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Data, analytics behind personalised shopping experiences

Unlocking the value of data is essential to optimise the customer experience, writes Lesley French. 

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Retail businesses hold significant amounts of customer data, collected on a daily, monthly and yearly basis. This data can tell incredibly meaningful stories about consumers’ needs and shopping habits, which, if leveraged in the right way, can contribute to any retailers’ growth and success. It’s critical that retailers use this data wisely to inform sales strategies and tailor their products and services to what consumers want.

Putting data into practice can help retailers get inside the minds of their customers, so they can proactively adjust strategies to drive sales. Using predictive analysis, where data is looked at to anticipate consumer behaviour and market activity, can help retailers gain significant competitive ground and offer customers valuable, personalised shopping experiences that keep them coming back.

Predictive analytics is powered by artificial intelligence (AI) and machine learning (ML, a subset of AI). AI works by applying computational operations to historical customer data that retailers have gathered via feedback forms, loyalty and rewards cards, mailing list details, and past online and in-store orders. The right tools and technologies can automatically crunch numbers to provide important, evidence-based insights into customer purchasing patterns and habits. These tools can even process details including socio-economic factors and customer demographics to help shape effective sales strategies.

AI and predictive analytics offer immense opportunities for growth to retailers of all sizes. It’s important smaller retailers in particular realise that AI-powered data analytics platforms can be affordable, accessible, and signal a critical turning point in offering personalised shopping experiences to their customers.  

In fact, research shows that 91 per cent of consumers are more likely to shop with brands that remember their shopping preferences and purchases and that make relevant suggestions regarding new products and services. Poorly curated shopping experiences are proven to frustrate consumers, and too many options and choices can leave consumers feeling overwhelmed. Customers value options that fall in line with their tastes, but offer diversity and change. This prevents consumers from growing bored and seeking out more exciting shopping experiences elsewhere, and it means customers don’t need to turn to other stores when looking for products they like. Studies have also revealed customers not only appreciate predictive suggestions but are happier to spend more money on products during fine-tuned and relevant shopping experiences. These shopping experiences are more seamless, productive, and rewarding for customers.

Also, consumers increasingly want their favourite stores and brands to offer relevant products ahead of time. This means retailers need to make decisions around ordering inventory with evidence-based confidence that these products won’t just sell, but will also prompt consumers to return looking for more items that they appreciate. And, the more data retailers leverage, the more effective these decisions will be.

Data-driven insights can tell retailers stories about customers they wouldn’t otherwise know. Customers’ decisions to purchase are often a result of emotion, trust, impulse, culture, income, location, and more. These finer, personal details carry a wealth of meaning, and ML can help retailers join the dots to connect personal characteristics to products and purchases. For example, by signing consumers up to customer accounts or loyalty cards, retailers can see what customers have purchased in the past, how much they paid, and at what time of day, or even year the purchases occurred. This helps retailers connect specific variables like location, to timing, and then to price point. Eventually, these variables can be connected to a new product, set to greet consumers as they browse a store’s website or walk into a shop.

Likewise, recording data that indicates customer age, occupation and gender can help retailers identify various customer segments, and see where the strongest product sales, returns patterns and even purchase decline come from. If a certain age group or segment is indicating fewer purchases, retailers can put into place sales strategies aiming to entice and restore these dwindling customer bases. Combining data around age with data indicating gender can help retailers far more effectively target specific areas of their customer base. And when paired with additional information, including customer location, these retentions strategies are even stronger. In this way, data analytics is particularly helpful for retaining customers.

Leveraging data with new technologies is indeed a realistic option, and massive opportunity, for retail businesses that want to know their customers better and move forward in the market. Data analytics can help retailers understand their customers like never before, and work to drive sales in sophisticated and personal ways.

Predictive analytics is increasingly being used by large retailers to better cater to their customers. However, this technology isn’t just reserved for large organisations. Smaller retailers might be surprised how it can help them make decisions that can level the playing field in the retail market.

Lesley French, general manager, ANZ, MicroStrategy Inc.  

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