Nov 26 2019
Data Analytics

How Small Retailers Can Keep Customers Coming Back, After the Holidays

Small Business Saturday is crucial, but business intelligence is key to establishing long-term relationships.

Black Friday. Cyber Monday. Small Business Saturday. They’re all just around the corner, followed by strong holiday retail spending. Research firm Deloitte predicts the average household will spend $1,496 this season, with 60 percent of shoppers splurging more than $2,100 on gifts and other holiday purchases. The National Retail Foundation, meanwhile, forecasts increased sales throughout the holidays with total growth of about 4.2 percent over last year.

The caveat? Holiday spending doesn’t hang around forever; according to historical data from the United States Census Bureau spanning the last 25 years, January and February are the lowest-volume retail months every year.

For small business retailers, seasonal changes demand a dual approach to sales: collecting big data when shoppers are out in droves, then analyzing data insights to deliver during the postseason slump.

Small Retailers Face Competition From Digital Natives

Competition is heating up in the retail sector. While more than 112 million shoppers now participate in Small Business Saturday, small retailers face increased competition from digitally native stores opening brick-and-mortar locations. Combined with growing demand for personalized consumer experiences — 62 percent of customers now expect individualized discounts and offers based on previous purchases — retailers need a holiday season strategy that anticipates and outmaneuvers the competition.

Technology helps small retail stores steal a march on competitors; by boosting website availability, companies can reduce physical store backups, while inventory-management systems ensure in-demand products are always in stock. Mobile apps can provide details about extended store hours and help consumers plan their visits, while advanced point-of-sale (POS) systems linked directly to Server Message Block (SMB) databases help capture useful consumer data.

What sets successful businesses apart from seasonal also-rans, however, is the ability to connect holiday sales with year-round conversions.

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Retailers Must Combine Data Sets for Long-Term Sales

Big data underpins the ability of any business to translate single sales into bigger strategies. But making the connection between seasonal sales and longer-tail trends requires the ability to combine two key data sets: internal and external.

Internal data includes transaction records, loyalty program information, historic buying trends and customer service interactions. Obtaining this data requires software solutions capable of linking common data sources — such as POS terminals, online order forms, mobile application accounts and customer service records — with customer relationship management (CRM) tools.

This data provides a baseline for current retail outcomes: Where are customers spending the most? What are their common complaints? Are they engaged with current brand marketing?

External data includes consumer demographics, such as age and spending pattern; location, including foot traffic; and larger market data, including industry sales at scale and current competitor sales. Here, sources are myriad: Research firms often release holiday and quarterly reports describing key data trends, while next-generation analytics tools help deliver contextual and location-based data to capture consumer foot traffic and engagement patterns.

How Data Analytics Helps Retailers Thrive Year-Round

While data-driven trends can help small retailers make the most of holiday spending, effective analytics are critical to survive the post-holiday slump.

Here, segmentation is important: Parsing data sets to define key customer demographics and spending preferences in turn helps retailers create targeted marketing and advertising campaigns that speak directly to consumer preferences and help drive all-year conversions.

Consider recent work from the ELTRUN Research Center of the Athens University of Economics and Business. Researchers found that basket-based analytics — which uses transactional data to help predict what consumers may purchase based on what they’ve already bought — can help identify customer segments, inform store layouts and improve advertising strategy.

For small retailers dealing with post-season staff shortages and significant inventory changes as holiday products are discounted and discontinued, however, delivering segment-based, consistent conversion volume depends on automated analytics tools capable of linking external and internal data sources, identifying key segments and providing actionable insights.

Seasonal sales account for a significant portion of small-retail revenue — up to 30 percent annually — but they’re not enough in isolation. While it’s critical to deliver a top-tier holiday shopping experience with in-store and online technology and outperform competitors by combining key data sets, surviving the post-season slump depends on automated data analysis that helps identify key segments and deliver consistent, year-round conversions.


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