Retail projections are predictive insights into spending behaviors that retail businesses use to plan their business strategies. The use of data analytics can vastly improve the accuracy and effectiveness of retail projections so businesses can start getting the most out of their forecasting models.
What Is Retail Forecasting?
Retail forecasting is used to properly allocate their resources the most effectively throughout different parts of the year. Accurate forecasts predict how much a company’s product or service is likely to be purchased, how much staffing and labor will be needed to meet demand, and other deep analytical insights.
Labor Forecasting in Retail
Using forecasting processes enables retailers to improve their workforce management so they can have better customer service, increase sales, and ensure they’re meeting staffing compliance standards at all times. By always having visibility into staffing, stores can be allocated the right amount of staff at the right time, every time.
Why Is Retail Demand Forecasting Important?
Planning for demand is essential for retail merchants to predict how much stock is needed to have on hand at specific locations at a given time, and to gain an understanding of customers’ brand affinity for their products or service.
The accuracy of forecasting is important for day-to-day operations, but especially for understanding seasonal trends and preparing for the holiday season when demand often sees significant shifts that must be accounted for.
With accurate forecasting models, retailers can expect the following benefits to their operations:
Maximize Profits & Gain Market Share
Accurate forecasting enables retailers to anticipate future market behaviors so they can strategically plan to gain market share over their competitors. Profits can also be maximized by using forecasting models to avoid spending money on unnecessary inventory, and never missing sale opportunities by not running out of stock.
Improve the Customer Experience
While data analytics for retail projections won’t improve the customer experience by itself, it enables retailers to do so by avoiding “out of stock” disappointment, and ensuring there is enough staff to provide customers a positive experience. This includes everything from properly accounting for the number of customer service representatives available, staff allocation to handle fulfillment, and shipping so customers don’t experience long delays.
By having a positive customer experience with a particular brand or service, customers are more likely to remain loyal and keep coming back for more.
Plan Advertising & Marketing Campaigns
Demand forecasting also helps retailers’ marketing teams get plenty of time to plan advertising campaigns and budgets. Using insights into expected consumer demand for certain types of products and services makes it easier to allocate ad spending, as well as determine campaign strategies that speak to what consumers are actually looking for.
How Do You Forecast Retail Sales?
Using data analytics, forecasting retail sales requires enough data to generate a projection. To most accurately forecast, businesses should use internal and external data. Internal day is typically a company’s own historical data, point of sale data, website data, and customer loyalty data. External data includes economic forecasts, industry trends, competitor data, and more. Having access to high quality external data from a data provider like Envestnet | Yodlee helps ensure the accuracy of retail demand forecasts.
Once data is gathered, four primary forecasting techniques can be used to create projections taking into account a variety of different market factors.
Qualitative Forecasting
Retailers can use qualitative data like market research or survey data to make accurate forecasts that increase their competitive advantage. By looking at competitive market trends for spending behaviors, businesses can forecast better informed marketing, segment, location, and advertising decisions.
Quantitative Insights
Investors and retailers can also use a quantitative approach to forecasting to prove their projections using historical data. Historical information only improves the level of meaningful insights it can provide overtime, so it can be used to characterize more external shopping behavior trends from market research.
Causal Model
The casual model takes controllable and uncontrollable demand factors into considerations. Controllable factors include price and marketing while uncontrollable demand factors include weather, trends, and political climate.
Time Series Analysis
This method is a more quantitative approach to demand and forecasting by using a mathematical approach to forecasting by using numeric inputs and trends.
How Retail Will Continue to Evolve
As digital payments have increased exponentially in 2020, the importance of big data in retail is not going anywhere. Predicting consumer spending, improving customer experiences, and improving forecasting demand accuracy are all possible with the right data and analytics programs in place.
AI and machine learning technologies will continue to be vital in streamlining retailers’ operations and improving customer satisfaction with products and services, and the sooner such technologies can be invested in, the better for retailers overtime.
As the market leader in data aggregation and data analytics, Envestnet | Yodlee has income and spending trends available for retailers during the COVID-19 pandemic. The trends can be used to inform retail forecasts in the immediate and long term futures of the crisis so you’re enabled to navigate the crisis with the latest data available.
