This Christmas Is Big Data’s Biggest Yet

22nd December, 2016 by
In the US and Western Europe, about a quarter of all personal spending occurs around the Christmas holidays. In the eight-week period from mid-November to mid-January, it all happens. Failure to carry the right products or miscalculate marketing and pricing, and a retailer could miss out on a huge slice of its potential annual sales with catastrophic results.

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It’s not surprising, then, that the sector has long led the way when it comes to the commercial exploitation of data. For decades, the big players have studied market trends closely and analyzed customer buying patterns in order to devise the most lucrative seasonal strategies. To do so they’ve been using ever more advanced computational techniques and ever bigger stores of data  –  from Wal-Mart’s game-changing data warehousing efforts in the late 90s to the emergence of Amazon, eBay and the growth of ecommerce and data-driven personalization in the 2000s.Big data underpins big developments and decisions.

Now we’re witnessing another stage in this evolution: the increasing use and accessibility of big data analytics to predict buying patterns and better target potential customers. All the big retail players have been working to exploit the technology for the past few years. As cloud-based big data analytics ‘as a service’ becomes more widespread we can expect to see plenty of smaller businesses following suit.

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A major sign that the insights from big data are giving retailers a significant seasonal sales boost can be seen in the growing increase in sales during the weekend of Black Friday and Cyber Monday. This has been the focus of many marketing efforts again this year. Black Friday 2015 saw a 223% sales uplift on the daily average, compared to a 62% uplift in 2014. Cyber Monday 2015 saw an impressive 139% sales uplift on the daily average, against 42% in 2014. This year, retailers hope they’ve done even better, although we’re unlikely to find out until after the holidays.

Given the intense competition in the sector, the big names are understandably cagey about revealing details of their big data strategies for this year, but given the growing range of big data analytics tools now at their disposal (see Big Data Analytics: The Best Tools For The Job), broadly we can glean what the savvier big-data-using organizations will be hoping to do…

#1. Predicting this season’s hottest sellers

Knowing what to stock is the first conundrum, and retailers would have begun their analyses months ago. By combining enterprise data (such as that from sales and CRM systems) with external data (such as customer web browsing patterns, social media sentiment, what’s being advertised, industry news feeds, etc), retailers can work out with increasing accuracy what is likely to be the year’s hit products.

#2. Inventory management

Once they have a good idea of what people will be buying, they can then begin to ascertain where demand will be strong or not. Here they’d be analyzing and correlating data like:

  • customer transactions
  • shopping patterns
  • location data,
  • regional ‘buzz’ around products on social media
  • demographic data and published research

This is true for both online and bricks-and-mortar channels. This will inform their decisions on how much stock to buy, and where to place it.

#3. Optimize pricing

Given the ease with which consumers can now compare an outlet’s prices against those of other suppliers, it’s vital retailers get the pricing right. Charge too much and people will vote with their feet. Pricing also needs to be dynamic, changing in line with competitors who will be constantly tweaking theirs throughout the season in order to maximize sales. Here companies will be automatically tracking and analyzing product prices across the board in real-time, allowing them to (for example) synchronize prices with competitors on an hourly basis to ensure they’re never undercut.

#4. Pinpoint specific target customers

By analyzing data such as social media feeds, customer browsing habits, loyalty data, purchase histories, demographic data and chatter in online groups and forums, big data analytics can pinpoint specific individuals who are likely to be interested in their products, meaning they can focus sales and marketing efforts where they’ll have the biggest impact.

#5. Segment prospects into target groups

Once a retailer has a list of potential buyers, it can use big data analytics to slice and dice them in various ways in order to inform its marketing and sales efforts at any point in the seasonal cycle. For instance, it could identify target customers likely to pre-order goods and target them accordingly with early-season offers and enticements.

#6. Know how, when and where to contact customers

As well as knowing who to contact, real-time analysis of data like customer preferences, web browsing data, wifi connection information, in-store beacon data and mobile app location data can tell a retailer whether a customer is walking into or past one of its stores, driving nearby, browsing its site, spending a long time in front of a particular display, etc. That customer can then be targeted individually using whatever channel is most convenient for them (social media, text message, email, app notification, etc). It’s all about developing a more personalized experience that matters.

#7. Entice buyers with personalized offers

The insights gleaned from historical and real-time analysis of big data ultimately allows retailers to maximize their chances of closing and maximizing a sale by delivering timely, targeted offers and enticements to customers at the right time, in the right way. For instance, if analysis reveals a customer has looked at a pair of shoes online and is now staring at them in the window of a store, the system could text them a code giving them a discount, or some suggestions of matching handbags in stock and a code for a further discount if they buy both. People want offers that match their needs.

Smarter seasonal shopping is just the start…

What this all heralds is a smarter seasonal shopping experience for all. Increasingly, retailers can:

  • accurately anticipate demand
  • have their products available when and where they’re needed
  • price goods dynamically
  • market products with relevant promotions
  • Set up timely offers tailored to those most receptive to buying.

Consumers too are benefiting from big data to find what they’re after faster, and at the cheapest price, so no business should think that it’s going to ease competitive pressure. We’ve barely scratched the surface of what’s going to be possible as the technology improves and the discipline matures.

And while the retail sector is still leading the way with big data analytics, the kind of techniques it has been pioneering are increasingly available to all businesses. Not every sector is going to be so focused on the Christmas period, of course – but the gift of better business insight all year round is one we can all look forward to unwrapping. Happy Holidays!

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