Sourcing and Procurement

Revolutionising category management with data analytics

Category management presents a business with multiple paths to success. The key to choosing the best way is difficult without category management analytics and actionable insights. According to a leading management consulting firm, retail businesses that use data-driven category management outperform their competition by 20%.

In a fast-changing business landscape, data-driven insights drive growth and profitability by strategically organising product categories. This helps businesses achieve their objectives and meet customer demands.

This article explains ways to use data analytics in category management, their outcomes, their impact on decision-making, and an ideal approach.

Data-driven insights in category management

Decision-making in category management must shift from traditional methods that rely on intuition-based judgment, manual analysis, and a hierarchical approach. Traditional methods lead to missed opportunities, inefficiencies, and undesirable outcomes.

There is a need for a paradigm shift in category management using analytics to bring precision, predict market trends, and discover hidden patterns. This is possible through advanced analytics, artificial intelligence, and machine learning to analyse vast data.
Data-driven insights help understand and predict customer preferences and emerging opportunities precisely. Businesses can collaborate with suppliers better to improve supply chain operations and efficiency.

Using data analytics to optimise category management

To effectively use data analytics to optimise category management, you must know your goals, segment categories, and customers and analyse your performance.

Outline category objectives

By defining objectives, you make the data analysis exercise fruitful. You want to ask yourself these questions:

  • What goals do you want to achieve under each category?
  • Do these align with the overall business goals?
  • What parameters will you use to measure your success?

Segment categories and customers

Segment categories and customers based on sales, profits, loyalty, buying preferences, behaviour, and demographics to identify those with maximum profitability. Tailor pricing, promotion, placement, and assortment strategies for each category.

Analyse category performance

After categorisation, analyse their performance against a benchmark using scorecards, reports, dashboards, and visualisations. Track and monitor KPIs such as market share, sales margin, inventory turnover, and customer satisfaction. Identify trends, threats, opportunities, and market gaps.

Outcomes of data insights in category management

Data insights in category management present diverse insights that allow businesses to stay ahead in a changing market:

Market trends and customer behaviour

Examine historical sales and purchase data and demographic information to identify shifting customer behaviour, market dynamics, and emerging trends. Develop custom marketing strategies and tailor the products and services accordingly.

Competitive landscape

Conduct a comprehensive analysis of customer pricing, product assortments, and market positioning to identify differentiating factors and competitor strategies. Identify gaps, adjust your positioning, and stay ahead of the competition.

Pricing and promotional strategies

Optimise your pricing and promotion strategies through pricing elasticity, market and brand seasonality, customer segmentation, and the impact of promotion on sales figures. Determine optimal pricing points, identify growth opportunities, and evaluate the effectiveness of marketing campaigns.

Approaching category management with data analytics

Businesses must incorporate strategy into category management, tie insights with opportunities, and dig deeper only when necessary. Here is a chronological sequence of steps towards advanced category management analytics:

  • Start with a bigger picture
  • Review the categories from a bigger picture, such as the national market across key channels. These insights give you a perspective to incorporate into tactical analysis and recommendations.

  • Drill down for tactical results and trends
  • Compare long-term (e.g. 52-week) and short-term (e.g. 12-week) trends. Analyse the customer decision tree to drill through the data for tactical results.

  • Use multiple data sources
  • Avoid a linear analysis and use data from several sources such as point of sale and consumer panel. Rather than looking at them in isolation, look across the sources to draw better insights.

  • Avoid generalisation of insights
  • Make the numbers more meaningful by tying in with a strategy and finding relevant benchmarks to create volume opportunities. Know how the index stacks up against the goal and compare your insights with those of your competitors.

  • Articulate a strategy
  • A well-developed strategy helps retail teams align short-term decisions with long-term business objectives. Know the guidelines, principles, and processes important for the strategy.

  • Incorporate retailer strategy and shopper
  • Be it assortment analysis, shelving, pricing analysis, or setting up promotions, make sure the strategies align with the buyer.

  • Use category thresholds
  • Finally, you want to ensure the strategies are driving sales. One of the ways is to look at each SKU and analyse its $ share as an indicator. Another way is to look at the performance of a bucket of SKUs over several years.

How can Infosys BPM help?

The Category Control Tower (CCT) at Infosys BPM is a futuristic genome-based solution for real-time insights and proactive alerts and is platform agnostic.

Read more about leveraging category management analytics at Infosys BPM.

Recent Posts