Komo AI

Shared search · Sep 12, 2026

How can I prepare Last Database data for CRM import?

To prepare Last Database data for CRM import, use this workflow:

  1. Export the data

    • Export the required tables as CSV or Excel.
    • Include a stable unique identifier, such as customer ID or email address.
    • Keep a backup of the original export.

  2. Keep one record per row

    • Use one clear header row.
    • Put each field in its own column; do not combine multiple values in one cell. 8

  3. Clean and standardize the data

    • Remove duplicate contacts or companies.
    • Correct invalid email addresses and phone numbers.
    • Standardize capitalization, country codes, dates, currencies, and address formats.
    • Remove unnecessary columns and blank rows.
    • Ensure each column contains only one type of data. 268

  4. Match the CRM’s field structure Create a mapping such as:

    Database columnCRM field
    first_nameFirst Name
    last_nameLast Name
    emailEmail
    phonePhone
    company_nameAccount/Company
    customer_idExternal ID

    Map every source field to the correct CRM field and convert values to the formats the CRM expects. 23

  5. Separate related records if necessary

    • Prepare separate files for companies/accounts, contacts, leads, and opportunities when the CRM requires them.
    • Preserve matching IDs so contacts can be linked to their companies and opportunities.

  6. Check required fields

    • Identify mandatory CRM fields.
    • Fill missing values where possible or remove records that cannot be imported.
    • Confirm picklist values exactly match the CRM—for example, use Active rather than active if that is the accepted value.

  7. Test the import

    • Import a small sample first.
    • Check field mapping, duplicate handling, relationships, dates, ownership, and special characters.
    • Test imports help identify problems before the complete migration. 3

  8. Run the full import and verify it

    • Review import-error logs.
    • Compare record counts with the original database.
    • Sample records manually and confirm that contacts, accounts, and related data were linked correctly. 12
    • Keep the original export until the CRM data has been fully validated.

A practical final checklist is: CSV format, UTF-8 encoding, one row per record, one header row, standardized values, no duplicates, required fields populated, mapped columns, and a tested backup.

Related

How can I use Last Database records in a spreadsheet?

You can use Last Database records in a spreadsheet by exporting them—ideally as a CSV file—and then importing that file into Excel or Google Sheets.

In Excel

  1. Export the records from Last Database as .csv or .xlsx.
  2. Open Excel and select Data → From Text/CSV.
  3. Choose the exported file.
  4. Confirm the delimiter—usually comma—and the text encoding.
  5. Select Load to place the records into a worksheet. Excel supports importing CSV and other delimited text files through this workflow. 1
  6. Convert the imported range to a table with Ctrl+T so you can filter and sort it easily.

In Google Sheets

  1. Open a new spreadsheet.
  2. Select File → Import → Upload.
  3. Upload the CSV file.
  4. Choose Create new spreadsheet or Insert new sheet.
  5. Confirm the separator and import the records.

Useful spreadsheet tasks

  • Filter records: Show only customers, dates, statuses, or categories you need.
  • Sort records: Sort by date, name, amount, or record ID.
  • Find duplicates: Use the database ID or email address as the unique value.
  • Calculate totals: Use formulas such as SUM, COUNTIF, or SUMIF.
  • Summarize data: Create a pivot table for totals by customer, month, status, or product.
  • Look up related information: Use XLOOKUP in Excel or VLOOKUP/XLOOKUP in Google Sheets.
  • Preserve IDs: Format fields such as customer IDs, ZIP codes, and phone numbers as text so leading zeros are not removed. Excel may otherwise interpret CSV columns using its default data-format settings. 1

Recommended layout

Keep the original import unchanged on a sheet named Raw Data, then create separate sheets for:

  • Clean Data
  • Reports
  • Charts
  • Lookup Lists

Use one record per row, one field per column, and a single header row. Save a copy of the original export before editing it.

If you intend to send the spreadsheet back into a database or CRM, do not rename or delete required columns, and keep a unique record ID so existing records can be matched rather than duplicated.

Related

How can I manage a large Last Database file?

To manage a large Last Database file, avoid opening and editing the entire file directly in a spreadsheet if it is slow or exceeds spreadsheet limits.

Recommended workflow

  1. Keep an untouched backup

    • Save the original file as read-only.
    • Work on a copy and record the export date and source.

