Data gathered

All year round and especially during the holidays, IBM helps retailers to synthesize data on customer behavior and the forces that influence it.

Some data comes from clients and partners (with their approval - see Data Responsibility @IBM). Other essential data such as weather information is owned by IBM or curated from trusted public sources. With this ever-evolving landscape, we help clients understand who their customers are, what they need, and how best to engage and delight them.

Featured data types

Web page views, orders, session length, cart abandonment, store foot traffic, POS transactions, inventory, marketing interaction, pricing, product catalogs and costs, customer attributes and reviews, store configuration, temperature, precipitation, social media, news stories, local events, economic indicators.

Data scrubbed

Data gathering securely curates authorized structured and unstructured data from digital and physical sources at the customer, client, and market level, but that's just the beginning of the journey. Dozens of validations are run to ensure that the data is comprehensive, honest, and as error-free as possible – in other words, “scrubbed”.

Up to 90 percent of a data scientist's time is spent on safeguarding data integrity to ensure that the right conclusions are reached. Working with Watson, time for data scrubbing can be dramatically reduced, with machine-learning based approaches that identify outliers and outages, impute missing values, and define appropriate baselines.

Insights revealed

Once the data is scrubbed, trends within and across customer segments and markets are analyzed and surfaced to help retailers understand their customers at the local level. These real-time insights are brought into easy-to-access tools that empower professionals to make critical decisions about how to best gratify their customers.

This year shoppers flocked to specialty stores over mass/club and department stores at a rate of roughly 3 percent, as compared to the same pre-Thanksgiving time period last year.

Toy & hobby shops also benefitted from a shift in foot traffic during the fall season.

Shoppers in the Midwest and Northeast are predicted to drive up to 14 percent farther on Black Friday than on a typical November weekend.

Going the extra mile for the perfect gift

This view represents the increases and decreases in distance traveled on Black Friday compared to other November weekends.

It's predicted that New Yorkers will continue to be a selective bunch, driving farther on Black Friday only for apparel and department store shopping (11 percent farther on average).

It’s predicted that Cleveland shoppers will travel an average of 10 percent farther on Black Friday to shop than they do on a normal November weekend.

It’s business as usual for Miami and LA shoppers on Black Friday – historically they actually travel 1 percent less far to shop when compared to other November weekends.

Barring a widespread large snow event, weather doesn’t effect holiday weekend spending but it does impact when, how and what is purchased.

Last November was the warmest recorded in 120 years, which delayed seasonal shopping.

60 percent of the Northern US is experiencing a colder November than last year, which is creating strong early demand for seasonal items.

This year, the biggest preseason shift towards shopping specialty items like Apparel and Toys was seen in Chicago and increased a combined 4 percent.

Consumers are expected to drive 7 percent further to apparel retailers on Black Friday compared to typical November weekend days.

Get more data-driven holiday retail strategies and insights