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Timeline NumpyData

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23/03/2020

Business idea

We realized the need to automate tasks and improve our times in SEO processes (data extraction, modeling and reporting). From this point, the idea of creating what today is Numpy Data was born.

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12/05/2020

Idea ripening and methodology

After weeks of thinking about it, we began to develop the processes and logic that have allowed us to automate all those daily repetitive tasks.

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19/08/2020

Research and documentation

Once all the necessary automations have been implemented, the mathematical and algorithmic parts began. What to do? How to do it? And for what?

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27/09/2020

Entire system self-analysis and reflection

We realized the infinite possibilities of this system and began to redefine each process, optimizing it even more. Added further code and implemented new functionalities.

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19/10/2020

Beta y Testing

Once all the necessary automations have been implemented, we start with all the mathematical and algorithmic part: What to do, how to do it, and what for?
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15/12/2020

Service pre-launch

We decided to launch the service in the future software, to make it scalable and ensure full customer satisfaction.

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13/01/2021

 Update 1.1

All the backlinks considered as outliers of your URL and the URLs of your competitors are now clustered and classified properly (none of them escapes now!!).
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19/01/2021

Update 1.2 

All clustered and classified backlinks within Numpy Data as “Disawov” are now called “Poor Authority”, which means they are either toxic and it is advisable to disavow them, or they will just not pass on any link juice to your URL.
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20/01/2021

 Update 1.3

To make an easier interpretation of the clusters and to ensure a millimetric and precise measurement of “The Authority and Naturalness” of your backlinks profile vs the TOP 3, we have created in your Dashboard three new tabs containing tables

«Keywords in Clusters»

«Traffic in Clusters»

«Referring Domains in Clusters»

There will be a direct explanation to everything !!

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15/02/2021

Update 1.4

Index section rebuild: we have included «Geo Backlinks» to geolocate customer and competitor backlinks distribution by country.

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25/02/2021

 Update 1.5

Crawling Site section restructuring (I): Empty, duplicate, long and short titles. Empty, duplicate, long and short descriptions. Empty, duplicate, long and multiple H1. Images without ALT text, large images and pagination.

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30/02/2021

 Update 1.6

Crawling Site section restructuring (II): Sitemap (URLS Noindex in sitemap, URLS in sitemap with index and status code 200, 3xx, 4xx, and 5xx).

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06/03/2021

 Update 1.7

New «Headers» tab in the «Report On Page» section, with all your URLs and the competitors’ headers sorted by columns.

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14/03/2021

 Update 1.8

New «TF-IDF Title and H1» tab in the «Report On Page» section, with matrices (corpus) and percentage grouped bars so that you can optimize your title and H1 based upon the prominence/relevance of each of those important words in the corpus.

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20/03/2021

 Update 1.9

New “TF-IDF Description” tab in the “Report On Page” section, with matrix (corpus) and percentage grouped bars so that you can optimize your description based upon the prominence/relevance of each of those important words in the corpus.

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30/03/2021

 Update 2.0

New «Sentiment Unigrams Analysis» tab in the «Report On Page» section:

  • Table with TF of your text and the competitors’.
  • Unigram Polarity.
  • Unigram Subjectivity.
  • TF-IDF coefficient of your text Unigrams and bar-graph representation.
  • TF-IDF coefficient of your competitors’ texts Unigrams and bar-graph representation.
  • TF-IDF percentage of your Unigrams.
  • TF-IDF percentage of your competitors’ Unigrams.
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20/04/2021

 Update 2.1

New « Sentiment Analysis Bigrams » tab in the «Report On Page» section.

  • Table with TF of your text and the competitors’.
  • Bigram Polarity.
  • Bigram subjectivity.
  • TF-IDF coefficient of your text Bigrams and bar-graph representation.
  • TF-IDF coefficient of your competitors’ texts Bigrams and bar-graph representation.
  • TF-IDF percentage of your Bigrams.
  • TF-IDF percentage of your competitors’ Bigrams.
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