Data Science in Retail: 4 Use Cases to Help You Increase Sales

By Dave Colgate

Cluster diagram showing different pricing groups based on willingness to pay

Data science is becoming widely used by companies of all sizes, from local SMBs right up to huge corporations such as Amazon. But what exactly can retailers gain from this new technology? Product recommender systems are often the first thought but curating better product suggestions isn’t the only way to use data science in retail. Below are four other use cases worth exploring if you want to maximise conversions and generate more sales.

1. Data-driven price management

You may love ‘happy hours’ and hate Uber price surging, but both practices share the same baseline idea – bump up the profits depending on the market conditions. Can retail companies get as good at attracting customers during ‘off hours’ and capitalising on high demand when the need is there? Yes, if they know how to piece their data together.

According to Deloitte, price management initiatives can boost profit margins by 2%-7% in just 12 months, generating an ROI of 200-350% on average. But few retail companies are actually taking advantage of this opportunity, mainly due to:

  • Low data maturity and analytics culture
  • Lack of visibility into all of the channels, product portfolios and customer segments.

Both issues are relatively easy-to-fix if you have a data science team and, once your data is prepped for analysis, you can choose to experiment with several price management strategies:

i) Personalise your discount/pricing strategy

Data science allows you to map similar customers into clusters based on their past behaviours and determine the ultimate price/discount combo that will make them convert.

Source: McKinsey

ii) Create segmented pricing

If you are not ready to go granular and personalise prices on a per-user level, you can still adjust your prices and offerings to cater to different audience segments. For example:

  • Value pricing – pitch a coupon or an extra discount to the bargain shoppers if they are buying at a convenient time (for example, they want winter boots in summer) or look at old inventory you need to clear out.
  • Standard pricing – pitched to the majority of your buyers.
  • Premium pricing – sweeten the deal for the premium-tolerant audience segment with an extra perk such as extended warranty (or another offer they are likely to respond to).

iii) Offer competitive real-time prices

Comparison shopping is at a peak with 87% of customers shopping on Amazon checking the price against the brands/retailer website. Considering how good Amazon is with price surging, manually benchmarking your prices with the competition is no longer viable. But with the help of data science and predictive analytics, you can create an advanced system that will help you automatically adjust prices depending on market conditions and competitors’ moves.

2. Data-driven attribution modelling

Conversions still remain a sore spot for retail companies. According to Wolfgang Digital’s new E-commerce 2019 KPI report, the average conversion rate in the EU retail sector is a meger 1.7% – the UK scores top.

Bar chart showing average conversion rates in Europe

And yet, despite the relatively low benchmarks, most companies still focus on traffic generation versus conversion optimisation. It …read more

Read more here:: B2CMarketingInsider

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