Integrating Retail Pro POS data and COVID-influx of ecommerce data for hyper-personalization
Amid recent retail turbulence, there has emerged an opportunity to provide customers a better, more intuitive shopping experience in the wake of a global pandemic that had serious repercussions in the retail sector.
With many shoppers staying out of brick and mortars due to COVID concerns, online shopping became significantly more popular, especially for buying everyday items like groceries and toiletry items.
As a result, retailers now have a year’s worth of data on new (and existing) ecommerce shoppers that can be integrated with store sales data from the Retail Pro POS for personalization, providing a more holistic customer view.
By preparing personalized and integrated customer solutions, retailers can be better positioned for success as the ability and customer willingness to visit stores increases.
Learning from your customer data
Hyper-personalization refers to enabling personalized, contextualized interactions across all channels, including sales and marketing.
A study from Ascend2 found that 62% of marketing professionals consider hyper-personalization to be critical, but only 9% have successfully implemented the strategy. Traditionally, personalized marketing would include, for example, inserting a customer’s name into an email or serving up specific content on a landing page. Personalized experiences in stores would stem from a salesperson’s ability to engage in clienteling based on the client’s history with a brand, especially in luxury retail.
Today, hyper-personalization uses intelligent tools like visual analytics software like Retail Pro Decisions to aggregate store and ecommerce data, the marketer’s email engagement data, website interactions, and other sources of third-party data to predict customer behavior.
AI algorithms can also compare a company’s shoppers with others online who display the same interests.
AI can aggregate similarities and predict future actions based on those that have already been taken by similar users.
That allows companies to deliver extremely relevant offers or product recommendations.
Rather than making recommendations to shoppers based on their own purchase history, AI compares their preferences and buying patterns to millions of others to discover more advanced, nuanced purchasing habits.
The strategy also builds brand loyalty: The more personal the customer experience feels, the stronger the relationship can be. Integrated data analysis combined with AI-powered loyalty and personalized marketing tools like AppCard for Retail Pro offers retailers something more than the competition.
Acting on data gathered during COVID’s ecommerce upsurge
With the sudden influx of customer data during COVID, retailers are learning more about what is truly important to customers, and what is not.
For instance, curbside pickup is a highlight coming out of the new normal shopping experience, a feature that in particular is helpful to parents of young children, those with disabilities or anyone on a tight schedule.
Prior to the economic lockdown during the first half of 2020, curbside delivery was pretty much limited to grocery pick up.
Retailers must leverage their data analysis capabilities while considering how recent customer trends will impact their supply chains.
They can then accurately respond to both vendors and customers in specific, relevant ways. By understanding the context of what customers want, retailers can adjust to meet those expectations. Retailers can move beyond providing customers with a robust product selection online and in-store. Today, the top retailers also offer a customized, cross-channel, personal shopping experience, resulting in loyal, satisfied customers.
