Jane walks into a store only to find that a product in question was out-of-stock on the shelves, but available at the warehouse – An instance of bad customer experience owing to the store’s negligence in demand sensing. In another, a company that uses automated order generation to replenish stocks found that two of its SKUs were getting overstocked as the company erred on the side of demand sensing.
Meeting customer demands every time means reading their demands in advance – Demand sensing is now a requisite business trait to facilitate effective planning and execution. And in a competitive e-commerce market, e-tailers are homing in on demand signals to ensure right stocks are available at the right time to fulfil customer needs.
Demand Forecasting is about making connections – connecting various factors influencing demand and evaluate how these factors impact the demand of a product in the future.
What connections do e-tailers target to achieve accurate demand forecasting?
Connecting Past Data and Present Realities
Looking at patterns from historic data puts forecast demand into motion in a way data is used for identifying patterns that are likely to occur in the future. But when it comes to accuracy of demand forecasting, it is the fusion of past historic data and present realities that helps in improving forecasting accuracy.
Turning to machine learning, e-tailers gain the ability to connect the past, present as well as the future. Machine learning rallies around the demand forecasting objective in finding patterns as well as unearthing forecasting errors committed in the past, and in learning and improving demand forecasting accuracy continuously.
Connecting Demand Accuracy to Customer and SKU
Customer plays a pivotal role in this demand forecasting equation. As customer needs and requirements drive inconsistency in demand, where meeting customer demands every time is a prerequisite to earning customer delight, it is the worth the efforts to ensure demand forecasting accuracy at the customer level.
In forecasting demand, the drive to measure sales, orders, unsold stocks and shipments is well augmented by infusing factors in terms of how customer preferences take an upward or a downward swing with regards to a specific SKU. The more granular the data gets, more influencers of demand that get stitched into the demand forecasting equation, more accurate the forecasting can become.
Connecting Varied Influencers
Weighing whether ‘Thanksgiving’ or ‘Black Friday’ offers the best holiday sales is one thing wherein what influence do these holiday shopping events have on product and SKU sales and demand is another. Improving accuracy of demand forecasting also sets the e-commerce focus on how promotions and events influence demand related to products and how demand fluctuates owing to weather changes or promotions run by competitors. And it can get more granular when contextual data is brought in with the objective to measure the accuracy pertaining to demand forecasting.
Connecting Challenges to E-commerce Demand Forecasting
The ordeals encountered in achieving accuracy in demand sensing are more in number, let alone nailing down the appropriate demand forecasting method. As the flow of new products increase, it becomes difficult to forecast demand for newly introduced products, where trends, data mining and cohort analysis collude to guide e-tailers in promoting accuracy in this forecasting exercise.
Demand sensing from an e-commerce perspective is also about the ability to read changing customer behaviors, understand how demand goes high or low during seasons and know the demand pattern for competition. In this omnichannel commerce scenario, channels making up for the scenario also have a say over demand patterns.
When an online digital retailer sought Saksoft’s expertise in infusing demand sensing into its operations, Saksoft helped the e-tailer in leveraging structured and unstructured data through Time-series, Regression analysis, Clustering models to make the most of a predictive forecasting model. The e-tailer took advantage of demand forecasting to enable effective administration of order and shipment levels, estimate lost sales and predict product demands. The demand sensing model also played a key role in helping the online digital retailer to take the average order value by 53%.
