Central support in data analysis, data set, or database, is the scientific data tool No. 1. Its popularity is growing with automatic learning needs (automatic learning): platforms provide data sets to train AI models. Regarding commercial needs, data sets serve to marketing and commercial interests. It is thanks to a database that is possible to refine the customization of its marketing campaigns, for example. The software allows you to create data sets with your CRM data.
Marketing and performance management strategy :Â the complete guide
What is a data set ?
A data set is an organized data set, structured in the form of a table. The data set, or the data set, is the basic support to analyze and use data. Companies use it for marketing and commercial purposes, and engineers use it for automatic learning.
This is how a data set can be seen :
This extract comes from the DVF set of public data (application for land values) produced by the General Directorate of Public Finance. In this example, an analysis of the data set can be related to real estate prices per square meter in the neighborhood. For a real estate agency, this analysis can help refine your sales price estimates.
What types of data are more in a data set ?
The figures and text are the most widespread data types in data sets used by companies for commercial and commercial purposes. A company that uses a B2C data set for marketing purposes, for example, has a list of customer names with their contact data, its purchasing history, the billing (CA) generated, its consumption preferences and its commitment to social networks.
To facilitate analysis and exploitation, the data format is ready to apply conditional filters. Boolean data, in particular, are very suitable. In the example, it is interesting to create a column that indicates “Yeah” either “No”depending on whether or not the customer bought a brand product. A filter in this column allows you to go to marketing campaigns: only customers who bought this product receive recommendations for additional products.
Frequent figures and text are often found in data sets, but all other data types can appear there: images, videos and audio. These data types are used in particular, in the context of automatic learning, to cause models. Example: In this extract of the hug face set, audio data can cause a model to recognize different accents or speak with different accents.
In what cases is a data set used ?
Data sets are used by the State to produce statistics. In other diverse fields, a database is used to highlight probable links (analysis) and deduce forecasts (exploitation).
A weather database, for example, highlights a link between the temperature and the period of the year, and a tendency to lose x degrees Celsius per year: it is possible to predict the temperature, in a certain period, for several years.
The data set, in this example, is useful for farmers. You could also imagine a pathist that uses this data set to schedule its client reminders for the annual chimney sweep.
Here are three frequent use cases of data sets.
Use a data set to aim your marketing actions
In marketing, a data set is, in a way, a gross version of the data extracted from the customer file. With data set software, this data extraction can be represented in a very readable and active format.
Data games are very useful to attack their marketing actions. The data analysis allows you to segment your audience and then communicate in a personalized way with each segment.
Use a data set to prospect in B2B
Commercial data is not considered personal data, they do not benefit from the protection by the GDPR: some are publicly accessible. Website offers data sets for prospecting purposes. These bases have demographic data, contact stockings, financial data and competitive data on a business list. On this basis, a company can prospect its target heart.
The exploitation of data, in this case, consists in using the means of contact of companies whose financial data corresponds to the prospecting criteria.
Use a data set for automatic learning
A data set is essential to cause a predictive model. The mechanism is logical: the model reads a colossal volume of data and deduces that if “this”so “that”. This learning allows you to execute orders: with each occurrence of “this”, “this” It happens.
Where to find data sets ?
It is possible to create a data set with their own data, extracted from surveys, forms, databases and CRM. There are also public data, which provide public data. Before using a database found online, you must verify your reliability in two aspects:
- Have the data have been obtained and disseminated legally and ethically, condition sine qua no Have the right to exploit them? Finding online a thrombinoscope of a large business school does not allow us to use it, for example, to the extent that it contains personal data.
- Are the right data? Analyze and exploit an incorrect, biased or even incomplete data set that distorts the results, which makes the exercise useless.
The data sets disseminated by public organizations, a prioriThey are reliable. The new companies also specialize in data sets for automatic learning: their online reputation constitutes a trust index.
Here are reliable sources where to find public data sets, download in CSV, JSON or SQL formats:
How to create a data set ?
A data set can be created very simply on an Excel spreadsheet, manually. Data set software automates the creation of data sets, when data volume is important. For optimal use, the software must be able to access CRM data or be integrated into it.
1 – Identify the necessary and relevant data
A data set meets an objective: generate statistics, understand a trend or even make forecasts. It is necessary to specify your objective, identify the necessary and relevant data.
Take a very concrete example. A B2B company wants to know if your blog promotes the conclusion of transactions, to decide whether or not to continue your marketing efforts to create content. To answer this question, she needs to link different data:
- The list of transactions concluded
- The list of contacts associated with transactions
- For each contact, activity in the blog
2 – Search or collect data
Data sources vary according to the objective. If the analysis is related to public data, we must go and look for them in the specialized press, in university documentation or even in public data. The creation of a data set for the analysis of your customer portfolio requires collecting data, using forms, sales data, a web analysis tool and a CRM that centralizes all the information.
3 – Enter the data in a structured table
Once the data set data has been identified and compiled, it is necessary to organize them to easily read the requested information. At this stage, it is advisable to refine the data using properties, conditions and filters.
In the example of the data game to analyze the marketing efficiency of a blog, the company may consider that the blog plays a role in decision making according to the number of articles consulted. Therefore, it is necessary to add a property for each contact: number of pages of the blog consulted. It is also possible to add a property related to the billing generated by each contact.
Add conditions then allow the requested information to be highlighted:
- If the number of blog pages consulted is greater than X, and if the client generates a higher turnover than y, then the blog is effective.
- If the number of pages is greater than x, and the billing is lower than and then the blog is indifferent.
- If the number of pages is less than X, and the billing is greater than y, then the blog is useless.
With a filter system, it is possible to obtain the proportion of customers for whom the blog played in the purchase decision. With the data set software, the results of the analysis can be done in the form of very legible graphics, of this type.
To go further, discover how to develop an effective marketing strategy by consulting the Ultimate Marketing Guide or discover Free Hubspot Marketing Software
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