6 Ways to Boost E-Commerce Business with Deep Machine Learning

machine learning

Deep machine learning is changing the way companies do business online, from understanding user behaviors to accuracy in inventory management and pricing, and from improved search results and customer support to more targeted online marketing. This advanced technology will be the benchmark for e-commerce as these elevated experiences improve metrics from traffic and retention to conversion and unit growth.

  1. Informed product suggestions to improve conversion and units per transaction. Among the biggest misses in e-commerce is the lack of personalized assistance which brick-and-mortar retailers can provide. Deep machine learning tackles that need as e-commerce sites employing the technology can provide relevant product suggestions based on user interests, past behaviors, and items that they are searching for. This is equivalent to a salesperson asking open-ended questions to uncover consumer needs for appropriate solutions, driving up conversion and unit growth potential.

  2. Improved customer service to drive customer retention, recommendations, and repeat traffic. Deep machine learning enables customer service chatbots to assist users better as they learn common questions, the success of re-directs, and product and service issues. As computers “understand” the user and their issues better, they can find more relevant solutions quicker for a better online experience which will drive user retention, social recommendations, and repeat traffic.

  3. Improved search function for faster and more relevant returns. Deep machine learning deciphers human context, making searches easier for the consumer without having to type in exact matches or worry about misspellings and other issues that could inhibit relevant returns. Deep machine learning analyzes past online behaviors and interests, deciphers context, phrasing habits, and employs features such as search ranking to sort search results by relevance for qualified results. Quicker and more accurate search results lead to improved retention, conversion, social proof, repeat business, and overall revenue growth.

  4. Predictive pricing to impact traffic and conversion. Retailers have always struggled to balance competitive pricing with margin driving pricing. Thanks to deep machine learning, e-commerce businesses can be more profitable through predictive market-right pricing; and when the pricing is market-right, more consumers are likely to visit a site from an ad and to be converted. Deep machine learning algorithms provide online retailers with predictive analysis about pricing trends, product rates, and online consumer purchasing behaviors to assist in traffic driving and conversion.

  5. E-commerce ad segmentation to improve targeting for inbound traffic and conversion. Deep machine learning analyzes consumer behaviors and preferences from historical searches to social media engagement and interests on profiles. This enables better targeting and segmentation of ads to drive qualified traffic to e-commerce sites and to impact conversion through customized marketing based on individual consumer online personalities.

  6. Inventory forecasting to improve margins, conversion, and unit movement. All retailers have experienced the pains of either not being able to accommodate consumer demand due to insufficient inventory levels or sitting on an excess of inventory, and both issues diminish profitability. Deep machine learning helps e-commerce businesses to predict inventory volume based on analysis of buyer and industry trends. This will help to minimize wasteful spending and the need for profit-diminishing markdowns. Retailers employing deep machine learning will benefit from increased conversion and unit movement by having the appropriate items available to sell when the consumer wants them.

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