Author: robert

Find out where to open a profitable new outlet

Proper planning of the network of points of distribution and customer service points is not an easy task. The decision to open a new outlet is a big risk. The more effort required to launch a new outlet, the greater the risk. The wrong location means not only sunken start-up costs, but also the need to maintain an unprofitable outlet later on.

Making the right decision requires taking into account a number of factors ranging from the demographic and income potential of the area to the activity of competitors and forecasts of the economic and labour market situation. The more areas to consider, the more complex the decision-making process.

At the same time, omitting important variables may result in a sub-optimal decision.

Advanced data analysis comes to help. Predictive models take into account hundreds of variables and identify key factors for the success of a location. This makes it possible to predict the future performance of the new outlet and the entire network. The impact of the newly opened outlet on the functioning of the existing ones must also be taken into account. The result of advanced optimisation algorithms that take
into account also the effect of cannibalisation, there is a recommendation for an optimal shape of the network. Thanks to the optimisation, we know where and in what order to open new outlets, and which outlets to close. It is also possible to predict with great accuracy how long it will take for newly opened outlets to reach profitability and when the investment will pay off. The results are visualised on maps so managers can easily understand the new network structure and make the best decisions.

Benefits:

• Understanding the factors that affect the profitability of individual outlets and the entire network
• Possibility of forecasting “performance” of a new outlet
• Better estimation of the time needed to get break-even point
• The possibility of objectively comparing many alternative locations under consideration
• Greater conviction that the decision on the new location was right

Clients: Leading marketer from retail industry, FMCG company

Diversify the offer depending on the Customer group

The customers belong to very diverse groups with different needs and habits. Some of the criteria for the division are simple and have long been used in marketing. For example, customers are divided by gender and age. However, such simple divisions are often insufficient in terms of business objectives. The more you know about the consumers, the more information you collect about them, the more sophisticated and precise the criteria for segmentation can be applied by your company.

As the number of variables to be taken into account increases, the labour intensity and complexity of the segmentation process also rises. However, these problems can be solved using machine learning methods. Clarifying algorithms can analyse customers for tens or even hundreds of features and distinguish natural segments (clusters).

Such segmentation may be behavioural, i.e. it may primarily take into account customer behaviour (both purchasing and other behaviour recorded by the company). This allows to achieve clearer and more business-oriented segments compared to the simple demographics-based segmentation still used in many organisations. Understanding the characteristics of customers belonging to particular behavioural segments helps both in making strategic decisions and in differentiating the offer and marketing communication, which can thus be more relevant and effective.

A huge advantage of solutions based on data analysis and machine learning is their scalability and applicability even for very large customer databases (going into millions). This makes it possible to assign each customer known to the company to the appropriate behavioural segment. On this basis, actions can be taken that are suitable to the segment profile.

Benefits:

• Possibility to focus strategically on the most attractive, profitable, developmental customer segments
• More effective communication by adapting the language and content to the customer segment
• Possibility to identify the segment to which a specific customer belongs
• Better understanding of similarities and differences between different customer segments

Clients: Leading marketer from retail industry, Marketer from retail industry, Marketer from retail industry

Show the customers what will encourage them the most to buy

Our recommendation systems select the content to be displayed on the website.

Recommending the right products at the right time significantly increases conversion and cart value.

Our systems include, in particular, cross-sell and up-sell, which translates into higher sales. By using historical sales data, relationships between categories, products, and website traffic history, the experts of Data Science Logic build models to suggest which products to display in the recommendation boxes. Our software integrates the recommendation system with the existing customer’s website and automatically controls the recommendations and records their impact on user behaviour. The data collected in this way return to models that are becoming more and more accurate and help to generate ever higher turnover.

What can be the applications of recommendation systems?

A natural area of use of recommendation systems are the websites and especially e-commerce. However, they are also successfully used to personalise the content of emails. The conclusions of the recommendations returned by the system can also be transferred to the surface layout and arrangement of products on the shelves and in brick-and-mortar stores.

Recommendation systems are also used in mobile applications, helping to display content that will increase user engagement, increase the likelihood of conversion to a buyer, or perform other desired action (e.g. a visit to an online or brick-and-mortar stores).

Benefits:

• Higher sales in online store
• Greater involvement of website users
• Higher usability of the mobile application
• Increasing expenditure and consumer and user satisfaction
• Better understanding of the relationship between product categories

Clients: Leading marketer from retail industry

Anticipate the “future” based on data

Predictive models, built by the experts of Data Science Logic, allow prediction of future consumer reactions and events, based on historical data. Advanced analytical algorithms discover patterns contained in a huge number of examples, counted in hundreds of thousands or even millions.

The prediction model built in this way can be used for consumer life style forecasting.

In which areas can predictive modelling be used?

Our models predict what kind of offer is most suitable for a particular consumer. This allows you to communicate with those who are really interested in your offer and invest your communication budget in areas where they will bring the greatest return. This way you avoid “spamming” consumers for whom the offer is not attractive.

Our predictive models predict the probability of purchase by a specific consumer, influenced by marketing communication. The variables such as communication channel (sms, email, digital advertising), its content and time of sending are taken into account. This allows us to tell you when it should be sent, to whom, how and what should be sent. This translates into measurable savings: there is no need to spend money on text messages that will not translate into sales and it is not worth offering a discount to a consumer who is already convinced to buy anyway.

The predictive models also predict at what stage of the shopping path the consumer is and what is the probability of moving to the next stage, under the influence of a specific stimulus. By combining consumer data from multiple sources, it is possible to precisely control the messages sent to the customer.

The predictive models are the “core” of many solutions built by Data Science Logic. They are used in the processes of distribution network optimisation, sales forecasting, in anti-churn models and in optimising marketing campaigns.

Benefits:

• Anticipating consumer behaviour in advance
• Forecasting future phenomena
• Better decisions based on reliable forecasts
• Understanding the factors influencing business-relevant phenomena (e.g. customers churn)
• Introducing changes in the organisation and business processes based on the conclusions of the model
• Possibility to simulate different future scenarios

Clients: Leading marketer from retail industry