Using Data Analytics To Take Control Of Black Friday

Many companies in sectors such as retail and e-commerce see their best business performance towards the end of the year. There are special sales like Black Friday or the end of year sales and then there are all the holidays — such as Christmas — that people are buying gifts for. The predictions for Black Friday 2024 are already all over the internet.

This can cause complete chaos for these companies though. If most of their annual revenue is tied up in the final quarter of the year then that means their logistics need to be ready to deliver more stock, they need to process more returns, and they need to handle more customer interactions. Then they need to scale back down for a quiet time in January.

The most important response to this is to build flexibility into the business. Business models like GigCX have emerged to help customer service managers build enough flexibility into their customer response during these extremely busy periods of the year.

However, there is another very important approach. Be armed with predictions of what is likely to happen. Which products will sell, when, and where?

This is where predictive analytics can help.

Every company has a sea of data, especially companies that are selling directly to consumers. They will have past sales records so they know what consumers like and dislike and when they are most likely to make a purchase. However, most of this data may not be in the right format for analysis so the required insights will be impossible to find.

These are the key steps that any business considering a predictive analytics strategy needs to consider:

1. Define your objectives: What insight do you want to find and how do you think it will affect your business? Are you trying to boost sales or get closer to what your customers want? Think carefully and define the end goals first.
2. Collect the data: think about what you already have, what you can draw on, and what might be useful. Integrating external data can often be useful to explore correlations — such as comparing past weather history against sales data.
3. Clean the data: clean it up and make it easy to search and build queries.
4. Tools and skills: install the tools you will need to search and find insights and ensure that your team has the required skills.
5. Build collaboration: don’t leave this to the sales team alone. Involve every department because they are all a part of your business success and each has different insight from their part of the business.

Preparing for the busy period in this way with predictive analytics can give any business a head start in a number of ways. The marketing team can focus on the products and services most likely to sell. The sales team can design more effective sales strategies that will be accepted by the customers and the customer service and logistics team can scale up to meet the expected demand.

Academics at the Wharton school believe that companies should not focus solely on enticing customers during the final quarter of the year. This is a very interesting point and is another way that predictive analytics can help a business.

Instead of focusing on attracting a customer once a year for a Black Friday deal, why not gather enough data on that customer to know what they need and prefer – so very individual offers can be prepared for the customer each month, or linked to special occasions like the birthday of the customer?

In this way, predictive analytics can be used to help companies prepare for the very busy Q4, but they can also drill down to create insights that will make some of these customers return more regularly throughout the year.

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