Does your email arrive safely in the inbox – or end up in the spam filter? Mailbox providers deploy email engagement metrics to identify reputable senders – so marketers should take heed of subscriber complaints. Examine the email journey, advises Guy Hanson, and how emails are perceived by recipients, in order to successfully reach subscriber inboxes.
With market research, point-of-sale information, millions of loyalty cards, and increasing volumes of online and mobile transactions, many retailers are sitting on huge pools of customer data; a data gold mine. Location data, social media insights and customer feedback from third-party sources are adding to this – with the upshot that many businesses are struggling to manage what they own.
It’s safe to say that data has become increasingly complex and, at times, overwhelming. To derive value and set themselves apart from the competition, brands must not only gather and store all this data, but turn it into actionable insights. All investment into holding, managing and analysing data needs to be linked with business outcomes in order to achieve optimal ROI.
If done correctly, the result is a win-win for shoppers and retailers alike. Shoppers benefit from an improved customer experience that is more relevant and helpful to them, and brands experience a lift in conversion rates and a boost in profits.
The data gold mine and data-driven personalisation
In-store loyalty cards were once the cornerstone of customer analytics. By offering basic rewards in return for consumer data, retailers would have the insights required to improve operations. But shopping channels have evolved. Consumers can now engage with retailers online, in-store and via mobile. As such, the trail of data has now grown exponentially.
This presents an exciting opportunity for retailers to track consumers across platforms – but it comes at a price. Today’s consumers are a lot savvier about the value of their data. In exchange for their details, they expect retailers to offer relevant products and promotions. Vital to the success of these incentives is personalisation.
According to Monetate, 83% of customers expect brands to personalise their online shopping experience for them. This includes advertising, the online journey and recommendations that take into account previous purchases and browsing behaviour. Many online retailers have already taken this on. In 2015, Shop Direct launched its completely personalised version of the Very.co.uk homepage, creating 1.2 million versions of its website to match the needs of its various customers.
Today, the online shopping experience is in the advanced stages of personalisation. Tomorrow’s task is updating and personalising the physical journey. Starbucks is looking to use a data-driven Artificial Intelligence (AI) algorithm to allow for a more bespoke barista experience. By using an app containing buying preferences (and taking into account contextual variables, such as local weather or regional trends), consumers can have relevant products and rewards recommended to them.
Elsewhere, fashion retailers are using smart mirrors in clothing stores to automatically read tags and display additional information while the shopper is trying an item on. The future will see digital shelving replace static price tags, dynamic pricing that reflects personal buying patterns and advanced scanning that automatically scan tags as soon as products are placed in a shopping basket.
Making content contextual
Personalisation is just one application of data analytics. Knowing a consumer’s preferences, interests and behaviour is a much more comprehensive approach to improving the customer journey. It allows retailers to provide useful and relevant content, as well as to predict future purchases, and in turn improve customer uptake.
To do this, retailers are looking to replicate expert in-store advice through digital means. In 2015, The North Face successfully piloted an online personal shopping platform powered by AI and IBM Watson. It worked by asking visitors a series of dialogue-based questions that mimicked the knowledge and efficiency of an in-store expert. After interpreting and evaluating the answers, a series of product and content recommendations would appear. This resulted in higher sales conversion rates and satisfied shoppers, who were willing to use the service again.
Elsewhere, eBay is using data insights and storytelling tactics to predict what customers might need. Someone buying clothes for a newborn can be retargeted with clothes and toys for a one-year-old a year down the line. It shows that with detailed data analytics, marketers can be at the right place, in the right time. A seemingly homogenous audience can be broken down into niche and distinct segments, allowing retailers to create targeted marketing content.
Faster order fulfilment
Data has long allowed retailers to forecast demand. But in recent years, big data and advanced machine learning techniques have improved this ability even further. In 2014, Amazon patented ‘anticipatory shipping’, a method that allowed them to cut delivery times. Algorithms would predict what customers might buy and ship the products in their general direction, all before a purchase is even made. Products can be shipped out to their relevant distribution centres in advance, thereby reducing the chances of late delivery returns.
The shipping experiences provided by Amazon and other tech giants have set the standard for other retailers to follow. According to Temando, 80% of shoppers expect to be offered same-day shipping, yet only 47% of retailers do this. And 77% of consumers want guaranteed weekend or after hours shipping, but this is only possible with 34%.
Seamlessness with tomorrow’s technology
Already, next-generation connectivity is changing the way we live and shop. Beacon technology is allowing retailers to send push notifications to shoppers as they walk around a store. So promotions are not only personalised, they are timely.
Similarly, Amazon has opened Amazon Go, a supermarket without checkouts. Instead of physical tills, mobile apps and location sensors are used to track the items a shopper picks up and also monitor their movements around the store. When they leave, the app simply charges them through their Amazon Prime account.
But it’s not only frontline retail that can benefit from innovation. Operations and behind-the-scenes functions can also be transformed with new thinking and intuitive technology. Just look at SAP, who are today testing digital eyewear in their warehouses. The idea is to put necessary information right in front of workers, instead of on a tablet or laptop, making things run smarter.
Together, it shows us that what seems like futuristic technology to many is, in fact, here today. Further developments in the Internet of Things, smart home devices and wearables will propel changes in the retail landscape even more. Forward-thinking retailers need to consider how these disruptive technologies will impact the way consumers behave and engage with a retail model, and how best to capitalise on this using advanced analytics.
New ways with data
For retailers, the purpose of data goes beyond just campaign measurement. The right insight can bring about improvements in the customer experience, allow for deeper personalisation, faster product delivery and greater cross-channel integration. Data volumes are growing, and so are its application areas in retail.
As we look ahead, 2017 will be a year in which brands that come up with bold ways to innovate and improve the shopping experience will gain an advantage. Sensor technology will further develop and expand, and the in-store experience will become just as exciting and complementary to the personal digital experience. Revolutionising the customer journey has only just begun.
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