5 Data Sources That Ecommerce Companies Should Excavate for Their AI Efforts – Dataconomy
In the eCommerce landscape, Artificial Intelligence (AI) is reshaping the game. This blog series explores how AI’s intelligent algorithms are revolutionizing online businesses, from personalized product recommendations to efficient inventory management. Join us for insights on leveraging AI to enhance the digital shopping experience and overall success in eCommerce.
Many e-commerce companies are looking to leverage artificial intelligence (AI) to reach more customers, improve their products and boost their sales. The trend can already be seen in e-commerce websites that feature recommendation engines, chatbots and smart assistants.
These types of AI technology adoptions are likely to continue. According to Gartner, 37% of organizations have already used AI in their businesses.
However, what’s often overlooked amid all the hype surrounding AI is how reliant the technology is on data. AI needs to have vast amounts of reliable data on hand to be able to perform effectively. And it’s not just any data pulled from some random sources. It should be data that’s relevant to your company and its business ecosystem. Therefore, a pivotal question that e-commerce companies looking to leverage AI should be asking is, do we have the right data and enough of it to fuel our efforts?
Here are five essential data sources that ecommerce companies can use to fuel their AI efforts.
Search and browsing histories in various channels across multiple devices can offer a wealth of information that organizations can leverage to help them understand customer behavior and offer relevant and helpful recommendations to individual consumers.
Such practices have been successfully used by many enterprises. For example, Amazon makes recommendations to its users based on their online activity on the website. This has helped further propel the e-commerce company to becoming a global tech giant.
Amazon says that 35 percent of its sales are generated by its recommendation engine. That’s a success rate of more than one in three clicks. With AI and ML engines becoming increasingly accessible to businesses of all sizes, solutions like PureClarity, which can generate e-commerce recommendations for individual site visitors based on their journeys, are likely to see increasing demand.
Past sales performance, inventory records, and present consumer behavior are significant data that can be carefully studied to identify and analyze trends and make smart product and service demands predictions.
This information is essential in proper inventory management to help avoid financial investments on unnecessary storage or stockpile. Stores and online marketplaces lose an estimated $1.1 trillion due to inventory distortion.
Integrated data from other important transactions such as shipping and purchase orders as well as reports from vendors and suppliers can help businesses plan and manage their procurement processes, inventory control, and product development. “Using inputs like customer demographics, sale prices, item promotions, competitor information and even weather,” notes Rod Daugherty, the VP of product strategy at Blue Ridge, “machine learning can help businesses predict and optimize demand and replenishment with far more accuracy than elementary or manual methods.”
With more consumers embracing digital commerce as a way to purchase goods and services, there’s also an increasing number of online fraud incidents. A 2018 study from Experian found that 63% of businesses have sustained losses due to fraud.
Fortunately, AI and machine learning can now help identify fraudulent or suspicious online payment activities through data analysis of online transactions. Collected payment information can be used to identify patterns of legitimate and fraudulent payment behavior.
Activities tagged as fraudulent can also be confirmed and rejected through intelligent systems. The anomalies and trends from this data can then be interpreted and recorded to help companies adapt and detect suspicious activities that even use new and more sophisticated deception tactics.
Online reviews have also become essential in the success of a business or product. In fact, 91% of consumers are more likely to use a business because of positive reviews. Unfortunately, this has also led to the proliferation of fake reviews online. Companies can leverage AI to identify legitimate reviews from fraudulent ones. While fake reviews typically include language tics that fail to reflect those of a normal online user, they can still be difficult to spot.
As such, rigorous collection of data, capable filtering systems, and artificial intelligence can go a long way towards ensuring online reviews are trustworthy. “It is extremely hard to apply machine learning trained by humans to understand double sentiment as things like sarcasm are complex to code,” notes Niv Elad, VP Engineering at Revuze. “However, self-learning systems sift through all industry products and categories to identify general sentiment and tone – and better understand cases of double sentiment.”
Identifying authentic user reviews that are meant to be taken literally can give businesses honest feedback about their products, which they can use to improve their services. Additionally, these reviews can also give them insights about the strengths and weaknesses of their competition.
AI-powered solutions offer actionable insights from the collected data of a brand’s and their audience’s social media profiles. These insights can help businesses track their engagement, identify consumer trends, build audiences, and find new ways to boost their reach. For example, insights from the engagement stats on a brand’s Facebook page can be used to run more successful ads based on a specific demographic and behavioral targeting.
AI-enhanced sentiment analysis of social posts, meanwhile, can help protect ecommerce brand reputations by identifying situations that call for immediate intervention. “Letting negative public feedback accumulate is a good way to damage your hard-earned reputation and lose customers, notes Chia-Luen Lee of Brand24. “Analyze all the possible reasons for spikes in positive and negative sentiment to manage your reputation, prevent PR crises, and identify what it is that your customers like (and dislike).”
By feeding social media signals into AI engines, companies can have a better idea of what their customers want, learn the language that can improve engagement, predict the time to publish posts to optimize clicks and conversions, and create more relevant and meaningful content to gain a competitive advantage in the market.
AI can help e-commerce companies understand their clients better, automate their operations, and offer seamless customer experiences. While the technology can certainly help businesses keep pace with the ever-evolving demands of today’s consumers, data collection and analysis will also be equally important.
Leveraging AI with the right data allows organizations to pinpoint problems and opportunities to make the necessary changes and adjustments to their business.
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