Scaling Personalisation
Now that they have become accustomed to what is possible using data-driven digital technologies, today’s consumers demand and expect seamless, frictionless shopping journeys, with experiences and interactions with brands that are custom-tailored to their personal preferences.
In their attempt to provide customised user experiences that are hyper-individualised, curated to build relationships and ensure loyalty, retailers the world over are turning to a growing ecosystem of software solutions, technologies and techniques - all dedicated to the concept of providing “tailored help.” In fact, the global personalisation software market is expected to grow from $620 million in 2020, to $2.2 billion by the end of 2026. A report by IndustryARC suggests that the size of the recommendation engine market will reach $12.03 billion by 2025, up from $ 1.14 billion in 2018, with a Compound Annual Growth Rate (CAGR) of 32.39% during 2020-2025.
According to a recent study by Mordor Intelligence on the state of the customer experience management market, Asia Pacific is the fastest growing market segment during the study period 2018-2026, with a Compound Annual Growth Rate (CAGR) of 17.9 %. This growth is largely due to governments' initiatives towards increasing urbanization and growing population. which are leading to increasing demand for digital products and services. In 2022, retailers in Asia-Pacific will have to quickly pivot to humanisation, deep personalisation, and applying insightful empathy, in order to meet ever-evolving customer expectations and supercharge modern customer engagements.
Clearly then, the number of personalisation tools and their usage is set to skyrocket in the future. So too is the number of diverse consumers to whom retailers must extend their personalisation efforts, creating an interesting dilemma for brands hoping to achieve personalisation and increasingly, hyper-personalisation at scale.
Why Scale Matters
To date, Asia has achieved a state of “deep retail”, in its personalisation evolution. This state is characterised by exciting innovations in Artificial Intelligence (AI) and Machine Learning (ML), blurring the boundaries between online and offline, and augmented and virtual experience curation for hyper individualised customer experiences.
Done correctly, personalisation enhances the lives of consumers and increases their engagement and loyalty to brands by delivering messages which are in tune with what customers really want -- and can even anticipate this. However, to delight and retain all of your customers, you need to make sure that end-to-end experiences are carefully designed, contextual, and personalised across all the different touch points. This requires personalisation at scale: creating individual customer experiences across your entire organisation based on the journey undertaken by each consumer -- and being able to deliver this across all your customers in real time.
For Asian retailers, running experiences at scale requires organisations to optimise their marketing, sales and service functions across complex global ecosystems -- all the while taking into account the considerable diversity that exists among consumers within the region itself, and in the wider global market. In order to break down the barriers to scaling personalisation, Asian retailers must also face up to certain challenges, including the paradox that exists between data, privacy, security, and safety, and the sometimes-contradictory nature of data collection itself, in terms of its return on investment for both the company and the consumer.
Achieving Personalisation at Scale
From a strategic standpoint, personalising at scale requires a fine balance between data handling and customer management, and the optimisation of internal operations, inventory, and supply chain. Achieving this can enable brands to fulfil individual customer desires in real time, and across all channels. As John Batistich, Non-Executive Director, General Pants Co observes: “The three streams I would have, in summary, is 1. the technology stack - How to build it, maintain it for different stages and size of business, 2. the marketing stream, which is about how do I track and retain customers and the third track is 3. supply chain - how do I create in that? How do I make same day or next day delivery or reality? How do I manage returns and better optimise? How do I make my supply chain more sustainable?”
For Asian markets in particular, there are other trends and technologies contributing to the facilitation of personalisation at scale. They include:
Customer Data Management (CDM)
For mobile operators in particular, investing in sophisticated Customer Data Management (CDM) systems and tools can help facilitate hyper-personalisation -- often in conjunction with Master Data Management (MDM) platforms. CDM tools incorporating AI/ML used with Master Data aggregation can provide a uniform, accurate and centralised view of the customer.
