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Enhancing ethical marketing by advancing consumer behaviour prediction

In a significant industry development, UK retailers are poised to embark on a transformative journey towards more ethical and inclusive marketing practices. By catering for a… View Article


Enhancing ethical marketing by advancing consumer behaviour prediction

In a significant industry development, UK retailers are poised to embark on a transformative journey towards more ethical and inclusive marketing practices. By catering for a wider range of customers promises to open exciting opportunities, which is good news for everybody.

Predyktable, a pioneering advanced data analytics company, is at the forefront of this new era. Predyktable’s CEO Phillip Sewell believes the key to achieving ethical and inclusive marketing is by better understanding and predicting how consumer behaviour shapes future purchasing decisions. He reveals the why’s and how’s in this exclusive Q&A.

What are you trying to change?

We believe that diversity and inclusivity should be at the core of UK retailing. It’s not just about being fair, it’s smart business practice too. Big financial and reputational benefits can be gained by those brands that seriously start embracing it.

To begin this journey, retailers need to better understand how to meet ever-changing community needs in highly challenging markets. With a deeper understanding of these dynamics, retailers can remove guesswork, bias, and uncertainty from decision making that hampers ethical marketing practices.

Retailers can start creating more diverse, inclusive, services, products and experiences tailored to better serve underserved or marginalised customers.

What are the barriers to change?

For most retailers, there are multiple challenges that constrain more ethical and inclusive future decision-making. We’ve highlighted them in our 2023 UK survey of over 100 marketers.

Most significantly, the study reveals shortcomings in existing data analytics tools, with over 65% of respondents finding them ill-suited to their forward-thinking needs. They are heavily reliant on historic data and insights, which is why 50% of marketers say they use poor quality data.

There’s also not enough focus on identifying and understanding wider external data sources. This means marketers miss out on valuable holistic insights of the market landscape, competitors, emerging trends and more. So, the quality and depth of insights aren’t there to support accurate predictions.

Moreover, the survey also exposes the underutilisation of predictive data analytics. A surprising 86% of marketers aren’t incorporating these tools into their decision-making processes, despite 70% recognising their pivotal role in potential success.

What is the change catalyst for retailers?

It’s all about helping retail marketers better understand and predict how diverse consumer behaviours impact on what when and how people shop. It’s a dynamic and multifaceted relationship that can significantly impact the strategies and decisions of retailers.

So how do we help them capitalise on this? 

What sets us apart is a cutting-edge data analytics capability that blends the power of rich data, state-of-the-art AI, and data science. Our ‘dynamic consumer behaviour engine,’ lies at the heart of this. It processes thousands of economic, environmental, demographic, social data sources, and our own extensively researched datasets including region-specific events.

These diverse data signals are combined with retail brands’ own historical data. Machine learning reveals complex, cause-and-effect, behavioural patterns, and relationships within the data. These feed bespoke, real-time, predictive insights and recommendations that augment forward marketing decisions.

As fresh data continually enriches the consumer behaviour engine, including the collective learning of anonymised client data sets, existing models are continually retrained with this data. Keeping the models current with shifting trends, behaviours, and sentiments is critical to consistently delivering highly accurate, timely predictions that are attuned to customers’ needs.

What type of results can we expect?

Equipped with these rich predictive insights, we believe retailers can start promoting greater diversity and inclusion through four key strategies:

  1. Inclusive targeting: by deeply understanding wider community needs and behaviours, hospitality marketers deliver products and services that resonate with underserved or marginalised communities.
  2. Eliminating bias: machine learning algorithms remove bias from prediction data, ensuring that marketing efforts avoid reinforcing stereotypes and discriminatory practices. For example, predictive models can ensure that pricing remains fair transparent and consistent for all customers.
  3. Tailoring recommendations: marketers meet the unique inclusivity needs of different communities using predictive recommendations to customise shopping experiences, products or services with physical or digital accessibility features, cultural considerations, and more.
  4. Predicting demand: equipped with predictive insights on dynamic demand, marketers deliver the right products and services, to the right place, at the right time, better serving underrepresented and vulnerable communities.

What is your key takeaway?  

Through advanced data analytics, we’re aiming to equip retail brands with predictive insights that unravel the mysteries of national consumer behaviour and its impact on the buying decisions of a diverse range of communities. This promises to pave the way for a new era of ethical and inclusive marketing, in harmony with the needs of these communities. Get this right and it fuels profit, brand equity and consumer equality. Everyone wins.

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