/Insights/The 4 stages of AI maturity in eCommerce

The4stagesofAImaturityineCommerce

Oct 2, 2023

9 min. read

For brands looking to maximize the value of AI, it’s often difficult knowing where you stand and how to take the step. That’s why our experts developed a model called The 4 Stages of AI Maturity. Check out this blog to learn about the activities, technologies, and data requirements involved in each stage of the AI journey.

With the rise of customer service chatbots, robotic process automation, smart shopping, and other innovations, business leaders across all industries and verticals are asking questions about AI—questions like: What AI solutions are currently available? Which ones are the best fit for my business? And how do I go about implementing them?

35% of businesses already use AI on some level, and 42% were actively exploring the possibilities of AI in 2022.

As a design and development consultancy that digitally transforms enterprises from the ground up, Codal often works with clients who are motivated to modernize their business by investing in AI, but they don’t know where to start.

Thus, our team of experts put together a simple model called The 4 Stages of AI Maturity. Here, we break down the tools and processes involved in each maturity stage, and how to advance from one stage to the next. In short, your business’ AI capabilities will ultimately depend on its ability to transform, centralize, communicate, and leverage massive amounts of data from various systems.

This guide was presented at the 2023 Bloomreach Edge Summit in Napa Valley, California, where Codal’s leadership team met with business leaders from all corners of eCommerce to provide helpful insight and evaluate their AI maturity.

Listen to Keval Baxi, CEO of Codal, discuss the four stages of AI maturity, and keep reading to learn more!

Top AI applications in eCommerce

AI is currently revolutionizing the online selling space. So before we move any further, let’s highlight some of the most impactful AI use cases for eCommerce merchants.

Sentiment analysis

Powered by natural language processing (NLP), big data, and machine learning tools, sentiment analysis involves monitoring public opinion online, then leveraging that data to enhance your brand reputation and the customer experience.

Consumer sentiments are pulled from various sources—from social media platforms, to website reviews, to forums—and organized on a centralized dashboard, where positive and negative emotions are identified, presenting trends and improvement opportunities via intuitive tables, graphs, and charts. This helps brands identify and evaluate the severity of specific customer frustrations, whether it be slow shipping, expensive prices, poor customer service, or inaccurate product descriptions.

The manual process of collecting and extracting these actionable insights from multiple locations over long periods of time wouldn’t be scalable—but with AI, it doesn’t have to be. With little to no human intervention, AI-enabled sentiment analysis shows you how people are talking about your brand online, so that you can make necessary, data-driven adjustments.

Dynamic pricing

Dynamic pricing—also known as surge and time-based pricing—is a business strategy that utilizes variable prices rather than fixed ones to account for various factors, such as demand, competition, scarcity, market trends, customer type, and more. For example, a clothing brand increasing the price of jackets during winter time, or an industrial supplier offering lower prices to longstanding buyers.

For eCommerce merchants, an AI-enabled dynamic pricing strategy can make a huge impact on sales. Today’s algorithms can scan internal and external data to make predictions about customers and the market, then automatically calculate and implement the optimal price of a product or discount—resulting in maximized revenue and a competitive advantage for your brand.

Chatbots

Chatbots and virtual assistants are becoming smarter every day, providing close, if not the same level of support to online shoppers as <em id="">real</em> customer service representatives. Research shows that today’s chatbots are able to satisfy 70% of customer interactions , and that 74% of customers actually prefer chatbots to human agents when solving their problems.

Using NLP to replicate real human conversations, these bots can answer basic questions, facilitate transactions, direct customers to specific products or services, and—when a true human touch is required—connect customers with a representative.

Plus, chatbots are available to serve customers 24/7, allowing businesses to save time and money, as well as reallocate customer support resources to more complex, hands-on issues.

Personalized product recommendations

According to a recent survey, 77% of consumers are willing to recommend and purchase more from brands that deliver personalized online shopping experiences.

A personalization strategy involves leveraging historical and real-time data to put the right products in front of the right customers at the right time. For example, a customer browses scarves on an online store, so the brand recommends scarves on the homepage during the customer’s next site visit, or sends them a marketing email to promote a current discount on scarves, or places retargeting ads for scarves on their social media, or all of the above.

The point is this: Personalized product recommendations increase customer engagement, conversions, and sales. With an AI-enabled eCommerce personalization platform like Bloomreach , your business can leverage data to automatically personalize its customer journey and marketing initiatives. Looking ahead, the more data you collect, the smarter the AI will become, resulting in even more personalized experiences.

Inventory management

AI is also being used to avoid common inventory management issues, such as inaccurate product data, overstocking and understocking, and shipment delays. Aside from human error, the real culprit behind these issues is a lack of access to historical and real-time data.

