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Are Your Data Solutions Excluding Key Customers?

Are Your Data Solutions Excluding Key Customers

Are Your Data Solutions Excluding Key Customers

As part of Solutions Review’s Contributed Content Series—a collection of contributed columns written by industry experts in maturing software categories—Olivia Hinkle, the Senior Product Marketing Manager at Validity, outlines how companies can ensure their data solutions don’t exclude essential customer data.

Customer data is one of the most valuable assets your company possesses. This information helps make vital business decisions, create compelling marketing campaigns, and continuously inform improvements to your product suite. While data solutions will vary based on the size and complexity of your business, best practices surrounding data management should stay the same.

Consistency, accuracy, and quality should always be top of mind. Organizations need dedicated individuals and technologies to curate and manage their proprietary data, or they’ll risk legal non-compliance and dissatisfied customers. Excluding key customers isn’t a shortcoming of the data solution you employ but the processes and governance you have in place.

In today’s macroeconomic climate, all customers must be considered ‘key.’ But depending on how your organization defines its key customers, here are a few things to keep in mind to ensure you’re adequately building data best practices to meet everyone’s needs.  

Segment by Customer Demographics   

Data needs and expectations vary widely based on company size, budget, industry, location, and other factors. Understanding the unique needs of each customer type will catalyze your efforts to build and implement best data practices within your organization.  

Larger companies typically have more complex data needs and higher expectations for data accuracy and analysis capabilities. They’ll likely require more advanced data management and analytics tools because they have an expansive volume of data. Secondarily, they also have more to lose if they get data collection, protection, and any resulting actions wrong. 

On the other hand, smaller companies often have simpler data needs yet fewer resources to manage them appropriately. These businesses are usually more focused on cost-effectiveness and ease of use rather than sorting through and protecting thousands of customers’ worth of data.  

Customers in different regions will also have differing expectations due to local regulations and technological infrastructure. Those in countries with strict data privacy laws may be more concerned about how their data is collected, stored, and used. Even your average consumer is worried about data privacy regarding SMS marketing. For example, recent research found that 96 percent of recipients wish companies were more transparent about how their data is used.  

Segmenting your data management practices by customer demographics will help you ensure you’re meeting everyone’s individual needs. But remember that ensuring you’re treating each customer equally—regardless of variances in size, location, spending, etc.—is only possible if the personnel responsible for a customer (i.e., Customer Success Managers) have access to clean and accurate data.   

Data Quality is King  

It’s impossible to execute on the vast majority of business functions without clean, accurate data. Trustworthy CRM data, for example, is vital to the success of any business. Yet research shows 95 percent of survey respondents report having data quality issues that seriously impair their ability to leverage their CRM fully. This illuminates a concerning disconnect between the significance we place on data cleanliness and our efforts to maintain it.  

Data quality is king. Running a report on the top 50 customers by revenue or the top customers in a given region or industry requires accurate data to run said reports. Attempting to run a promotion or campaign to the top ‘n’ customers in any given sector is impossible if some accounts have missing segmentation attributes like Industry or Company Size.  

Deploying data quality tools can help pick up the slack by identifying anomalies and automating their remediation. But technology can only treat the symptoms of poor data management rather than the root cause. Some actionable steps your business should take now to improve data quality include: 

  • Obtain leadership buy-in: Ensure your leadership team is fully aware of your company’s data management issues. When leadership considers data a priority, obtaining the resources you need for organizational changes and investments becomes much more accessible.  
  • Establish a data management team: Many organizations lack a clear data governance plan. A cross-functional data management team featuring members from the sales, marketing, operations, and IT departments can help your company maximize the value of your data, ensure data quality, and streamline your data processes across the board.  
  • Appoint a CRM data quality manager: So much data flows into CRM systems that managing this information should be a full-time responsibility.  

In tandem with data management technology and a governance plan, these initiatives will help your business overcome its toughest data quality challenges and ensure you’re consistently operating with the most accurate, up-to-date information.  

Look at Product/Service Adoption  

Another helpful tactic to ensure critical customers aren’t excluded is examining your product and/or service usage. Determining how much value a customer gets from your offerings can be incredibly beneficial as you look to upsell, make product improvements, and/or expand into new markets. Many organizations go through this process, but few surface that data to those that need it. Some metrics to consider include the following: 

  • Adoption rates: Is this customer using your product/service? How many employee seats are currently active?  
  • Frequency of use: How often does the customer use your product?  
  • Duration of use: How long does the customer spend on the product for each session?  
  • User engagement: Does the customer actively engage with the product? You can measure this by looking at page views, click-through rates, and duration of use.  
  • Segmentation: Do specific customer segments prefer different products or capabilities?  
  • Customer feedback: What are customers saying about your product? What is your NPS rating? What type of help desk tickets are you receiving? Are they all regarding a similar issue, or do they come from a similar customer segment?  

These analytics will help you improve your product suite, bolster customer satisfaction, and set your sales team up for success. Without data surrounding the value current customers derive from your services, salespeople will have difficulty renewing contracts and upselling. Furthermore, it will equip them with the use case data they need when selling to new prospects.  

A Two-Pronged Approach  

It is a non-starter to assume your data is correct or that employees will update the information when they find incorrect info. Data must be constantly checked, cleaned, updated, refined, and so on. Employees probably won’t do this voluntarily, so consider investing in automated data solutions. Some tools will automate data management scenarios and processes once these have been defined and built, freeing up precious time for administrators and/or data stewards to focus on other high-impact tasks.  

The capabilities of various data solutions can vary dramatically. But data management best practices should not. No matter what tool you decide to use or the specific data needs your company and its customers face, the combined power of technology and a clear data governance plan will prevent you from excluding key customers from your decision-making and broader initiatives. 


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