Your CRM Might Be Filled With Bad Decisions Waiting To Happen

Data-driven decisions start with good data

• Bad quality data in your CRM assets can be the root cause of bad decisions.
• Data quality issues in your CRM have a negative effect across sales operations, marketing operations, customer success, and finance. Incomplete data compromises RevOps efforts.
• Four common types of bad data to look for in your CRM and their potential causes.
• How to improve your CRM data quality.

Your CRM depends on good data quality

Many data-driven organizations rely heavily on data stored, generated, and aggregated within their system of record: more often than not, their customer relationship management (CRM) system. Whether it be for reaching out to customers, converting an opportunity, or reporting on sales or marketing performance, it is safe to say that we use our CRMs to collate all these insights to aid in our decision-making. After all, this is what CRMs are built to do. This coincides with Gartner’s definition of CRM as “a business strategy that optimizes revenue and profitability while promoting customer satisfaction and loyalty. CRM technologies enable strategy, and identify and manage customer relationships, in person or virtually. CRM software provides functionality to companies in four segments: sales, marketing, customer service and digital commerce.”

Data is fundamental for CRM processes, especially when it comes to positively impacting business operations and goals, for example, lead nurturing and customer retention. But just imagine running well-thought-out strategies that are based on incomplete data. Unreliable data puts decisions and future planning at risk. Essentially, bad data in your CRM assets can be the root cause of bad decisions.

There’s a misconception that the CRM is only to be thought of as a sales tool. Yes, it’s a powerful sales tool, but that’s only scratching at the surface of its value. The time has come for organizations to recognize that its use case goes far beyond sales and is a valuable tool when shared among the revenue team. With that, we need to recognize that bad data in the CRM doesn’t merely mean that sales has to spend their valuable time hunting down a new phone number. Rather, bad data in the CRM means decisions that impact the entire organization are at risk.

Martechseries.com shared the results of a survey sent to over 1,200 CRM users. It indicated that 95% of respondents report data quality issues seriously impair their ability to fully leverage their CRM system. In addition to this, a staggering 81% of respondents noted that CRM data quality isn’t a high-priority initiative for leadership.

Four types of bad data and their causes

When data quality issues penetrate your CRM, not only is user trust put at risk, but it also ultimately has a negative impact on the effectiveness of your RevOps initiatives — that’s a negative effect across sales operations, marketing operations, customer success, and finance.

So, as data practitioners, what should we consider “bad data,” and what are the potential causes?

• Incomplete and missing data. Simply, this is when customer attributes are unknown or were left out of the customer record. When a contact record is missing the title value, for example, it’s difficult to do the segmentation that is critical for account-based marketing (ABM) outreach. Even more basic, the lack of identifying attributes such as business name, country, and telephone number makes the simplest outreach time-consuming. Incomplete data makes it difficult to nurture leads and compromises RevOps efforts.
• Stale data. Unlike food, stale data does not emit foul odors, but it may leave a bad taste in your users’ — or worse, your customers’ — mouths. Today’s organizational landscapes change quite frequently. Be it divestitures, M&As, or going out of business, you risk dampening your customer engagement strategies if you don’t keep up with these insights.
• Incorrect data. Two probable scenarios contribute to this type of bad data in your CRM: 1) stale data that doesn’t get refreshed, and 2) inaccurate data entry. For the latter, data gathering can be challenging when no standards are in place for input data qualification. Even with the best intentions, mistakes can happen; even the most reliable ETL (extract, transform, load) processes could fail systematically as data is being processed.
• Duplicate data. One can argue that there are scenarios where duplicate data needs to be created to meet internal and/or external policies, but duplicate data is considered harmful when its existing version serves no business purpose and reduces the effectiveness of the decision-making process. Unintentional duplicate data will provide misleading results when aggregated without knowledge of its presence.

Four tips to improve data quality in your CRM

Don’t let your CRM and RevOps processes fall prey to bad data. If left unattended, the quality of data in your CRM systems will become less and less reliable, even with an influx of fresh data. Your “fresh” data, too, will be part of the problem in due time. According to Deloitte¹, on average, 71% of consumer data contains errors to some degree — and that’s before decay even starts to set in. In the same vein, Gartner² approximates that, in recent years, bad data cost businesses $15 million on average annually.

Here are four things you can do to get started on elevating the reliability of your CRM with good data-guiding principles:

• Institute data standards. The compounding effects of unregulated freehand entry of crucial data can be debilitating to data analysis and integration projects. Lack of standardization makes it difficult to aggregate, group, and articulate data. Ironically, inconsistency is the one consistent factor. Challenge your RevOps teams to go beyond the minimum acceptable data entry, like requiring a minimum number of characters for address data. The reward will be better habits and better data. Provide users the error-preventing assistance of a type-ahead text function to help them auto-fill sections for faster and more accurate input. Couple this with a search-before-create capability accessing both internal and external referential data. This not only inherits the standards available from existing data/dictionaries but also empowers users to input better and more accurate data. Follow up with KPIs to show how the data is improving quarter over quarter and year over year. Many CRMs have built-in features that can be implemented to improve the quality of data on entry. This eBook describes some of the tricks that you can implement in Salesforce to boost data quality.
• Develop data quality habits. Consistency and standards are key for a successful data practice, but these need to become regular habits to provide a backbone for data quality. Examine these two elements. Where are they in your processes? Habits can be adjusted, continued, enhanced, or stopped. They accomplish the actions on which data quality relies. Standards offer a baseline on the acceptability of the results of these actions.
• Enrich and refresh. Data fundamentally describes transactions, people, places, things, events, other data, etc. As those attributes change, our data will also shift and change. This is why it’s so vital that we refresh our data and enrich it with additional insights. Acquiring new data is only one part of the solution. Keeping it relevant as needed is the other half. We know that data decays over time; customer data is believed to decay at the rate of 30% per year.³ Strategies for enriching and refreshing data on demand or periodically become more pertinent to organizations depending on data for decision-making, but before doing so, understand the readiness of your technology landscape. Are you able to handle API connectivity, or should it be batch refresh? How often do you need to refresh? Ensure that the business is included in the creation and implementation of these strategies, as the speed of the business will dictate the initiative. D&B Connect for Salesforce enables businesses to trust their CRM data with always-on data maintenance and management powered by the Dun & Bradstreet Data Cloud.
• Establish an archive strategy. Because the cost of data storage plummeted in the past 10 years, data is living longer than ever. However, this did come with a price: the price of managing and accessing data. Old and immaterial data can easily pollute an environment like your CRM faster than you can spell D-A-T-A-S-W-A-M-P. Provide context on what is material data and bake that into the policies on keeping data live in your CRM environment. As for the rest, archive it in a manner that allows you to reinstate it when needed. Review the archiving policies periodically to ensure that your CRM data is meeting your business needs.

CRM success depends on data and habits

Your CRM environment is a socio-technological tool — it carries human behavior into its processes. The insights it delivers are directly related to the habits you allow into the process. Applying the guiding principles above will provide the framework for curtailing bad behavior and elevating the level of your CRM’s success. As a direct result, it will better your data quality and thus the decisions you make as well.

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¹ https://www2.deloitte.com/us/en/insights/deloitte-review/issue-21/analytics-bad-data-quality.html

² https://www.gartner.com/smarterwithgartner/how-to-stop-data-quality-undermining-your-business

³ https://www.business.com/articles/prevent-data-decay-from-ruining-your-crm/