Now, warehouse managers can rely on AI algorithms to scan inventory data and make predictions around demand. Based on this knowledge, the AI determines which products should be reordered, how many units, when the orders should be placed, and so on—allowing the warehouse to eliminate redundancies, save shelf space, and conserve resources.

AI maturity stages & data readiness

Based on the professional experiences of our product innovation and strategy experts, Codal has created The 4 Stages of AI Maturity—a guide to help businesses understand where they’re at in their AI journey, along with the role that data plays in progressing from one stage to the next.

The 4 stages of AI maturity in eCommerce

Take a look at our model to see which stage your business falls into:

Stage 1: Exploration

In the initial exploration stage, your business is starting to learn about the benefits and challenges of AI in eCommerce, and has perhaps started using third-party AI solutions—such as automatic text generators like ChatGPT, image generators like Canva, and keyword generators like SEMRush. Your business is also starting to understand how external AI solutions leverage specific inputs of data to produce a desired output.

This early in the journey, you shouldn’t be concerned about collecting and managing massive amounts of data in house—that comes later. Rather, you should just be focusing on learning as much as possible about AI solutions and best practices, developing partnerships with AI experts, and setting clear goals for the implementation and management of AI to improve your customer experience and internal operations.

Stage 2: Adoption

Now, you and your stakeholders have a solid understanding of the AI capabilities available, and it’s time to test them out. From using libraries to translate product descriptions, to adding a chatbot on your site for faster customer service, you can start integrating AI tools on your website—many of which will seamlessly plug into sites powered by Shopify, BigCommerce, Commercetools, and other widely used eCommerce platforms.

At this stage, your team is manually exporting, analyzing, manipulating, and leveraging data from multiple sources, with zero data synchronization between systems. As your company grows—taking on more customers, products, technologies, and of course, data—this process will need to be automated to maximize your AI capabilities and overall business scalability.

Stage 3: Guidance

In this stage, on top of improving the customer experience through chatbots, product recommendations, dynamic pricing, and so on, your team is leveraging AI in the back office to guide real business decisions through predictive analytics. Your AI solution converts massive amounts of data from disparate sources into actionable insights, visualized with graphs and charts on a centralized platform.

This is made possible by automatically cleansing, enriching, deduplicating, transforming, and centralizing information through automatic data pipelining. Hundreds of thousands of data points from various locations are collected every day and organized into one database, which AI algorithms use to recognize patterns and trends that inform all kinds of business initiatives, from improving the customer experience, to lowering operational costs, to simplifying workloads across departments.

Stage 4: Automation

Finally, your organization is replacing entire hierarchical spaces with AI solutions. In this fully automated approach, AI is managing and making decisions for end-to-end business workflows—such as reordering and negotiating the price of inventory—while human employees simply monitor the algorithm’s performance.

Here, your data pipeline is fully tested and producing the most accurate data possible, enabling the AI to complete high-level business tasks with no consistent human intervention.

Case study: Structuring data personalizing the customer experience in automotive

Codal recently launched three new online storefronts for Aftermarket Performance Group (APG), a family of automotive brands that provide a wide range of aftermarket car parts, accessories, and components to a target audience made up of auto shop owners, dealers, and enthusiasts.

APG aspired to better connect with its customers through personalized website content, product discovery, and email marketing. To achieve this, Codal implemented one of our key technology partners , Bloomreach—a personalization platform offering a full suite of AI-powered solutions for eCommerce merchants.

Through a pixel embedded on APG’s DTC sites, Bloomreach collects data around individual user behavior, including search queries, viewed product pages, and items added to their cart. Then, AI algorithms use that data to deliver personalized content, arriving in the form of future search results, product recommendations, marketing emails, retargeting ads, website promotions, and more. Once the APG team has established specific personalization rules from their Bloomreach dashboard, AI handles the rest—and admins can always make quick and easy updates to the logic based on ever-evolving business strategies, customer expectations, and market trends.

Codal architected a modern technological ecosystem that allows the client to take advantage of cutting-edge, AI-enabled technologies like Bloomreach. In a matter of three months, we took APG from Stage 1 to Stage 3 of our AI maturity model, and are currently working on advancing the business to Stage 4.

To learn more about our work with APG, read the full case study here.

Take your business to the next stage of AI maturity

Codal can help your business transform legacy systems, centralize and structure data from multiple sources, and ultimately lay the foundation for meaningful customer experiences and automated workflows—powered by AI.

From adding a chatbot to your website, to streamlining inventory management, our team can build, evaluate, recommend, integrate, and optimize AI solutions that accelerate the growth of your business.

Topic

Unified commerce

Product engineering

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