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How to avoid getting left behind in the personalisation arms race

The internet is abound with stats about the value of personalised customer experiences: everything from customers paying more, to increased referrals and improved loyalty.

Amazon, Google and Facebook are increasingly using their vast data sources to personalise ecommerce experiences (think Instagram Commerce) and new retail business models are also emerging that use personalisation as their main differentiator. For example, China’s eCommerce company Pinduoduo allows customers to club together to tell manufacturers what they want (often cutting out retailers all together) and online retailer Stitch Fix now uses over 4,000 personal ‘stylists’ to create digital personal shopping experiences for each of its almost 3 million customers.

Personalisation is rightly a hot topic. But how can retailers avoid being left??

1. Deeply understand customer behaviour

Analytics tools have long been the basis for helping retailers understand customers. Used well, they can be great at highlighting trends, correlations and hypothesising causality. However, while analytics is great at showing us what is happening at an aggregate level, it’s not very good at helping us understand why  customers behave in certain ways. This deeper understanding of customer behaviour is critical for effective personalisation.

Connecting together data from  marketing, finance and merchandising, gives more insight on behaviour across the customer journey, but it is just the first step. Customers’ decisions in each moment are  complex  and different for each individual. Retailers need to understand these clearly if they want to create more relevant and targeted experiences. When breadth of customer data becomes the key, it can be difficult to see how existing retailers can compete with the big technology giants whose range of products  permeate every corner of our online and increasingly offline - lives.

However, retailers do have a  competitive advantage of their own: the merchandiser. Merchandisers have the power to bring a qualitative understanding of customer attitudes and behaviour to the retail experience, but their value is often overshadowed   amid the buzzwords of ‘big data’ and ‘AI . To generate deeper qualitative insights, merchandisers should engage with customers on a personal basis to gain a deeper understanding of shopper motivations. Combining these insights with expertise in digital user experience and eCommerce analytics will enable the creation of personalisation strategies that purely quantitative-driven personalisation will miss.

2. Start building your algorithm arsenal

Technology can help time-limited eCommerce managers create a relevant personalisation experience at scale.

Although many retailers are moving to cloud infrastructure from the likes of Google Cloud Platform, Microsoft Azure and, scarily for retailers, Amazon Web Services, it is important to remember these technologies are not the only game in town..  Given the huge market share held by these three companies, and recent statements from Amazon about dominating the machine learning market, there is a danger it will homogenise the personalisation ability of retailers whilst also risking business model intermediation as the technology giants take an increasing share of eCommerce revenues.

In order to remain distinctive and unique, retailers should therefore develop their own unique algorithms, combining the qualitative insights of their own merchandisers with the analytical capability of data scientists. By applying a culture of experimentation, brands can combine these resources and test strategies themselves, via game theory or war game approaches, in order to give them the personalisation edge they need in an increasingly monopolised marketplace.

3. Design privacy into the user experience

A recent Accenture study found that 83% of retail customers are willing to passively share their data (e.g. buying history), and 74% are willing to actively share their data (e.g. preferences), in exchange for personalised experiences.

However, this doesn’t mean that all personalisation is a good thing. Customers can frequently find hyper-personalisation creepy or untrustworthy. Facebook and Amazon (Alexa) have both been impacted recently as their efforts to personalise experiences have broken the trust of their customers. But by intricately understanding how customers value their own data and privacy, personalised experiences can be designed that gain trust rather than break it. Spotify and Netflix are examples of businesses that have built trust with customers over time and now use transparent user experience cues e.g. ‘Recommended because you watched / listed to X’ to generate personalised experiences that work.

Personalisation is already the next arms race in the ultra-competitive world of digital retail. Investing in deeply understanding customer behaviour, building an algorithm arsenal and designing privacy into the user experience are three strategies that can be applied in order to avoid being left behind.

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