AI Innovation: A guide to modern retail customer retention strategy

AI Innovation: A guide to modern retail customer retention strategy

Today, the modern consumer is constantly shifting across buying channels, which means Organisations that adapt to this are able to improve their user experience and drive better relationships with their audience across various points of contact. Artificial Intelligence (AI) helps drive these experiences to ensure a consistent and unified engagement for every journey the consumer chooses to take.

As people change their behaviours, retailers, marketers, salespeople and customer support reps will need to react fast, as they are likely to have multiple touchpoints with a retailer & expect their journey between each touchpoint or channel should be seamless. This means retailers will need to pursue one holistic approach – an omnichannel experience that consumers can willingly use anytime & anywhere, driven by AI.

Major factors such as customer sentiment and brand image help drive customer retention in today’s market. By understanding customer sentiment alongside delivering a consistent brand image, retailers will see an improvement in customer retention and consumer relationship.

From providing a seamless omnichannel experience to better personalisation, artificial intelligence opens up a lot of new possibilities. By tapping into the power of data far faster and more efficiently than humans ever could, AI can improve the overall customer experience.

More resources: AI Myths Debunked

How AI Helps Retailers Boost Customer Retention

Artificial Intelligence is a key cornerstone of many retail customer retention strategies, by allowing brands to interact with their customers and nurture the relationship in an organic and personalised way. This is key to building strong customer loyalty and promote retention, while also effectively engaging with an audience to drive revenue creation opportunities.

In this article, we will examine five ways that AI can help you improve your customer loyalty.

1) Predictive Analytics Keeps Stock Levels Relevant

Before diving into this point, a quick definition of what predictive analytics is:

Predictive analytics describe the use of statistics and modelling to determine future performance based on current and historical data. Predictive analytics look at patterns in data to determine if those patterns are likely to emerge again, which allows businesses and investors to adjust where they use their resources to take advantage of possible future events.

Customer engagement, specifically how your customers view and interact with your brand, is a key factor in your marketing efforts. Organisations that have a 360-degree view of their customers and their needs at the core of their marketing strategy at set to be successful.

Big data has changed the way marketing and sales teams engage with customers. It has changed the way that businesses market to their customers, which in turn, helps companies increase their profits. From our own research and case studies at Data Clarity, companies using personalised marketing and campaign optimisation increase sales around 40% – read more about that here.

AI and big data analytics provides the business intelligence you need to bring about positive change, like improving existing products or increasing revenue per customer by generating insights into customer behaviour and predict what customers are looking for with accuracy.

Product and merchandising teams can use these powerful insights to accurately predict sales in particular the colours, styles, fit and sizing that their customer base actually wants to purchase. Production teams need to know the right quantities of fabric to order to reduce stock waste.

Case Study

Sephora uses predictive analysis for their email marketing campaigns. With this innovation, they are able to easily keep track of their customers’ purchase histories and then, based on the date of purchase, they calculate how long it will take for the product to run out.

When the product is about to run out, they send their customers emails with relevant offers for the product. In addition, they feature complementary products in their emails, which is a great way to ensure that customers remain happy with and loyal to the brand.

More resources: Analytics and Insights

2) Personalised Recommendations Generate Unique and Personalised Experiences

Seamless and personalised experiences are essential in today’s increasingly crowded retail market. Consumer expectations, and the desire to shop in various ‘channel-hopping’ ways, means that being able to deliver seamless experiences across each touch point across every channel is the only way to win consistent customer loyalty.

To develop a successful personalisation strategy, the key is to understand your customers’ preferences. Manually running through each user’s history is too labour-intensive for any organisation, AI can help you discover exactly what your customers like quickly and easily by identifying patterns in purchasing behaviour and trends. Based on these patterns, organisations can tailor the content shown or product suggestions at scale.

Master data management enables retailers to build a complete profile of an individual customer. When used correctly these insights can be used to personalise marketing messages, tailor in-store experiences and deliver consistent value, leading to an increase in customer engagement and loyalty.

This is done by combining the data from multiple systems such as your CRM, EPOS or E-Commerce systems and de-duplicating the data to ensure a single point of reference for each customer. The benefit of this approach is the removal of siloes. Across the whole business, every team and department have access to the same information for each customer.

Sales, marketing and operations daily activities become aligned to optimise the sales cycle from end to end to improve the customer experience, create more efficient sales pipelines, and reduce wasted resources, including marketing budgets, time gathering insights and excess stock (which you can read about here).

Case Study

In 2023, Spotify had debuted a new AI DJ to provide personalised playlists based on a listener’s tastes. The DJ is a personalised AI guide that understands a listener’s music taste so well that it can choose what to play.

Listeners were more likely to listen for a longer duration when they were presented with personalised playlists. From 2019, they found that 80% of listeners sought out a track on their own after discovering a song in their personalised playlist. Not only that, but the number of times that listeners saved a track went up by 66%.

