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How demand planning is shaped by three A’s of consumer behaviour
Today, numerous factors influence purchasing behavior, from the economy to convenience. In response, retailers have to deploy a more agile approach to th demand planning.
This is largely due to greater pressures on profit margins. By forecasting sales, retailers can develop a more nuanced approach to pricing and inventory. Maintaining a balance is crucial. Whereas excess storage space leads to increased overheads, low inventory might lead to losses. Therefore, businesses need to strike a delicate equilibrium. Through better order management and stock allocation, companies can drive revenue and control overheads. However, the characteristics of effective demand planning are shifting. If before companies focused on the ‘four P’s’ of product, price, place, and promotion, it is now crucial for them to reconfigure. Now, businesses must devise a customer-centric strategy for demand planning, characterized by the ‘three A’s’ of customer behavior that are explained in more detail below.
The three A’s of customer behavior deciphered
1. Affinity
The primary key to influence the sales cycle is affinity – which is otherwise known as consumer confidence. According to the research conducted by British consultancy firm KPMG, 74% of industry leaders in the retail sector considered consumer trust and loyalty to be ‘very’ or ‘critically’ important to their business success.
2. Ability
Although brand loyalty is important, it has to be facilitated by ability. Essentially, this means that no matter how well a consumer regards a brand, they have to have the means to purchase before they become a customer. When retailers consider the factors that affect ability, such as earnings, employment, and economic confidence, they can begin developing a more refined approach to demand planning.
3. Attitude
Although the end goal is a purchase, there are numerous external factors that influence customer behavior. For instance, personal, social, and political conditions could affect purchasing patterns. Although these external factors can have positive effects on customer behavior, they might also impact them negatively. Therefore, it is critical for the businesses to deliver sensitive marketing content that takes into account consumer’s attitudes.
Further strategies for demand planning
Ultimately, there are many aspects of customer behavior that are outside of a business’s control. Although they can develop nuanced, emotionally intelligent marketing content, in the end, their reach can only extend so far. However, there are some tools that facilitate a more pragmatic approach to demand planning that are detailed below.
1. Promotion event planning
Promotion event planning is a granular approach to demand that businesses can apply to specific products and timescales. For instance, it might focus on one particular retail location, product, week, or day to analyze demand. The process forecasts a given store’s inventory requirements, and then communicates this information back to a centralized distribution center. Through this automated process the system can model buying patterns based on historical data, maintaining equilibrium in inventory.
2. Machine Learning
Machine learning is revolutionizing demand planning. Using artificial intelligence, retailers can configure assortment, space, category, and price taking into account financial plans. This approach combines real-time, granular prediction technology with machine learning techniques to develop an ever more agile approach to demand planning. Ultimately, this technology will expedite the decision-making process and identify areas for growth, whilst mitigating risk.
Combining customer-centricity with technology
The customer should be unequivocally at the center of any business’s demand planning strategy. However, technology is increasingly able to support and refine this approach, allowing retailers to gain a more complete, evidence-based picture of purchasing patterns. As these technologies become more widely available, companies should seek to implement data-driven demand planning capabilities into their business plan as soon as possible. Once technology manages the calculations, enterprises can focus on enhancing the customer experience.