Overcoming 4 MSME Database Challenges for Better Sales Prospecting

Sales | 5 minutes to read

14th Jul, 2022

Data accuracy is an issue for many MSME enterprises, leading them to lose ground in the competitive field. Here are some common MSME database challenges that businesses need to fix for improved prospecting.


    • Inaccurate MSME database creates waste and affects profitability of sales prospecting campaigns

    • It is not hard to identify the factors which cause errors in the database and fast track the revenue cycles

    Poor or bad database is one of the valid reasons why prospecting for the Micro, Small, and Medium Enterprises (MSME) industry is daunting. Gartner believes that poor data quality costs enterprises an average of $15 million annually. Many MSME enterprise struggle with the accuracy of data for sales and marketing prospecting and lose ground on the competitive field. It leads to Higher consumption of resources, maintenance costs, inaccurate mail deliveries, increased churn, negative publicity, misinformed decisions, etc. In a nutshell, inaccurate MSME Database seriously dents revenue lifecycles by causing errors in the marketing automation and CRM systems.

    Culprits of Poor MSME or SME Data

    SME data is essential for businesses to have in order to make well-informed decisions; however, this data is not always accurate. There are several factors that can cause errors in SME data, ranging from a human error to technical difficulties. Here are some of the culprits of poor MSME or SME data:

    1. Poor Data Management Techniques:

    In many cases, businesses simply do not have the proper systems in place to effectively manage their data. As a result, information can easily become lost or corrupted, leading to inaccurate records. Another common cause of errors is entering data manually. This is often done in an attempt to save time, but it can lead to transcription mistakes that can be difficult to catch. In some cases, businesses may also input data into the wrong fields, causing further inaccuracies. To avoid these issues, it is essential to have a good data management system in place and to carefully review all data before it is entered into the database.

    2. Fast-changing Nature of SME Data: 

    SME or MSME data is fast changing. It changes as the business leaders change jobs frequently. This makes it difficult to keep track of the latest data and trends. However, by using robust data maintenance practices, businesses can keep track of their SME or MSME data easily and effectively. A data management system can help businesses to track changes in job title, company size, industry, etc. By tracking this information, businesses can stay up-to-date with the latest trends in the SME or MSME data landscape. In addition, businesses can identify potential opportunities and challenges that they may face on the path to prospecting.

    3. Disconnected Infrastructure:

    Gaps between new and old IT infrastructure lead to silos in  SME database. The IT infrastructure should be intelligently integrated when businesses trade data with thousands of business systems. Problems arise when cloud and on-premise systems use different data formats which are incomprehensible to each other.  This causes inconsistencies and errors in data as the smoother processing of data is disrupted. In order to avoid this, businesses need to make sure that their IT infrastructure is intelligently integrated from start-to-end. This will allow for smoother data processing and will help to avoid errors.

    4. Wrong sources of data: 

    Unreliable sources of data cascade errors in the entire SME database. This can have devastating consequences, as inaccurate data can lead to incorrect decision-making by sales and marketing teams. It is therefore essential that efforts are made to ensure that data regarding SMEs is sourced from reliable sources. There are several ways in which this can be done, such as verifying data with multiple sources, or only using data from sources that have a credible reputation of quality.

    The Average Cost of Poor MSME Data

    IBM believes that inaccurate SME data costs the entire U.S. economy a whopping 3.1 trillion every year. Gartner predicts that the average monthly financial loss caused by poor data is $9.7 million. The CRMs like HubSpot and Mail Chimp charge for every single contact. If half of the email ids pushed into the email campaign are inaccurate, companies end up wasting a lot of money on irrelevant targets.

    Poor data also inhibits B2B’s marketing success – It can cause loss of reputation and leads. As per the 1-10 rule proposed by George Labovitz and Yu Sang Chang, preventing bad data costs $1 while rectifying existing problems costs $10.

    Ways to Correct MSME database 

    Luckily, there are a few things that salespeople can do to correct an inaccurate MSME database:

    1. Deriving Data from Authentic Sources:

     It is important to leverage data sources that have a proven track record. There are several ways to identify authentic data sources. One is to look for data that reputable organizations have collected. Another is to look for data that experts have verified. Leveraging reliable data sources can help teams to make better decisions for the business.

    2. Robust Data Management Workflows: 

    Smart enterprises leverage a cohesive hybrid mix of automated digital tech and manual verification methods to collect and correct data. The collected data is distilled through multi-layered validation, whitespace discovery, profiling, and updation process. A combination of techniques helps in building a strong database that can be enriched after small intervals.

    3. Start-to-end integration: 

    Data integration eliminates silos by consolidating data from multiple sources into a single database. This can make it easier to generate reports and perform analytics, as well as reduce the amount of duplicate data. Data integration can be a complex process, but the rewards can be significant. By breaking down data silos, organizations can gain a better understanding of their operations and make better-informed decisions.

    4. Human Validation: 

    In order to ensure that the data is accurate and reliable, it’s important to have it validated by human beings. This means that someone with knowledge and experience in sales prospecting reviews the MSME database to verify its accuracy. This extra step may take a bit longer, but it’s worth it to ensure that the sales team is working with the best possible information.

    Watch this Video: Leverage these future forward strategies to build database for sales prospecting

    Experiencing Accuracy Issues in Database? Contact Denave

    As a global “Intelligent Data Services” vendor, we offer the most comprehensive and accurate MSME database which is built with the help of AI/ML based bot profiling with manual reverification methods. Our database is based on advanced:

    • Whitespace Discovery Techniques Increasing market penetration and net new customer acquisition
    • Data enrichment / maintenance capabilities Cleansing and enrichment of data for improved sales prospecting
    • Data Analytic Techniques — Illuminating new insights for improved ROI

    We offer both pre-packaged and custom curated lists, as per diverse requirements. Our team can help you discover your ideal customer profile with the help of historical sales propensity analytics.

    We would love to talk to you about how we can help you achieve your sales and marketing goals. Would you be interested in scheduling a call? Contact us

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