More resources: Disparate Data to Actionable Insights

3) Customer Loyalty Programs are led by Insights

Whenever a customer interacts with a brand – such as viewing an item online, purchasing an item instore, leaving a customer review – organisations are gathering valuable data that can be used to drive customer loyalty. When this information is unified and analysed, organisations can create specific customer personas segmented by age, location, interests, and more.

Read more: A Guide to Unifying Data

With these segments, organisations can better personalise the incentives that are offered to customers and create personalised loyalty programmes. Instead of following a one-size-fits-all model, organisations can tailor a programme to meet customers’ requirements. So, if an organisation has five different customer personas, the marketing team can come up with five different loyalty programs. Each one can have an incentive that meets the specific needs of the particular audience segment.

Another way AI can transform traditional loyalty programs is through digitising them. With chatbots, organisations are moving their loyalty programmes online to drive personalised customer loyalty at scale.

Case Study

Marriott International is one of the big brands that has adopted this strategy: they have linked their loyalty program to their chatbot, which is available on Slack and Facebook Messenger.

A customer can use their chatbot to see if a hotel room is available in New York. Even if they do not end up booking a room, the chatbot sends them links to content that they may be interested in. Their bot-based loyalty program allows members to review various benefits and explore frequently asked questions.

More resources: Customer Personalisation

4) Price Optimisation with AI

One of the primary differentiators for consumers when considering different brands with similar products, or fill similar needs for the individual, is price. Therefore, price should be a key consideration for all retailers.

The balance here is to ensure suitable profitability for the organisation, while providing clear and competitive value for the consumer across regions. Artificial Intelligence aids organisations to meet this delicate balance.

After scanning large amounts of data and analysing different pricing scenarios, AI tools can generate pricing suggestions. This suggestion is usually create from: past transactions, pricing of competitors, contextual data, to conduct a win/loss analysis.

The findings can help companies gauge the willingness of people to pay a certain amount for any given product. It is a solution that can helps organisations predict the price that is most likely to generate sales, keep up to date on trends in the industry and can help negotiations with key suppliers.

More resources: How a data strategy boosts a Sales and Marketing team

5) Product Innovation based on Data

Another under-utilised benefit of AI is product innovation. With artificial intelligence, organisations can better understand what their customers and key personas want. This data can also help drive merchandising and product design decisions to meet customer requirements.

By removing data silos that traditionally obscure useful cross-departmental information, R&D departments can use customer data, sales data and stock data to help guide what customers are looking for so they can come up with solutions that address the audience’s specific pain points. This can help organisations gain a key competitive advantage in an era defined by consumer empowerment.

With AI personalisation, organisations no longer need to aim in the dark when creating new products. Data-driven companies can give their target audience what they actually want and need in a timely manner, underpinned by relevance.

6) Hyper-personalisation is the Way Forwards

Hyper-personalisation is the most advanced way brands can tailor their marketing to customers. Think of your favourite coffee house, where the barista knows your order by heart, or the online store that suggests products you’re likely to adore based on your behaviour.

In today’s dynamic business landscape, delivering personalised experiences has become a paramount strategy for companies looking to stay competitive and build lasting customer relationships. Hyper-personalisation, achieved through the strategic use of data, advanced analytics, and AI, is emerging as the key to meeting the ever-evolving expectations of modern consumers. Hyper-personalisation, achieved through the strategic use of data, advanced analytics, and AI, is emerging as the key to meeting the ever-evolving expectations of modern consumers.

Case study

Starbucks excels at hyper-personalisation. With the help of AI, the coffee brand uses real-time data to send users unique offers based on their preferences, activity, and past purchases. With more than 400,000 variations of hyper-personalised messages to send, coffee lovers always feel as if their communication with the brand is tailor-made.

We live in an era where customers expect us to understand their wants and needs, and as organisations become greatly customer-centric and data-driven. Hyper-personalisation is the new, modernised solution.

More Resources: The Power of Data Analytics and AI in Hyper-personalisation

Final Thoughts

Building customer loyalty takes a lot of time and hard work. Smart companies are not only using AI to deliver a superior customer experience and thus brand loyalty, but 61% of organisations plan to employ a Chief AI Officer in the future

To attain real value out of AI, organisations need access to high quality data. This requires aggregating data across disparate systems into a single, unified view throughout the organisation. Once in place, businesses will gain true insights which will be key to strategic decision-making.

AI technology, including machine learning and big data, can help organisations to understand customers better and predict their future behaviour. By analysing customer behaviour, companies are able to ascertain their requirements, likes and dislikes. Accordingly, organisations can offer content and products that these customers will like and improve their experience when interacting with the brand.

When customers feel like their needs are met and their voices are heard, they are likely to be more satisfied with a brand. This, in turn, can turn them into loyal customers.

At Data Clarity, we are on top of the challenges and opportunities Artificial Intelligence presents for retailers. We offer multiple data-driven solutions for organisations to benefit from customer data, data management tools and analytics.

For more information, contact our experts today.

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