How Artificial Intelligence is Helping Optimize E-commerce Content


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    The use of AI in writing content

    The debate over AI versus humans is a long and storied one, with endless arguments and evidence on either side. In the last few years, machines have become efficient. Automation has largely replaced rote and mechanically-focused jobs. However, many believe that automation can never take over jobs that require human reasoning and creative thinking. Thus, we find it difficult to envision machines writing compelling content. However, the reality is that machines are already writing content and they are getting good at it. A popular example of artificial intelligence for content creation is Quill, an NLG platform developed by Chicago based company Narrative Science. Using Quill, you can automatically generate anything from stock market reports to sports articles.

    The desire to automate product content creation

    Content is what ultimately sells products online. While design can grab buyers’ attention, and attractive prices can influence decisions, customers won’t buy products they know nothing about. Creating product content is the most challenging and time-consuming activity while making your E-commerce website. In general, the product content creation process involves the following steps :

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    Fig: Product Content Development Process

    Wrong or less detailed product content will put off customers. The desire to automate product content creation is thus strengthened because it aligns with the goals of the process i.e., to improve product content quality and to increase quantity.

    Some unique challenges in automating product content creation

    Every step in the product content creation process presents some unique challenges while automating the process.

    1. Data Gathering

    In this step, you need to search for products using a unique Product ID on their brand website, retailer websites or marketplaces (in decreasing order of trustworthiness) to gather relevant product data. This raw product data is unstructured and unclean. The challenge here is to make the AI learn how and where to look for product data.

    2. Attribute Filling

    This step involves extracting attribute information from the raw product data and inserting those attribute values in their corresponding fields in the attribute sheet. These curated sheets created here must contain detailed, accurate and standardized attribute values for each product (this is the content that will be displayed on your product page). In practice, per day attribute sheets are filled for thousands of products. Each product has 20-50 attributes. Here, automation is tough simply due to the volume of the products and the number of attributes per product.

    3. Copy Writing

    Creating quality copy for products is crucial for your E-commerce website as it leads to better user experience and also makes your site more trustworthy. This is absolutely beyond the scope of the current AI developed by Narrative Science as product copy writing involves identification of best product features combined with polished writing to quickly and precisely make the customer understand the “need” of the product.

    4. Multimedia Content Development

    Images and videos are the most compelling ways to engage customers and can be used to convey vital product information and thus drive sales. There’s enormous scope for automation here as current AI’s are very capable of image editing tasks such as scaling, retouching, color correction, watermark removal, etc.

    Applications of AI in product content creation

    Now that we are aware of the challenges, let’s take a look at some of the ideas for completely automating each of these steps.

    1. Data Gathering & Attribute Filling

    • Gathering product data by web scraping on the brand website
    • Structuring and normalizing the raw product data by referring available data for similar products (some attributes values are filled by doing this)
    • For the remaining unfilled attributes, it must use machine learning techniques for extracting them
    • Lastly, it must ensure that the generated attribute sheet has no missing values for mandatory attributes and that the attributes are entered according to your website guidelines

    2. Copy Writing

    • Identify the best product features that are it’s USP
    • Tailor the product description in such a way that it is not only easy to understand for customers but also more likely to appear in search engine results
    • Perform spelling and grammar checks to ensure error-free content is published on your website

    3. Multimedia Content Development

    • Image editing – scaling, retouching, background removal, etc.
    • Extracting certain attributes such as color, size, etc.
    • Removing fraudulent text, promotional text, etc. from the product images

    What AI’s can do as of today?

    Every idea listed above cannot be materialized with today’s technology. However, some of the aspects of the product content creation process can be automated using current Artificial Intelligence.

    1. Data Gathering & Attribute Filling

    Attribute filling

    You can use a dictionary based approach to group similar attributes. Ngram models are used to extract the attribute value for products.

    Product classification

    You must input all the available product attributes to help the AI classify the product into an existing category or create a new one. Currently, AI’s developed in this area can categorize up to half million products per day.

    Hard rule-engine based checks

    A rule-engine takes product data as input and performs checks such as formatting checks, syntax and guideline checks and consistency checks and highlights the errors so that they can be resolved as soon as possible.

    2. Copy Writing

    Spelling and grammar checks

    As discussed earlier, automating copy writing is not possible with the current technology but you can use AI for spelling and grammatical checks, presence of restricted words, etc.

    3. Multimedia Content Development

    Attribute extraction

    Category, size and color are the product attributes that can be identified using current AI’s for image processing.

    Text removal

    OCR algorithms are helpful in identifying and removing unnecessary text from product images.


    Product content creation is currently beyond the scope of NLG platforms. While complete automation of the product content creation process is difficult, automating certain aspects of this process such as partial attribute filling, product classification, image processing and quality checks can be implemented. Having an optimized semi-automated product content creation process will be beneficial as productivity will improve, accuracy will rise and TAT (Turn-Around Time) will be smaller. It’s time for you to understand that automating product content creation is not a radical concept but something you need to do to make sure your business survives in the long run!


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