Ecommerce can’t quite duplicate the experience customers have shopping at a local store where a friendly clerk who knows them personally can make helpful purchase  recommendations. But it’s getting there. And generative AI (GenAI) is leading the way. In fact, GenAI is poised to revolutionize the entire marketing funnel, plus back end fulfillment functions as well.

What is generative AI?

The term “AI” is everywhere in marketing copy, but what does it actually mean, and how does GenAI differ from traditional AI?

Traditional AI is basically about recognizing patterns and making predictions or decisions based on those patterns. AI can, for example, analyze a consumer’s purchase history (along with other data) and recommend the most likely item for a successful upsell — or analyze data from pumps in a factory and predict when they need maintenance before they actually fail. The key point is, all the analysis and decision-making is based on rules created by the system designers.

GenAI is focused on creating new content, whether it be a product description, an image, an answer to a customer’s question, or a model of a molecule that’s likely to cure a target disease. In contrast to traditional AI, GenAI doesn’t rely on rules. It trains itself. This is crucial. The training process is called machine learning (ML). It’s basically a highly sophisticated trial-and-error process. Through millions of iterations, a machine learning system learns how to get the “right answer,” based on available data. 

Interestingly, the designers of the system cannot always know how the ML system arrived at that answer. For example, a search query might deliver five responses, which can be ranked in any order. Based on the data available to it, a GenAI-powered search engine will reliably put the “best” response at the top of the results list (with “best” defined as most likely to sell, highest profit margin, or whatever is desired). However, it may be impossible to know exactly how the system arrived at its decision. 

Advanced GenAI systems use neural networks, which mimic the way the human brain functions. Like humans (and unlike conventional computers), they assign weights to various inputs based on their perceived importance for determining the “right” answer. This highly complex process, sometimes referred to as fuzzy logic, is what lies behind the remarkable success of GenAI.

Generative AI's role in ecommerce

GenAI is having a transformative impact on ecommerce. At the heart of its impact is personalization. GenAI can leverage personalization data from multiple sources to help deliver the shopping experience most relevant to the individual customer, and most likely to result in  high AOV conversions. In addition, GenAI can enrich the customer experience with two-way communication in real time, and automate other ecommerce processes to improve efficiency and reduce costs. Taken together, personalization, CX enrichment and improved efficiency can make ecommerce one of the most attractive sales channels, both for B2C and B2.

Rapid adoption

Given these benefits, it’s no surprise that GenAI is being rapidly adopted by businesses large and small. In one recent Harvard Business Review study, 31% of the respondents said they were using GenAI within their ecommerce operations. More broadly, 36% were using it to generate marketing content. According to another HBR report, 69% of respondents using GenAI achieved improvements in speed and efficiency.

There is little doubt that GenAI gives ecommerce companies a competitive advantage. By offering immediate answers to consumer questions, GenAI overcomes one of the biggest consumer frustrations in online shopping, lack of accurate information.  

Personalization 

Among all of GenAI’s benefits for ecommerce, personalization – or more precisely, hyper-personalization – is arguably the most important. Hyper-personalization differs from earlier personalization efforts because it takes more data into account and operates in real time. It evaluates not only basic purchasing history, but product details like color or brand preferences, plus navigational history, information from social media and even the time of day or the local weather. Based on data like this, it presents tailored product recommendations, other content that’s likely to meet a customer’s needs and perhaps a customer-specific price.

Generative AI in ecommerce is a win-win proposition In one study, roughly half of American consumers stated that they wanted targeted product recommendations. In fact, over 70% of them already expect AI personalization as part of their online shopping. In addition to improving the customer experience, personalized product recommendations boost sales. It’s widely reported that GenAI — using personalized recommendations — can increase sales as much as 20%. And according to one study, personalization increased average order value for 98% of the participants.

Customer Experience

At the front end of the marketing funnel, GenAI can create personalized email marketing messages. It dramatically expands search possibilities for customers by extending this activity into the realm of images. When customers see something online that they want to buy, perhaps while they’re engaged in social media, they can cut the image, paste it into the search box, and quickly discover that item. This gives consumers another way to search, one that is often simpler, more convenient and faster than trying to put a description into words.

When the customer journey ends in an abandoned cart, which happens roughly 70% of the time, GenAI can help increase conversion rates through a variety of techniques such as  streamlining the check-out process with features like automatic fill-in, and offering specials or dynamic pricing. But the main benefit is the degree of back-and-forth interaction that can happen during the shopping experience.

GenAI chatbots and virtual assistants make ecommerce customer interactions richer and more satisfying by, for lack of a better word, humanizing them. The key capability of these digital entities is their ability to understand and converse in natural language. By offering immediate answers to consumer questions, chatbots overcome one of the biggest consumer frustrations in online shopping, lack of accurate information. And they clearly increase engagement, as the growing adoption of chatbots demonstrates – 67% of consumers have used a chatbot within the last year.

