Retail solutions
Grow your customer value and win new market share
Watch a location analytics video demoData insights to help retailers build competitive omni-channel strategies
We help retail brands extract insight from their data, to optimise customer experience, and grow customer value and brand loyalty.
From location analytics to loyalty schemes, with 15 years working closely with IKEA brands world-wide, Ikano Insight now assists all retail businesses to adjust quickly to consumer behavioural shifts, and become more competitive in an omni-channel market.
We’ll help you precisely target new customers online and offline through our retail specific business intelligence solutions.
Retail solutions
Location analytics
Identify and track competitor customers,
map retail catchment areas,
and directly target market share growth
Customer analytics
Communicate with your target customers based upon their behaviour, life-stage, demographics and preferences.
Marketing analytics
Target the value potential in your customer base, and take action to maintain growth and stem decline
Pricing analytics
Price and discount elasticity modelling predicts customer behaviour and enables you to maximise return and profitability
Product analytics
Target your customers with next best product offers using recommendation engines to increase purchase frequency and value
Location planning
Plan new store locations based on actual customer movement and competitor analysis
Our work
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Chat with our experts at a time that suits you, to explore how advanced analytics will change your business.
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Retail Solutions Frequently Asked Questions
What are the core elements of retail BI?
The core elements of retail business intelligence (BI) encompass robust data collection methodologies, advanced analytics techniques, intuitive data visualisation, and comprehensive reporting mechanisms. These elements work together to empower retailers to unlock actionable insights from the vast amounts of data that they hold.
Why is business intelligence important in retail?
Business intelligence holds paramount significance in the retail sector due to its ability to catalyse data-driven decision-making. By leveraging BI, retailers can unearth invaluable customer trends, optimise inventory management, streamline operations, revolutionise marketing strategies, and ultimately gain a competitive edge in the dynamic retail landscape.
How does data integrate with other platforms used within retail business?
Data integration lies at the heart of an interconnected retail ecosystem. Through meticulous data integration practices, retailers can consolidate disparate data sources, including point-of-sale systems, CRM platforms, e-commerce portals, supply chain systems, and more. This seamless integration empowers retailers to achieve a comprehensive and holistic view of their business operations and customer interactions, allowing them to make insightful analysis and informed decision-making.
How do Ikano Insight work with their clients?
Ikano Insight adopts a consultative approach in their collaboration with clients, engendering a close partnership rooted in data analytics expertise. Through their proven methodologies, Ikano Insight assists retailers in unlocking the full potential of their data. Their services encompass comprehensive data analysis, granular customer behaviour insights, optimisation of marketing campaigns, deployment of machine learning, impactful self-service reporting via data visualisation, and data-driven business decision support, all aimed at increasing market share, driving growth and expansion, and enhancing customer value.
What types of retailers can benefit from data analytics?
The benefits of data analytics extend across nearly all retail verticals, including brick-and-mortar stores, e-commerce retailers, omnichannel enterprises, supermarket chains, and fashion retailers, among others. Irrespective of retail type, business intelligence empowers retailers to discern customer preferences, refine inventory management strategies, optimise pricing models, enhance marketing effectiveness, and strengthen overall business performance.
How long is a typical project?
The duration of a data analytics project depends upon its scope and intricacy. While project timelines can vary, a typical engagement may span several weeks to months. Factors influencing project duration include dataset size, complexity of analysis, availability of requisite data sources, and specific project objectives. A well-executed data analytics project ensures a comprehensive exploration of insights while balancing efficiency and thoroughness.
