The 10 Dangers of Bad Data Quality in Salesforce
Good data quality is an essential attribute for any organization. Calling duplicate leads, emails that bounce, undelivered packages, useless reports, and forecasts that are way off are all caused by bad data. Research from scientists and consulting firms shows that data quality is important for your organization's success. And with the emergence of new technologies like cloud computing, AI, Machine Learning, and social media, the importance of data quality has become even more prominent. This article will discuss 10 proven areas, inside and outside your organization, where good data quality will offer a substantial competitive advantage.
Bad data is continuously entering your Salesforce org through poor data migration processes, external data sources like web forms or lead lists, or employees' data entry. The consequences are undeniable. Data decay causes your existing data to get less useful over time. Your employees might suffer the negative effects, and your customers will hardly give second chances.
You might think improving your data quality is costly, tedious, and time-consuming. It is not. With the right solutions, automatically cleanse your existing data in Salesforce under your own rules. Better yet, prevent inadequate data from entering, regardless of the source, and make your sales, marketing, support, and success teams happy and more productive.
Internal Impact
1. Inefficient outcome: how sales, marketing, and support teams are being affected
Consult your sales team, who will inform you that handling duplicate or incorrect contact information takes them out of their productive state. Instead of focusing solely on contacting their clients, they must first authenticate contact details and search for duplicate records. If they fail to do so, they may end up unintentionally contacting an existing customer under the impression that they are a new lead, resulting in wasted time, disgruntled employees (which will be discussed later), and an unsatisfactory customer experience. It is certainly advisable to take steps to prevent this scenario from occurring.
The same goes for marketing teams. Bad contact data leads to many bounced emails. These harm your sender's reputation. Bad sender reputation leads to emails ending up in the SPAM folder of your contacts. It is tough to engage existing leads if they don't even get to read your email - the benefits of clean, formatted data are plenty in this example.
Last but not least, bad data quality also affects support and success teams. Due to duplicates or unverified records, the same issue may be investigated multiple times. When the support team cannot verify accurate customer records, it significantly increases the time spent responding to inquiries as they must search manually. The outcome? Delays, frustrated customers, and demotivated employees.
Furthermore, bad data quality can result in inaccurate customer segmentation, which prevents the support team from properly handling specific customer requests by categorizing them incorrectly.
2. Low employee satisfaction
Which Salesforce admin at one point didn't want to throw their laptop out the window after hours of data transformations in Excel to get data imported in Salesforce? Which salesperson has not angrily thrown down the telephone after yet another call to a non-existent telephone number?
Scientific research shows that working with high-quality data significantly improves employee satisfaction and loyalty to their company. Bad data scares away your best employees. With unreliable information, employees can't trust the accuracy of their work or make informed decisions quickly.
We all know how difficult it is to find talent, so let us ensure we hold on to our best colleagues onboard.
3. Bad CRM adoption
Since the beginning of CRM systems, users have always returned to their trusted spreadsheets, lists, and notebooks to enter and find their data. Why? Because users don't trust the data in the CRM or can't quickly find what they need to perform their job. Old habits die hard. Your employees will work in the way that is most practical for them. Productivity and accuracy take a back seat.
Gartner found that inadequate data was the number one reason for failed CRM implementations, and we can all agree on why. You may experience this daily.
4. Bad collaboration
Cooperation often involves sharing data between teams and staff or interpreting data together. When data quality is bad, this creates mistrust between colleagues and teams. Each department has its own fragmented version. Research has widely shown that trust and performance are positively related.
Lead ownership and sales commission conflicts can occur on even the best sales teams. But with duplicate leads in your system, you are asking for trouble. The same goes for empty or unvalidated addresses, leading to territory conflicts down the line.
5. Unnecessary spending
Everyone can agree that inferior-quality data incurs costs and that the reverse is also true: high-quality data leads to cost savings. These costs can be defined in many ways but are generally distinguished into direct and indirect costs. Direct costs are immediately visible and known to management, whereas indirect costs are unknown.
Direct costs include delivering a parcel to an invalid address, leading to an undelivered package or lost shipment, increased postal fees, and, even worse, canceled orders. Many software solutions or services base their pricing on the number of records or users, so having duplicate records leads to higher software fees. Take email marketing software or Salesforce Marketing Cloud, for example.
Indirect costs are less easy to define and quantify. More time is spent working with bad data, which slows growth. Indirect costs include (opportunity) costs for making decisions based on flawed data—more on this in the next paragraph.
6. Non-data-driven decisions
Bad data equals bad decisions. Think of uninformed pricing policies and poor planning, focusing on the wrong customer segments. Duplicates and incorrect address data can throw a wrench in allocating the optimum sales capacity to each region. Forecasts will be off, and reports will be hard to generate and unsuitable for data-based decision-making.
7. Compliance & risk management issues
Many laws regarding privacy and data portability have been enacted in recent years. Most famous are the European General Data Protection Regulation (GDPR), the UK Data Protection Act (DPA), and the Personal Information Protection and Electronic Documents Act (PIPEDA) in Canada.
Common in all those regulations is the right to personal data access, deletion, portability, and consent.
Having duplicates in your database makes compliance with these laws near-impossible. If a lead opts out of email communication and you change the setting for one record but leave the duplicate record on opt-in status, your lead will still get a commercial email. The same goes for access, deletion, and personal data exports.
Meeting customer expectations is challenging enough as it is. Having your data work against you certainly makes it worse. Legal problems cause you to make unnecessary expenses and tarnish your company's image.
External Impact
8. Bad user experience
According to Deloitte, consumers are continuously raising the bar and are more likely to share bad experiences online. This tendency is true for both consumers and business buyers.
No psychologists here, but we know for sure that customers are more likely to share bad reviews because they are more passionate about their negative experiences and want to prevent others from having the same experience. Frustration? Most likely. Bad reviews can also be a way of venting.
In all cases, negative reviews can help you improve if you know how to address them properly, but even in this case, you need to rely on your data. Good data enables you to reduce dissatisfaction. Negative reviews are easier to detect and address.
9. Lack of trust from customers
Let's be honest. Who gives second chances? Poor data quality can severely damage customer trust, which is essential for long-term relationships. Inaccurate and segmented data leads to a loss of confidence in your company's ability to serve customers effectively, be reliable, and secure their data. Customers are unlikely to trust you again. They also make sure that everyone around them is aware of it. Moreover, some people don't trust your company and haven't even hired your services or tried your products.
Not just about addressing bad reviews but losing business opportunities due lack of trust. This takes us to the next point.
10. Lost business opportunities
In short, poor-quality data closes doors. It's better to be safe than sorry. Make sure you do an excellent job of handling your customer's data from the beginning and save yourself from losing them. Customers may leave you at a different stage of the way. Some of them are more optimistic and patient than others. Still, poor management of unreliable data won't make them stay.
Conclusion
You might think improving your data quality is costly, tedious, and time-consuming. It is not. With the right solutions, automatically cleanse your existing data in Salesforce under your own rules. Better yet, prevent inadequate data from entering, regardless of the source, and make your sales, marketing, and support teams happy and more productive.
