The retail industry is on the cusp of another big data disruption. As the application of machine learning becomes more mainstream, retail brands now have to think and decide how they will navigate the next age of artificial intelligence. Blended Digital co-founder Damon McMillan discusses. 

The adoption of machine learning (ML) for retail has exponentially increased over the years. A 2020 Research And Markets study reports artificial intelligence (AI) in the retail market is expected to reach $15.3 billion by 2025. From market insights to logistics operations, ML has improved and changed the way brands operate, as well as how they understand and interact with their customers. 

Smarter supply chains

Machine learning is transforming existing supply chain models in the retail industry. Enabling more effective demand planning, cost reduction initiatives and on-time shipments. 

For instance, Amazon applies ML to power their supply chain and 1-hour deliveries. Thanks to ML, they can predict demand before stocking their warehouses - informing their forecasting team which products, and in what quantity, to buy.

Improved customer experience

ML has especially been a game-changer in customer experience (CX). By learning individual customer preferences and behaviours, ML enables brands to deliver a more personalised CX at every touchpoint, giving brands the opportunity to give the customer what they want, before they even ask for it. 

One good example is the Japanese multinational personal care company Shiseido. They scan and capture customers’ skin conditions based on various attributes like skin tone and environmental factors.

Through ML, Shiseido then recommends a personal skincare kit most suitable for each customer. Adjusting their recommendations as customers continue to track their skin condition and feed it back to the brand’s system.

Today, retail organisations are on the precipice of democratised machine learning that will further improve the impact on CX. Before we’re able to realise all the possibilities ML has to offer though, we first need to understand the critical factors at play to make it happen.

Looking inside out - current challenges in the retail industry

Customer expectations are continuously evolving. And with the advent of digitisation, access to more information and more choices than ever, customers also expect better services, better products and better experiences.

This affects how retail organisations choose and adopt marketing technologies (MarTech) that will help them remain relevant and competitive in their markets. 

However, this also leads to some problems. Today, 92% of companies globally fall short of meeting customer expectations with their MarTech and CX initiatives. What’s even scarier is that well over half of these digital transformation projects fail. 

So what’s the crux of the matter?

MarTech paralysis

Every CMO today is stuck in a paradox. The pressure to transform and adapt to the fast-paced evolution of CX and MarTech is enormous. However, the risk of doing so is so great that implications of failure can put their companies years behind.

Meanwhile, the number of MarTech solutions and channels has also increased over the years. 15 years ago, there were only 5 core marketing channels. Today, there are over 70. 

In 2011, there were only 150 MarTech solutions. By 2019, the number grew to 7,040. The pressure combined with technology overload has overwhelmed leaders, affecting how they choose the solutions that best fit their needs. 

Disconnected from technology, data and customers

A lot of retail organisations also feel disconnected from their own technology, data and ultimately their end customers. This stems from the failure of having a connected model supported by a connected strategy and team. So what causes the said failure in the first place? 

What many retail organisations fail to realise is that MarTech is more than just choosing the right platform. To ensure a successful technology transformation and great CX, you have to carefully take into account your data, people and existing processes.

Without the appropriate resources, technical and operational capability, you will not be able to effectively leverage MarTech and ultimately machine learning to drive value. 


In the age of the customer-first model, many retail organisations still focus on the channels to which they can reach their market instead of looking at data to better understand their customers.

As a result, they lose focus on the metrics (e.g. customer lifetime value) that truly paint a complete picture of the progress of their CX strategy. 

Limited intelligence - current limitations of AI 

While AI and ML allow brands to create more meaningful business-to-customer interactions, they still have their limitations. In fact, there remains to be a gap between the accessibility and availability of AI marketing platforms and the needs of marketers today. 

The majority of AI features in MarTech at present are a “one-size-fits-all”. The reality, however, is that every organisation has its own bespoke customer data points, objectives, requirements and complexities - all of which current AI features will not just “plug and play” to achieve. 

Take for example the common industry use cases (which you might already be doing) of MarTech AI features like:

  • Send time optimisation guiding marketers on the best time to connect with customers based on their engagement data points;
  • Next best product recommendations which are based on customer preference; and
  • Sentiment analysis and real-time interaction management that help brands deliver relevant experiences and value at the right moment.

While these capabilities allow you to get insights about your customers and optimise marketing initiatives, they can be limited by your data points. Further, most AI marketing features don't seamlessly integrate with your other existing technology stacks which should allow them to learn better based on your company’s complete data. 

But everything is about to change. 

The golden opportunity - Driving value with democratised machine learning

Market-based access to data and other ML tools are lowering entry barriers to train and launch more sophisticated AI algorithms. Further, several AI marketing platforms are now emerging to drive customer engagement and value from real-time integrated and customisable machine learning. 

The best part: it’s not only accessible to data analysts and data scientists. These platforms are bringing ML capabilities to all stakeholders of a business, including marketing teams.

In the next two years, we will be seeing retail marketers having greater access to AI customer modelling. They will be integrating ML in their marketing platforms with simple drag-and-drop frameworks, easily constructing, mixing and matching to take charge of the customer experience through the lens of AI. 

Leading the future of retail - What retail organisations can do today

With all these golden opportunities almost within our reach, the next question for retail organisations is: “What can you do today to make the most of the existing innovations whilst also preparing for the future?”

Use data to understand customers

Organisations need to shift from being channel-centric to becoming more customer-centric. Use your data to understand customer behaviours and maximise the insights you get to drive relevant interactions throughout the customer journey.

Understand how AI fits into your business

Consider how your chosen AI products will be able to use your own company data. Will it help you reach your business objectives? Will it give you a clearer view and understanding of individual customer data and help identify key signals to interact with them over their lifetime?

Identify the right metrics

Like any other marketing investment, the impact of your AI products should be measured against your marketing KPIs. In addition to standard metrics like open and clickthrough rates, look at other CX metrics such as cost per acquisition, retention rates, annual average value of customers and customer lifetime value. 

Ensure stakeholder buy-in and alignment of goals

Buy-in from stakeholders is important in any project. Ensure your digital transformation efforts are in-sync with their goals, and that they understand the value AI brings to the whole organisation. Finally:

Connect your data, people, process and platforms

Data, people, process, and platforms are four key components that need to align. Do you have quality data ready to be fed to your ML models? Is your team ready to implement ML into your other marketing platforms? What are your current data analytics and MarTech capabilities that can be built upon? 

These four components need to be intrinsically connected throughout your technology and marketing departments to ensure a successful technology transformation, adoption and ultimately great customer experience.

Over the next 12 months, we’re predicting to see a big acceleration in AI.

The gap between what’s been promised and AI's real-world applications will start to close further improving modern retailing across physical and digital touchpoints.

Lead the future of retail by building your own organisational capability for building machine learning modelling and ensure your MarTech strategy is connected with your teams and business objectives.

comments powered by Disqus