¿Cuáles son las formas más efectivas de limpiar y transformar los datos desordenados de clientes potenciales?

I do almost all of my table work now with Clay, but if you don't have Clay - Airtable is another alternative. You can use Excel/Sheets functions but it's just way easier to visualise and work with those other tools.

When addressing the challenge of incomplete or messy lead data, I've found that combining information from various sources can be highly effective. This method helps fill in gaps where specific fields in the database might be missing. However, having the right IT resources, budget, and willingness to invest the time and effort needed to make this happen is crucial. It's more than just rectifying data errors; it's about understanding the underlying reasons for these gaps. This perspective can reveal valuable insights, like emerging trends or market segments. Cleaning and transforming lead data is an ongoing process, requiring constant attention and strategic thinking to enhance lead generation performance effectively.

How to Clean Data Identify data discrepancies using data observability tools. Remove unnecessary values. Remove duplicate data. Fix structural errors. Address any missing values. Standardize data entry and formatting. Validate and correct values against a known list of entities. Develop a data quality strategy.

Whenever working with lead data make sure you understand the demand and need of the campaign you are working on. Understand the ICP, Understand and research the prospect and get as much information as much possible before you approach the prospect. Do it for all the leads in your data. Ultimately you will see that irreverent leads are getting filtered

I might be a bit old school, but a good old countif function on excel has always helped me to de-dupe data :) that way you can merge any fields where you might have some data in some fields that is missing in others

The reflex to fill in missing lead data can be strong, but understanding the 'why' behind the gaps can transform our data handling. These missing values aren't just empty spaces—they're potential signposts pointing to trends, disengagement, or even new market segments. Before we turn to data enrichment tools or deletion, let's consider the narrative these blanks may be crafting.

The key to ensuring you have few if any missing values is both to prepopulate data capture fields and to make them mandatory. It's best to prepopulate a mandatory field, which then enables the users simply to pick from a select list of options. By making the field mandatory, the data entry process stops until the lead chooses an answer. But keep the amount of such fields to a minimum! The more you ask for, even if prepopulated with information to choose from, the more friction you cause in your data entry process. You will get less participation, fewer leads. Next, for open fields, give instructions about how to reply. "In 5 words or less...", "What 3 items...", "What one idea..." Offer a starting point on how to answer the question.

CHANGE THAT HABIT: Takes 14 days or 66 days to change a habit depending on who you ask. If you have a team and your the manager and they have a team lead you can say "for 14 days I want all of you to send your list of what leads you wrote notes for to (Team Lead) and they are going to judge where you are falling short on your notes for the leads or if you are missing the notes altogether. This is our primary focus for the next 14 days. Then make a video for the team to go off of as a video SOP and train your team lead on what to look out for and correct the team on when they see there lists for the next 2 weeks.

There are easy formatting errors that can really throw off your data, but can also be easily fixed with simple scripts. For example, the phone number field. Do you use dashes? Do you include a country code? Do you have numbers separated by country format? When working with global teams, dates can also become unusable based on different date formats across the world. You need to take a standardized approach, or you risk messy data and confusion. Emails may be the most important part of your data. I have used many email verification tools (Kickbox is one I like a lot), and they can help you easily identify bounced emails which you can review for formatting errors or misspellings.

One of the most complicated problems to resolve in lead data quality is having to scrub and enhance the data before it may be reliable enough for dashboard display and decision-making. Depending on the size of your data set, these data transformations can only be done programmatically, through specialized algorithms and highly technical routines. Among the top software service providers in the industry you'll find Informatica Cloud Data Quality, DemandTools, and Dataloader.io, but you may also depend on Dunn & Bradstreet Optimizer for Data Management, which not only may clean but enrich your data with market information and demographics. Some of these routines could run in real-time, transactionally. Others require period data batching.

If you are struggling with clean data, leverage a 3rd party data provider who can give you clean, accurate data. Once you have the clean data, don't simply add it to your CRM - where the bad data already exists. Do a contact purge where possible (e.g. missing fields, bad emails). Otherwise it's like adding new fish to a dead pond. Everything will eventually die as well. Ensure LinkedIn URL for each contact is a mandatory field. People update LinkedIn far more often then they do CRM!

Set minimums for every team member to be scrubbing, cleaning, validating. Is it 5 added records a day? Is it 15 validated records a day? It’s one of the most necessary jobs that needs to be done. It’s also a grueling task. Everyone pitching in helps. A little at a time, each, isn’t such a mountain to climb.

Let someone else do this for you. Unless this is something you already have a skill set in, don’t waste your time on it. Hire a professional to clean up your data. They’ll know how to organize it and ensure everything is straightened up. Your time is better spent focusing on what’s most important in your own role.

Implement validation rules to ensure that lead data confirms to predefined patterns and is accurate. Use data verification services to validate email addresses, phone numbers and other data.

Having a standard template for lead list uploads will be helpful for you and your team, so that way there is a standard of the minimums needed to keep data clean. Supplementing with a third party data provider can then also give you more clean and accurate data. Same for any lead gen forms, which is most important if you don't have a third party provider, making sure you have the minimum information needed on your forms to help your database.