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PriceSpy Competitor Price Monitoring Solution
2012.07.12

The purpose of PriceSpy technology is to provide answers to pricing, price monitoring and product comparison problems in e-Commerce when faced with thousands or tens of thousands of products. Such high volume of products can no longer be handled manually and this is where data and text mining methods come in really handy. The technology is analogous to working with Lego bricks; the various problems are treated using various modules.

PriceSpy technology components:

 

  1. Product/keyword mapping
  2. Google ranking analysis
  3. Data mining from webstores
  4. Data mining from price comparison websites
  5. Automatic product comparison

The starting point of the technology is keyword search; our customers include companies with many years of on-line trading history hence they are in possession of a set of keywords of at least 1000 expressions that best describe their scope of activities and products. If that is not available to you, we have the technology to collect the relevant keywords, as is described under the text and data analysis of webstores.

Further to this, our potential customers include dealers and brand distributors who sell large volumes of products through on-line retailers and whose pricing policy has internalised what is known as price limit.

1. Product/keyword mapping

 

One of our basic services is product-keyword integration, which constitutes the foundation of all our other services.

Example for product/keyword mapping:

toy microphon

86632-toy_microphon.html

toymicrophone

86632-toy_microphone.html

toy microphone

86632-toy_microphone.html

toy with microphone

86633-toy_microphone.html

 

There are two possible methods for mapping: on the one hand data is extracted from Analytics, or - if that is not available - Google ranking analysis is used instead. We have found that the first method produces more accurate results.

What can mapping be used for?

  • For SEO: for the comparison of individual title and meta-keywords
  • For the automatic generation of long tail AdWords campaigns
  • Competition analysis

2. Google ranking and analysis

 

Using the set of keywords received from our customer, we determine the positions of the given URL's on Google's search-results page. The set of keywords may contain many tens of thousands of terms. Depending on the purpose of the activity we may change the depth of the query, i.e. we may want to examine the first 10, 20, 50 or 100 Google search results. Further to this, we may filter our query for individual countries. As the final result of the query, we end up with three lists. Before going on to describing the lists, let us explain the meaning of URL types. There are three main types: webstore, price-comparison, and others. The URL types which we want to make a query for can be pre-set in the system, but this needs to be done once only. In all future queries, the system will "remember" the types of URL we had queried before. We are developing the technology that can automatically recognise URL types therefore manual intervention will no longer be necessary. The lists only contain webstores and price-comparison sites.

Queries may be scheduled and reports will be automatically generated for the selected times. Reports may be generated monthly, weekly, daily.

2.A.  Keyword - URL ranking list

The list contains:

  • Keyword
  • Ranking
  • Type (organic/paid)
  • URL
  • Change (change is always indicated against the previous search)

 

2.B.  Keyword / webstores list

The list contains the number of webstores identified for any specific keyword in the breakdown of paid and organic search-results in the top N (e.g. 10 or 20) position.

 

2.C.  Store summary

The list contains:

  • URL
  • Shows the number of keywords a store comes up for in the top N list.
  • How many keywords has the store improved or gone behind in position relative to the previous query

What to use these lists for?

  • To examine the effectiveness of our own SEO activities
  • List 2.A provides the foundations for a complex price-optimisation campaign. See case study
  • Competition analysis and monitoring

3. Data mining from webstores

 

This component collects the fundamental product data (name, brand, price, etc.) and - if available - further product properties (e.g. size, batch, etc.) from an arbitrary set of webstores. There may be an arbitrary number of product properties; they are generally stored as property value pairs. There is an initial setup stage to data collection as well, after which we set the frequency of the collection.

The meta content of a page (title and keywords) may also be extracted as a product property. This also enables us to determine the keywords assigned to specific products. Further to this, using product comparison (section 5), we can define the common products of competing stores and hence we can arrive at a set of keywords than can be used to describe our own page.

What can this be used for?

  • For competition analysis and price monitoring
  • For keyword generation and product and keyword mapping

 

4. Data mining from price comparison websites

 

Price comparison sites are unavoidable actors in e-Commerce. In certain segments they have virtually exclusively occupy all top positions on Google hit lists. Therefore, if a webstore wants to have a competitive edge over its competitors by offering better prices, it is no longer sufficient to look at the pricing of the competition on the first page of Google's hit list, but you will also need to know the prices offered on price comparison sites by the competition for the given product. This module has been designed to tackle this issue allowing for data mining from price comparison websites.

What can this be used for?

  • Competition price analysis and monitoring
  • For the price limit monitoring systems of wholesalers and brand distributors

 

5. Automatic product comparison

 

Finding out whether two digital representations of a product actually cover the one and the same product is not an easy task. To put it simply, the problem is that different merchants might use different names for or assign different properties to the same product.

For example:

  • Leica Jogger 28 Optical Site Level + Tripod + Level staff
  • Leica Jogger 28 Optical Site Level Set

or

  • Zinc tablets 20mg
  • Zinc tablets 60 ea
  • BioCo Zinc tablets

Merchants usually do not publish the universal ID's of the products so we had to develop a data mining technology that enables product identification using the characteristic properties of the products.

With the help of a probability model building on text mining methods and using machine learning, our software can identify two products as one and the same with relatively high accuracy even if they are represented under different names and with different properties in various webstores.

As a result, we are able to locate alternative web offers for the same product including offers from price comparison websites. Further to this, we can also monitor price changes over time. By doing so we can build a central database containing the offers of all major webstores and price comparison sites by products.

What can this be used for?

  • For the price comparison of the same products by different merchants
  • For the price optimisation of long-tail campaigns
  • For the price limit monitoring systems of wholesalers and brand distributors

 

Case study

 

The following case study presents a specific example of use. The advantage of this method is that PriceSpy can automatically run the test for the entire set of products.

 

One of our clients complained to us that he had the feeling that one of his competitors had cut its prices well below his, and asked us to check if he was right.

For the analysis we needed:

  • to analyse the data of the two stores (in fact, in this case only one, because the client's data was made available to us in a CVS file)
  • the cross section of the product sets of the two webstores
  • to run a price comparison analysis, and a
  • Google rank analysis

 

The price difference between the products is shown in blue in the figure.

d

The Google rank analysis revealed that more expensive products should not be advertised using Adwords because the price difference was also represented on Google's search-results pages.

 

The situation is similar on price comparison sites. Therefore, if we want to advertise a product, we must first align the price to the best offers available on the market.

 

To sum up: if a product is listed with prices other than the lowest for one or more keywords on Google's first page, then:

 

  • There is no use advertising with Adwords for the given keyword
  • Reducing the price of the given product is worth considering

 

Since we now know the results of the automatic product comparison, we also know which products our competitors do not advertise. It is worth focusing on the e-Marketing of these products.

Feasibility

 

The following example is the sketch for a feasibility option.

Let's take, for example, a brand distributor with many hundreds or even many thousands of products and Internet-based retailers. If the brand distributor prescribes a minimum price limit for the retailers, then he will sooner or later be facing the problem of retailers breaching the rules. The PriceSpy technology, however, is great tool to build up an automatic price-monitoring system.

The system performs data mining at regular intervals, classifies the extracted product data, examines instances when the price limit was breached, documents the case, then reports to the brand distributor at regular intervals.

 

The system performs data mining at regular intervals, classifies the extracted product data, examines instances when the price limit was breached, documents the case, then reports to the brand distributor at regular intervals.