Picto Dropcontact - demi rond jaune et vertPicto Dropcontact - Shapes droite

Lead Quality: The Dark Side of Your Data

data quality is essential for a successful CRM

Whether Star Wars fans like it or not, data problems are an integral part of our galaxy.

The amount of data is constantly increasing. Yet organizations, regardless of their field of activity or size, do not realize the strategic importance of data quality.

On average, respondents believe they lose 12% of their revenue due to insufficient data quality, according to the Experian Marketing Services white paper.

Among the different data types used to increase revenues and achieve sales objectives, contact information 👤 plays an essential role: lists of prospects, leads, customers...

Their sources are numerous (company websites, call centers, sales teams), and data will only be gathered in Excel spreadsheets in a second step, with very variable quality.

the quality of the data is often highly variable

Accurate and sufficient information, however, ensures real added value in the business relationship.

The effectiveness of your prospecting will be directly impacted thanks to consolidated information: from personalization of emailing campaigns to the follow-up of the customer relationship.

Shit in, shit out 💩

The use of data is becoming more and more popular as solutions flourish to exploit, harvest, or visualize them.

According to Experian Marketing Services:
Software tools can be implemented to verify structured customer information, such as email addresses, postal addresses, and mobile phone numbers. Standardized and validated information makes it easier for companies to find existing accounts (...)

When data are used correclty, they enable IT, sales, financial services, or marketing teams to make the most out of them in their professional context. All this is only possible if they are using quality data!

your sales/marketing teams need to be fed with quality data!
Take the Gremlins: feed them the wrong way, and you might be disappointed with the result!

Indeed, the quality of the raw material 💎 is the most important when building a data strategy: it is impossible to build anything viable without solid and healthy foundations!

Reporting tools are another excellent example: they only reach their full potential if the data is clean ; hence the famous quote in data processing: "shit in, shit out".

This type of tool represents an expensive investment that will not pay off unless we use them properly. 💸

Ten years later, we often feel the same disappointment regarding big data because nothing has changed for data processing: algorithms need "clean" data to work more than ever.
Source: lespetitescases.net

A considerable waste of time ⏰

One statistic comes up regularly on the subject: "data scientists" spend almost 80% of their time selecting, cleaning and preparing data (New York Times 2014 and Forbes 2016).

Regardless of the source of data inaccuracies, correcting them is an additional workload that could be eradicated.

Whatever the sector, we spend hours on repetitive tasks with no added value 😩: they take up 80% of the time spent on data processing in general.

stoping wasting time - enjoy

We will, therefore, address the main themes in the area of contact data quality in a mini-series of articles that will be published in the coming days.

In each of them, we will present the solutions that allow you to get rid of these tedious tasks and focus on the essential: your business!

Table of contents 👇

Ep.1 - Cleaning

Ép.2 - Emails

Ép.3 - Data merging

Ep.4 - Enrichment

Did you like this article? Then do not hesitate to visit our site and test our solution:  🎁We offer you the first 100 contacts! 🎁

Most frequently asked questions

Why is data quality important?

On average, respondents think that 12% of their revenue is lost because of bad data quality, according to the Experian Marketing Services white paper. It is therefore crucial to have clean, usable and, most of all, updated, data.

What kind of data do company use to complete their contact database?

There are various sources of data: a company's website, call centers, and sales teams. Data are then stored and centralized in an Excel file or a CRM.

How much time is spent processing data on average?

Data scientists spend almost 80% of their time selecting, cleaning and prepping data - a considerable amount of time that must be reduced to focus your business efforts on sales.
Share this article

Let's Get Started !

Don't waste a single minute
on your CRM data!