Multichannel shoppers are on the rise and provide a lucrative opportunity for savvy retailers. According to recent stats from WorldPay, multichannel shoppers spend between 50% and 300% more than single-channel shoppers. Purchasing decisions are no longer exclusive to one channel, so it follows that retailers shouldn’t limit themselves to this either. Before they buy, shoppers are now accessing multiple touch points – including web, mobile, social media, in-store, email and contact centres – for product information and comparisons. How many times have you been in a store and witnessed shoppers on their mobile phones scanning barcodes and looking up products? How many times have you done this yourself?
Researching products, showrooming and looking for deals prior to making a purchase are all part of the modern shopping process. What’s more, as shopper’s flit from channel to channel, both online and offline, they expect to receive the same personalised and engaging experience. It’s now more important than ever for retailers to ensure that each and every interaction with a shopper is both highly individualised and consistent across all channels.
Retailers have been experimenting with personalisation for years now, but many are just scratching the surface of what’s possible. Here’s an arsenal of techniques to help establish omnichannel personalisation in today’s modern age of retail:
Every shopper is unique, and whether they’re a first-time visitor or a loyal customer, the need for retailers to deliver a relevant and personalised shopping experience is the same. However, demonstrating knowledge of the preferences of first-time shoppers can be challenging, given the lack of data insights available to retailers.
Also known as ‘Wisdom of the Crowd’, collaborative filtering is a personalisation technique that collects information on the behaviour of other shoppers and uses that insight as a source for determining interests and preferences of new visitors. An example would be Amazon’s ‘customers who bought this also bought’ product recommendation technique. While this is useful for offering similar or complimentary products based on what others shopping for the same item have purchased, it does not reach shoppers on an individual level.
To achieve true real-time personalisation, retailers need to move beyond this basic first step and aggregate multiple data points, including segmentation, behavioural profiling, purchasing history and social data to create a more complete profile for each unique shopper.
This technique delivers a more personalised experience, provided the shopper profiles are updated in real time. Incorporating deep customer profiling with real-time updates allows retailers to deliver the most relevant experience based on shoppers’ active browsing sessions, and not solely based on historical data or group behaviours.
Personalisation strategies will only ever be as good as the data they are based on. Therefore, the more quality data a retailer has, the better the shopper experience. A more personalised customer experience will positively impact the business as a whole.
In order to create a complete view of each shopper and deliver a seamless personalised experience, data must be interconnected across channels. For example, data collected at point of sale (POS) in-store, or through reward cards should be available for online profiles and vice versa. This data can then be used in real time to update a shopper’s profile. Imagine browsing for a shirt online and then visiting the retailer’s physical store to try the shirt on and eventually buy the product. If the retailer keeps sending you display ads or email reminders for the same shirt you bought, it’s clear the retailer’s many channels are not talking to one another. Using data poorly (or rather not using data at all) can make or break a shopper’s loyalty. Good usage of data on the other hand can uncover a wealth of useful information to better target, retarget and personalise future experiences.
Even the most well-planned omnichannel strategy cannot create that essential seamless shopping experience without personalisation. Making sense of data, breaking down silos in order to share data and then using this data to deliver individualised customer experiences is the job of personalisation. However, without the right technology and resources to support the omnichannel vision, it can be difficult for retailers to create a truly powerful and meaningful shopping experience.
Predictive modelling uses multiple techniques such as data mining, machine learning and AI to analyse an online shopper’s data to predict future behaviours. The intricacy of the knowledge garnered from predictive modelling means retailers are able to deliver a relevant shopping experience – even for a first-time shopper. The use of AI and machine learning focuses on how shoppers think about things emotionally or aesthetically, understanding their responses and behaviour, and then applying these insights to better target and deliver the most relevant experience to a segment of one across all touch points. The information is generated in real time so that all personalisation is in-the-moment and therefore highly relevant.
By using machine learning and AI to predict what the shopper By will respond to in the moment, retailers can present alternatives for purchase so that shoppers either buy more or convert more frequently. In this vein, the technology then optimises outcomes for the business and the needs of the shopper, which is a powerful proposition.
Omnichannel is here. The reality is that consumers already search and shop via every channel, and they expect their experiences to be seamless and personal. Retailers should be embracing personalisation as more than a feature, but rather a long-term strategy for business success and customer loyalty. Omnichannel personalisation should be an easy sell for retailers, and an even easier sell to shoppers who expect their favourite brands to know their preferences.
Meyar Sheik is CEO and co-founder of Certona