7 Best Practices to Cleanse Sales Lead Data

1. Establish Sales and Marketing Data Cleansing Rules

For accurate data cleansing, you should set rules that ensure that the data is correct, consistent and useable.

For example, ensuring customer’s postal codes and state codes agree, making sure the addresses used for marketing campaigns are all standardized the same way, etc. The fields you will want to set rules for can be unique to your business; but it may include: job title, role, email ID, Mobile number, industry, revenue, etc.

• Identify and leverage the best, from a wide array of data cleansing techniques.
• Assess sales datasets where majority of errors occur.
• Do a root cause analysis to chart out an action plan to reduce/eliminate those errors.
• Define data quality metrics relevant to marketing campaigns, and monitor improvements.

2. Prioritize Data to be Cleansed

With marketing data pools getting bigger, chances of erroneous data and inaccuracies increase. Identifying and rectifying errors from entire marketing data is challenging. It’s time to prioritize customer lead data that needs cleansing and enrichment.

• Identify the set of marketing data which needs cleansing. It can be the one collected from disparate sources or manually entered by your sales and marketing teams; or a combination of both.
• Update the sales and marketing data cleansing plan; as per changing data quality and company priorities.
• Start small and make incremental changes. Repeat the process several times for continued improvement in the sales data of priority leads.

3. Check Data at Source for Mistakes

Fixing the marketing data before it becomes an erroneous entry in your database will save you hours of time, stress and dollars down the line. First step is to identify data entry errors, statistics and patterns.

For example, if you are required to enter data from customer survey or feedback forms with too many handwritten fields; you are more likely to get data quality issues in such data. So if your business is constantly producing data from such resources forms; it is crucial to fix mistakes in data at source.

4. Manage Data & Duplicates

In spite of stringent checks, if duplicates percolate in your marketing data; actively detect and remove them. Once you are done with deduplication, here is the sales and marketing data cleansing checklist:

• Data validation: Check accuracy and quality of source data before processing it.
• Standardizing the data: Ensuring same type of data exists across all columns in the marketing database.
• Normalizing the data: Check if all your sales contact data is recorded consistently.
• Merging the data: Combine relevant sales and marketing data scattered across multiple datasets in a new file.
• Aggregating the data: Sorting and expressing the data in a comprehensive manner.
• Data filter: Narrowing down the data to include information that is relevant to sales team.

5. Append Data

Appending or adding missing sales lead data to current customer contact information will grow your database; but with outdated, incorrect or duplicate data.

But appending data followed with validation, standardization, normalization etc., will help you reach correct customers, create better engagement, maximize retentions, and optimize results of your marketing strategy and improve sales. To avoid bad consequences of ‘dirty data’, cleanse and append your sales and marketing data regularly.

6. Implement Automation

Your plan to improve the quality of your marketing database is developed, metrics to measure data accuracy are defined, data duplicates and errors are removed, and overall database is enriched with real time data. The next step is to automate data cleansing process to achieve data governance.

• Automated data cleansing solutions search for data anomalies and discrepancies through algorithms or lookup tables.

• Algorithms can be scheduled with data cleansing scenarios specifically tailored to your business needs.
• Software bots used for automated data cleansing, run at intervals without human intervention.
• Data cleansed by programmable bots helps build consistent view of customers, vendors, products, locations etc.

Automation ensures real time data cleansing which helps you with clearly defined sales approach, business intelligence, master data management etc. It also helps you adhere to data security parameters, compliance guidelines and of course the data standards of the industry you belong to. Automation reduces human intervention, hence errors and streamlines the overall marketing database scrubbing activities.

7. Measure Data Accuracy

This is a tricky task as the answer to data accuracy cannot be a “Yes” or “No”. With geo-coding locations, accuracy is a matter of degree (or minutes and seconds). Whereas when recording names, accuracy is all about “correct” spelling; and what the individual says is correct, irrespective of any standards or rules.

Measuring and managing data accuracy without understanding the context in which sales and marketing department is going to use it; is nearly impossible. Invest in the time, tools, research, and hire data cleansing companies if necessary to measure the accuracy of your data in real time.