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With hundreds of online retailers and thousands of products to select from, an average modern-day shopper compares pricing across many e-commerce websites quickly before settling for the lowest-priced alternatives. So, retailers had to execute millions of changes in prices daily in this never-ending race to become the lowest priced without losing any possible margin.

Recognizing, categorizing, and matching products is the initial step to comparing prices across different websites. However, there needs to be a correction in how products get represented across various e-commerce websites; this procedure is moderately complex.

Let’s go through an example:

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What’s required here is a price intelligence solution that initially matches products in many websites accurately and swiftly and then allows auto-tracking of competitor price data constantly.

Price intelligence solutions are already available, then why not use them?

Many challenges are there with mandatory solutions available in the market – the major one is that they are not working appropriately. It’s like complying with the procedure of getting actionable data that assists retailers in acquiring competitive advantages to do that in reflection.

Here are the different solution types that we get in today’s market:

  • Developed systems within: Solutions created by retailers themselves usually depend on substantial manual data aggregation with weak product-matching abilities. As professionals develop these solutions, they deal with significant operational challenges in the form of updates, maintenance, etc.
  • Data scraping solutions: These solutions come with no data standardization or product-identical capabilities and lack the power to deliver applicable, actionable insights. Moreover, it’s a struggle to scale them up to accommodate massive volumes of data during peak times such as promotional campaigns.
  • DIY solutions: These solutions need manual research with data entry. Because of human intervention levels and required efforts, they’re exclusive, slow, hard to scale, and of questionable accuracy.

AI has all the answers!

The competitive price intelligence solutions of Actowiz Solutions are specially designed to assist retailers in achieving competitive advantages precisely by offering timely, accurate, and actionable price insights allowed by matching products. We offer retailers access to detailed price data on millions of competitor products as often as they want.

Our technology stack primarily includes the following.

1. AI and Data Aggregation

At Actowiz Solutions, we aggregate data from various web resources across composite web environments – constantly with higher accuracy. Being in this industry for a long, we’re used to a lot of data that we can utilize to train product-matching platforms.

Our datasets comprise data from millions of products collected from many verticals and geographies in retail. These datasets have hierarchically organized data based on the retail taxonomy. There’s information like categories and subcategories; at top levels, we have essential product information like titles, descriptions, and other relationships. Our architectures of Machine Learning with a semi-automated data-building system, improved by a skilled QA team, assist us in interpreting the needed information and making labeled datasets with exclusive tools.

2. AI and Product Matching

Product matching at Actowiz Solutions is made through a combined platform that uses image and text recognition capabilities to precisely identify comparable SKUs with millions of products and e-commerce stores. Products also get classified depending on their structures, and a standardization layer is intended based on different image or text-based attributes. We use a collective deep learning architecture personalized to Computer Vision problems and NLP precise to us.

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While technically a vital part of a product matching procedure, we use a semantics layer and deserve specific mentions because of its influential capabilities.

We use customized and state-of-the-art word representation methods like BERT, ELMO, and Transformer for capturing intensely contextualized text having better accuracy. An intra-attention or self-attention mechanism studies the association between the words in the question and the last parts of a description.

Image data processing begins with object recognition to classify any product area of interest. Then we use deep learning architectures like Inception-V3, ResNet, and VggNet, which we have accomplished using billions of categorized images. Then, we use different pre-processing methods like face removal, skin removal, flexible background removal, image quality improvement, and scrape image signatures through Machine Learning and deep learning-based algorithms to recognize products exclusively across millions of indexed products.

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Lastly, we competently allocate billions of images from different stores for quick access and facilitate searches on a vast scale utilizing our image-matching engines.

3. Use of Human Intelligence

In situations where the confidence score of machine-driven matches is lower, our team of Quality Assurance (QA) experts validate the output.

Our team does things like:

  • Finding why the confidence score is lower
  • Confirm the correct product matches
  • Find a way of encoding knowledge into rules and feed that back to an algorithm

We’ve created a self-improvement feedback loop that improves with time. This procedure helps us rapidly match products at a considerable scale with high accuracy (usually more than 95%). Our system has gathered knowledge over years of our operations that will be hard for anybody to repeat.

4. Data Visualization and Actionable Insights

When the matching procedure is completed, the pricing is aggregated frequently, allowing retailers to improve their pricing constantly. Usually, pricing insights are characteristically consumed using our SaaS-based web portal that includes reports, dashboards, and visualizations.

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Instead, we can take part with interior analytics platforms using APIs or produce and provide regular spreadsheet reports per the customers’ preferences.

Conclusion

    There are numerous advantages of our solutions. Detailed pricing improvement opportunity-associated insights produced appropriately empower retailers to meaningfully improve their competitive position across product types, categories, and brands, with the ability to influence their pricing perception amongst consumers. All these insights, if used at high granularity in the long run, can assist in maximizing revenue using price optimization on a larger scale.

    Our solutions also help in driving process-based and operational optimization for retailers. These modifications assist them with better alignment to efficiently take a data-driven approach to price and help them achieve cleverer retail operations worldwide.

    All these wouldn’t be possible if a product matching procedure integral to this system were expensive, unreliable, or inefficient.

    If you want to know more about Actowiz Solutions’ proprietary product matching platforms and their advantages to brands and e-commerce businesses, contact Actowiz Solutions now!

    You can also reach us for all your web scraping services and mobile app scraping service requirements.

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