The Data-Driven Edge: Intelligent Actions through AI Powered Decisions
Key Takeaways:
- Smart, fast decisions are vital in a highly competitive retail landscape
- AI-powered intelligence can help maximize revenue and customer satisfaction
- Natural Language Processing enables personalised human-like query’s, that can support quicker data analysis. Comprehensive planning and domain expertise can help shape the right AI decisioning strategy
Introduction: Outperforming The Competition With AI In Retail
With retail businesses around the world under ever-increasing pressure, many are turning to AI. The combination of stronger competition, greater consumer demand for personalization, fast-changing marketplaces, and pricing complexity mean retailers are having to operate smarter and more efficiently than ever before.
Retail companies using AI and machine learning technology are outperforming those who don’t. And when margins are razor-thin, and consumers can switch brands at the drop of a hat, this can make a real difference to success. This blog explores how AI-powered analytics and decisioning can be applied in practice, and drive sustainable revenue growth long-term.
Why Static Reports and Dashboards Are Holding Retailers Back
Historically, retailers have relied on traditional business intelligence tools to drive insights and inform their decision-making. However, as these tools lack the predictive capabilities and real-time responsiveness of AI, a BI-led approach is increasingly falling short.
Businesses still using BI tools have found that the time lag between data collection and decision implementation is too long. Human bottlenecks in analysis and interpretation slow things down further, and processing complex scenarios with many different variables often proves too difficult. As a result, businesses can’t respond to market changes quickly enough, miss new opportunities, and don’t always make the right decisions.
How AI-Powered Insights OfferA Competitive Advantage
Deploying AI decision agents allow retailers to move away from descriptive, reactive retail analytics towards more prescriptive, automated actions. Real-time processing capabilities, predictive modeling at scale, multi-variable optimization and self-learning all come together to enable a more proactive, dynamic and accurate approach.
This can be applied across key areas:
Dynamic Pricing
AI-powered pricing systems can adjust prices in real-time, based on many different data streams and variables, and have enabled some retailers to increase gross profit by as much as 10%.
These systems combine real-time competitor price monitoring, demand elasticity inventory movement, and analysis of trends, seasonal fluctuations and different customer segments. As a result, pricing can be constantly adjusted to maximize revenue, improve inventory turnover and reduce the manual workload of pricing.
Supply Chain Optimization
With modern retail involving such huge volumes of data, across customer interactions, inventory levels and market trends, real-time decision-making is essential to connect pricing decisions with supply chain realities.
AI solutions can improve the scale and accuracy of demand forecasting and inventory optimization, accounting for supplier risk, route logistics, quality control and seasonal demand. This can help right-size stock levels all year round, lower operational costs, improve supplier relationship, and avoid customer dissatisfaction caused by poor product availability.
Customer Behavior Prediction
The one-size-fits-all approach to customer engagement no longer works in today’s retail landscape. Personalized pricing, experiences and promotions that move beyond basic demographics are essential to drive higher conversion rates and customer lifetime value.
AI promotion strategies and pricing have made a real difference in this area, supporting an increased ROI for marketing campaigns. This is achieved by analyzing the widest range of data sources possible - purchasing history, browsing behavior, social media sentiment, customer service interactions and more - to deliver individually tailored recommendations.
It also helps reduce churn and improve customer satisfaction by sending the right messages to the right people and the right time, from promotions and suggested actions to cross-selling and up-selling opportunities.
Natural Language and Voice Insights
The use of Natural Language Processing (NLP) to support data analysis, can help retail staff get the insights they need faster and more easily than ever before. Complex SQL queries can be delivered as questions in simple English, and the NLP model can deliver answers to those questions in easily digestible form. This can expand access to data and insights across an organization, speed up and democratize decision-making, and can be used to automate report generation and data visualization.
A Strategic Roadmap for Deploying AI and Retail Analytics
Businesses that don’t deploy AI-powered analytics and other technologies for retail will continue to fall further behind competitors - but getting a deployment right demands careful planning and the help of expertise. From our experience in supporting retail businesses with AI, these four strategies represent the best way forward:
Identify Suitable Pilot Projects
Starting with small-scale projects that can quickly deliver tangible results can prove the value and concept of an AI led analytics deployment. This starts with an assessment framework that identifies and prioritizes KPI’s and considers technical feasibility. Common pilots include competitor price monitoring automation and inventory-based pricing adjustments, as these can quickly provide clear, demonstrable improvements in revenue, margin, process efficiency and customer satisfaction.
Build a Robust Data Foundation
Having good-quality, integrated data, available in real-time, is the bedrock of AI success. This should encompass critical data sources such as transaction and sales information, competitor pricing, inventory and supply chain data, and wider indicators across customer, market and economic sentiment. A good data strategy will leverage insights from this data through a scalable architecture, cloud-based storage, APIs, and data security and compliance measures.
Partner with Domain Expertise
An expert partner who understands the specific challenges of retail is essential. They should be able to demonstrate a proven track record of implementation, agile development methodologies, a collaborative partnership model and a human-centric design approach. This will ideally reach end-to-end, from initial concept, through implementation, to post-deployment support and ongoing optimization.
Embed Ethical AI
Ensuring that AI deployments are ethical remains one of the biggest challenges in AI engineering. It’s vital that AI decision-making is transparent, free of bias, fair in its decision analysis and doesn’t introduce bias. This can be achieved through regular responsible AI audits, and a continuous approach to monitoring across model performance evaluation, testing and validation. In turn, this will also help alleviate concerns around change management, user adoption and trust in AI.
In Summary: Finding The Right Partner To Maximize Retail AI Potential
The use of AI will continue to grow in the months and years ahead, with the global market set to reach over $800 billion by 2030. It won’t be long until AI is considered a necessity rather than a differentiator, so if your organization hasn’t done so already, now is the time to move towards increased AI integration across business functions and adopt AI-powered decisioning.
Ciklum is perfectly placed to help you in this endeavor, as a strategic partner as well as a technology provider. We can help you develop an AI strategy and roadmap, develop custom decision solutions, and give you all the support you need behind the scenes across data architecture, integration, change management, training and optimization.
To find out more on how we can help you establish scalable, future-proof AI solutions with accelerated time to value, talk to the Ciklum team today.
