The 4 Stages of AI Maturity for E-Commerce Companies
By Carl Bleich
The 4 Stages of AI Maturity for E-Commerce Companies
Table of Contents
Artificial intelligence is rapidly changing the world of e-commerce and thanks to the advancements made in generative AI, online retail will never be the same.
As technology continues to revolutionize the way shoppers engage brands online, e-commerce companies are embracing AI solutions at an unprecedented pace. From personalized recommendations to streamlined customer service, AI has become a cornerstone of success in the digital marketplace.
But before you can fully understand the ever-changing landscape, you have to understand where your company stands today with its digital and AI maturity. And if your company is just beginning its journey with AI-powered SaaS technology or doesn’t quite know where to start, don’t worry — you’re not alone.
Moving both too fast or too slow in adopting AI-powered technologies can be damaging to brands. Having a deep understanding of where your business fits in the marketplace and what specifically each AI-powered technology can do to benefit your brand is essential before investing your resources.
In this guide, we’ll explore the four distinct stages of AI maturity, developed by enterprise digital development consultancy Codal. We’ll uncover the challenges and opportunities presented at each stage, offering actionable advice to help you achieve AI-driven excellence. We’ll also dive into popular use cases that will help e-commerce companies understand which AI-powered solutions their collected customer data has them ready to adopt right now.
Whether you're just starting to explore the possibilities of AI or looking to enhance your existing AI capabilities, use this guide as your roadmap. Our aim is to empower you with the knowledge and tools necessary to make informed decisions and stay ahead of your competition.
Common AI Use Cases in E-Commerce
Before we dive into the different stages of maturity and where your company might fall, let’s learn more about the most common use cases in digital commerce today.
AI has been a game-changer for early adopters in e-commerce, revolutionizing the way businesses operate and can interact with their customers online. By leveraging AI technology, e-commerce companies are able to enhance various aspects of their operations, and as a result provide a more seamless and personalized shopping experience for their customers.
With its ability to analyze vast amounts of data and provide valuable insights, AI has become a powerful tool for businesses looking to enhance customer experiences and drive growth in the digital marketplace.
If you aren’t yet using AI to your company’s advantage, these are a few AI-powered use cases that you’re missing out on:
Using AI to make predictions about customer behavior has allowed e-commerce companies to both better serve their customers and generate more revenue.
AI-powered predictive analytics offer businesses invaluable insights and the ability to make data-driven decisions. By applying sophisticated algorithms and machine learning techniques against a rich set of historical data, predictive analytics allows companies to forecast future trends, customer behavior, and market dynamics. This use case holds immense importance for businesses looking to stay ahead of the curve and gain a competitive edge.
With AI-powered predictive analytics, businesses can analyze vast amounts of historical and real-time data to identify patterns, trends, and correlations. This enables them to anticipate customer needs, personalize marketing strategies, and deliver targeted offers at the right time, ultimately enhancing customer satisfaction and driving sales.
Predictive analytics empowers businesses to optimize inventory management and supply chain operations. By analyzing historical data, market trends, and external factors like global economic indicators or world events, AI algorithms can generate more accurate supply-and-demand forecasts. This allows businesses to stock inventory efficiently, minimize stockouts or overstocks, and streamline their operations, leading to improved cost efficiency and customer satisfaction.
Predictive analytics empower digital commerce companies to make data-driven decisions, a must in online retail today.
AI-powered product recommendations have become a cornerstone of the e-commerce industry, revolutionizing the way businesses engage with their customers.
This powerful use case utilizes sophisticated algorithms to analyze vast amounts of customer data and browsing history, enabling businesses to deliver personalized recommendations that resonate with individual preferences. The importance of AI-powered product recommendations cannot be overstated, as it significantly enhances the customer experience and drives sales.
Personalized product recommendations have the ability to capture customers' attention, guiding them towards relevant products they may not have discovered on their own. By leveraging AI algorithms, businesses can understand customer behavior, preferences, and purchasing patterns, allowing them to tailor recommendations that align with each individual's unique tastes. This level of personalization builds customer trust and loyalty, ultimately leading to increased engagement and higher conversion rates.
The impact of AI-powered product recommendations extends beyond immediate sales. By offering personalized suggestions, businesses can create a more seamless and enjoyable shopping experience for their customers. This not only increases customer satisfaction but also encourages repeat purchases, as shoppers feel understood and catered to. The convenience and efficiency of AI-driven recommendations ensure that customers find what they're looking for quickly, reducing frustration and improving overall satisfaction.
Sentiment analysis, also known as opinion mining, is a powerful application of artificial intelligence that allows businesses to understand and analyze the emotions and opinions expressed in text data. By utilizing natural language processing and machine learning techniques, sentiment analysis enables businesses to gain valuable insights into customer sentiment, helping them make informed decisions, enhance brand reputation, and improve customer experiences.
With social media platforms, online reviews, and forums playing a significant role in shaping public opinion, businesses need to stay attuned to what customers are saying about their products, services, and brand as a whole. Sentiment analysis provides a systematic way to monitor and analyze these conversations, enabling businesses to gauge public perception and sentiment accurately.
