Category: Oferta EN

Get the right information at the right time

Effective decision making requires easy access to the right information at the right time. Adequate reports often have to be based on data from many different sources. Manual preparation of reports is time-consuming and error-prone. Not only does it entail high labour costs and expenses, but it can also cause delays in accessing information for managers. The automation of the reporting process helps to increase the speed of decision making. The reports are refreshed in intervals according to managers’ needs and delivered in the best format for decision making. Clear and interactive dashboards allow for quick assessment of the situation and immediate reaction. The attachments in the form of tables and spreadsheets help to make in-depth analyses and discover the causes of the phenomena. An accessible form of visualisation (also on maps) helps to understand the problem intuitively and to take appropriate actions.

Benefits:

• Always up-to-date information available to managers
• Clear form of presentation
• Faster decision making
• Saving time for preparing reports
• Less chance of errors and delays in reporting

Clients: Leading marketer from retail industry, Marketer from retail industry, Customer from the financial sector, FMCG company

The sale of many products and services is characterised by strong seasonality. Seasonality models built by Data Science Logic allow to identify such patterns at the level of product category or even specific SKUs.

This allows you to plan your sales activities in advance, ensure adequate staffing and stocks of materials and goods. A good knowledge of seasonal patterns also helps to plan a calendar of promotional activities. Regular monitoring and analysis of the seasonal aspects of business helps to detect changes in consumer habits (e.g. the increasingly early start of the season due to climate change) and adjust the offer accordingly before the competition does.

Benefits:

• Adjusting the calendar of promotional actions to seasonal patterns
• Proactive influence on seasonality of sales through properly planned marketing activities
• Detecting changes in consumer habits and the associated seasonality

Clients: Leading marketer from retail industry

Anticipate how much you will sell in the future

Many different factors influence the level of sales. Ranging from prices, weather, moves of the competition, promotion, advertising budgets to the general economic situation and labour market. The effects of some factors are postponed. In this case, predicting what level of sales can be expected in future periods becomes a big challenge. However, predictive modelling comes in handy.

Advanced statistical models take into account dozens of different factors. They combine data from internal sources with external data, such as the weather, forecasts of the Polish Central Statistical Office (GUS), competition and identify relationships between these factors and the level of sales.

This allows for forecasting sales of the entire network or by individual outlets, product categories and even specific SKUs in different time horizons (e.g. 30 days, 6 weeks, a quarter). The forecast may focus on the sales value, the number of products sold or the number of customers. Predictive models built by the experts of Data Science Logic benefit from the latest advances in machine learning that make it possible to achieve high forecasting accuracy with deviation of up to a few percent.

Each subsequent week the models take into account new sales data and are calibrated to make forecasting more and more effective. The results of forecasts are presented in an intuitive graphical form so that managers can easily make decisions based on them.

The knowledge of future sales trends and the factors influencing them allows us to plan the level of stock , the capacity and staffing of sales outlets/customer service centres well in advance.

Benefits:

• Better planning of production, orders, stock levels, staffing of the outlets
• Better understanding of the direction and strength of sales drivers
• Identification of long-term sales trends and short-term deviations
• Identification of the threat to the execution of sales plans in advance and the possibility of taking timely countermeasures

Clients: Leading marketer from retail industry

Identify your customer’s life stage and adapt your offer to it

The customer’s life cycle varies from industry to industry. The number of stages that can be distinguished, their duration and the periods between them vary. Sometimes the life cycle is more linear, sometimes the individual stages are repeated. However, it is almost always possible to indicate moments in a customer’s life that determine the change of their needs and habits. Managers in each industry are usually aware of their customers’ life cycle. This knowledge often comes from qualitative research and experience. The problem is to indicate the stage of life of a particular customer to whom you want to address the offer. With the help of advanced data analytics, combining information from multiple sources such as transaction data, loyalty program data, website traffic, qualitative and survey research, we can model the life cycle and forecast the flow of customers between different stages. The customers usually send signals indicating their readiness to move to the next stage. Early recognition of such signals is possible thanks to machine learning and analytics, which allows to reach the customer with a suitable offer before the competition does.

Benefits:

• Understanding of the customer’s behaviour, which shows that they are transitioning to the next stage of their life cycle
• The ability to identify the life stage of a particular customer
• Better tailoring of the offer to the situation and needs of a specific customer

Clients: Leading marketer from retail industry