A large food retailer faced the challenge to drive profitability from its e-commerce operation, with capillary distribution as a critical concern. Given the interdependency between several decisions (e.g., hub locations, fleet size, vehicle capacities, delivery slots available to the client), a tool became necessary to test distinct scenarios and find potential improvements.

Detailed impacts needed to be estimated for each scenario, to empower decision makers. Besides tactical planning, the tool would also be leveraged for budgeting purposes.


A simulation tool was designed and implemented to draft a prospective budget for each given scenario. Behind it lies a predictive model that estimates travel distances by leveraging demand densities.

Historical orders, business trends (e.g., demand growth by region) and the scenario’s parameters (e.g., active hubs, delivery slots) are key inputs. For certain parameters, the model can adopt a prescriptive stance (e.g., fleet size).

A dashboard complements the simulator to provide an overview of past performance. KPI's are common to both tools: cost and service level (e.g., share of on-time deliveries)


A solid planning environment (simulator + dashboard) became available to the team in charge of e-commerce distribution. A user-friendly interface enables a quick interpretation of the outputs.

As a consequence, higher planning consistency and faster improvement cycles were achieved. Tests to the simulator, based on past decisions and ensuing results, showed global deviations under 2% for the prospective monthly budgets.

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  Everyone likes a good deal especially when they could be part of it.  In the past years, promotions have been continually growing in sales and consumer behavior influence. However, the demand variability imposed promotes stress in the whole supply chain, especially concerning the expected demand of each product – promotional forecast. Being able to accurately predict promotional demand involves many decisions where typically analytical models delivered superior results and unbiased insights. Yet, there are several challenges placed every time that deserves to be considered regarding this process.   Shrinkage vs service level Although promotions face an increasing impact on the consumer, one pivotal fact is the need to have products available on the shelf to the end customer. If too much product is placed for a promotion it could result in shrinkage or negative margins due to product natural life-cycle. Subsequently, this might induce an over-dimensional operation with higher risk and capital invested. As opposite, an out-of-stock situation will impact customer satisfaction and loyalty. For manufacturers, out-of-stock could be even more damaging and lead to a competitive disadvantage with a loss of brand equity and loyalty. As promotional impact increases the more imperative will be to have a balanced situation and by using a more rigorous forecast less stock will be needed to fulfill possible forecast-demand deviation and, consequently, decreasing risk.   Promotional factors Predicting promotions is considerably more complex than predicting non-promoted products. The number of factors influencing demand change within promotions and, for similar promotional conditions, the data available is considerably less or non-existent. Marketing campaigns, price, store display, geographic location, brand, gifts, and many other features consistently impact how clients react to promotions. Nowadays, there are several models and methodologies to tackle this problem, however, it is necessary to have the analytical expertise to parametrize and understand the black box that not always deliver the expected outcome. The probability of ending up at an overfitted situation with meaningless results is higher, but when correctly applied is possible to take advantages increasing forecast accuracy and, especially, promotional knowledge. With an advanced understanding of each feature impact, promotional plans have more information to meet the expected results and be in line with organization strategy.   Products interaction New products introduction and promotional assortment add even more complexity into this process. With an increasing product diversification, the differences with similar products are narrower and, in many cases, irrelevant for client requirements and satisfaction. This creates an intense network of product interactions where one promotion has an impact on others promoted and non-promoted products. The introduction of new products enhances the density of the problem by requiring a forecast without historical data to support the analysis. On this subject, product attributes and similar products have an important role in order to prepare and get a sustainable forecast. By looking for attributes instead of individual products it is possible to get close to future demand and products interactions - leveraging the available information. Promotional forecasting is a process ready for improvement and there are plenty of options to achieve it. With good processes and technology, organizations can continue to exploit the benefits of promotion without tarnishing it by using inaccurate forecasts. This consequently leads to better promotions plans with better information, which helps further forecast to be more accurate. What follows is a significant cost reduction and a synchronized supply chain. Besides, having a good methodology could be the core point to leverage the business and take advantage over the competition.  


