The retail sector is renowned for being hyper-competitive; and yet, the majority of retailers only analyse a small portion of retail analytics to glean actionable insights. Read on to discover how we’re helping retailers optimise operational processes, make more accurate predictions, and drive revenue on Google Cloud.

retail analytics

Consumer behaviours are forever evolving, most recently due to the COVID-19 pandemic and resulting lockdowns. In fact, online retail reached a record proportion of total retail sales in January 2021 at 35.2% - compared to just 19.5% in the previous year (Office for National Statistics, Monthly Business Survey).

With change set to continue, how can retailers effectively plan for the future?

If your business and customer data is under-utilised, then you can’t make well informed decisions. For retail organisations, this can lead to inaccurate demand predictions. What’s more, if you’re operating an outdated legacy system, inefficiencies can build up over time and ultimately jeopardise the customer experience. 

In order to accurately predict and drive revenue, your retail analytics centre has to run like a well-oiled machine. At Appsbroker, we specialise in helping retailers harness on-demand cloud storage and compute power, in addition to Google’s unique machine learning and AI capabilities, to optimise operational processes and supercharge the customer experience.

Here are three ways we’re helping household name retailers modernise their data, applications and infrastructure to predict and drive revenue for years to come.

1. Up-to-the-minute insights with insightNOW deliver award-winning digital product launches

In 2020, a British retailer sought to achieve two firsts: deliver its first ever digital-only global product launch, and unveil the first ever product in its personal care range with a difference.

The business sought Appsbroker’s help using insightNOW to achieve instant, actionable insights - enabling the customer to gauge interest prior to the event across 30+ territories, interpret performance, and make data-driven decisions to support the launch.

On launch day, the business was able to live stream the event from its flagship store. The real-time tracking on the digital campaign landing and product pages instantly confirmed the launch’s success.

This was coupled with live direct digital sales reporting, revealing the retailer generated $280K in just two hours on a high end product that was only available to purchase directly at the time. Within 24 hours, the customer had achieved over $1.5M in global sales - making it the most successful product launch and number of sales vs target in the first month in the brand’s history!

The deep dive funnel analysis and customer journey reporting enabled the retailer to optimise conversion rates and overcome the marketing challenges associated with positioning a high-end product in a competitive marketplace.

Related Read: Overwhelmed by Surge Buying? How Real-Time Insight Can Help You Manage

2. Machine learning models predict pricing elasticity and turn pricing theories on their heads

While machine learning has increased in uptake in recent years, moving ML models past the Proof of Concept (PoC) phase into production is a common sticking point for many organisations.

In just six weeks, we built and ran a series of machine learning algorithms to help a retail manufacturer of frozen foods predict how pricing impacts profit - and the results were fascinating! 

Prior to our work, the customer was ready to drop the price of its most popular fish fingers brand by 5% to stay competitive. However, our machine learning algorithm revealed the business actually needed to increase its pricing on certain fish finger products to strike the perfect balance between popularity vs price - driving revenue to its lower priced products to maximise profit.

The frozen foods manufacturer was able to identify over $4m in additional profitability in just one product category, an additional $21m across its key products in the UK, and approximately $60m globally in one year!

“Automated decision making is giving businesses the freedom to operate and control their own destinies. I believe machine learning and AI has the ability to transform every part of every business.” 

Andy MacInnes, CTO, Appsbroker

From personalisation to fraud detection and everything in between, explore more machine learning retail use cases now and be inspired!

retail analytics

3. Data modernisation creating truly omnichannel experiences

Finally, we helped a leading British supermarket consolidate its online and in-store retail analytics to provide an end-to-end view of the customer and eliminate guesswork.

The retailer wanted to be able to identify customers across online and in-store sources to deliver highly targeted marketing campaigns. In addition, it wanted to be able to report on and attribute ROI back to third party advertisers.

We created a secure encrypted data ingestion pipeline to process all daily in-store transactions and join them together with online shopper IDs. The matched shopper ID baskets are then sent to Google Analytics.

As a result, the supermarket is now able to personalise the customer experience across multiple channels, deliver targeted marketing campaigns, and present sales performance analytics back to third party suppliers across online and in-store.

Targeted campaigns have resulted in a 10x increase in online orders per week, with the new solution automatically scaling to accommodate this sharp increase. The retailer can now draw a clear line of sight between a customer viewing a product online or in-store and completing a transaction on the opposite channel, resulting in a truly omnichannel experience.

Related Read: Data Modernisation Is a Retail Essential

Why choose Appsbroker for your retail digital transformation?

As this handful of retail stories shows, no matter where you are in your digital transformation journey, we can help you take control of your data, improve decision-making, and drive revenue on Google Cloud.

“It’s of strategic importance to work with a provider who will give you the best capability - and the clear winner is Google.”

Andy MacInnes, CTO, Appsbroker


Our deep expertise a
nd passion for Google Cloud Platform, coupled with our proven Agile methodology enables rapid Application Modernisation, Data Modernisation and Infrastructure Modernisation.

At Appsbroker, we’re passionate about delivering smarter ways of working and building productive, lasting relationships. Our collaborative approach, robust processes, and consistently high standards of service across the entire customer life cycle result in extraordinary business outcomes.


Explore our latest
retail case studies now to discover how we’re helping organisations like yours make more accurate predictions and drive revenue through retail analytics. 
retail analytics

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