Retail
Big Data Analytics
Use Cases
Retail Big Data
Analytics

 Use Cases

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Nowadays data proves to be a powerful pushing force of the industry. Big companies representing diverse trade spheres seek to make use of the beneficial value of the data.

Thus, data has become of great importance for those willing to take profitable decisions concerning business. Moreover, a thorough analysis of a vast amount of data allows influencing or rather manipulating the customers’ decisions. Numerous flows of information, along with channels of communication, are used for this purpose.

 

A review of the top Big Data Analytics use cases for retail:

Recommendation engines

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This kind of analytics algorithm compare the past searches and purchases of the user with other customers with the same likes. Then it can suggest new items based on the products bought by similar users

It can be applied even when we don't have futher information about user previous behaviour, the so called "non personalized recommendation".

The typical approach suggest new product to our users, but we can look at the reverse path. For example, we want to promote a product, the algorithm can select the users with the higher conversion rates for a personalized marketing campaign

Customer segmentation

Different users have different tastes and likes. A one size fit all marketing strategy will underperform and deliver a poor ROI

This kind of analytics algorithms has the ability to segment your customers in different groups that shares the same demographics characteristics or buying behavior. In order to create a personalized marketing campaign that will deliver the right message to the right user at the right time via the right channel.

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Propensity to buy / churn

This algorithm can be used to estimate the probability of a potential customer to buy an accommodation or travel.

If a customer has a lower probability to buy, a discount could be added to increase the conversion rate

It could be applied also to cross-selling scenarios, for example offer different extras depending on the customer

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Market basket analysis

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This process mainly depends on the organization of a considerable amount of data collected via customers’ transactions. Future decisions and choices may be predicted on a large scale by this tool. Knowledge of the present items in the basket along with all likes, dislikes, and previews is beneficial for a retailer in the spheres of layout organization, prices making and content placement

The insight information largely contributes to the improvement of the development strategies and marketing techniques of the retailers. Also, the efficiency of the selling efforts reaches its peak.

 

Warranty Analytics

Warranty analytics entered the sphere of the retail as a tool of warranty claims monitoring, detection of fraudulent activity, reducing costs and increasing quality. This process involves data and text mining for further identification of claims patterns and problem areas. The data is transformed into actionable real-time plans, insight, and recommendations via segmentation analysis.

The methods concentrate on the detecting anomalies in the warranty claims. Powerful internet data platforms speed up the analysis process of a significant amount of warranty claims. This is an excellent chance for the retailers to turn warranty challenges into actionable intelligence.

 

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Price optimization

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Having a right price both for the customer and the retailer is a significant advantage brought by the optimization mechanisms. The price formation process depends not only on the costs to produce an item but on the wallet of a typical customer and the competitors’ offers. The tools for data analysis bring this issue to a new level of its approaching.

The algorithm presupposes customers segmentation to define the response to changes in prices. Thus, the costs that meet corporates goals may be determined. Using the model of a real-time optimization the retailers have an opportunity to attract the customers, to retain the attention and to realize personal pricing schemes.

Location of new stores

Data science proves to be extremely efficient about the issue of the new store’s location. Usually, to make such a decision a great deal of data analysis is to be done.

The algorithm is simple, though very efficient. The analysts explore the online customers’ data, paying great attention to the demographic factor. The coincidences in ZIP code and location give a basis for understanding the potential of the market. Also, special settings concerning the location of other shops are taken into account. As well as that, the retailer’s network analysis is performed. The algorithms find the solution by connection all these points. The retailer easily adds this data to its platform to enrich the analysis opportunities for another sphere of its activity.

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Customer sentiment analysis

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Customer sentiment analysis is not a brand-new tool in this industry. However, since the active implementation of data science, it has become less expensive and time-consuming. Nowadays, the use of focus groups and customers polls is no longer needed. Machine learning algorithms provide the basis for sentiment analysis.

The analysts perform sentiment analysis on the basis of natural language processing, text analysis to extract defining positive, neutral or negative sentiments. All the spotted sentiments belong to certain categories or buckets and degrees. The output is the sentiment rating in one of the categories mentioned above and the overall sentiment of the text.

Fraud detection

The only efficient way to protect your company’s reputation is to be one step ahead of the fraudsters. Big data platforms provide continuous monitoring of the activity and ensure the detection of the fraudulent activity.

Using the data analysis mechanisms within fraud detection schemes brings benefits and somewhat improves the retailer’s ability to protect the customer and the company as it is.

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