Retail data analytics provides data on inventory levels, supply chain movement, consumer demand, and sales, crucial for marketing and procurement decisions. Retail analytics gives insights into consumer, business, and organizational processes.
The competition is fierce across borders and channels. Continuous growth opportunities lie in leveraging meaningful insights. With insights, businesses can deliver targeted promotions, better-focused goods and services. They have presented a talent management challenge for companies.
Draup analyzed the impact of Data and Analytics in business transformation and showcased how retail HR leaders can reimagine their talent management strategies to build a robust data & analytics team.
Retail must move from predictive to prescriptive analytics
While descriptive, diagnostic, and predictive analytics provides past data, allowing businesses to identify patterns within the organization, prescriptive analytics enables the data collection from both inside and outside the organization, validates critical decisions, discovers new business opportunities, and identifies problem areas blocking sales opportunities.
Online shopping is about a personalized consumer experience. Prescriptive analytics helps brands forecast outcomes from a set of the past consumer experience. Remedial action (prescriptive) is far scientific than forecasting (predictive).
Prescriptive analytics takes companies from insight to foresight, offering customers concrete proposals.
Impact of Data and Analytics in the retail industry
- It improves operational efficiency by analyzing consumer behavior using their shopping data to calculate the average checkout wait time.
- Using consumer intelligence & analytics, companies can analyze consumer behavior into positive, negative, or neutral.
- It improves product offerings & services by predicting emerging trends and targets the right consumer, helping companies stay competitive.
- It reported an average 8% increase in revenue and a 10% reduction in costs.
Big data reveals patterns, trends, and associations, especially relating to human behavior and interactions. Here are examples of how retail companies use data and analytics.
Inventory and supply chain management: Poorly maintained inventory is a retailer’s nightmare. Optimizing the supply chain can increase operational efficiency and performance, reducing costs. Predictive analytics answers what and when to store or discard, thereby removing uncertainty or stocking products just based on a hunch.
Campaign management: Learning about your customers can help you target consumers with personalized messages. Companies can use predictive analytics to craft marketing strategies. Data-based decisions reduce decisions based on instincts or guesses. It can identify the channels and the times that require an increase in your marketing spend and resources.
Behavioral analytics: Various consumer interaction points like social media, e-commerce sites, card transactions, and so on can provide data that can make it easy for retailers to track people and analyze their shopping behavior and assess the impact of merchandising efforts.
Retailers can use the current data points and those captured in earlier campaigns to build models linking past behavior and demographics to score every customer as per the likelihood of them buying certain products. It can also predict churn based on the past data on the subscription.
To navigate the future of Data & Analytics in organizations, HR leaders must reevaluate their existing talent management and workforce planning strategies.
Hiring an AI-talent for digital transformation
Successful implementation of AI is the first step towards retail success. Additionally, human intelligence is also essential. Retail companies must hire data-savvy professionals that include engineers and data scientists. There are two kinds of AI-talent implementation companies must have in their talent pool.
- Data science talent: Data science talent involves engineers and scientists trained to work on a deep data pool and a well-organized platform that integrates and makes data available across the company.
- Analytics-enabled talent: Some managerial roles include marketing and analytics to drive business performance, resulting in greater productivity and operational efficiency. Analytics teams identify customer wants using social analytics, unusual activity on the network from real-time dashboards, or forecasting inventory using predictive analytics.
Retail companies must rethink their existing team structure to utilize the analytics teams. Instead of a centralized analytics team serving different divisions and business functions, modern companies must switch to a dedicated analytics team for each business division and function that takes on highly defined and specific workloads.
We looked at Amazon’s organizational structure and saw that each of its business units with specific responsibilities and objectives have its analytics team headed by a Head Executive, functioning independently.
Rebuilding and reskilling
AI/ML engineers understand a larger problem and the technical ecosystem they are designed for, not merely the algorithm needed to identify the data set’s anomalies and outliers. AI professionals must understand this system’s interconnectedness that makes up the problem.
AI sits at the intersection of human behavior, decision-making, and technology. Engineers may create the software but may fail to integrate the technical and business elements to arrive at a solution that addresses the problem holistically.
Companies need not hire just engineers. They must fill other positions, including AI project manager or AI ethicist. These people must be well-versed in the company’s corporate mission, values, and standards, along with having familiarity with the technology strategy and plans of your business – skilled problem solvers and innovations will be there along the way.
Analyzing the skills landscape
Traditional skills are facing an existential crisis and data analysts must train on advanced skills sets augmented by AI, ML, NLP, computer vision, etc., so that they can gather data, find patterns easily, write reports, collaborate with stakeholders, present their findings, and create systems.
Companies must also look at a cost-effective alternative to meet the high demand for Data & Analytics talent. Looking at candidates from non-traditional backgrounds is an alternative way to hire AI/ML talent for analytics.
People who have crossed over industries, changed roles, or have demonstrated that they are connectors and integrators offer the adaptive thinking necessary to build the future of AI. HR teams can hire these people.
Reskilling is another way to meet the demand, which otherwise will lead to 1.37 million job losses. Draup has found that reskilling can save up to 50% of the cost per employee.
A company can reskill a ‘Systems Engineer’ to evolve to a ‘Big Data Analyst’ role. The sample case study shows how.
This whitepaper is written with data gathered from the proprietary sales intelligence platform by Draup. It can explore diverse job roles and locations, provide ecosystem insights into location, impact on traditional roles, intelligence into talent, and many others.
Draup’s platform can identify disrupted job roles and in-demand skills in data & analytics so that it can help talent managers analyze the emerging roles, predict optimized future career paths, and map out a reskilling journey with courses/certifications to fast-track transition.