Businesses around the world have understood the value of data analytics and the digital era has made it impossible for a business to thrive without leveraging the advantages of it. We are all aware of the amount of voluminous data the retail providers generate on a daily basis. The retail businesses are left with no option but to explore various ways of leveraging data as per their requirements. However, a major chunk of repository data might not be of any assistance if not transformed into relevant insights for customers and market trends. To ensure that the entire data generated and collated is being translated efficiently, many retail businesses are still on the lookout for a one stop solution to address their concerns.
The odds are most businesses across industries are familiar with Robotic Process Automation (RPA). As the technology attracts significant benefits by automating existing repetitive, high-volume paper-intensive tasks, the human workforce could focus on creating business intelligence and serving customers better. While RPA is proving itself to be industry agnostic, the initial installation costs of the technology is sufficient to reap significant ROI. But when RPA is coupled with artificial intelligence and machine learning techniques, retail businesses are offered the luxury of employing real-time predictive analysis to derive a sophisticated value of all the data collected by data analysts to ensure higher sales outcomes in the future.
Read: Top 5 Use Cases of Robotic Process Automation (RPA) in Retail
Predictive Analytics with Cognitive Automation
A comprehensive analysis of large volumes of voluminous sales data is instrumental for marketing campaigns targeting a specific and a real audience. However, the retail providers are challenged with improving customer conversion rates, reducing customer churn rate, and lowering customer acquisition costs. RPA can be the best fit for sales analytics. With cognitive automation, the retail landscape can analyze the sales performance of a product at a specific geographical location. Artificial Intelligence (AI) and machine learning algorithms will provide real-time insights that contribute to the sales analysis.
According to a report, around 10-15% of the in-store stocks are returned, eventually devoting a significant amount of time towards sending the stocks back to the warehouses.
Cognitive automation also factors in exceptions, recognizing your high value customers, their motives behind the purchases, analyzing purchase patterns, and the best channels to provide a surreal digital shopping experience. Keeping these things in mind, let’s look at a success story of a leading CPG distributor in Canada.
The multi-national distributor used to gather weekly sales data at a store level manually. The data collected were fed and mapped into product codes. Due to the varying product cycles in their firm, cross-selling analytics and churn prediction were a huge challenge. With the salesforce serving over 2000 customers, the company is looking to overhaul their sales activities around key accounts and is trying to enhance better vision towards sales planning.
The manual trade-spend analysis process was performed and shared with stakeholders in the following fashion:
The above manual process consumed at least 2 to 3 days to share sales reports.
With the help of cognitive automation, the CPG distributor obtained valuable insights on future sales prediction and churn rate minimization, the current attrition risk, and cross-selling opportunities. Furthermore, the sales analysts learned how to deploy machine learning algorithms and predictive analytics for dynamic pricing and reducing churn rate. Also, the brand partner’s hopes of increasing the financial fortunes through operationalizing predictive sales across all sales teams were a reality.
And now the revised automated sales analytics workflow goes something like this:
Thanks to intelligent automation, the distributor was able to complete the same process within a few hours or on the same day. Moreover, the distributor witnessed 100% accuracy and around 50% reduction in churn rate with increased bandwidth of financial analysts.
Given the amount of voluminous data to analyze and the exceptions to consider, manual sales analytics are a frantic affair at consumer product firms as analysts scramble to collate sales data on a regular basis. But with cognitive automation, the software bots can compile sales data over the weekend and have the detailed reports ready when the accounts team arrives on Monday morning.
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