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Retailers and Online Businesses

All About Understanding your Customers

Intelligent marketing in today’s competitive environment is all about understanding your customer on a deep level. Old-school direct marketing and selling what “you think customers want” are methods that are doomed to fail. The reality is that customers make purchasing decisions based on a wide variety of reasons; more often than not, repeat business is directly related to the seller-buyer relationship. Engaging with customers and providing them with the specific goods & services that they want is a key component of finding the “loyal customer.” But how do you know what customers want?

Demand Forecasting Challenge: Too Much Data

In the age of Big Data, there is a vast quantity of information available for analysis, particularly in the retail and online marketing environment. The type of information is varied and includes datasets such as historic sales data, consumer demand data, price variance data, lead rates, trends, social media, customer feedback, weblogs, online buying behavior, Website usage, and loyalty programs. The sheer quantity of data stops most companies in their tracks: they want to take advantage of the information, but don’t know where to begin.

Using DSS for Demand Forecasting

DSS is particularly useful when analyzing customer data, because it will enable your company to provide immediate and personalized interactions with your customers. Predictive analytics will empower your company to improve both retail and online marketing:

  • Pricing: Determining price points no longer has to be a guessing game. Use historic sales data and customer trends to understand exactly how much money your customers are likely to spend based on a wide variety of factors (e.g., time of year, distance from retail location, IP address, online buying behavior, etc.). Your models can include datasets from any data source, from complex SQL databases to simple Excel spreadsheets. This enables you to create highly-targeted retail and online marketing forecast models that will show you which price points are most likely to turn potential customers to proven buyers.
  • Inventory Management: Balancing product inventory is a tricky business: too much stock results in excess storage costs while too little has product availability (therefore impacting customer retention and usually increasing customer churn) repercussions. Data Science Studio can help your business find the right balance by using demand forecasting to predict production & consumption quantities. Demand forecasting uses machine learning and user-friendly visualizations and web apps to convey how your customers are likely to engage with your products based on the dimension of your choosing, such as the time of year, income fluctuations by region, or perhaps a combination of both. The datasets you use are entirely of your choosing!
  • Customer Service and CRM Initiatives: At the end of the day, it’s all about how successfully your company engages with your customers. Understanding this metric is largely related to how effective your business is at collecting, tracking, and measuring customer interactions — these “engagement indicators” are windows into your business-customer relationship. DSS is highly effective at enabling users to collect, clean, and analyzr these types of datasets for better understanding of what specific changes need to be made. Perhaps employee policies need to be modified? Business hours? Loyalty programs interactions? The “right answer” for your company depends on your unique circumstances, which is exactly what Data Science Studio is more than capable of helping you accomplish.
  • Online and Retail Marketing Recommendations: A key question in understanding customer behavior is, “How do I know what customers want?” The old-fashioned approach was to blindly offer all of your products to see what happens; even worse, making assumptions about customer needs based on unmeasurable factors (e.g., gut feelings, intuition, market experience).

Predictive analytics in DSS empowers your organization to gain a much deeper understanding of your market than your competitors, effectively using a wide variety of raw data to present the most likely outcome(s). Historic sales data, the weather, social media content, and even traffic data can all combine in different ways to paint a picture of what your customers are looking for and when they want it. This enables your company to use DSS for creative forecast modelling, such as building a recommendation engine for sales associates that provides on-the-spot information about past purchases, or perhaps suggesting recommendations based on similar customers. In short, DSS-powered demand forecasting provides realistic recommendations that can profoundly impact your company’s bottom line.

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