Bridging the Gap Between Big Data and the Consumer

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The UK retail sector has had a difficult couple of years; it’s predicted 23,000 stores and 175,000 jobs will be lost this year – but is it all doom and gloom – or are we witnessing an industrial transformation?

UK shoppers are more connected than ever before, with the average consumer spending 24 hours every week online, and the majority using three or more connected devices to access the internet. A recent PwC survey concludes consumers are truly multi-channel, using every shopping channel, tool and device available to them, from laptops and tablets to social media and virtual assistants. This continual connectivity generates massive volumes of data, which retailers can use to better understand consumers; who they are, what their interests are, and what they’re likely to buy next. But for the majority of retailers, bridging the gap between data and the consumer by turning masses of unstructured information into actionable insights seems like an overwhelming task.

So how can retailers make use of big data to get ahead of the competition?

Finding the right shoppers

Making sure their marketing messages are delivered to the right people is the first way retailers can harness data to their advantage. Using artificial intelligence, retailers can evaluate individual visitors in real-time using a wide range of data types including contextual, behavioural, transactional, and demographic data to build detailed user profiles, understand their context or position in the customer journey, and predict their likelihood of converting.

Rather than using traditional machine learning, which is rule-based and therefore limited in its effectiveness, retailers should look to more advanced techniques such as reinforcement learning. This form of deep learning trains algorithms based on a system of positive and negative rewards, increasing efficiency through improved decision-making at individual user level. Reinforcement learning algorithms can predict how likely shoppers are to take action at different levels of the funnel, for instance whether they will click on a banner ad and whether they will make a purchase, creating a single score that quantifies the value of the user. They can then check the accuracy of those predictions using a continuous feedback loop, constantly improving performance. With the ability to make decisions in 0.1 seconds, reinforcement algorithms can instantly qualify online traffic and decide whether or not to bid on ad impressions, as well as how much to bid, to ensure the most effective use of marketing budgets. 

Sending the right message

Reaching the right shopper is only half the story; retailers can also use data to drive personalisation and ensure the right message is delivered. Today’s shoppers want retailers to treat them as individuals, with a recent Salesforce survey revealing 76% of customers expect companies to understand their needs and expectations, so using personalisation to deliver relevant messaging is essential.

Retailers can employ techniques such as dynamic creative optimisation (DCO) to tailor marketing messages to the individual in real-time. They can leverage data points such as browsing behaviour, purchase history and location to select the most appropriate creative elements, including product, call to action and special offer. These individual elements can be combined to produce the creative version most likely to drive action for that specific shopper, ensuring the message is aligned with their unique needs.

The challenges retailers faced in the last 18 months are likely to remain this year and beyond, so marketers must take every opportunity to differentiate their offering. By using AI-based tools to ensure personalised marketing messages are delivered to the right people in real-time, retailers can bridge the gap between big data and the consumer, making the most of their budgets to deliver relevant, engaging messaging that really drives performance.

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