Artificial Intelligence for e-commerce data

July 15, 2020

In this article we are introducing to the reader new rules imposed by Ecommerce in this new technological era, but also the issues your company could face in reference to this new ecommerce age

In recent years, online Commerce is acquiring great importance in the growth strategies of companies and this fact is a big challenge for brands to manage their online sales channels as ecommerce does not follow the rules of the offline world. This fact causes Category Management to get uncontrolled quite easily. The difficulty of accessing the information in order to manage the distribution and sale of our products.


1. NEW RULES IMPOSED BY THE ECOMMERCE:

ECOMMERCE DIMENSIONS:

Assortment & Stocks

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Nowadays thousands of retailers, plus new ones appearing every day, control the sale of our product. They decide whether to sell the product, replenish its stock and choose the assortment depending on the target: managing poorly your portfolio.

  • Thousands of retailers
  • New retailers popping up constantly
  • Retailers managing poorly your portfolio
  • Out of Stock increasing your opportunity cost

Pricing & Promotions

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Most retailers use dynamic pricing systems to assign a price to their products. Thus, the price varies constantly and becomes nearly unpredictable. Moreover, shoppers have access to offers of the same product at different prices. And the easy comparison with competitors’ discounts generates an escalation of promotions that ruin margins.

      In summary:

  • Dynamic pricing causing continuous price variations
  • Harmful promotions
  • Offers based on user location
  • Competitors tracking you

Search & Digital shelf

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The share of shelf now depends mainly on search results, that is, how often our products appear on the first page of the most popular search keyword sets. And this dramatically varies with the search algorithms that the retailers use. This ranking problem may artificially prioritize products of their own brands, or of certain sponsors, or simply be poorly developed.

    To sum up:

  • No EANs available
  • Multiple titles and variations for the same product depending on retailer
  • Low performing SEO
  • Multiple languages

Content & conversion

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The content included in the product pages is defined by retailers and often does not respect the manufacturer’s guidelines: poor descriptions, low quality product images or reviews spread all over the web. This deteriorates our brand awareness, SEO, conversion and customer engagement.

      In conclusion:

  • Unstandardized and low quality product images
  • Poor descriptions
  • Reviews are spread all over the web

2. A BIG DATA PROBLEM

To sum up, manufacturers need to gain control over their online retailers by collecting data in all their meaningful retailers at least twice a day. 

Let’s do a quick example of the human resources needed for such a task for a medium manufacturer:

  • Imagine you sell 500 products
  • Your products are sold, in average, in 6 main retailers
  • That is, you need to track 3.000 product pages
  • On average you need to look at those product pages a couple times per day. That’s 6.000 visits
  • Manually extracting this data would take you around 1 minute per visit
  • In total, humans would spend 6.000 minutes gathering data, 100h/day
  • A human works for 8h/day so you would need 13 people working full-time non-stop to get the data

Obviously, this is not the way to go. Companies prefer to automate the scrapping of all this data creating rules to reach the elements of interest such as stock, price, category or title. However, it is not an easy task. These rules, known as “extraction template” or “scrapping template”,  may vary by retailer over time and even inside the different categories inside the same retailer. These are some of the big issues one finds when scraping data from retailers at scale.


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Now let’s go one step further. Such template also need great operational energy. You are guessing right: if the website’s source code changes it might affect the extraction template, breaking some rules and losing data. When gathering data to feed our Product Monitoring tool, we need to consider hundreds of retailers, which burdens the operations workload. At Shalion, we have developed an AI tool able to automatically scrape the important product information from previously unseen retailers.

We are proud to introduce you  the AI weaponry that allows us to empower our client’s Category Management and Product Distribution providing:

  1. (near) Real time data
  2. Scalability
  3. Accuracy
  4. Synthesized, curated and analyzed data
  5. KPI convertible information

“Brand monitor” enables you to follow the offering of your products and your competitors within the different e-sellers allowing you to react and to take action regarding:

  • Stockout of your products. Which retailers do I have stockout of my products?
  • Price and historical price changing.
  • ¿Who is winning the Amazon buy box (the first recommended seller option for a search of a product)?
  • What is the score of my reviews in the different e-retailers?
  • What % of products are in promotion?
  • What is the coverage of all my assortment within the different e-retailers?
  • How well are my products described in the different e-commerce sites?

And much more information that we could provide.


Do you want to know more about our tool? Do you have the feeling that your products are being sold in multiple e-retailers but you don’t have all the control that you would like? Let us help you, drop a line to Shalion:Let us improve your performance.

Previously published in:
Herreros, Enrique (February 21,2020) Shalion's Keynote at Beauty Innovation Days 2020