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Whatever the country is, when the mobile commerce data from online retailers and brands is compiled, the key findings states that purchase and transaction through mobile devices are continuously increasing.
• For year-on-year, the growth in time spent by mobile app category, the shopping app has made the biggest gains with a 54% increase in usage.
• 53.9 percent of all retail e-commerce is expected to be generated through m-commerce by 2021, that’s up by 19.4% from 2017.
• The worldwide mobile commerce revenues amounted to 96.34 billion U.S. dollars in 2015 and are set to surpass 693 billion U.S. dollars in 2019.
It’s obvious. There is nothing surprising as it’s a logical extension of the way smartphones are in the use. The way mobile devices are creeping into our lives portrays a positive picture of mobile usage in the future. This is the reason that the retailers have increasingly put their sights on and investing heavily their resources into E-commerce app development. Advocates believe that it’s a golden ticket to potentially augment the sales and customer experience. However, the things don’t turn out as anticipated. When the last decade was analyzed, the E-commerce sales via mobile have not been able to outperform the sales through the desktop.
It begs bigger questions. Why is mobile E-commerce not getting that much bigger now? Why are the customers not jumping toward the app bandwagon? Has the experience to deliver by Ecommerce not reached the point where it should be? And if so, How an E-commerce engineer can provide the best customer experiences?
The old-age technology- Big data analytics has answers for this, which allows online stores to listen to the customers, understand and adapt to their views of the world for mapping the customers’ needs while they accordingly make customer experience strategies and customer-centric decisions.
With big data technology, handling, organizing and managing the growing massive data have become a breeze. It helps the Omnichannel retailers to stay in front of the new breed of the customers with real-time analysis of the customer behavior and solving the issues off the bat. This in turn makes the customers happier and their purchase effortless.
Giant retail players like- Amazon, Alibaba, Walmart, Netflix, and others have kept technology at the core of their online stores to redesign the services, personalize the offers, and added channels to satisfy the customers eternally. Additionally, this helps them in evaluating the marketing campaign strategy and gaining a competitive advantage over others.
In the present scenario, the retailers are facing the biggest hurdle with the high volume and variety of data from different types of devices, which are difficult to link, correlate and interpret. The challenge has compelled the retailers to adapt to the path of big data for tracking the entire customer’s journey at the micro – level which can even be an average Joe has never realized.
After analyzing customers’ behavior across devices, they are grouped into different segments for targeting the audience in a better way. The categorization holds a ton of value during holiday campaigns to draw the attention of existing ones and to capture new eyeballs. The real-time monitoring and predictive analysis help in understanding every customer’s journey from entry to exit, and reveals the potential gaps that interfere with the customer experience, which in turn, helps in planning the future offers.
The customers never support discrimination, but it works when leveraged during pricing. With the increased prevalence of internet marketing, dynamic pricing has emerged as a new phenomenon. Based on this, prices of the products are dynamically changed according to the location, product type, time of the year, and demand in the market for maximizing the seller’s profit and capturing the largest share of customer attention.
The reason behind is that customers tend to shop from the retailer who offers the products at the best price. When a seller using dynamic pricing tool provides the product at the competitive price, the customers will be there. The pricing scheme strategy significantly impacts the importance of customers’ satisfaction when they purchase. Amazon employs the exact strategy where the price of products is varied by 5%, 10% and 15% on a daily, weekly or monthly basis.
Here comes the importance of the mix of a customer’s past purchase, browsing behavior and the market trends, which online vendors leverage to get the rich insights out of the data. The automated data analyzing has invented a recommendation system where the customers receive the tailored messages or emails for the products they want to have. There are different algorithms inbuilt inside the recommendation engine that help the retailers in recommending nearly what fit the best with the customer’s needs on every device a customer uses, which consequently benefits both.
In addition to recommendations, the discounts, coupons or additional cash-back messages are also sent to the customers according to their purchase history and browsing patterns. It enables repeat visits and stimulates to shop impulsively. With personalized offers and custom recommendations, the customers are made to feel that the retailer shows interest in catering to individual preferences and needs. Also, wants them to easily get their favorite products and purchase them at the best price, which in turn makes the retailer their favorite.
The customers’ spirit will get dampened as soon as they see their favorite product added to the cart or the one they just want to buy has run out of stock. With data analytics, the out-of-stock nightmare will fly away as it facilitates smart inventory management.
E-commerce can seamlessly manage the right levels of the inventory where adequate stocks are maintained not on the grounds of guessing game, instead, it is based on the customers’ search and purchase information. The data helps in predicting the customer’s demand ahead of the time, so that shelf space can be optimized alongside optimum discounts to increase the bottom line.
Nothing other than customer feedback is a powerful weapon which unfolds actionable insights to know a customer’s reaction towards a brand’s offers and the experience it delivers. Again, the brands cannot assess the customer’s feedback on an individual level, here is where data analytics to the rescue.
With sentiment analysis of reviews and ratings, the reviews which are not in similar formats are reviewed and the combination of data is stored. Through machine learning and text analytics, the reviews into positive and negative categories are separated to gain the meaningful insights illustrating which touch points satisfy the customers and which burst them into fumes. Improving the customer services according to their feedback inches the brand closer to the customers, win them over and build a long-lasting relationship.
Taking extra caution for the negative reviews and addressing them double-quick ensures customers that the brand considers them as an important asset and values them. Be on high alert and never sleep!
The last words
Retail space has become an accelerating marketplace where seeking proactive methods to extract the value out of the gigantic data sources in innovative ways. It is essential to gain a deeper understanding of the customer’s data for pleasing the customers in their complete buying process.
Not leaning on the customer’s data means, checking the pulse of your customers and getting to the grips of the customer experience is improbable. This indicates that your venture is on the verge of falling apart. It’s an age of customer experience where the retail industry is standing on the pillars of customers’ success. Don’t dare to overlook it, else you are risking at the helm to be overlooked.
Tap into the customers’ data to sell beyond the services or products that is delivering the unmatched experience no matter what device the customers are using, get the measurable conversion, sales, and revenue in the return. Keep soaring high by scoring high on the customer experience chart embracing big data.
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