Creating an engaging customer experience is vital for any business. Businesses are collecting and using data to improve the customer experience. Retaining online customers is becoming more complex as customers these days are putting a premium on shopping experience in addition to price and quality of products. Data Science and Big Data Analytics can help businesses in providing a better shopping experience that leads to higher customer satisfaction and loyalty in the long run. Advanced analytics tools are capable to collect, store, analyze and utilize historical and current data in an attempt to provide better customer experience. Hence, these tools are more widely leveraged by digital retail companies.
Here’s how retail companies are harnessing their data to enrich the customer experience and retain customers in the long term-
Predicting customers’ purchasing patterns from their past behavior – The most important aspect of any advanced analytics tool is to predict customer needs and their behavioral patterns. The data analyzed from the customers’ past behavior can be utilized to understand their current needs and eventually predict which product they will purchase. Analytics tools are leveraged and recommendation engines recommend related products that the customers are looking for. For example, makeup products company Sephora’s Visual Artist App recommends products based on its customers’ previous purchase history. Demographics of the customers, combined with their purchase history, allow the app to predict what products the customers will buy.
Sephora Visual Artist App collecting the current data of the visitor to predict and recommend the products that she will purchase.
Real-time results – Predictive Analytics provide the businesses with real-time results to tailor their strategies accordingly for optimizing the customer experience. They collect customers data in real time and develop recommendations based on customer feedback and preferences. The algorithms built into the data-driven systems of Youtube or Netflix will provide their viewers’ with further recommendations depending on the type of videos they have watched, if they have skipped a particular song, etc. This will enhance the customer experience as the analytics will know what will work and what will not.
Youtube recommending wide varieties of videos based on the visitor’s past search results.
Personalized services to the customers – According to Forbes, more than 85% of mobile marketers have reportedly achieved higher engagement, greater revenues, and more conversions into their businesses by offering personalized services to their customers. A well-drafted personalized marketing strategy derived from the already existing data from the database always is effective. Amazon and Spotify successfully implemented analytics into their operations that would suggest related products and music to their customers’ tastes and preferences.
Amazon recommending laptops of different companies, along with a personal suggestion about the best selling product.
Optimized pricing models – Price optimization is understanding customers’ reaction to various pricing strategies by using data analytics techniques and finding the best price for the company. Pricing models are drafted based upon factors such as costs, competition, season, etc. Change in the price of one product will create a change in the sales of other products. Data Science predicts this change and helps in computing the pricing models. The Airlines and Hospitality Industries, in the 1980s, started leveraging big data analysis for optimized pricing decisions – to offer competitive pricing with maximized revenues. The data about the time of booking, type of customer, geography, etc, were the factors considered for customized pricing.
Connecting to customers touch points – Customer data can be acquired from various internal and external touch points. Every data collected from these touch points is essential for branding. Hence, analyzing all the touch points becomes a crucial element in providing enhanced customer experience. The data collected from various pre-purchase, purchase and post-purchase touch points will help in analyzing how the customers feel about the product and the company. The technology giant, Apple, is considered to be the leader when it comes to customer touch points. With its innovative product descriptions, easy-to-navigate website, and easy access to customer support – it provides a consistent pathway to its customers.
Businesses are slowly realizing the importance of data in the customer experience. Data, when used strategically with the help of Data Science can do a lot more. As we move ahead, we’ll see a lot more applications of data analytics in customer satisfaction and experience.
- Data plays a critical role in enhancing customer experience and satisfaction.
- Businesses are leveraging advanced analytics tools to improve the shopping experience.
- As we move ahead, we will see much more granular use of data in customer experience.