4 Ways AI-Based Data Analytics Is Increasing E-Commerce Sales

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Image by Gerd Altmann from Pixabay

Artificial Intelligence has emerged as a much-talked about topic of late, and the world of marketing is set to be transformed in the near future.

As we enter a new era where artificial intelligence (AI) is set to transform the way we live and how businesses engage with consumers, there is no better time to understand how big data analytics, machine learning and deep learning will continue to transform the way marketers and technologists create more relevant experiences for their customers.

Below, we have collated some of the top applications and use cases of data analytics in marketing, and gathered some success stories from companies working in this space.

1. Crafting high-quality content

Brands know they need to be constantly providing their consumers with engaging, valuable content. Through strategic focus on the purchase journey and product integration, the success of content marketing can be gauged by the performance of pages in Google search results.

More so, for e-commerce brands that sell products on one particular channel, it is crucial that a single page on their site links to other channels like social media or product pages that drive purchases.

Data science and data architecture teams use data from Google to track customer behavior and trends in search data and product ratings for online stores.

The biggest challenge for any marketing team is the complexity of multi-channel shopping experiences, which require different signals to drive the right customer interaction at the right time. Now, with technology platforms like Google Shopping data, marketers can be assured that they are using the right metrics to optimize and personalize their website for each customer.”

2. Finding product distribution patterns

With the growing e-commerce industry, it is essential for marketplaces to track not only product purchases but also the customers’ purchasing preferences.

This leads to a massive amount of data that will need to be worked with to unearth insights, especially as many customers buy several products in a particular category.

Fashion e-commerce marketplace Lyst uses both tools and technologies to efficiently acquire and analyse product data to deliver a new, refined shopping experience. It collects information on items people want to buy across every channel, using data from Google Shopping, Pinterest, and their own data.

By looking at the trends of shopping behavior, specific buyers, and categories for all their customers, the fashion marketplace hopes to create highly personalized shopping experiences.

3. Product discovery via visual clues

The process of discovering products is not just about the way one interacts with a website or browse through images. It is also about understanding visual cues like the colour combinations or patterns in fashion photographs, the fonts in headlines, and the wording on product packaging.

Through machine learning, fashion apparel brands are able to connect image search bar to CRM data, which allows them to identify the best keywords to drive traffic and conversion. By correlating the exact colors and patterns in clothing pictures and the keywords that have previously been used to drive traffic to the site, many brands generate interesting visual cues that inspire customers to click on the product.

Having visibility into the data points involved is also important, as it can reveal relationships that weren’t previously apparent.

4. Helping brands deliver an engaging & profitable shopping experience

By leveraging these tools, e-commerce marketplaces are able to provide differentiated customer experiences and fulfill their promise of giving a superior online shopping experience.

By providing an insight into the exact product a customer is looking for, merchants are able to anticipate the level of interest they will show to the product, increasing their chances of sale. They are also able to determine how a product will appeal to customers based on the patterns and color combinations they prefer, improving their chances of reaching the most qualified customers.

Gathering the data required to understand customer trends can take up to three months, which is a long time in an already crowded e-commerce industry. Marketplaces that can rapidly analyze customer data and generate insights are able to work with brands to create the shopping experience that shoppers are looking for.

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