Critical Element to Keep the CRM Engine Humming

Sales | 5 minutes to read

24th Aug, 2021

Poor data quality coupled with abysmal analytics has a severe impact on the effectiveness of a CRM system. Adopting best data practices can help enterprises bolster the productivity and efficiency of their CRM processes and maximize ROI


    • Data is the bedrock of all CRM systems to optimize the sales, marketing, and customer support services

    • Inaccurate and bad customer data can hurt the sales, productivity, and even the brand image of an enterprise

    • Enterprises can adopt smart measures to salvage the bad databases and maximize the productivity and effectiveness of their CRM processes

    When scaling up rapidly, businesses and marketing leaders usually have inflated expectations from CRM and marketing automation tools. There is an assumption that investing in the most expensive or feature-rich digital solution will guarantee success, ensure sustainable growth, and improve customer engagement. However, most leaders do not realize (until it is too late) that there is something far more important than the CRM system itself.

    What is the one thing that decides the efficacy of CRM systems and how can businesses focus on it? What is the cost of ignoring data quality and how can it be prevented?

    Why is data the bedrock of all CRM tools and processes?

    Data helps businesses understand their customers better and manage the relationship more effectively. Customer data is, essentially, how enterprises listen to their customers to plan sales, marketing, servicing, and virtually every other customer-facing business function. It doesn’t just hold economic value but is also key in building a stronger customer relationship and meeting their expectations.

    Data about customers helps in identifying spending habits and choosing the most impactful marketing strategy. In other words, it helps in increasing engagement and retention, personalizing recommendations and experiences, and building a comprehensive database of customers. All these data points and sets eventually feed into customer relationship management (CRM) software and tools, which simplifies the process of customer management. Furthermore, analyzing this data can help with sales forecasting, business planning, implementing market segmentation, increasing loyalty, and making smart decisions.

    Thus, the importance of clean, accurate, and reliable data at every step of the CRM process cannot be overstated enough. It equips sales and marketing professionals with valuable customer insights and helps in setting the overall customer engagement goals, strategy, and approach.

    CRM is undergoing technological transformations making it indispensable for the businesses. Read the blog on CRM evolution to know more.

    The cost of bad data in CRM

    When data with high inaccuracies is used in CRM tools and processes, it can be detrimental to sales, revenue, productivity, and even the brand image. Reports suggest that when poor quality data is used in even the most sophisticated CRM systems, it can still lead to losses of millions of dollars per year!

    According to IBM, data of poor quality costs organizations more than three trillion dollars each year. Gartner says that, on average, businesses lose $9.7 million every year because of bad data. A disengaged audience, damaged reputation, decreased productivity and efficiency; these are just a few ways in which dirty data impacts businesses.

    Remember, once inaccurate data becomes a part of the system, its impact is registered well beyond the short-term sales target. Cleansing the data, correcting mistakes, and undoing the damage also becomes an increasingly expensive process. The costs of storing bad data, sending irrelevant communication or engaging the wrong customers become prohibitively high if left unchecked. Vitally, using bad data in sales and marketing impacts customer service and support as well.

    What causes data to become unusable?

    There are many practices that lead to data going bad, and segregating them as per different stages of the data lifecycle can help understand them better:Data collection sources

    If the data collected at the source is incomplete, inaccurate, or even fraudulent, it will naturally undermine the entire process. It can happen when customers give incorrect information about themselves or do not provide complete data. This commonly happens when they are asked to sign up or register in order to avail a benefit or enjoy an exclusive perk. Similarly, the sources of data could be compromised or simply contain incorrect information.

    Data entry or migration

    For many organizations, data collection and data entry into the CRM system are separate processes. However, if data entry professionals aren’t trained to be consistent, it can lead to significant errors or omissions during the data entry or migration processes. Any mistakes made here can lead to inconsistent, overlapping, or duplicate customer databases that can reduce the utility of data.

    Data management

    Data management refers to a set of ongoing processes and practices to organize, validate, and maintain data hygiene. This can include data enrichment, authenticating contact information, removing erroneous data, and merging duplicate datasets. However, many organizations fail to implement these measures and do not have strong data management frameworks or practices to maintain data standards.

    Data decay

    Over time, the validity of all databases erodes due to natural reasons. For instance, people may move to a new address or change their phone numbers, or professionals may change their roles or organizations. Similarly, the demographics of people change with age, location, and experience. These natural processes lead to data decay and render perfectly good data useless by making it untimely and irrelevant. According to some reports, 20% of all contact information, like addresses and phone numbers, changes every year.

    Know the best practices to takedown data decay. Watch the Webinar (recording) “The curse of data decay on sales prospecting.”

    How can organizations salvage bad databases?

    1.     Distinguish between good and bad data sources

    First, identify what kinds of data are collected and their sources. Then segregate these sources into good and bad, and try to determine whether the source itself provides inaccurate data or whether the issue lies in the data entry/migration process.

    2.     Prioritize remedying the biggest sources of bad data

    Begin with fixing the largest source of bad data by reconfiguring the relevant data collection, entry, or management process. Investigate and look for quick fixes in the CRM that can help change several data entry points with minimal effort. Then replicate this for all identified sources of bad data, both big and small.

    3.     Undertake comprehensive data cleansing and enrichment

    Make data cleansing and enrichment a periodic activity that is undertaken with strictness and dedication. Filtering, editing, merging duplicates, validating, and classifying information should be an ongoing process to continually improve the quality of the data.

    4.     Get expert help

    Involve CRM experts and providers to help arrest the spread of bad data, limit bad quality data practices, and deploy comprehensive data management models. Getting external support and help ensures that no sources of bad data are overlooked. Furthermore, specialized professionals bring with them the knowledge of the best data hygiene strategy and industry best practices.

    What are some best practices in CRM database management?

    Consistent data policies and processes for all teams

    All the teams that interact with the data must be trained to follow standard operating processes with precision. Every individual who builds, manages, or uses the database must be absolutely clear regarding their role in maintaining the quality of the data.

    Deploy easy-to-use CRM tools and solutions

    Go for user-friendly and intuitive CRM solutions that minimize errors and mistakes. Train all employees and managers on how to input and process data in these systems and make it a part of the onboarding process for vital roles in the relevant function.

    Use automation tools to reduce human errors

    Leverage digital tools and AI-powered solutions to simplify data input and editing processes. This not only saves time and resources but also reduces the error rate. Many smart CRM solutions assist in data cleaning as well.

    Establish data quality standards and implement them

    With the help of talented experts, design a suitable data management model and implement it uniformly across the organization. Keep updating this model to include the latest best practices and respond to new data-related challenges.

    Read this case study to know how Denave’s CRM tool helped maximize revenue.

    The importance of good quality data in CRM: The conclusion

    Data is the fuel for all modern day business processes and functions, and customer relationship management is no different. The use of accurate, reliable, and valid customer data can help bolster the productivity and efficiency of internal CRM processes and help businesses grow sustainably. It is critical to maintain the quality of data used for business processes as bad data can have serious ramifications on the company’s bottomline, customer engagement, and future. Using the right set of tools and practices, businesses can maintain customer databases effectively and ensure that the data serves its purpose.

    CTA: Get in touch with us to know more about Denave’s expert CRM and Lead Management Systems and how we can help your business grow.

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