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Home Artificial Intelligence How to Boost Retail Sales through AI-Driven Strategies

How to Boost Retail Sales through AI-Driven Strategies

1/03/2024
(10 min)
Gregory Ovsiannykov
Managing Director
preview ai in sales

Virtual assistants, VR fitting rooms, highly personalized product suggestions – the world of online shopping is far from what it used to be only a decade ago. 

The modern retail market is dominated by Artificial Intelligence (AI), an advanced technology that makes things simple for businesses and users. 

Though many companies are still considering AI a costly and complex integration, others have harnessed its untapped potential and reaped the gains of accurate predictive analytics, automation of all vital processes, retail pricing optimization, and data-backed decision-making.

If you’re still not sure whether your retail business needs AI solutions, just look at a couple of stats I’ve come across: 

  • AI in the retail market is predicted to exceed $85 billion by 2033 – compared to a market cap of $4.84 billion in 2021.  
  • Machine Learning (ML) occupies 30% of all AI use in retail as of 2022. 
  • The largest market for AI use in retail is North America, and the fastest-growing market is Asia-Pacific. 
  • The CAGR of this market niche is a whopping 32%+

The Place of AI in Retail 

AI is the use of advanced computer algorithms to perform human intelligence-like activities, such as learning, reasoning, and decision-making. These algorithms first learn based on datasets they’re fed and then develop on their own, prone to self-correction and self-improvement. 

Due to these specifics, AI models are widely used in predictive analytics and process automation. 

Retail businesses have a variety of options for benefiting from AI tools, such as: 

  • Customer behavior analysis 
  • Inventory management 
  • Demand forecasting 
  • Personalized marketing 
  • Pricing optimization 
  • Fraud detection and prevention 

Let’s consider each of these implementation aspects in more detail in the next section.

How Can AI Boost Increased Retail Sales?  

How to improve sales performance in retail with the help of AI solutions? Here are the top ways I’ve found to work the best in this digital business sector.  

Customer Behavior Analysis 

An insight into who your customers are and what they prefer is valuable knowledge that can help you offer more personalized services. 

AI-driven analytics gives a 360-degree view of customer preferences, and predictive modeling using those analytical data can provide targeted product recommendations. 

Another option is using AI-powered chatbots for quality customer service improvements. All these efforts can help you build better relationships with your target audience. 

Inventory Tracking 

Due to AI programming advancements in demand forecasting, your business can now avoid overstock and stockouts. 

Smart AI systems can conduct inventory tracking and optimization in real time, thus elevating your inventory management efforts to a new level. 

These innovations are a sure cost-reducing step that also improves customer satisfaction with your retail services.  

Predictive Demand Analysis and Forecasting  

Accurate demand forecasting is critical for retail businesses, as it helps them optimize stock levels and production based on real-time demand dynamics. 

“According to my experience, accurate demand forecasting is a game-changer for retail businesses. Machine Learning algorithms can provide the precision needed for predictive demand analysis, benefiting retailers of all sizes.”

Max Roshchyn

Global RGM lead at Kraft Heinz

Customization of Marketing Efforts  

Customers are likely to develop more brand loyalty if they receive custom tips and product recommendations based on the analysis of their preferences and behavior. 

These in-depth insights into your customer profiles can boost your cross-selling and upselling potential, translating into promo optimization, greater revenues, and customer satisfaction. 

Real-Time Price Adjustments  

The use of retail pricing optimization software can enhance the company’s profit margins while keeping customer satisfaction consistently high. 

AI algorithms can be trained to offer dynamic pricing based on the intersection of demand, competition, and customer purchasing patterns. 

Anti-Fraud Measures  

Online businesses are vulnerable to a great number of cyber threats and exploit risks, with criminals targeting sensitive personal and financial details. 

Anti-fraud AI software can visualize patterns and anomalies in financial transactions to alert the company about suspicious activities and protect customers and businesses from breaches or theft.

real time anomaly detection

AI Strategy Deployment in Retail 

Starting out with AI revenue growth and optimization software can be tricky. Here is a step-by-step guide for AI model implementation in a retail business. 

#1 Business Needs Analysis 

You won’t benefit from AI if you don’t identify a specific use case for this technology in your specific business. Focus on your vital business needs and goals. Determine the gaps or inefficiencies AI can resolve. 

In most cases, AI apps work best with process automation, decision-making aid, customer experience enhancement, and data accuracy. 

#2 AI Software Selection 

Now that you know what AI integration will be used for, it’s time to choose a specific AI solution that meets your special needs. 

These may be chatbots, NLP products, machine learning and modeling software, and the like. The world of AI is vibrant and diverse, and you should choose the best-matched option for your business strategy. 

#3 Data Strategy Design 

At the core of proper AI algorithm training lie huge datasets, which you should collect and provide for the system to work as it should. 

You have to develop a clear plan for data collection and organization: identify the data you need for AI strategy implementation, determine how you will collect and store it, who will do all data-related tasks, and what insights you’re planning to get from AI tools based on that data. 

#4 AI Team Compilation 

AI integration is impossible without an appropriately qualified team, so it’s time to find a reliable tech partner with in-depth AI expertise. 