Super-Apps
Smartphones and mobile devices have already achieved huge penetration in Asian markets, and mobile apps provide a unique opportunity for brands to eliminate friction and increase personalisation.Super-apps take this to the next level, adding new layers of functionality to an existing base app, to systematically build a complete ecosystem that can use AI, location data, and other relevant inputs to dramatically improve the customer experience. Super-apps like WeChat have become central to the lives of many in China. Other examples include TikTok’s shoppable live streams, Pinterest shopping, and Instagram shopping, all of which drive significant value for all stakeholders in the digital economy.
How Pomelo Fashion is Using Amazon Personalise to Achieve Personalisation at Scale
Image Source: https://www.techinasia.com/jdcombacked-pomelo-launches-b2b-platform-fashion-brands
Since its launch in 2013, Thailand based Pomelo Fashion has grown to become a global eCommerce service that sells clothes and accessories on its website, on Android and iOS apps, and in physical kiosks to nearly two million customers in more than 50 countries. The brand currently has 18 retail locations throughout Southeast Asia and employs 500 members of staff across its corporate offices, retail stores, and warehouses.
For years, Pomelo Fashion depended for its personalisation on an algorithm which ranked products on category pages, such as “Dresses,” “Blouses,” and “Pants & Bottoms”. This ranking was done on the basis of page views and sales and combined the trends of the past 30 days with lifetime customer behaviours, product prices, and newest releases. The rank was calculated daily and stored in a database and provided an identical experience for every user by country.
However, with products discovered through category pages accounting for 38% of the brand’s purchase income, Pomelo Fashion felt the need to increase the relevance of products shown on these pages to individual consumers. Enhancing its personalisation algorithm with Machine Learning (ML) would improve the quality of recommendations on category pages and allow individual customers to be routed to selections on other pages such as “Colour Swatch,” “Shop the Look,” and “Just for You,” which generate 30% of Pomelo Fashion’s revenue.
The brand had been working for some time with Segment -- a customer data system that collects, schematises, and loads sales data from Pomelo Fashion’s mobile app, website, and kiosk services on Amazon Web Services (AWS), to provide a 360-degree view of customers and allow for real-time personalisation. With the release of the Amazon Personalise private beta in June 2019, Pomelo Fashion saw an opportunity to furnish itself with the infrastructure necessary to create personalised experiences at scale, to help with product discoverability. Pomelo Fashion also enlisted the services of Braze, a customer engagement service whose Connected Content feature uses recommendations from Amazon Personalise to customise Pomelo Fashion’s cross-channel campaigns, and deliver messaging experiences at scale.
Image Source: https://segment.com/
As Shane Leese, business intelligence director at Pomelo Fashion explains: “Using AWS would also provide regional availability and help Pomelo Fashion set up the new logic to personalise its categories and sorting to each shopper. We were trying to build in-house event tracking but were looking at a pretty messy set of event data. Our AWS solutions architect could see this would be a long road, so he suggested getting Segment on board to save more developer time than it would cost. With the data flowing from Segment, we didn’t have to build a lot of infrastructure to make this happen.”
The new personalisation logic looks at customer product interactions (clicks, purchases, add-to- cart selections, etc.) as a basis for predicting which products they are most likely to find interesting. Machine Learning correlates customer and product details, to enable the model to more accurately locate similar products and customers.
The results have been impressive. Using AWS has increased gross revenue from category pages by up to 15%,and boosted click-through rates from category to product pages by up to 18%. Add-to-cart clicks from category pages are up by a factor of 16, and Pomelo Fashion’s Return On Investment (ROI) increased by 400% within one month.
Moving forward, Pomelo Fashion plans to add tracking for size selections on its product detail page, ask basic sizing information at key points of the customer journey, and implement a series of filters that remove less relevant products from its category pages, based on a customer’s purchasing history.
A Final Thought...
With so many pressures on brands today to connect with customers, the question remains how can they embrace authenticity at scale while orchestrating this journey?