Predictive analytics uses data, statistical algorithms and machine learning procedures to identify the probability of expected outcomes from historical data, enabling organizations to increase their productivity, competitive advantage, improving what companies do and how they do it.
Franchise system retailers can face growth challenges in a changing and competitive retail marketplace, and franchise managers and store owners must choose the right technology to remain relevant, unique and appealing to customers.
Data and predictive analytics tools are helping retailers around the world cater to specific and changing customer tastes and drive sales in smart, accessible ways.
How do predictive analytics applies to retail franchises?
Predictive models use actual results to develop a model that generates predictions of value for different or new data. These models store the results in the form of predictions that represent a probability of the company’s target variable based on the estimated importance of a set of input variables. Any franchise can use this tool to increase revenue, reduce risk and optimize operations.
Predictive analytics and data analysis are invaluable for retailers serving a wide range of customers. Getting into the minds of customers is increasingly essential for franchise managers and store owners who want to deliver better shopping experiences to their customers, which, in turn, will drive sales and business success.
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What benefits does predictive analytics bring to retail franchises?
Most franchise businesses possess significant amounts of customer data that, if used judiciously, can help them provide customers with timely and intelligent shopping suggestions that adhere to brand requirements. Applying artificial intelligence based predictive analytics to customer data can help retailers predict what types of products their consumers are likely to buy, when and at what price.
Not only can predictive analytics help retailers offer the right products to their consumers, but it can also indicate in advance what customers need. In many cases, this means retailers can predict what customers will want before the customer knows it. This information can help retailers make evidence-based decisions about inventory with confidence. And beyond sales, these products are likely to motivate customers to stay loyal to franchises and brands.
AI applies computational processes to existing consumer data collected through customer feedback forms, membership data, loyalty and reward cards, mailing list information, and past online and in-store purchases. Emerging technologies can analyze this data and provide retailers with essential information about customers’ upcoming shopping needs and even reveal information such as customers’ socioeconomic backgrounds, locations and varied demographics to influence successful sales strategies.
Variables such as customer age, occupation, gender and family can also help retailers form an idea of the needs of their various customer segments, identifying where the greatest number of sales and interest lie, and which customer groups will be best served by the business. For franchise retailers, retaining customers is essential for growth.
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AI and predictive analytics can offer franchise companies of all sizes significant growth opportunities. Processes driven by these tools can be an important catalyst in retailer growth and success stories.
The more relevant data franchise owners and store managers can collect and analyze, the greater their understanding of consumer shopping habits. Data analytics enables accurate stories about individual customers and customer subgroups, helping retailers understand the many variables that drive in-store sales.These personal factors have deep meaning, and machine learning can help retailers connect these details so they can recommend the right products and make new sales.
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