You may source some experts from the in-house team but don’t forget about the impressive potential of team augmentation. 

We at RNDpoint provide dedicated development team and team augmentation services to help clients compile teams with a comprehensive tech stack for AI solution development and deployment. 

#5 AI Algorithm Training 

Now that the data and statistics are compiled in one place, and your team is up and running, it’s time to proceed to AI model training. 

Feed the data into the AI algorithm, set its decision-making rules, and watch it learn to engage in related decision-making. 

At this stage, the engagement of AI data scientists is vital, as they can streamline your model’s training and make adjustments underway so that it produces accurate results. 

#6 AI Model’s Integration 

As soon as the model is adequately trained on historical data and produces consistently accurate results, it’s time to apply it to real-time decision-making. 

At this stage, you should integrate it with business operations and ensure its alignment with the existing IT infrastructure. 

It’s vital to ensure smooth integration without troubling disruptions of your retail business’s operations, which is possible through proper staff training and technical staff support. 

#7 Performance Tracking and Evaluation 

The AI model’s successful integration is not the end of the process but rather its beginning. 

From now on, you need to monitor its performance against a set of well-defined metrics and criteria, which reveal the model’s contribution to your organizational success. 

It also makes sense to analyze the impact of integrated AI solutions on the business aspect they were meant to improve.

#8 Iterative Upgrades 

AI algorithms’ logic is not set in stone. It should be upgraded and adjusted as soon as the market dynamics change. 

It’s important to have a dedicated AI expert who can monitor the AI model’s propriety and update its mechanics in case your business needs change or the environment evolves.

AI-Related Business Challenges and Risks 

Obviously, I would never call AI usage a panacea for all business challenges and goals. 

This innovative technology is still a black box for many, with a lot of implementation challenges and risks surrounding it. 

Here are the two most important aspects to consider before launching your AI project. 

1. User Data Privacy and Ethics of Use  

Since AI algorithms and models are trained on large datasets, the source of that big data often becomes a matter of ethical and legal debates. 

“I suppose that one of the key challenges in AI adoption is the ethical use of user data. Privacy concerns and the potential for bias in AI models are critical aspects to consider before launching any AI project in the retail sector.” 

Max Roshchyn, Global RGM lead at Kraft Heinz

Every state has some form of privacy legislation in place today, which regulates users’ private data utilization for business purposes. 

The very fact that AI uses large data pools poses significant threats to privacy. Besides, datasets for AI model training may get into the wrong hands and abused, with uses ranging from theft to cyberbullying. 

Another ethical dimension is proneness to bias. AI models won’t be biased only provided their training data is also unbiased. 

However, checking the big dataset for bias is a task next to impossible, thus causing the potential risk of AI’s promotion of long-held discriminatory and biased opinions and decisions based on those subjective beliefs. 

2. Expenditures on AI Introduction  

AI technology is relatively new, so using and developing custom-tailored AI applications is costly for many enterprises. 

Simple AI models cost around $20,000 and can reach hundreds of thousands of dollars. 

The main AI-associated risk I currently observe in the market is the uneven access to AI among traditional businesses. It can create a discriminatory environment in the digital space, limiting some companies’ access to data and concentrating the power in the hands of the few data-holding giants. 

Retail Case Study: AI-Driven Pricing Optimization

RNDpoint has vast experience in AI software development and has implemented a series of successful AI models for clients in the retail business. 

Here is one of the case studies we can share to showcase our professionalism and in-depth understanding of all AI strategy integration aspects. 

How it all started. A Dutch grocery store contacted RNDpoint for AI development services with an aim to regain its market share and offer better value for the customers’ money. 

How we did it. The RNDpoint’s team analyzed the client’s business framework and identified two areas for improvement, developing two AI models to address them.

ai in retail

The SKU segmentation model focused on the key value categories (KVC) and key value opportunities (KVO) for the client to inform price optimization based on customer analytics

The price optimization model was further employed to calculate optimal dynamic pricing in relation to the SKU segmentation model’s parameters and competitor prices. 

What our client got. As a result of these models’ joint work, the client managed to attract customers with optimal prices without violating its profit margin expectations. 

How we can help. RNDpoint delivers professional AI services for retail based on your project needs and requirements. Turn to our managers for AI consulting and harness the power of AI algorithms for your business growth. 

Conclusion 

AI and retail go hand in hand, with a variety of AI strategies applicable for retail metrics improvement, process automation, and smarter revenue growth management

You can implement a huge set of sales strategies and techniques to boost your market presence, win customer loyalty, and optimize your vital business processes at all levels. 

It’s high time to hire AI developers and get the best out of the AI potential for your business. 

What is the ROI I can expect from implementing AI solutions? 
Does RNDpoint implement AI strategies for retail businesses? 
What are the key areas where AI is used to enhance retail sales strategies? 
What are the key challenges associated with implementing AI-driven strategies in the retail industry? 
What kind of support and training do you provide for smooth implementation? 
Can your AI solutions integrate with my existing retail management system? 
How user-friendly are your AI solutions for someone not very tech-savvy? 

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Gregory Ovsiannykov
Gregory Ovsiannykov
Managing Director
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