Top 3 Demand Forecasting Challenges Facing the Retail Industry in 2019
In today’s data-centric world retail environment has grown even more complex. With the influx of consumer data, businesses like retail need to have a better mechanism for demand forecasting in order to improve their customer service and stay ahead of the competitors. A good demand forecasting model enables businesses to smartly use their historical data [...]
In today’s data-centric world retail environment has grown even more complex. With the influx of consumer data, businesses like retail need to have a better mechanism for demand forecasting in order to improve their customer service and stay ahead of the competitors. A good demand forecasting model enables businesses to smartly use their historical data on consumers and help them to plan strategies for future trends. Also, demand forecasting techniques help companies to anticipate when the demand will be high and establish a long-term model that can help in business growth. Retailers with the help of demand forecasting model can eliminate their dependency on instinct and intuition for decision-making. However, demand forecasting seems to be easy but in practice, retail businesses face critical challenges in building a demand forecasting model that can help them to deal with the ballooning complexities in the retail environment. In this article, our retail industry experts have listed out a few challenges that players in the retail industry are poised to witness in 2019.
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Demand Forecasting Challenges
Challenge #1: Using an integrated system to track customers and business
Over a period of time, most retail companies have built out their operations and have deployed systems as they need them. This often means retailers have one system for their enterprise resource planning and another for customer resource management. Though such systems have solved the incremental needs of retail companies, the ongoing digital transformations along with the need to maintain interoperability have proved to be a barrier in two ways for retailers. First, it is resulting in duplication of information in both systems that create a siloed approach and impacts efficiency. Second, with such a system it is difficult to gain actionable insights into unstructured data sets. Lack of unintegrated system makes it difficult for retailers to improve their business operations.
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Challenge #2: Applying the correct methodology for strategic decision-making
Retailers are making strategic decisions by setting goals for the company at large rather than for specific regions. Without a focused approach, retailers fail to gain visibility into where growth is expected. Also, such an approach does not help them to identify the regions that outperform in the competitive market and which ones underperformed against easy regional targets. Therefore, having a proper demand management process in place is important for retail businesses to get away with instinct based decision-making.
Challenge #3: Leveraging unstructured datasets to forecast the next step
Today players in the retail industry are struggling to leverage external data or customer profiles effectively. Customer profiles offer detailed insights into what customers want enabling companies to understand demand better. Also, by using external data businesses can improve their accuracy of forecasts by accounting for external forces that were overlooked previously. Building demand forecasting models can make it possible for retail companies to distinctly segment customers and analyze the demands and preferences of target customer segments.
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How can Quantzig help you take your demand forecasting to the next level?
Quantzig offers demand forecasting solutions that help businesses to create accurate demand capacity and plans. Our demand forecasting solutions and help companies to boost their forecast accuracy, improve service levels, manage portfolios, and maximize return on demand planning efforts. Quantzig, with its expertise in offering demand planning and forecasting solutions, empowers businesses to discover hidden performance drivers, increase profitability, improve collaboration, validate their business strategies, gain a 360-degree view of demand, and optimize ROI on operational spend.
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