Table of Contents
The Client
The client is a leading e-commerce retailer based out of Germany. The retailer was looking at expanding its online product offerings by sifting through the massive datasets in their repository to find hidden patterns and uncover meaningful insights.
The Business Challenge
Today, it doesn’t matter if you run a business online or in the real world, all that matters is how efficiently you can analyze the gigantic troves of information to extract granular insights lying at your disposal. This is considered to be the barometer that determines the success ratio of pure-play e-commerce retailers. Owing to the data explosion and the impact of technological advancements in today’s world, businesses own huge volumes of datasets, which act as a valuable source of actionable insights.
Businesses collect trillions of bytes of data on customer transactions, suppliers, internal operations, and market competitors. Such datasets are predicted to grow in the coming years, due to which businesses are focusing on the incorporation of mining solutions and text analytics to exploit the true potential of data. Penetrating deeply into the textual datasets by leveraging web mining solutions empowers retailers to gain a better understanding of customers’ sentiments around what they offer. As a result, customer experience sprints from the mundane to marvelous, and customer loyalty gets a solid push along with revenues.
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Top Challenges Faced by the Client
The e-commerce boom has presented both challenges and new opportunities for retailers that are looking to expand globally by improving their online presence and expanding their digital footprint by leveraging web mining.
Problem Statement 1
The e-commerce retailer faced predicaments in collecting and analyzing the huge datasets spread across geographies and business units, owing to the unstructured nature of the datasets.
Problem Statement 2
The client found it challenging to extract insights from unstructured datasets related to business outcomes, sales, and suppliers. Being able to sift through the huge data repositories and structure the datasets in the form of intelligible information was challenging as the data existed in silos.
Problem Statement 3
Organizing the data in a structured manner and linking it with internal data proved to a major challenge that prevented the client from analyzing the user-generated content to optimize business decisions.
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Solution Offered and Value Delivered
We followed a three-pronged approach to gain the desired insights for this web mining engagement.
Phase 1: Web Data Collection
The first phase of this web mining engagement revolved around collecting and documenting the available information. The gathered information was then analyzed and populated into desired formats to discover prevalent market trends, new opportunities, and drive sales while cutting down costs.
Phase 2: Discovering Hidden Patterns
The second phase of this web mining engagement focused on pattern discovery. The structured datasets were analyzed to understand the browsing behavior of online customers and to determine the success of their marketing efforts.
Phase 3: Analyzing the Data Patterns
The last phase of this web mining engagement focused on using the extracted insights to fine-tune product development, pricing strategies, and customer experience. This helped them offer incremental value to the shopper’s online experience while achieving a remarkable 3X improvement in customer satisfaction rates.
The last phase of this web mining engagement focused on using the extracted insights to fine-tune product development, pricing strategies, and customer experience. This helped them offer incremental value to the shopper’s online experience while achieving a remarkable 3X improvement in customer satisfaction rates.
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Working hand-in-hand with the e-commerce retailer we gained a better understanding of their business processes and unique challenges. In line with this understanding, we put together a dedicated team of our web mining and text analytics experts who co-ordinated with their in-house team to completely transform their online business strategy. To drive down costs and compete better the e-commerce retailer had to leverage web mining, improve decision making, and develop new and innovative products to create new revenue streams, explore new markets, and better understand customer needs. Quantzig’s web mining and text analytics solutions offered the desired insights that helped the client to devise a new business strategy aimed at driving revenue from online channels.
Though e-commerce retailers have come a long way, the absence of in-person interaction is the only area where they still taste defeat at the hands of brick-and-mortar retailing. The only way to inflict a fitting counterblow is to transform the online customer experience through analytics-driven, data-backed decisions.
What are the Different Types of Web Mining?
Web mining can be divided into three types based on the data that is used. It includes the following types:
- Web usage mining
- Web content mining
- Web structure mining
Why Collaborate with Quantzig?
Owing to the complexity of datasets, extensive analytics, and research skills are required to perform web mining. The analytics and web mining experts at Quantzig possess comprehensive data mining experience and the domain knowledge to effortlessly process voluminous quantities of data and deliver valuable insights to your organization.
You might also be interested in taking a look at Quantzig’s webinar which highlights the benefits of web crawling and data mining techniques.
Every business is unique and so are their needs. Request a free demo to find out how our web mining solutions can help yours.
