Vous analysez les données pour prendre des décisions de vente. Comment assurez-vous son exactitude et sa fiabilité ?

Actually, I find the following to be the most important - maintaining accurate and up-to-date customer profiles in CRM for tracking revenue. This will ensure that every transaction is correctly attributed to the right customer, providing clear data for analysis and developing targeted marketing strategies, and identifying key revenue drivers. Discrepancies in customer addresses or the presence of multiple, incorrect profiles can lead to significant reporting errors. Such inaccuracies can result in misguided business strategies, wasted marketing efforts, and an overall skewed understanding of the business’s financial health. Incorrect reports not only hinder strategic decision-making but also affect the credibility of the business’s data.

Data sources include internal sales data, which provides insights into past sales performance, customer behavior, and product trends. CRM systems are vital as they track customer interactions, sales pipelines, and account histories, enabling sales managers to understand customer needs and tailor their strategies accordingly. Market research and industry reports offer external perspectives on market trends, competitor activities, and potential opportunities. Financial data, including revenue, profit margins, and cost analysis, helps in assessing the financial health and performance of sales initiatives.

CRM’s are only as good as the information we put into them so It is crucial to have all team members on the same page with regard to the proper use of the tool. A healthy relationship with the CRM as a whole makes managing easier as we can trust the data and hold individuals accountable that need support.

Imagine running a company with everyone using different sales spreadsheets. A single, unified data source ensures clarity and consistency, fostering trust and reliable decision-making across your organization.

1-Establish Reliable Data Sources: identify and leverage trusted data sources, such as CRM systems, sales automation tools, and market research data. 2- verify Data Quality: Implement processes to verify data quality, including data cleansing, standardization, and duplicate removal. 3-Utilize Data Validation Tools: employ data validation tools to check the accuracy and integrity of data. This can include range checks, data type checks, and consistency checks. 4-Conduct Regular Audits: perform regular audits to assess data accuracy and integrity. This helps identify and correct any discrepancies. 5-Provide Training and Guidance: train users on data entry best practices and provide clear guidelines on how to maintain data accuracy.

Eliminate Duplicates: Locate and remove duplicate records. Fix Errors: Correct inaccurate or incomplete data entries. Unify Formats: Ensure data uses a consistent format (e.g., dates, currency).

Removing duplicates: identify and eliminate duplicate records to prevent skewed analysis. For example multiple entries of the same sales can inflate revenue figures. Handling missing values: implement strategies to address missing data like imputing missing values with average or medians or using predictive models ti estimate missing data points. Standardization: ensure data is in consistent format ( e.g: currency, units of measurement) to facilitate accurate analysis. This normalizing categorical variables and ensuring numerical data is within expected ranges. Outlier detection: identify and investigate outliers to determine whether they are errors or significant data points. Removing erroneous outliers ensure the data is integrity

Making sure your data is accurate and reliable starts with good data cleaning. This means getting rid of duplicates, fixing mistakes, and filling in any missing pieces so your data is complete and correct. Check your sources and cross-reference the info to spot any inconsistencies. Using automated tools can make this easier and cut down on errors. By focusing on data cleaning, you set up a solid base for accurate analysis and smart sales decisions, leading to better results.

Data Cleansing: Identify and correct errors or inconsistencies in the data. This might involve removing duplicates, correcting typos, or filling in missing values.

When we get data- we need to check from which source we got this data- spend little time in checking raw data- you will be able to make out any error and then once you are satisfied with accuracy of data- you can go ahead with decision making...

As a Territory Executive in the tobacco industry, historical sales data was crucial for navigating market complexities. During a previous role, a surge in sales preceding the national budget in Bangladesh sparked initial excitement. However, past trend analysis revealed similar spikes tied to pre-budget product hoarding due to anticipated price increases. This historical insight validated then current data and guided strategic adjustments, enabling us to capitalize on opportunities while proactively preparing for market fluctuations.

Building on data without considering historical context can lead to significant business failures. Imagine a procurement manager isolated in a cave, cut off from all information, who decides to evaluate market requirements for vitamin C supplement tablets based solely on sales figures from the COVID-19 pandemic year. This approach is akin to a smartwatch on a recently amputated arm displaying a congratulatory message for losing 70 kilograms of weight—completely missing the critical context.

Numbers tell a story. Comparing past data vs present provides the opportunity to both identify trends as well find opportunities and challenges faced by the business previously creating new windows of opportunity.

Completely understanding your sales data and noticing trends will help you set accurate sales goals and forecast your pipeline correctly.

Obtaining real-time qualitative input in the field is essential to test the sales data. As quantitative input is always backward looking, such data is no guarantee for future sales. Sources as market research, ad boards, co calling, attending sales trainings and organizing sales and marketing workshops are some of the realtime tools which have proven valuable . Inputs can be used to direct marketing strategy, discover new challenges and opportunities in sales and fine tune the tactics and messaging. The proof of the pudding is in the tasting !

Ensure your data is current and reflects recent trends and customer behavior. Outdated data can lead to misleading conclusions.

By keeping an eye on your sales in real time you can make quick adjustments if a problem arises. If you don’t keep a close eye on your sales you will lose deals quickly and will not be able to save them.

One of the most important aspects of data verification, often overlooked, is ensuring the data is initially complete. Depending on the decision's significance and the industry's complexity, it may be necessary to involve a consulting panel to provide additional insights. For example, in the complex real estate industry, input from government officials, mortgage experts, building materials industry leaders, and seasoned economists is essential to evaluating forecasts in light of current and future trends.

It's important to verify data in the sales process to ensure accuracy, enhance customer relationships, improve targeting and segmentation, increase conversion rates, and maintain compliance. This ultimately leads to better decision-making, operational efficiency, and customer satisfaction, all crucial for achieving sales success.

I love data, but ensuring its efficiency and accuracy is paramount. Involving stakeholders in the data analysis process is crucial. Their qualitative insights provide valuable context that can balance and validate quantitative data. Sales team members, for example, offer perspectives on why certain figures may not match expectations. This feedback loop identifies blind spots, combining data precision with human experience. Sometimes, relying on qualitative input is better for specific targets, and AI can further exploit these insights for robust sales decisions.

In retail sales, engaging stakeholders in data analysis is crucial for holistic insights. Sales teams, with their daily customer interactions, offer qualitative viewpoints that supplement quantitative data. Their insights uncover subtleties such as customer preferences and evolving market dynamics, which numerical data may overlook. This collaborative method deepens comprehension of sales trends, enriches decision-making, and integrates data-driven insights with frontline expertise. It ensures strategies are adaptable and informed by real-time conditions, especially in scenarios like prolonged store closures or shifts in consumer behavior and market boundaries.

Document Your Process: Keep a record of the steps you take to clean and analyze the data. This helps ensure transparency and reproducibility. Data Skewness: Be aware of potential biases or skews in the data that might influence your analysis. Consider alternative perspectives and data sources. Use Multiple Data Sets: Triangulate your findings by using data from various sources to get a more complete picture.

First check data source If it is correct or not Don't get stuck with Garbage in- Garbage Out issues Don't jump to nh conclusion quickly Check for ground realities as well

Apart from the reliability of the data and its sources, the pool it was obtained from is also key. Firstly the demographics/location/size of the pool are important. (20% of 40 people is only 8 people. Not a large pool by any means). (All from one neighborhood also not the same). Secondly, clarity on what one is looking for & why, in the data being sifted through.

Always keep clean records and always update your data will improve and shorten your sales cycle . Having your data accurate will improve your sales forecast.