AI-powered sentiment analysis revolutionizes e-commerce ad campaigns like never before. This intelligent tool, friendly to both beginners and professionals, reads between the lines of customer feedback, giving you a nuanced understanding of your audience's emotions and sentiments. The result? Smart, data-driven ad strategies that resonate with your customers on a personal level, boosting engagement and sales. It's not just about reading data, it's about comprehending emotions, making AI sentiment analysis an authoritative game-changer in the e-commerce arena.
By leveraging sentiment analysis, businesses can identify positive and negative sentiments associated with their brand, products, or services. This valuable information helps them identify areas of strength and weakness, allowing for targeted improvements and addressing customer concerns. Understanding customer sentiment allows businesses to tailor their strategies, messaging, and offerings to better meet customer needs and preferences.
Customer segmentation is a crucial practice in marketing that involves dividing a company's customer base into distinct groups based on shared characteristics, behaviors, or preferences. This AI-powered strategic approach allows businesses to better understand and cater to the diverse needs of their customers.
By segmenting customers, businesses gain valuable insights into their various consumer groups, including the unique needs, desires, and pain points of each segment. With a deep understanding of their target audience, businesses can develop highly personalized marketing campaigns that resonate with specific segments, leading to increased relevance and engagement.
An essential aspect of customer segmentation is the ability to prioritize resources effectively. By identifying high-value segments, businesses can allocate their time, budget, and efforts where they are most likely to yield optimal results. For instance, a digital commerce company may discover that a particular segment has a higher customer lifetime value or a greater potential for cross-selling. This knowledge allows businesses to focus their resources on nurturing those relationships, maximizing ROI and long-term customer loyalty.
Using AI to connect with a diverse customer base more effectively is a great way to ensure you’re meeting as many needs of your customers as possible.
AI-powered dynamic pricing leverages advanced algorithms and machine learning to dynamically adjust prices based on a variety of factors, such as demand, competition, and market trends.
Consumer behavior is constantly evolving, and traditional static pricing strategies may fall short in capturing the full potential of demand and profitability. This is where AI-powered dynamic pricing steps in, offering businesses the ability to adapt their pricing in real time. By analyzing vast amounts of data, including historical sales, competitor prices, and market conditions, AI algorithms can accurately determine the optimal price for each product at any given moment.
AI-powered dynamic pricing allows businesses to maximize revenue by pricing products at their most profitable points. By identifying price elasticity and demand patterns, AI algorithms can automatically adjust prices to balance profitability and sales volume. This ensures that businesses can capitalize on high-demand periods and respond quickly to changing market conditions, ultimately driving growth and profitability.
Using AI to manage your inventory is helping e-commerce companies predict demand fluctuations and ensure that products are always at optimal stock levels.
Traditional inventory management methods often rely on manual analysis and historical data, which may not fully capture the complexities and fluctuations of market demand. AI algorithms, on the other hand, can process a wide range of variables, such as sales history, market trends, and even external factors like weather or holidays, to make accurate demand predictions. This allows businesses to optimize their inventory levels, ensuring they have just the right amount of stock to meet customer demand without tying up excess capital in unnecessary inventory.
AI-powered inventory management systems enable businesses to automate and optimize their replenishment processes. By continuously monitoring sales data, stock levels, and lead times, AI algorithms can proactively trigger purchase orders or production schedules. This ensures that businesses can replenish their inventory in a timely manner, avoiding stockouts or overstock situations. Furthermore, AI systems can optimize the order quantities and timing based on factors like supplier performance, production capacity, and transportation constraints, leading to cost savings and improved supply chain efficiency.
The Stages of AI Maturity
If those use cases (or other AI-powered use cases) sound like they may benefit your company, it’s time to start taking a closer look at the AI maturity of your company and where exactly it stands right now in the marketplace.
Codal breaks down AI maturity into four different stages: exploration, adoption, guidance, and automation. Understanding which stage your company falls in — which is impacted greatly by data readiness — is the first step in planning your company’s roadmap with AI-powered technology.
In the exploration stage, e-commerce companies are just beginning to learn about AI-powered technology solutions. The business is learning about the benefits and challenges of AI in e-commerce and is investigating or in the early stages of using third-party AI solutions such as ChatGPT.
The exploration stage is the starting point where businesses begin to recognize the promise of AI for their operations. It's a stage where companies are experimenting with AI technologies, conducting pilot projects, and testing different use cases. They may be exploring AI-powered solutions for inventory management, customer segmentation, personalized recommendations, demand forecasting, or dynamic pricing.
During this phase, businesses are focused on gathering insights, understanding the capabilities of AI, and evaluating its potential impact on their specific industry and market. They may be investing in research and development, collaborating with AI solution providers, or building in-house AI expertise. The exploration phase involves learning from both successes and failures as companies discover what works best for their unique business needs.
In this stage, companies are starting to understand how external AI solutions leverage specific inputs of data to produce their desired outcomes. There is no requirement or need to have a specific amount of data at this stage — that comes later in the model.
