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.
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:
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:
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.
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:
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.
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.
And yet, despite the relatively low benchmarks, most companies still focus on traffic generation versus conversion optimisation. It may seem that playing the numbers game is a good strategy (more traffic = more sales) but you can actually get more with less by drilling down deeper into your analytics data and identifying which channels bring the best ROI and secure the highest conversions.
Data-Driven Attribution Modelling – a custom model mapping conversions and sales to respective touchpoints/channels in a customer’s journey – can help you with that. This model shows what kind of marketing sequences lead to the most sales, what creative assets play a major role in the process, and why some customers did not end up converting.
The kind of insights you can get include:
Effectively cross-selling even a small-ticket item can lead to massive profit increases. One furniture retailer decided to pitch an item worth 6% of their average order value (AOV) to shoppers. In just 41 days post-implementation, they had increased AOV by 4.6% and secured an additional $180,000 (£142,400.00) in monthly revenue.
Using data science in retail can help you increase profits without running numerous A/B tests. And you can even pitch personalised offers to different customer segments to further pump up conversions and sales. Adding predictive analytics to the mix will give you an extra edge: you will be able to see exactly when to upsell/cross-sell to meet your business goals.
For instance, you can create an algorithm to identify the key value items (KVIs) and key value categories (KVCs) that are making a big difference to your bottom lines and pitch them accordingly to different shoppers. Those can be:
Determining who your most profitable and loyal customers are is relatively easy these days. But, traditional analytics fails to tell you when those shoppers are starting to purchase with less frequency, what leads to that and why they switch to a competitor altogether.
Data science can help you explore those root causes. You can identify the dependencies between different customers’ choices/behaviours and apply that data to predict their future actions. Here are a few examples of what you can achieve with CLV modelling:
If you feel that your marketing budgets are growing, but your sales numbers stand still, exploring how you can use data science in retail may be the right option for you. Yes, it may sound like a lofty investment, but the ROI is definitely there.
If you need advice, call our specialists today on 02392 830281 and we’ll be happy to help.
Dave is head of SEO at Vertical Leap. He joined in 2010 as an SEO specialist and prior to that worked with international companies delivering successful search marketing campaigns. Dave works with many of our largest customers spanning many household names and global brands such as P&O Cruises and Harvester. Outside of work, Dave previously spent many years providing charity work as a Sergeant under the Royal Air Force Reserves in the Air Cadets sharing his passion for aviation with young minds. He can often be found in the skies above the south coast enjoying his private pilot licence.
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Categories: Machine Learning
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