The quality of your client's CRM data is critical. It impacts operations throughout organizations and customers throughout the customer journey.
However, there is often a disconnect in organizations' understanding of how the quality of their data impacts their bottom line.
In this article, we'll explain some important concepts for why data maintenance is critical and provide some resources that can help you to make the case for your data management services to your prospective clients.
Bad data means marketing teams are unable to effectively segment contacts and personalize communications, negatively impacting the outcome of campaigns. This lack of personalized messaging also impacts brand reputation in the eyes of customers. And without reliable associations between contacts and companies, account-based marketing is difficult.
Sales teams are also highly impacted. They waste time fact-checking inaccurate data, and less time means fewer deals, killing morale and negatively impacting commission-based roles. Sales reps step on each other's toes working duplicate records. Low quality data can also impact lead scoring and lead routing, leading to poor opportunity prioritization.
Customer support and success teams also feel the sting of low-quality data. Like sales reps, they also waste time checking inaccurate data. This impacts their ability to serve customers and impacts critical KPIs like ticket close times, lowering morale.
Across an organization, bad data impacts your ability to report on key segments and make accurate data-backed decisions. The time that employees have to spend double-checking data, fixing mistakes, and being less effective in their jobs impacts morale.
Armed with this information, the ROI of data management becomes clear, and you can convince your clients of its necessity.
Learn More: The Business Impact of Not Maintaining CRM Data
Many organizations believe the one-off expense will save them money over continuous data maintenance. This is not true.
A one-off data cleanup project is never just a single project. Issues continually stack up in your CRM, requiring regular cleanup projects. Data cleanup projects have many hidden costs:
- Planning and orchestration costs: Data cleanup projects are resource-intensive. You must collect information, understand your data issues, gain access to systems, define goals, research cross-departmental data issues, and coordinate with disconnected teams.
- Between-cleanup data degradation costs: Between cleanups, new data with issues are flowing into your CRM, degrading data quality.
- Opportunity costs: What could you have accomplished in the time you spent planning and executing a giant data maintenance project?
Ongoing data maintenance allows you to minimize costs and the impact of data issues, maximizing ROI.
The process of cleaning data relies heavily on mundane, repetitive tasks. But software can automate many of those tasks, freeing your employees from tedious data maintenance while improving morale. Over time, you can refine and improve upon the solutions that you have in place and don’t have to reinvent the wheel for each data cleanup project.
With continuous cleanup and automation, the time required to coordinate and execute cleanup projects is minimal because you are always building on what you have in place. You’ll have automated solutions for common problems and can focus on new issues as they arise.
Learn More: The Hidden Costs of One-Off Data Cleanups
At Insycle, we have developed the 4 Phases of Data Management Evolution, which is a series of phases that companies go through as they prioritize data management and take their data from chaotic to optimized.
These same phases also apply to your clients, and you can use Insycle to take your cliients from "Chaotic and Undefined" to "Optimized.".
Let's examine how Insycle helps with this:
- Undefined & Chaotic. All companies start in this phase. Before you can fix issues in a database, you have to identify what issues are present. Companies may have a vague idea about what issues exist in their database and how its impacting their business goals, but they are just hunches until proper analysis is done.
- Visibility. Companies in this phase have audited and analyzed their CRM data, identifying the most critical issues that need fixing. Start with the Customer Data Health Assessment, which surfaces data quality issues that negatively impact your marketing, sales, and support efforts and guides you through the process of fixing them.
- Standardization. Companies in this stage have identified, planned, and fixed a majority of their most pressing issues their database and are beginning to feel the effects of standardized, reliable data.
- Optimization. With working solutions, you can then deploy automation to ensure your biggest issues are identified and fixed on an ongoing basis, without the manual time investment. Instead, your teams can focus on building upon the solutions they have in place.
Reaching the Optimization phase delivers numerous benefits to your clients. With reliable customer data quality, they'll be able to reduce time wasted on manual data maintenance, maintain a single customer view, improve segmentation and personalization, improve morale, and provide a better experience to their customers.