It’s important for e-commerce companies to navigate the exploration phase strategically and with a clear understanding of their goals and objectives. By staying informed about the latest AI advancements, collaborating with experts, and continuously learning from industry best practices, businesses can position themselves for success in the rapidly evolving world of AI-driven e-commerce.
In the adoption stage, e-commerce companies are beginning to wade into the AI waters, so to speak.
Stakeholders understand the AI capabilities that are available in the marketplace, just not which exact ones are perfect for their specific business or use cases. The output here involves integrating AI tools with e-commerce platforms to test the impact on business operations.
E-commerce companies in the adoption phase have recognized the value and benefits that AI can bring to their business. They understand that AI has the potential to enhance customer experiences, optimize operations, and drive growth. As a result, these companies are investing resources, both in terms of finances and talent, to implement AI technologies across various aspects of their business.
In the adoption phase, e-commerce companies are strategically deploying AI use cases. They may be partnering with AI solution providers, building in-house AI teams, or utilizing AI platforms to accelerate the integration of AI into their existing systems and processes.
This is the phase where companies recognize that AI technologies thrive on large datasets, and thus, they are actively gathering and organizing their data to fuel AI-driven insights and decision-making. This involves capturing customer behavior, transactional data, and other relevant information to train AI models and improve the accuracy of AI-enabled applications.
The adoption phase of AI maturity for e-commerce companies represents a significant step toward realizing the potential of AI technologies.
The progression from the adoption to the guidance stage comes when you are taking your data and using it to power AI that helps make business decisions for your company with the help of human interaction.
In this stage, it’s essential that the data you’re using to train your AI models is 100% accurate. It would be to your advantage to consolidate your data into one source and make enhancements to it, on top of ensuring its accuracy.
Once you’ve properly prepared your data, you can begin to leverage AI to guide decision making through predictive analytics and other forms of business forecasting. The AI solution at hand will convert massive amounts of data from your sources into actionable insights for your company.
While you’re relying on AI for key outputs in the guidance stage, that AI doesn’t yet work independently from human oversight. Your commerce-driving team must be trained to work hand in hand with your AI-powered technology. This will ensure outputs are accurate and useful to your business.
The automation stage is the final stage of AI maturity, where your company has tested the AI models and trusts them to the point where no consistent human intervention is necessary.
For example, if your company needs to know when to order additional inventory, it would be fully relying on the AI model to make that decision rather than your commerce-driving team. Your commerce-driving team’s responsibility is to monitor the quality of data going into the AI model to ensure that it can make proper decisions.
The automation phase of AI maturity for e-commerce companies is an exciting time that brings significant advancements and opportunities. As businesses embrace artificial intelligence and automation technologies, they can streamline various processes and enhance their operations.
Codal Collaborates To Use AI To Power Food Production Systems
In collaboration with a leading chemical manufacturer, Codal built an AI-powered solution that automates the ordering and reordering cycle of chemical sprays for food production systems, helping to streamline pest control management and increase demand for pesticide products.
Data is first collected from IoT-enabled bug traps, showing the volume and types of pests in specific farm locations. Then, AI and ML algorithms analyze this data to determine the optimal pesticide product and how much of it should be applied to any given area.
The necessary pesticide product is then automatically ordered or reordered, with no human intervention required. The pest data is further leveraged to program autonomous crop-spraying drones with the most accurate coordinates for their next spray cycle.
Codal’s collaboration with the leading chemical manufacturer showcases the impact that AI can have on a business operation in the automation stage.
Prioritize Your AI Maturity With Bloomreach and Codal
Regardless of where your company stands with its AI maturity, Bloomreach and Codal are prepared to work together to help you achieve your AI-related goals.
Whether it be learning how to first offer personalized product recommendations to your loyal customers or taking a fully automated approach to digital commerce, the combination of Codal’s data-driven engineers and the Bloomreach Commerce Experience Cloud will help your company win online.
If you’re looking for more information on your AI maturity and what it means in this ever-changing landscape, contact us today.
Codal is an enterprise design and development consultancy that specializes in eCommerce, helping brands solve complex problems and accelerate growth through elegant, data-driven digital solutions. Since opening its doors in 2009, Codal has worked on the leading edge of eCommerce, crafting award-winning DTC storefronts, AI-powered shopping experiences, B2B sales portals, marketplace apps, and more. By delivering best-in-breed front- and back-end solutions, Codal enhances the customer experience, maximizes internal efficiencies, and drives revenue for brands across industries.
Bloomreach personalizes the e-commerce experience. It unifies real-time customer and product data so businesses understand what customers really want. By connecting that understanding to every channel, the e-commerce experience becomes limitless — continuously reflecting a changing customer as they shop. With the scale of AI, this creates endless new paths to purchase, greater profitability, and fast business growth. Bloomreach products include: Engagement, a marketing automation platform; Discovery, an e-commerce search solution; and Content, a headless content management system. The company has multiple AI patents and serves 850+ global brands including: Albertsons, Bosch, Puma, and Marks & Spencer.