We all know that customer behaviours have undergone profound changes, and the need for businesses to rethink their engagement strategies leveraging first-party data has become more critical than ever before. With the existence of Big Data and Artificial Intelligence (AI), brands can now analyse data from online and offline touchpoints with ease to gain understanding into their customer behaviours, allowing businesses to understand when, where, and how to engage them.
In one of our client case studies, we leverage the power of AI to move away from the rule-based segmentation. We segment customers of a retail brand by their past spending behaviour, creating a comprehensive 360° customer profile that includes demographics, spending patterns, purchase timing, preferred products, and frequently visited touchpoints and POS. This allows the brand to identify the best way to engage with each of the customer throughout their brand journey and enables the creation of personalized and meaningful experiences that build trust, loyalty, and long-lasting relationships with customers.
In this study, we leverage demographic data, online ecommerce spending data, as well as offline spending data including the POS preferences to develop 4 segments, powered by AI algorithm. The 4 strategic segments identified were: Very Important Customers (VICs), Spontaneous Explorers, Regular Locals, Regular Tourists.
The segmentation provides a good starting point to analyse and understand granular customers behaviours, and to define engagement strategy based on their persona. We observed that it is crucial to engage high-value customers at the right time before they downgrade to a less profitable segment. The below graph illustrates the average customer monthly spending since their first purchase – it shows clearly that if no engagement was made to the Spontaneous Explorers in their 6th month with the brand, they will leave the brand or have a tendency moving to a less valuable segment. For VICs, the story reversed – they do not begin with big purchase, but with the right push in their 6th month, they can grow into a profitable segment.
Furthermore, we inferred that an omnichannel strategy results in an increase in average customer LTV (Lifetime Value) leading to greater retention. By combining the insights derived from both online and offline customer behaviours, brands can unlock the potential to craft highly tailored promotions and campaigns that resonate with each segment’s distinctive preferences across various touch points.
Our study uncovered a compelling revelation: Spontaneous Explorers demonstrated a strong inclination towards eCommerce channel, contributing to a remarkable 72% to the total eCommerce sales. Harnessing this valuable insight in conjunction with the above Moments That Matter analysis empowers marketers to strategically design acquisition campaigns, such as captivating first-time offers, and reactivation campaigns, featuring enticing comeback promotions to this segment on eCommerce channel. Meanwhile, in offline channels, VICs serve as significant drivers of sales performance. Combining with the VIC persona, this suggests that brands can plan cross-sell campaigns in physical stores to maximize the customer values.
Offline channels hold considerable influence alongside the digital landscape. As customers return to physical stores, strategic store-level campaigns are essential. Our analysis unveiled opportunities to optimize customer engagement by tailoring campaigns to specific Point of Sale (POS) locations. By leveraging insights on segment preference to POS, brands can allocate resources and focus marketing efforts for maximum impact. The accompanying graph illustrates the sales turnover and new customer recruitment metrics at each POS, as well as the segment preference to POS, based on their top spending in each POS. With these POS level insights, brands can develop store-specific campaigns that resonate with each segment, and thus further improve customer experience and cultivate customers loyalty.
Our study exemplifies the potential of first-party data in segmenting customers with insights that accelerate the business expansion. Indeed, segmentation is just one (and usually first) instance where Customer Relationship Management (CRM) and marketing teams can embrace a data-driven approach to reshape their engagement strategy. Brands must be equipped with an overall suite of CRM capabilities including the right data, tools, and people to enable advanced analytics use cases like demand forecasting, churn prediction and LTV prediction. In the upcoming (and final) article in this series, we will illustrate how data and AI can be used in customer relationship management and marketing, offering data engagement strategies for the post-COVID era.
To find out more, see our third and final article: Rethinking APAC’s Customer Engagement Strategy Post Covid Using First-Party Data (3/3).
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