Whether Star Wars fans like it or not and this cover, 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. Source: e-marketing.fr
Among the different data types used to increase its turnover and achieve its commercial objectives, the contact information 👤 plays an essential role: lists of prospects, leads, customers...
Their sources are multiple (company site, call centers, sales teams), and this data will only be concentrated in Excel spreadsheets in a second step, with very variable quality.
Accurate and sufficient information, however, ensures real added value in the business relationship.
The effectiveness of your prospecting will be directly impacted, from the personalization of emailing campaigns to the follow-up of the customer relationship, thanks to consolidated information.
Shit in, shit out 💩
The use of data is becoming more and more democratic 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. This standardized and validated information makes it easier for companies to find existing accounts (...)
Adequately addressed will enable IT, sales, financial services, or marketing teams to make the most of their data in their professional context. All this is only possible if they are fed with quality data!
It is indeed on the quality of the raw material 💎 that its proper downstream use will depend: it is impossible to build anything viable without a solid and healthy base!
Reporting tools are another excellent example: they only reach their full potential if the data is clean ; hence the famous adage in data processing: "shit in, shit out".
This type of tool represents an expensive investment that will not pay off if we are not careful how to use them properly. 💸
Ten years later, we often feel the same disappointment regarding big data because nothing has changed for data processing: algorithms need more than ever "clean" data to work.
A considerable waste of time ⏰
Regardless of the source of data inaccuracies, correcting them is an additional workload that could be eradicated.
This is the same ; whatever the sector, we spend hours on repetitive tasks with no added value 😩: they thus occupy 80% of the time spent on data processing in general.
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 thankless tasks and focus on the essential: your business!
Table of contents 👇
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! 🎁