce weekly data cleansing activities to employees. For example, sales reps may clean up expired opportunity close dates and other CRM data, which is no longer relevant.
- Data cleansing should be complemented with measures on sales-related processes improvement
Keeping your sales data clean is not a merely technical activity. It requires reviewing lead and opportunity management processes to unify standards for data input for different systems used in a company. For instance, sales reps use Salesforce Sales Cloud integrated with a marketing automation platform. If sales reps and marketing specialists do not have unified standards for data input (unified lead and opportunity name format, phone number format, etc.), duplicated data in CRM will be a result.
- Data cleansing should be supported with proactive technical measures
According to 1-10-100 Rule, it takes $1 to verify a CRM record when it is initially inputted, $10 to clean it later, and $100 to do nothing (a result of lost opportunities and wasted time). To prevent the revenue drain caused by discordant sales data, you should take proactive technical measures to maintain data quality. For instance, an in-house Salesforce administrator (or the one from a Salesforce support service provider) can enable automated deduplication with Salesforce duplicate rules that help to identify and manage (delete, merge, etc.) duplicate records. The administrator can also introduce validation rules. They prevent the entry of data not compliant with the internal quality standards of the sales data in your company. For instance, a rule can validate that the opportunity’s close date isn’t prior to the current day or that the opportunity amount is a positive number.
Well-performed sales data cleansing embraces data hygiene activities on the business and the technology sides. Dedicating a good amount of time and effort to keeping your sales data clean solves the problem of poor data quality and brings increased accuracy of sales reports and forecasts, sales reps’ productivity and shorter sales cycles.