Capitalize on Data Analytics to Boost Customer Experience in the Retail Space

Capitalize on Data AnalyticsBig Data needs no introduction. Everyone is talking about it and many companies are using Big Data to achieve a number of business goals. But in an unrefined state, Big Data is just a collection of large volumes of data that really doesn’t tell us anything. Trying to make sense out of the huge pile of information is one of the biggest challenges retailers face with Big Data. This is where advanced analytical tools come into play and filter the unwanted data and offer useful information. Big Data has transformed the way retailers conduct business with consumers. It has offered them valuable insights into customer shopping behavior and industry forecasts necessary to stay ahead of the competition. Retailers realize the power of deploying analytical tools to understand their customers better, one of the crucial parameters for business success.For any retailer, the main goal is to increase the ROI. We have a packed marketplace with several vendors selling different types of analytical tools. Although the names may sound the same, a closer look at the features and the capabilities of each tool would reveal sizeable differences in the techniques being used, the results it offers and the business value it delivers.

Business requirements and goals may vary from one retailer to another. An analytical tool that may work for one retailer may not work for their competitor. The good thing about online shopping is that retailers can send in personalized product suggestions to customers. Imagine a customer shopping in a brick-and-mortar store. The sales person would not know anything about the customer’s likes or shopping behavior. But online shopping offers a peek into what the customer likes when he/she buys a particular product. It helps retailers send customized offers, deals and discounts. This way the retailers won’t be spamming their inboxes with unwanted offers and will be sending only relevant information to a specific group of customers.

Although shopping online offers an insight to what the customers like based on the product they buy, it doesn’t say anything beyond that. In order to make a more precise decision about what to send and what not to send, it is essential to deploy the right analytical technique.

The process of choosing an analytics tool is a challenge in itself. There is no straightforward guide to tell what tool one must opt for. Start by determining the nature of the business and understand what the requirements are. This will give an overview of what tool is best suited for the business. For example, a retailer may want to enable limited distinction when dealing with a first –time customer. Deducing customer behavior based on the purchases they made can be done using collaborative filtering. When targeting a broad segment of customer base for promotional activities, clustering algorithms may be used. This is very useful when trying to group customers based on similar buying patterns. The result of this technique would be a general increase in the customer response rates, say about 5-6% when compared to the conventional question-based grouping.

In a competitive market if retailers wish to succeed, they should try and understand the future behavior of customers. This will help them set a strategy to analyze the behavior and offer personalized services. A regression model may be deployed to fulfil this objective, increase conversion rates and boost performance. A time-to-event model can also help retailers get a deep understanding of this aspect and improve performance rates by 50%, in addition to a significant lift in the response rates.

Today, well-informed consumers are driving the retail industry. Sending a promotional offer may or may not encourage a customer to buy a certain product. If a customer is already planning to buy a product irrespective of the offer mailed to him, will that change customer behavior? So, retailers have to weigh their decisions carefully to determine if an investment is worth their time and effort. It is also necessary to figure out what kind of promotions will have a huge impact on a customer or what kind of offers will change customer behavior. Up-lift model, a leading-edge analytical technique, used in combination with time-to-event model will help retailers predict the change in customer behavior that would occur to a particular retailer action. This model offers the best insights that a retailer requires to decide where to allocate marketing spends to generate higher ROI.

Decision models help retailers consolidate and compare the outputs of several predictive model results and other key decision elements to manage complex decisions. It also aids in detecting key drivers and spots the best offers taking into account customer and retailer constraints.

No matter what approach a retailer chooses to deploy, it is necessary to ensure that clean, high-quality, and easily accessible data is available. A data warehouse is vital to connect all the enterprise-wide data. Another critical factor is the support and commitment from the top management that understands the usage of these tools and trusts its capabilities to influence business decisions.

More and more analytical tools are making its presence in the retail space. New technologies such as video and customer sentiment analytics are being used extensively by many companies to harvest better results. Depending on the requirement, it is up to the retailer to decide where and how to apply an analytical technique to ensure better profits and service. They can combine more than one technology to create an analytic landscape of their own. They can plug it to their marketing strategy to increase customer insights, deliver excellent service to their customers and improved value to business.

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