  2. Check the file size and row count

    • Excel supports up to 1,048,576 rows per worksheet.
    • Google Sheets has a 10-million-cell limit, and performance commonly declines with very large or formula-heavy sheets. 24

  3. Use a database for the master copy

    • Import the data into SQLite, Microsoft Access, PostgreSQL, or another database.
    • Databases are better for large files because you can filter and aggregate only the records needed instead of loading everything into memory. 2
    • Add indexes to frequently searched fields such as customer_id, email, or date.

  4. Split the data logically If you must use spreadsheets, separate files or tabs by:

    • Year, month, or quarter
    • Region or business unit
    • Customers, contacts, orders, and transactions
    • Active versus archived records

    Keep the same column headers and preserve a unique record ID in every section.

  5. Create a raw-data layer

    • Keep imported records in a Raw Data sheet.
    • Do calculations and reports on separate sheets.
    • Import only the columns and rows required for the current analysis; keeping raw data separate and querying smaller working ranges reduces spreadsheet workload. 4

  6. Reduce spreadsheet overhead

    • Remove unnecessary columns, formatting, images, and blank rows.
    • Avoid merged cells in the data table.
    • Replace thousands of repeated formulas with pivot tables, Power Query, or efficient query formulas.
    • Use Excel’s .xlsb format where appropriate to reduce workbook size and improve handling of large files. 6

  7. Process in batches

    • Import or update records in manageable batches, such as one month or 50,000–100,000 rows at a time.
    • Test each batch before continuing.
    • Export filtered summaries rather than the complete database whenever possible.

  8. Validate after cleanup

    • Compare record counts before and after processing.
    • Check duplicate IDs, missing required fields, invalid dates, and broken relationships.
    • Keep an error log and retain the original backup.

When to stop using a spreadsheet

Move the file permanently to a database or data warehouse if it:

  • Exceeds the spreadsheet’s row or cell limits
  • Freezes or crashes during filtering
  • Requires many users to edit it
  • Contains linked tables or millions of transaction records
  • Needs frequent updates or automated CRM imports

The safest setup is: database as the master copy, spreadsheet as a filtered analysis or reporting layer.

Related

How can I divide Last Database records into smaller segments?

How can I use Last Database data for different marketing campaigns?

Use your Last Database as a central contact list, then create targeted campaign segments instead of sending the same message to everyone. Database segmentation divides contacts into groups with shared characteristics, enabling more relevant campaigns and easier performance measurement 14.

1. Clean and prepare the data

Before segmenting:

  • Remove duplicate contacts.
  • Standardize country, industry, job title, and date fields.
  • Verify email addresses and phone numbers.
  • Add fields such as:
    • Customer or prospect status
    • Product interest
    • Location
    • Industry or company size
    • Purchase history
    • Last interaction date
    • Engagement score
    • Marketing consent and unsubscribe status

Keep a unique contact ID so records can be traced back to the original database.

2. Create useful campaign segments

SegmentExample criteriaSuitable campaign
New leadsAdded within the last 30 daysWelcome and educational email series
Highly engaged prospectsOpened, clicked, downloaded, or attended an eventProduct demo or sales campaign
Inactive contactsNo interaction for 90–180 daysRe-engagement campaign
Existing customersPrevious purchase or active accountRenewal, support, or loyalty campaign
Product-interest groupsViewed or purchased a particular productProduct-specific promotion
Geographic groupsCountry, city, or sales territoryLocal events or region-specific offers
Business groupsIndustry, revenue, company size, or roleB2B industry-specific messaging
High-value customersHigh purchase frequency or lifetime valueVIP benefits, upgrades, or retention offers
Lost opportunitiesPreviously contacted but did not purchaseNew offer, case study, or follow-up campaign

Common segmentation approaches include demographic, geographic, firmographic, behavioral, technographic, and psychographic data 4. Event attendees, lead scores, product lines, and lost opportunities are also practical campaign segments 3.

3. Define an audience rule for each campaign

For example:

Include contacts where country = UK, product_interest = Software, and last_activity < 90 days; exclude unsubscribed = Yes and customer_status = Current Customer.

Use both inclusion and exclusion rules. Exclusions prevent customers from receiving prospect promotions, inactive contacts from receiving excessive messages, and unsubscribed contacts from being contacted.

