One of the biggest challenges retailers face during the holiday shopping season is a trap of their own making.
Many customers have become accustomed to receiving steep discounts on certain pivotal shopping days. This is particularly true with Gray Thursday, Black Friday, Cyber Monday, and Mobile Tuesday.
As Jack Aaronson notes in a blog post for ClickZ, the concept of “Pavlovian marketing” stems from the notion that companies train customers to shop with them only when they know they’ll receive discounts.
Customers have become so accustomed to receiving markdowns at certain times that they’ll wait to shop until retailers make such bargains available.
Of course, not all shoppers buy into the belief that they’ll only get the best bargains between Thanksgiving and Mobile Tuesday. Pundits like Andrew Schrage of Money Crashers note that deals that are associated with Cyber Monday often extend past this popular shopping day.
And while some shoppers will procrastinate and wait until the last minute to finish their holiday shopping, others will continue to scour for special deals right up until Christmas.
So, what are some ways in which retailers can use data discovery tools and techniques to identify potential buyers who might be predisposed to purchasing particular products within certain price ranges? One way is by spotting potential buyers as they peruse a retailer’s web page for certain products.
If a visitor to a web site lingers for an extended period of time without taking action, the retailer can use data discovery tools to determine the right time to launch a chat window pop up from a customer service agent offering to help the customer.
Meanwhile, retail business leaders can use self-service BI tools and dashboards to drill down on recent transactional data to identify customers who respond to particular offers during the holiday shopping season and other potential buyers who don’t convert.
Further analysis and statistical modeling of the data can help retail executives determine with a high level of probability the percentage of prospects who may respond to a new offer if it’s set at a specific discount (30% off versus 25% off) or within a certain price range ($21.99 to $39.99 versus $24.99 to $44.99).
Taking this a step further, retailers can also use data discovery tools to identify similar traits that exist between customer groups that purchase certain products (e.g. affluent single women 45 to 59 who purchase upscale grooming products for men). Retailers can then turn to their prospect databases and match those traits to prospects with similar traits who have not yet converted.
This highlights another significant challenge retailers face in the run up to the holiday shopping season – the ability to predict the volume of sales they can expect during this time.
Data analysis as well as data discovery and predictive analytics tools can help retailers forecast their gross revenues during this time. Accurate sales forecasting can also aid in determining appropriate staffing levels to support in-store, online, and mobile operations.
While the use of data analysis, data discovery and predictive analytics tools can help retailers forecast holiday sales volumes, these tools can also assist in evaluating would-be buyers and determining effective approaches to help convert them.
- Subscribe to our blog to stay up to date on the latest insights and trends in data analysis, data discovery and predictive analytics.
- Please join us on Tuesday, December 18th at 1 p.m. EST for our complimentary webcast, “TIBCO Spotfire Delivers Game-Changer – Brings The Power Of Discovery To Big Data,” presented by Lou Jordano, Director of Product Marketing, TIBCO Spotfire. In this webcast we will demonstrate the generally available version of Spotfire 5, the next generation of data discovery and business analytics, offering breakthrough capabilities to speed analysis of big data.