Case Study
and Market
Measure up against competition and define steps for brand enhancement
Apiron Technologies’ client is one of the most successful independent German distributors of a wide range of components, peripheral and software products in Germany and all over Europe with a strong growing presence in the Scandinavian countries.
The client’s product is a leading innovative solution, already sold to more than 50 million happy customers worldwide. Apiron Technologies was assigned to perform a competitor and marketing analysis for the specific product in the Scandinavian markets and Italy.
The methodology approach includes data crawling, natural language processing (NLP), data analysis and finally reporting of the results. Although the crawled reviews are in non-English languages, namely Danish, Finnish, Norwegian, Swedish, and Italian, all the information has been translated using in-house technology tools.
During the Natural Language Processing (NLP), two methodologies were applied
• Sentiment Analysis algorithms
Processed reviews and marked them as positive or negative.
• Meaning Extraction techniques
The main topic and subtopics of each and every customer review were segregated. Based on these results, the most important product technical specifications (features), product price range, and brand awareness were identified per product category and specific client base.
In summary, the following data analysis methodology was applied:
Data mining / Data crawling
Searched the web and gathered data related to customer reviews and product specifications via Apiron Technologies in-house proprietary software.
Natural Language Processing (NLP)
Processed and analyzed reviews via machine learning-based meaning extraction tools in order to isolate the product specifications that are at the top of the customers’ interests.
Data Analysis
Isolated the top product specifications from the crawled data and based on the top selling products per data source, the respective client’s products were positioned accordingly.
All these tasks have been achieved via an in-house data science and AI platform: Pythia.
Data Analysis
Having gathered specifications and product price ranges as our main criteria, the company positioned its client’s products in the market against their competitors. For the analysis the company used exclusively data that are available online. Key metrics for the analysis output were:
• The quality and features of the competitive products based
on the specifications that the customers were interested in.
• The price range for the respective products and brand awareness.
Results showed:
Our report, a result of data mining and expert analysis, was presented to the marketing department as the basis to plan the next step for those markets. More specifically:
• Provided the most important features and specifications by price range, competitors and user insights per country.
• Identified the reasons product market penetration for Italy was so low based on demographics and market analysis.
Product review customer insights example
The following example of razor products depicts quality ratings of the brand and the competition. Results were generated by a sample of 39K reviews from a total of 542 SKUs for the UK market (Dec2020). The Research and Development department was able to use the analysis results to improve an existing product or launch a new product based on customers’ reviews.
Competitor Price Tiering/Popular Products example
The following example displays how a brand’s marketing department can analyse the performance of the brand in the Scandinavian market specifically in Denmark, Finland, Norway, and Sweden to identify and rank major competitor brands and products.
By comparing price tiers and feature ratings, insights about the competition and ideas about new products were generated.
An example follows regarding how a brand’s marketing department can do the following:
• Comparing price tiers and features ratings
• Getting insights of the competition
•Launching new price strategies and products
Here we can view the output of the analysis of all reviews and comments for a specific electronics category (blenders) per brand from the top visited sites (sources) for the region.
• Comments and reviews were translated
• Sentiment analysis ranked content as positive/negative
• Further analysis shows the top performing brands and
positive/negative customer insights.
Market analysis for razors example
This example presents how a brand’s sales department reviewed products per e-commerce source (the top 4 were selected) based on major marketing features and price tiering.
The information depicted here displays each brand’s popular products (SKUs) per price tier for each selected major e-commerce site.
Information and ranking were used as input for various brand departments to align marketing campaigns with features, kick off promotional campaigns or set a new pricing policy for specific products.
Major features were extracted using NLP and ranked by importance as shown in the table.