GenAI-powered virtual assistants can also help customers configure or customize products that require this step. They can even provide visual previews, letting customers see a piece of furniture in their home or virtually try on clothes. The most sophisticated can remember previous interactions so customers don’t have to repeat any steps or provide the same input more than once.

Chatbots and virtual assistants are available 24/7 so customers can get help when they need it, not just during business hours. During the work day, they can answer routine questions, leaving human assistants free to handle more complex issues. Chatbots clearly increase engagement, as the growing adoption of chatbots demonstrates – 67% of consumers have used a chatbot within the last year. The net result is increased customer satisfaction with reducing staff needs. There is little doubt that GenA-powered chatbots give ecommerce companies a competitive advantage.

Operational efficiency and automation

GenAI can make an important contribution to the bottom line through  automation, which significantly reduces the costs of manual labor. Content creation is a good example. Anyone who has experimented with a large language model (LLM) such as ChatGPT or Gemini knows how quickly they can write a blog post or an email message. This capability can be dramatically scaled and combined with graphic capabilities to create literally all the content required for an ecommerce retailer. This includes product descriptions, sales copy, product images, FAQs, operating instructions, videos and more. 

The primary benefits of content automation are speed and cost reduction, but there are two other important benefits. Copy can be optimized via automated SEO for external search engines – primarily Google – and it can also personalized.

Product categorization is another example of a time-consuming manual task that can be automated using AI solutions such as Query Categorization. (To be clear, this function can also be handled by PIM systems.) In marketing, GenAI can automatically generate personalized email messages based on consumer behavior. By analyzing internal and external factors in real time and passing that data into a GenAI tool, it can optimize pricing for maximum revenue.

When customers do make an order, GenAI can play an important role in automating the fulfillment process, including customer communication such as thank-you notes, shipping information and the like. GenAI can monitor inventory levels and predict demand on an ongoing basis, preventing excessive inventory levels or stock-outs that result in lost sales.

Implementation considerations

Build vs buy

As with all major IT initiatives, build vs. buy is a key early decision that can have important short- and long-term implications. In the case of GenAI, the scales are weighted towards a buy decision, for several reasons. 

The most important argument in favor of buying is the complexity of the technology itself. GenAI really is rocket science. Simply finding the individuals to build a GenAI team is difficult, with a current talent shortfall estimated at 250,000. Furthermore, this gap between supply and demand means that the available talent is expensive.

A second concern is the speed of the technology’s evolution. With so many players working to advance AI, there’s a strong possibility that a) a third party will come up with a platform with out-of-the-box capabilities that exactly meet your company’s needs and b) the platform you developed in-house will quickly become obsolete vis-à-vis the broader market. 

If you already have a relationship with a major player, it may well be that the next iteration of their platform will incorporate GenAI.

The argument for an in-house solution revolves around proprietary data and highly personalized content creation. Third-party solutions may not be able to conveniently manage the data you collect, particularly if it’s not from mainstream sources. Also, no third-party will ever deliver the experience differentiation that you can build yourself. Finally, the build solution gives you the highest possible level of control. 

Here's a quick check list of the issues you need to evaluate in making your build vs. buy decision.

• Cost: Which approach makes the most sense in terms of ROI?

• Talent: Are you prepared to make a long-term investment in specialized talent?

• Time-to-market: Which approach is faster, and how much does that matter?

• Proprietary data: Would proprietary data that shouldn’t leave your organization be involved in the development or testing?

• Unique UX: How much of a competitive advantage is a unique customer experience?

• Control: Will your competitive advantage be diminished if you’re forced to depend on third parties for upgrades and new feature availability? 

Data quality and availability

GenAI systems in ecommerce make decisions and generate output based on the data that’s available to them. If there are problems with the data, these systems can’t deliver useful results. There are three key issues.

• Quantity. The accuracy of a GenAI system’s decisions is directly related to the amount of data used to train it. The obvious question – How much is enough? – can only be answered on a case-by-case basis by experienced statisticians or data scientists, but it must be addressed. 
• Quality. The data that any given company has about its customers decays at a rate of roughly 30% per year. People die, change names, move, change jobs and so on, and some sort of ongoing monitoring system needs to be in place to maintain a high degree of data hygiene.
• Security. All ecommerce systems make use of personally identifiable information (PII), like credit card details. The ecommerce team needs to work closely with the information security organization to make sure that PII is appropriately encrypted, safe from breaches, gathered only with consumer consent and handled in compliance with the governmental regulations that apply in various jurisdictions. To repeat, the risks related to PII are very high and security experts should be involved. 