4. Match the message to the segment

Do not merely change the recipient list. Adapt the:

  • Subject line
  • Offer
  • Landing page
  • Call to action
  • Send time
  • Follow-up sequence

Examples:

  • New leads: “Here’s how to solve [problem]”
  • Existing customers: “Get more from your current plan”
  • Inactive contacts: “Would you still like to hear from us?”
  • High-value customers: Early access or an exclusive benefit
  • Product-interest segment: Benefits and use cases for that specific product

5. Export or connect each segment

You can manage segments using:

  • Saved database queries
  • Separate spreadsheet tabs
  • CSV exports for an email platform
  • CRM or marketing-automation audiences
  • Dynamic segments that update automatically

Marketing-automation tools can filter contacts into or out of campaigns and use engagement or lead-stage data to trigger appropriate content 3. If your platform supports it, use dynamic segments rather than repeatedly exporting lists manually.

6. Test and measure separately

For every segment, track:

  • Delivery and bounce rate
  • Open rate
  • Click-through rate
  • Conversion rate
  • Unsubscribe and complaint rate
  • Revenue or qualified leads
  • Cost per conversion

Measure each segment separately; this shows which audiences respond best and helps you adjust targeting and messaging 5. Start with a small test group, compare two subject lines or offers, then send the stronger version to the remainder.

Recommended structure

Maintain one master database with fields such as:

text
1Contact_ID
2Name
3Email
4Location
5Industry
6Customer_Status
7Product_Interest
8Last_Purchase_Date
9Last_Engagement_Date
10Lead_Score
11Consent_Status
12Campaign_Status

Then create campaign views such as:

text
1Campaign_Audience =
2Customer_Status = Prospect
3AND Product_Interest = Product A
4AND Consent_Status = Yes
5AND Unsubscribed = No

Do not create permanent duplicate databases for every campaign. Keep one clean master file and generate temporary, clearly named segments such as ProductA_Prospects_Sep2026.

Finally, use only data for which you have an appropriate legal basis and honor consent, opt-outs, and applicable privacy and marketing regulations.

Related

How can I get the best value from Last Database data?

How can Last Database help discover new business opportunities?

How can Last Database support a growing sales operation?

Last Database can support a growing sales operation by turning prospecting data into a repeatable, measurable sales process:

  • Standardize your ideal-customer targeting. Filter prospects by country, city, industry, company name, job title, technology, and other available company attributes instead of letting each rep build lists differently. 59
  • Build focused territory and account lists. Create separate segments by geography, vertical, company size, role, or technology so managers can assign territories and reps can run specialized campaigns.
  • Give reps faster access to decision-makers. Use business email, phone, executive-contact, and company data to reduce manual research and increase selling time. 510
  • Enrich your CRM. Add missing contact, company, industry, location, role, and technology information to existing accounts; deduplicate records and establish rules for ownership, status, and follow-up. Last Database describes its data as usable for CRM-enrichment workflows. 612
  • Automate lead sourcing. Its REST-style API supports programmatic searches for email, phone, and fax records using country, city, industry, and keyword filters, returning JSON for internal tools or CRM workflows. 9
  • Scale gradually with usage limits. The API currently caps results at 100 records per request; listed plans range from 100 requests per day on Free to 10,000 on Pro and 100,000 on Enterprise. 9 Use smaller test segments first, then increase volume as conversion data justifies it.
  • Create a measurable outbound engine. Track delivery, reply, meeting, qualification, opportunity, and revenue rates by segment, source, rep, and message. Invest more in segments that produce pipeline—not merely the largest lists.
  • Improve handoffs and forecasting. A shared, enriched account database helps sales development, account executives, marketing, and customer teams work from consistent records and identify expansion opportunities. 2

A practical rollout

  1. Define your ICP and exclusion rules.
  2. Pull a small sample for two or three priority segments.
  3. Validate fields, remove duplicates, and sync only useful records into the CRM.
  4. Assign leads using territory, industry, or account-owner rules.
  5. Run personalized, compliant outreach sequences.
  6. Review conversion and data-quality metrics weekly.
  7. Refresh records and expand only the segments that generate profitable pipeline.

Use appropriate consent, suppression, opt-out, and regional privacy controls; Last Database itself recommends compliant email and marketing practices. 5

Related

Explore
Our CRM data governance framework | by Willem Koenders | ZS ...mediumHubSpot Data Model Updates: What New CRM Flexibility Means ...linkedinSales Data Management SystemslinkedinB2B Databases: Why They Are Essential for Lead Generation Successlinkedin
Empowering Lead Management: How Data Engineering & Reverse ...mediumcrmlinkedinPowering Lead Management: Why Data Engineers Matter in Sales ...mediumlifecyclemarketing #crm #martech #systemsthinking | Harshita ...linkedin

Ask your own follow-ups

Komo answers with sources. Pick up this thread in your own history and keep digging.

How can I prepare Last Database data for CRM import? · Komo