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Diagnosing data decay: a step-by-step guide for companies selling to restaurants

July 17, 2023 | Trends and Advice | by Grant Gadoci

This is the second blog in a 3-part series designed to help companies targeting the restaurant industry better understand, diagnose, and ultimately resolve CRM data decay. Read part one here.

Understanding and diagnosing data decay is crucial for companies selling to the restaurant industry, as it allows for targeted efforts to combat this issue.

In our previous blog, we explored the concept of CRM data decay and its potential costs to businesses. Now, it’s time to assess whether or not your organization is facing the challenges of data decay. In this blog, we present a step-by-step guide to help you diagnose data decay effectively.

In many cases, organizations are not even aware that they are collecting dark data or they generate way more data than they can interpret

Step 1 – Assess candidate fields by data entry point

When diagnosing data decay, it’s crucial to consider both the fields within your CRM and the data entry points. Different data entry methods may result in varying levels of data decay, and understanding these entry points can help identify fields that are most likely to suffer from decay.

Consider the following factors when assessing fields and data entry points:

  1. Data entry methods: Evaluate the methods used to enter and update data in your CRM. Are updates primarily made manually by sales representatives, or do you rely on list purchases for mass updates? Understanding the data entry methods is important because certain methods may introduce higher risks of data decay. For example, manual updates by sales reps may lead to inconsistencies or missing information over time, while list purchases may provide moment-in-time snapshots that quickly become outdated in the rapidly changing restaurant industry.
  2. Field importance and usage: Examine the importance and usage of different fields within your CRM. Fields that are critical for sales, marketing, or customer service activities are more likely to experience data decay if the data entry points are not actively managed. Identify fields that play a significant role in your business operations and focus on assessing their quality, accuracy, and completeness.

By considering the data entry methods and the importance of fields, you can determine which fields are most prone to data decay and prioritize them for evaluation and maintenance. This assessment will help you target the areas of your CRM that require immediate attention and ensure that the most crucial data remains accurate and up to date.

RECOMMENDATION: 5 CRM fields to prioritize when selling to restaurants
Based on my extensive 15-year experience in the industry, here are the five industry-specific fields that are most susceptible to severe data decay:

  1. Unit Count: The number of units or locations associated with a restaurant. This should be assessed monthly, and should have a very small margin of error.
  2. Ownership: Is this Account (associated with a brand or concept) part of something bigger? Unparented Accounts significantly slow down SMB and MM reps.
  3. POS Type: The point-of-sale system used by the restaurant for transactions. For companies selling software, POS documented on an Account should be non-negotiable.
  4. Service Type: The type of service offered by the restaurant, such as full-service, limited-service, or buffet/cafeteria. This should be a picklist, not free text, and should be aligned with NAICS.
  5. Website: A restaurant’s website is the primary means for both record deduplication and Account disambiguation, particularly in the restaurant industry long-tail. Blank or non-normalized data should be avoided at all costs using field validation and automated workflows.

Note: these are just a few examples. We recommend you also consider secondary attributes—such as menu offering, territory, and where the brand is in its growth life cycle—along with any custom fields that are unique to your business.

Choosing fields and reviewing data entry points to assess data decay.

Step 2 – Define how to measure data ROT

Now that we have identified the fields to validate in Step 1, the next step is to determine how to define the level of data decay within these fields. One useful tip is to use the ROT acronym, which stands for “Redundant, Obsolete, and Trivial” data.

Data ROT provides a framework for measuring the quality, accuracy, and completeness of your data. By evaluating these aspects, you can gain valuable insights into the extent of data decay within your CRM.

  • Redundant data: Identify redundant data that exists in multiple fields or records within your CRM. Redundancy not only takes up valuable storage space but also increases the likelihood of inconsistencies and inaccuracies. Streamline your data by removing duplicates and ensuring that each piece of information has a single, reliable source.
  • Obsolete data: Obsolete data refers to information that is no longer relevant or valid. Evaluate how much of your data falls into this category. As an example, look for inaccurate unit counts, out-of-date ownership hierarchies, or missing or inaccurate POS types. Regularly update or remove obsolete data to maintain the accuracy and effectiveness of your CRM.
  • Trivial data: Trivial data refers to information that holds little or no significance for your business objectives. Evaluate the relevance and usefulness of each data point in your CRM. Identify fields or records that contain trivial information that does not contribute to your sales, marketing, or customer service efforts. Streamline your data by eliminating unnecessary or irrelevant data points.

To accurately measure data ROT and evaluate how much data is missing or decaying, consider partnering with a qualified market intelligence provider. These partners have expertise in data assessment and can perform comprehensive evaluations of your CRM data. They can identify data gaps, inconsistencies, and areas of improvement, providing you with actionable insights to address data decay effectively.

By incorporating the measurement of data ROT and seeking assistance from market intelligence partners, you can gain a more comprehensive understanding of the quality and completeness of your data, enabling you to make informed decisions and take targeted actions to combat data decay.

Using ROT to define the level of data decay within prioritized fields.

Step 3 – Analyze a subset of records

With the fields identified and data criteria defined, the next step is to analyze a subset of records in order to gain a deeper understanding of potential data decay within your CRM.

To begin, it is important to select a representative sample of records. Here are some tips to help you get started:

  1. Sample size: I strongly recommend using 10% of your Account list as the sample size, with a minimum of 100 records and a maximum of 1,000 records. This range ensures a sufficient number of records for analysis without overwhelming the process.
  2. Exclusions: Remove any Accounts that are outside the initial scope from consideration. This could be based on factors such as industry or country. By focusing on relevant Accounts, you can obtain more accurate insights into data decay specific to your target market.
  3. Randomization: Randomization provides a more comprehensive view of data decay across different types of Accounts. Consider using hidden fields or formulas to ensure that your sample is randomized and includes a variety of Accounts from all segments of the industry. Avoid solely selecting Accounts that you know have fields with the best or worst data integrity.

Once you have selected your candidate records and isolated the primary fields for analysis, it’s time to look for inconsistencies, outdated information, or missing details that may indicate data decay. During this primary analysis, pay close attention to patterns or trends that emerge. Identify common issues or areas where data decay is prevalent within the sample. This analysis will provide valuable insights into the overall health of your CRM data and will likely drive the conversation on how to reduce or avoid data decay in the future.

By examining a subset of records and conducting a thorough analysis, you can gain a clearer understanding of the extent of data decay in your CRM and take informed steps to address and mitigate the issue moving forward.

Recommended steps to perform a thorough analysis of CRM records to assess data decay.

Step 4 – Give yourself a grade… and a plan of action

Based on your analysis, give your organization a grade that reflects the extent of data decay within your CRM. Assess the overall quality and reliability of the data in the chosen fields. This grade serves as a baseline measurement of your data decay situation.

Next, develop a plan of action to address the identified data decay issues. Consider implementing data hygiene practices, such as regular data cleansing, data verification processes, and automated data updates. Explore industry-specific data enrichment services or technologies that can help you maintain accurate and up-to-date information in your CRM.


Diagnosing data decay is a crucial step in combating the challenges it poses to companies selling to restaurants. By following this step-by-step guide, you can assess the extent of data decay within your CRM and develop a targeted plan of action to address the issues.

In the final blog in this series, we will present a solution to fix dark data at its source, helping you regain control of your CRM data and maximize its value when selling to the restaurant industry.