Integration with existing systems

An AI-driven ecommerce system may need to integrate with a large number of external enterprise applications, such as the core ERP system, plus the Customer Relationship Management (CRM) system for customer data, (including purchase history), Product Information Management (PIM) for inventory and the Content Management System (CMS) for access to appropriate content. All of these systems may store data in different formats, and a data integration capability is necessary so they can all be available and work together. 

Ethical and privacy considerations

The use of AI in ecommerce raises important ethical concerns. The first is privacy, or what some regard as “electronic stalking.” How much tracking and data collection is reasonable for the purpose of personalizing online interactions, and how much is an invasion of privacy? The second concern has to do with fairness and bias. With dynamic pricing systems, is it fair that different individuals or demographic groups should pay different prices, or be offered more or less favorable payment options that may reflect bias?

Making sure that potential customers aren’t driven away by AI-enabled business practices is obviously important, and the primary best practice is transparency. Ecommerce sites should clearly state what data is being collected and how it’s being used. Also, it’s best to give customers opt-out options, e.g. a click box that says, “Do not share my data.” 

Finally, you need to make sure that new AI-based capabilities don’t violate laws such as Europe’s GDPR or California’s CCPA to name just two.

Practical steps for getting started

Step 1: Assess current ecommerce operations and identify areas for AI enhancement.

• Conduct a thorough analysis of existing ecommerce processes, systems, and data assets.

• Identify pain points, inefficiencies, and opportunities for AI-driven improvement.

• Prioritize use cases based on potential impact and feasibility.

Step 2: Define clear objectives and KPIs for AI implementation

• Set specific, measurable goals for generative AI implementation, aligned with overall business objectives.

• Define key performance indicators (KPIs) to track progress and measure success.

• Establish baseline metrics and targets for each KPI.

Step 3: Evaluate and select appropriate AI tools and partners

• Research and compare available GenAI solutions and vendors.

• Evaluate options based on factors such as functionality, scalability, integration capabilities, and cost.

• Consider partnering with AI development firms or consultants for custom solutions and expert guidance.

Step 4: Develop a data strategy and infrastructure

• Assess current data collection, storage, and processing capabilities.

• Identify data gaps and quality issues, and develop a plan to address them.

• Invest in data infrastructure and tools to support GenAI development and deployment.

Step 5: Implement GenAI solutions incrementally

• Start with pilot projects in high-impact areas, such as product recommendations or content creation.

• Monitor results closely and then iterate based on feedback and performance data.

• Gradually scale successful solutions across the organization, ensuring seamless integration and user adoption.

Step 6: Foster a culture of continuous learning and improvement

• Encourage experimentation, innovation, and knowledge sharing around GenAI.

• Provide training and support for employees to develop AI skills and adapt to new ways of working.

• Regularly review and optimize AI models and processes based on changing business needs and technological advancements.

Future trends and predictions

Increased adoption of GenAI across the ecommerce value chain

GenAI will clearly become an increasingly important factor in ecommerce. According to one study, 80% of retailers are either using GenAI or piloting projects, and 90% intend to increase their investment in GenAI in the coming fiscal year. GenAI will impact virtually every ecommerce function, from front-end marketing to fulfillment. Given its potential for operational efficiency and sales results, it’s likely to become a standard feature of ecommerce platforms and tools

Convergence of GenAI with other technologies

GenAI may well be integrated with other technologies to address specific concerns. For example, blockchain could be used to limit access to IP that needs to be kept secret. GenAI could also power augmented reality experiences. In this latter case, the experience itself could conceivably become a product available alongside physical goods. In fact, it’s impossible to predict what innovations may emerge as a result of GenAI.

Growing importance of AI ethics and governance

The importance of ethical issues related to GenAI will likely increase. In recent times, digital technology has moved faster than the legal institutions with the power to govern it, but those institutions will catch up, and the result will be a growing body of regulations. Along with this, widely recognized industry standards and best practices related to ethics and governance will emerge, perhaps sponsored by industry organizations.

Shift towards AI-driven, personalized ecommerce experiences

AI-driven personalization will become the norm in ecommerce over time. The shift will gain momentum due to three factors: growing consumer demand for personalization (which is increasing with each generational cohort), the business results AI delivers, and the continuing advances in technology. 

Technological advances in particular will lead to new levels of customization, with platforms able to incorporate increasing amounts of data from more and more sources. Chatbots will continue to grow in sophistication, which will result in greater interactivity. 

Conclusion 

GenAI is poised to revolutionize ecommerce, and that’s not an exaggeration. Personalization gives consumers a more satisfying experience while increasing key revenue metrics. The efficiencies of automation GenAI delivers reduced costs and thus boost the bottom line. The technology itself is becoming available in all types of architectures, with solutions for companies of all sizes. To learn more about Algolia’s search and generative AI solutions, schedule a demo with our team today.