Business Inteligence
Artificial intelligence can help with Business intelligence by modeling the dynamics of the business.
Use Artificial intelligence (AI) and Machine learning (ML) to unlock insights already present in data that are hidden from a traditional Buisness Intelligence (BI) system by modeling the dynamics of the business.
What is Business Intelligence?
Artificial Intelligence (AI) and Business Intelligence (BI) are often misused terms and even confused with one another.
Business Intelligence also called Business Insights is used to create insights from data produced by the company's activities, such as sales numbers, media investment, distribution channels, etc. The insights are then used to aid in decision-making. It does this by combining learnings from business analytics, data mining, data visualization, and other data tools.
Business Intelligence also called Business Insights is used to create insights from data produced by the company's activities, such as sales numbers, media investment, distribution channels, etc. The insights are then used to aid in decision-making. It does this by combining learnings from business analytics, data mining, data visualization, and other data tools.
Customer Case: Sustainable Growth
Identification of actionable growth opportunities in +10 countries across five products.
Artificial Intelligence and Business Intelligence
In order to move beyond simple dashboards of historical data, one needs artificial intelligence to decompose the various drives of the KPIs and to be able to start planning future actions.
Business Intelligence focuses on visualizing past data and some very basic insights using spreadsheets. But in order to gain true insights into the data one needs to go beyond that and start using complex models that model the dynamics of the business this is where AI comes in.
Equipped with these advanced models one can take BI to a whole new level. Making it possible to
- Understand what drives the KPIs
- Plan the impact of future actions
- Optimize for the perfect action
- Find synergies within the drivers for several KPIs
Benefits of combining Artificial Intelligence and Business Intelligence
- Forecast the impact of decisionsWith AI one moves beyond gaining insights into past actions, rather starts working with the forecast of the impact of future actions. Making it possible to plan and compare various scenarios.01
- Fast AnalysisWith the ability to continuously load the latest data, one can directly see the impact of the latest activities and track the performance. Thus shortening the feedback loop, enabling one to quickly identify the most cost-efficient activities02
- Data-driven decisionsWith direct access to the data, one can track the exact impact of what has been done it is easier than ever before to understand the impact of previous decisions, and what impact new decisions will have.03
- Increased organizational efficiencyWith tools that allow the organizational leaders to get direct access to the data, they can option a holistic view of the business. Makes it possible to see how the activities of the various departments impact one other.04
Customer Case: Budget Optimization
Sales budget savings of up to 25% compared to existing spending.
Where Artificial Intelligence can help
There is a wide range of areas where our models add value, here is an example of some of the models we support.
Sales Forecasting
Investigate the impact of drivers such as macroeconomic factors, seasonality, trends, weather data, distribution channels, media investment, etc. on sales. This makes it possible to better plan and predict various changes to see what will have the biggest impact.
We support predicting future sales based on given input data, picking what variables can be changed and optimizing those to maximize sales, and comparing various scenarios to see how they compare to each other.
We support predicting future sales based on given input data, picking what variables can be changed and optimizing those to maximize sales, and comparing various scenarios to see how they compare to each other.
Customer Churn
Customer churn is the percentage of customer that stops using a service within a specific time frame. Given that there is usually a cost of acquiring new customers, it is vital to minimize churn. There are a lot of factors that impact churn such as brand loyalty, price, customer satisfaction, etc.
By building a model that analyzes customer churn, it is possible to understand what impacts it and how much. When one knows that we make it possible to simulate various different strategies to mine customer churn and pick the best one.
By building a model that analyzes customer churn, it is possible to understand what impacts it and how much. When one knows that we make it possible to simulate various different strategies to mine customer churn and pick the best one.
New customers
New customers are always nice to have, but what drives new customers may be different from what minimizes customer churn as it comes down to establishing a new relationship not maintaining one. That is why it is key to be able to decompose the various drives of new customers in order to focus on how to acquire new customers while at the same time minimizing Customer Acquisition Cos(CAC)
Pricing Dynamics / Pricing analyses
Get an understanding of how pricing will affect sales, and thus be able to accurately predict spikes in demand and sales. Such one anticipates the needed inventory ahead of time, thus improving inventory velocity and customer satisfaction.
Impact of branding
Measure how different drivers such as customer satisfaction, media investments, pricing, events etc. impact the brand of your business. Having that knowledge enables you to make optimal decisions in how to improve the value of your brand.