Explore how AI-powered e-commerce platforms revolutionize SMBs by enhancing pricing analysis, inventory management, and data security through encryption and blockchain technology.
AI-powered e-commerce platforms scale SMB operations by providing sophisticated pricing analysis and inventory management. Encryption and blockchain applications significantly mitigate concerns about data security and privacy by enhancing data protection and ensuring the integrity and confidentiality of information.
A 2024 survey of 530 small and medium-sized businesses (SMBs) reveals that AI adoption remains modest, with only 39% leveraging this technology. Content creation seems to be the main use case, with 58% of these businesses leveraging AI to support content marketing and 49% to write social media prompts.
Despite reported satisfaction with AI’s time and cost-saving benefits, the predominant use of ChatGPT or Google Gemini mentioned in the survey suggests that these SMBs have been barely scratching the surface of AI’s full potential. Indeed, AI offers far more advanced capabilities, namely pricing analysis and inventory management. Businesses willing to embrace these tools stand to gain an immense first-mover advantage.
However, privacy and security concerns raised by many SMBs regarding deeper AI integration merit attention. The counterargument suggests that the e-commerce platforms offering smart pricing and inventory management solutions would also provide encryption and blockchain applications to mitigate risks.
Regressions and trees: AI under the hood
Every SMB knows that setting optimal product or service prices and effectively managing inventory are crucial for growth. Price too low to beat competitors, and profits suffer. Over-order raw materials, and capital gets tied up unnecessarily. But what some businesses fail to realize is that AI-powered e-commerce platforms can perform all these tasks in real time without the risks associated with human error.
At the center is machine learning, which iteratively refines algorithms and statistical models based on input data to determine optimal prices and forecast inventory demand. The types of machine learning models employed vary across industries, but two stand out in the context of pricing and inventory management.
Regression analysis has been the gold standard in determining prices. This method involves predicting the relationship between the combined effects of multiple explanatory variables and an outcome within a multidimensional space. It achieves this by plotting a “best-fit” hyperplane through the data points in a way that minimizes the differences between the actual and predicted values. In the context of pricing, the model may consider how factors like region, market conditions, seasonality, and demand collectively impact the historical sales data of a given product or service. The resulting best-fit hyperplane would denote the most precise price point for every single permutation or change in the predictors (which could number in the millions).
What machine learning contributes to this traditional tried-and-true econometric technique is scope and velocity. Whereas human analysts would manually deploy this tool within Excel, using relatively simple data sets from prior years, machine learning conducts regression analysis on significantly more comprehensive data sets. Moreover, it can continuously adapt its analysis in real-time by feeding it the latest data. This eliminates the need for a human to spend countless hours every quarter redoing the work.
In summary, machine-learning regression ensures that price points are constantly being updated in real time with a level of precision that far surpasses human capability.
As for inventory management, an effective methodology within machine learning’s arsenal would be decision trees.
Decision trees resolve inventory challenges using a flowchart-like logic. The analysis begins by asking a core question, such as whether there is a need to order more products to prevent understocking. Next, a myriad of factors that are suspected to have an effect on this decision are fed to the model, such as current stock, recent sales, seasonal trends, economic influences, storage space, etc. Each of these factors become a branch in the decision tree. As the tree branches out, it evaluates the significance of each factor in predicting the need for product orders against historical data. For example, if data indicates that low stock levels during certain seasons consistently lead to stockouts, the model may prioritize the “current stock” branch and recommend ordering more products when stock levels are low during those seasons.
Ultimately, the tree reaches a final decision node where it determines whether to order more products. This conclusion is based on the cumulative analysis of all factors and their historical impact in similar situations.
The beauty of decision trees is that they provide businesses an objective decision-making framework that systematically and simultaneously weigh a large number of variables — a task that humans would struggle to replicate given the large volumes of data that must be processed.
The machine learning techniques discussed earlier are just examples for illustration purposes; real-world applications are considerably more advanced. The key takeaway is that e-commerce platforms offering AI-powered insights can scale any SMB— regardless of its needs.
Balancing AI with data security
With great power comes great responsibility, as the saying goes. An e-commerce platform harnesses the wondrous capabilities of AI must also guarantee the protection of its users and customers’ data. This is especially relevant given that AI routinely accesses large amounts of data, increasing the risk of data breaches. Without proper security measures, sensitive information can be exposed through cyber-attacks.
When customers are browsing an online marketplace, data privacy and security are top of mind. According to a PwC survey, 71% of consumers will not purchase from a business they do not trust. Along the same lines, 81% would cease doing business with an online company following a data breach, and 97% have expressed concern that businesses might misuse their data.
Fortunately, e-commerce platforms provide various cybersecurity measures, addressing security compromises and reassuring both customers and the SMBs that host their products on these platforms.
Encryption is a highly effective method for securing data transmission and storage. By transforming plaintext data into scrambled ciphertext, the process renders the data indecipherable to anyone without the corresponding decryption key. Therefore, even if hackers somehow manage to intercept data exchanges or gain access to databases, they will be unable to make sense of the data. Sensitive information such as names, birthdays, phone numbers, and credit card information will appear as meaningless jumble. Research from Ponemon Institute shows that encryption technologies can save businesses an average of $1.4 million per cyber-attack.
Block chain technology contributes an extra level of security to e-commerce platforms. Transaction data is organized into blocks, which are in turn linked together in a chain. Once a block joins the chain, it becomes difficult to tamper with the data within. Furthermore, copies of this “blockchain” are distributed across multiple systems worldwide so that the latter can detect any attempts to illegitimately access the data. An IDC survey suggests that American bankers are the biggest users of block chain, further underscoring confidence in this technology.
The argument here is that SMBs can enjoy the benefits of AI while maintaining data privacy and security. The right e-commerce platforms offer tried-and-true measures to safeguard data and prevent breaches.
Having your cake and eating it too
The potential of AI in SMBs remains largely untapped. As such, those daring enough to exploit machine learning to empower their business logics may reap a significant dividend over competitors who insist on doing things the old-fashioned way. By automating essential functions like pricing analysis and inventory management, businesses can achieve unprecedented levels of efficiency and accuracy. The e-commerce platforms providing these services are equipped with robust cybersecurity features, providing valuable peace of mind for SMBs.
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