  Increasing loyalty by executing customer retention and acquisition promotional campaigns has been one of the main marketing objectives for the last decades. While marketing and commercial teams work hard to deliver the best value to customers, the brand-customer relationship is becoming more dynamic and multi-layered with customers having increased competitor awareness and preferring to use proximity stores to cherry-pick shopping baskets.   Whether on retail, banking or any service companies, the strategical planning of loyalty targeting and promotional activity is becoming a very complex task which, if not built upon solid profitability evidence, quickly turns into a  resource-usage and margin-diminisher managerial problem. While the promotional strategy is typically enforced on a top-down matter, the customer response complexity and dynamicity urge a bottom-up approach that manages to ensure one-to-one ROI focused on real customer needs and basket growth opportunities. A broad analytical vision of the customer behavior history and future response predictions are the most valuable assets towards ensuring the promotional value proposition is suited to customers’ needs and ready to drive long-term profitability.   Embracing the dynamic customer’s lifecycle A typical promotional strategy is usually defined over some sort of low-dimension customer lifecycle segmentation, more often than not based on the core RFM variables (Recency, Frequency, and Monetary value). However, both customers and companies are engaging in a multitude of ways, in which different products and channels are tailored to target specific customer needs, such as convenience, large-basket destination stores and online retailing. In this era, the increasing customer price awareness along with the emerging promotional make loyalty a much more dynamic and hard to acquire concept. While RFM models have very valuable predictive power and provide a simple but effective way of segmenting customers, they fail to pick up on the necessary consumer behavior detail to fully optimize the marketing relationship. One could dive even deeper and find such different behaviors within the same customer, perhaps across different product categories. Multi-product companies typically face very different competitors across their assortment range, and customer retention and churn measurements should also include this diversity dimension. Otherwise, the company may never extract the full potential from its customers and may be losing more money than they even realize. Broad lifecycle segmentation is no longer enough to respond to the evolution of customer loyalty, as companies need to figure out the driving needs for customer shopping and understand their true potential value and, ultimately, profitability. The pursuit of one-to-one ROI Figuring out the customer level ROI is all about the understanding potential for customer improvement and the relationship between customer investment and expected probability of growth activation. Analyzing customer potential is two-fold. It involves computing detailed customer-need specific lifecycle segmentations and also mining association rules to estimate them for customers with insufficient transactional data. Customer level information is obviously extremely rich on providing information for this value. The next step is to get answers on how to acquire that value potential. Past promotional campaigns and extremely frequent A/B testing should be deployed to accurately analyze the efficiency of each promotional vehicle, communication channel and promoted products on converting customers’ potential value into loyal recurrent transactions. Developing these predictive models enables companies to avoid the most common mistakes of promotional planning: Focusing on quantity instead of quality: Having too many promotions will, at most, maintain customer’s cost perception while eroding most chances of sustainable profitability; Ignoring customer data on the promotional definition: Suppliers are the main source of promotional funding, but the value of such mass promotions are limited regarding the impact of customer growth and acquisition. When strategic guidelines are spread down from general segmentations, customers may end up getting targeted for promotions they don’t need, on channels they don’t respond to, and with a disproportionate value regarding their potential brand engagement. The only way to avoid this issue is to strategize not on the promotion and channel usage, but on how different customers should be targeted. Before deciding on what to offer to each customer, companies should strategically define: Which segments to invest and disinvest in? What is the target ROI to decide on whether customers should be contacted? What impact will the available campaigns have on customers’ shopping patterns? How much will each customer segment, if targeted, increase market share or profitability?   Then, by compiling all the type of discounts and promotions that may be included in campaigns, companies should be able to look at the expected response for each pair of customer-promotion and figure out the expected customer response in shopping behavior.   Overcoming organizational challenges Effectively planning promotional activity across the company, making sure both customer needs and supplier funding opportunities are being considered, poses three main challenges to large companies: Managing and navigation huge amounts of data; Building a robust promotional planning process that brings all stakeholders’ needs and capabilities on board; Ensuring the customer-level promotional portfolio is correctly planned instead of having promotional silos that may have cross-promotional effects and thus erode profitability.  There is no point in devising the ultimate promotional campaign for the customers if the commercial teams can’t fund it and there is also no point in engaging in non-profitable promotional activities that contribute little towards achieving the targeted customer value proposition. Overcoming these difficulties requires developing all the processes and frameworks that enable all stakeholders to work together during promotional planning stages. Having the ability to plan, control and evaluate mass and customer-specific promotional activities is the only way to ensure marketing investments have sufficient returns and to avoid them turning into a margin-consuming spiral activity. Only then can companies pursue purposeful customer marketing strategies and develop promotional proposals that leverage customer knowledge into sustainable growth.