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Prosper from the Holiday Retail Rush with Data Analytics

29 Nov 2019 | Share

November and December are make or break months for retailers. Shoppers throng to make the most of Black Friday deals and trawl the internet in desperation to find the perfect gifts for their family. In 2018, worldwide Holiday season spending grew 5.4% to $998.2bn, and on Black Friday alone, Brits are expected to spend an average of £220 each on items they probably don’t need.

Most of this recent growth is driven by the ease and convenience of internet shopping. The advent of widespread next day delivery now allows the less organized among us to leave our Christmas shopping until the 11th hour. Retailers cannot afford to be so disorganized though. So, what needs to be done to make the sales and deliver the best customer experience this year?

  1. Get ready for a massive spike in website traffic.

Some of the leading causes of website slowdowns include:

  • Third-party website components/apps
  • Overburdened APIs
  • Servers with lack of scalability unequipped for peak traffic
  • Web pages with slow loading times resulting from too many graphic components

Walmart compared performance to conversion rates and found that the overall average site load time for converted visitors was lower (~ 3.22 seconds) than that of the visitors who didn’t convert (~6.03 seconds).

  1. Get your website mobile ready

Mobile devices accounted for 67% of all digital traffic on Black Friday 2018, up from 61% the previous year.

  1. Prepare your distribution centres

Get your distribution centres ready to handle an increase in volume of deliveries and returns.

Don’t get stuck in the past

It’s important to remember that data analytics is not just an opportunity to understand your historical performance. Today, with an analytical database you can drive competitive advantage, especially at a time when your customers are generating more data than ever before.

How?

  • Deep Personalization (email, advertising, in-website offer / suggested purchases)

For example, you can use analytics to create customer microsegments and curate personalized gift guides for your customers to drive cross-selling after you’ve attracted them with discounts.

  • Optimize product content based on sentiment analysis

Sentiment analysis shows you the vocabulary of customers, retailers need to make sure that their own content matches up. For example, a project by Accenture uncovered that ‘seasons’ are discussed in 47% of retail customer content but only 27% of product content.

  • Harness predictive analytics

Combine natural language customer service and review data with store, website and logistics sources. That way you can predict when bottlenecks are going to disrupt your operations on an ongoing basis.

  • Use dynamic pricing

Run analytics and automation to set prices in near real-time according to activity on the website, competitors’ pricing, product availability, item preferences, order history, expected profit margin, and other factors.

  • Optimize your inventory

The technology is available for retailers to analyze billions of transactions and variables. This can range from past sales, searches on a website, weather information, even concert calendars in a particular geography – and predict what customers are going to buy before they order it. The retailer can even trust an AI engine to automatically purchase the necessary items from their suppliers with no human intervention. It would be simply impossible for a human to analyze and recommend the variety of sizes, colours, and products that a machine learning system can. This level of predictive/prescriptive analytics will allow companies to recommend products to their customers – boosting retention, while simultaneously having these products in place to fulfil orders much quicker. The end result? A happy customer.

  • Use sensors and beacons in physical stores

Data is generated by customer behavior within stores. You can track the interactions of customers, identify pain points/bottlenecks as well as lay outs that work particularly well. Test different lay outs across stores early in the season and continuously optimize them based on the data.  This technology is already in use by numerous retailers including Target, CVS, Nordstrom and Walmart.

Of course, this is not the only peak season during the year. These are considerations that have to be front of mind at all times for any successful retailer. If you want to find out more about how Exasol can turn data into real business benefit, check out our retail insights here.

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Now that we’re well and truly in the age of data, what’s coming next? 

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