Thanksgiving was right here! Half of the globe witnessed some crazy shopping kicking off the entire holiday season, and retailers had a whale of a time, offering luscious discounts and consumer gifts at half the prices.
Before the weekend Thanksgiving sale, 69% of Americans, close to 164 million people across the US were estimated to shop– and they had planned to shell out up to 3.4% more money as compared to last year’s Black Friday and Cyber Monday sale. The forecasts came from National Retail Federation’s annual survey, headed by Prosper Insights & Analytics.
In such a brimmed-to-the-full holiday situation, pricing is not the sole determining factor – customer experience, service offerings and promotions, a cumulative of all these factors starting with data plays a major role in amplifying a company’s competitive advantage.
Timely, accurate data is a pre-requisite
Large-scale events survive on ROI (Return on Investment). And how to determine ROI? Of course, with timely, accurate data. It is crucial for businesses to ensure that their employees take significant decisions based on a comprehensive view of the organization’s business efforts instead of siloed data sources locked in individual boxes.
For this, having a well-administered self-service analytics strategy is an absolute necessity. Stakeholders need to access customized data sources, enabling teams including supply chain and marketing to identify outliers and modify strategies as events take place. Real-time analysis helps retailers discern issues the moment they happen. For an instance, if your e-commerce website shows some problems in loading products or showing wrong products in the cart, you can immediately address the issue and fix the problem, instead of discovering the issue next day, when the window of rectification might get closed.
Also, predictive analytics aids retailers fathom customer patterns from previous years to optimize their purchasing experience today throughout the entire customer buying journey, from advertising to payment.
For SAS Predictive Modelling courses, DexLab Analytics is your go-to destination.
Omni-channel strategy for a seamless retail experience
To make the customers’ face shine in the light of holiday shopping, it is imperative to introduce a successful omni-channel strategy structured on smooth, sounding foundation of data. Aggressive competitors develop state of the art dashboards exhibiting each level related to customer journey, sales, marketing, data engagement and supply chain at one single place. To give a tough competition to such setups, you need to work tirelessly to prepare efficient customer predictive modeling that could counterfeit rival initiatives. Customer analytics also nurtures customized experiences by reviewing product offerings that targets demographics across diverse regions.
Welcome a new retail landscape with the proliferation of mobile technology
“58 percent of consumers use mobile phones to research product information in stores and 40 percent use mobile phones to download digital coupons,” – according to a survey by RetailDive.
From this result, you can understand how positive customer experience has started reaching beyond the simple brick-and-mortar showrooms. The best example is Alibaba, a giant Chinese e-commerce retailer who is sweeping the world with its newest booming technologies, like Hema app and Alipay e-wallet. Equipped with a multitude of data at hand, Alibaba nabs customer preferences and frame recommendations both in their physical store as well as on a shopper’s smartphone.
More and more companies are investing in mobile technology, as it’s about time to establish potent analytics strategy with mobiles for an ultimate omni-channel experience. They suffice to be the key performance indicator – to learn more about customer analytics, look up to DexLab Analytics. Their program-centric Predictive modelling of market risk using SAS courses are stellar.
Watch Customer & Marketing Analytics video here
Interested in a career in Data Analyst?
To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.