Explanatory analysis: understanding the past to act with confidence
Explanatory analysis involves analysing historical data to identify the causes of a past event. Its aim is to answer the question: Why did it happen?
Use case by Inflow: improving sales through explanatory analysis
Let’s imagine an e-commerce company that noticed a sudden drop in sales in the last quarter. Using explanatory analysis, it can :
- Identify whether this drop is due to seasonality, a change in consumer behaviour or an increase in competition.
- Study the impact of previous marketing campaigns and adjust future strategies.
- Gain a better understanding of the customer profiles that have stopped buying and develop re-engagement actions.
Explanatory analysis tools and methods
A number of tools and methods are used to carry out effective explanatory analysis, including:
- Statistical analyses: these enable trends to be detected and correlations between different variables to be identified.
- Business Intelligence (BI) tools: platforms such as Power BI or Tableau make it easier to visualise data and highlight key insights.
- Causal analysis: this helps to understand the cause-and-effect relationships between different factors that have influenced an event.
By applying these techniques, Inflow helps its customers to isolate the key factors that have impacted their performance, so that they can adopt decisions based on concrete facts.
Predictive analysis: accurately anticipating the future
While explanatory analysis focuses on the past, predictive analysis seeks to anticipate the future. It uses statistical models and machine learning algorithms to detect trends and make projections about what might happen.
Use case by Inflow: predicting market trends
Let’s take the example of a company in the fashion sector that wants to anticipate trends over the coming months in order to adjust its production. Using predictive analysis, it can :
- Identify which types of product will be most in demand, based on sales history and consumer behaviour.
- Adjust its stock and avoid surpluses or shortages.
- Optimise its marketing strategy by targeting the right segments at the right time.
The technologies behind predictive analysis
Predictive analysis is based on advanced technologies, including :
- Machine learning and artificial intelligence: algorithms such as neural networks and random forests are used to identify complex trends in data.
- Big Data databases: these make it possible to process massive volumes of information to obtain accurate forecasts.
- Modelling tools: solutions such as Python (with Scikit-learn) or R are used to design predictive models.
Thanks to its expertise in data science, Inflow implements high-performance predictive models, enabling companies to adopt a proactive approach and anticipate market fluctuations.
Complementarity of the two approaches: a winning duo
Far from being opposites, these two analyses work hand in hand. A company that knows how to explain its past performance has a solid basis for making relevant forecasts.
At Inflow, we combine these two approaches to offer comprehensive and actionable analyses. By first analysing past data, we enable companies to understand their strengths and weaknesses. We then use these insights to build reliable predictive models and turn data into strategic leverage.
Example: optimising stock management
A distribution company can :
- Use explanatory analysis to understand why certain products sell better at certain times.
- Apply predictive analysis to anticipate the volumes needed and adjust supplies accordingly.
This combined approach helps to optimise costs, avoid losses and respond effectively to demand.
The importance of expertise and support
Even with the best tools, making the most of these analyses requires in-depth expertise and a well-defined strategy.
Why call on a specialist agency like Inflow?
- Expertise in advanced technologies: we use the latest innovations in artificial intelligence and machine learning to guarantee highly accurate analyses.
- Personalised support: every company is unique, and we tailor our solutions to your specific needs.
- A strategic vision: beyond the numbers, we help our customers integrate these analyses into their decision-making to maximise their return on investment.
Common mistakes to avoid
When implementing explanatory and predictive analyses, certain mistakes can limit their effectiveness:
- Relying solely on averages: these often mask major variations in the data.
- Not updating models: predictions need to be regularly adjusted to take account of new data.
- Underestimating data quality: erroneous information can distort all analyses.
By avoiding these pitfalls and relying on solid expertise like that of Inflow, companies can exploit the full potential of their data.
In a world where data is king, knowing how to explain and predict is a strategic necessity. Thanks to Inflow‘s expertise, companies can make the most of their data and take informed decisions based on reliable, high-performance analyses. Contact Inflow today and turn your data into a competitive advantage!
