With 96% of online retailers stating data management is a priority for their marketing function, it is apparent that data influences every aspect of a brand’s digital strategy. Additionally, 58% of retailers claim improving customer retention is their top priority for data-driven marketing, highlighting how vital an informed approach is for driving long-term revenue, particularly in light of recent global events.
Thanks to the rise of both the digitally savvy shopper and online privacy regulations, retailers are facing the challenge of meeting higher consumer expectations with a shrinking amount of data and limited resources. Coupled with unforeseen challenges from Covid-19, it’s more important than ever that marketing budgets work hard and are used in the most effective way possible. Those retailers that refine their data strategies, such as Ikea, for the current digital landscape can boost engagement rates, unlock greater potential from fewer assets, generate a higher ROI on marketing spend, and reallocate their resources to maximise productivity.
Shoppers have never been harder to influence. The majority occupy digital environments for almost six hours per day, navigate an estimated 5,000 ads, and are increasingly particular about how and when they engage with both brands and retailers. Competition is also mounting from private labels; with 78% of UK consumers considering cost the most important purchasing factor.
As a result, retail marketers look to deploy highly personalised user experiences to connect with customers under these conditions. When building this experience, retail businesses must factor in a competitive overview of market prices to engage users and prompt a conversion. To do this effectively and in real time requires a sophisticated approach to data.
Utilising a platform that collates consumer data from across disparate channels enables marketers to form a holistic view of their customers. From this consolidated data, AI-powered tools can provide actionable insights in real time to augment marketers’ analytical capabilities and provide context to consumer behaviours. This approach optimises the consumer experience by delivering high quality personalisation throughout the marketing funnel. For example, if a consumer engages with a specific product, AI tools can initiate customer service functions, such as chatbots, and tailor the brand interaction to the consumer, driving further engagement.
Building a highly personalised user experience is no less reliant on data than it was before the implementation of GDPR. Third-party sources, however, are running out in the new age of consumer privacy, meaning retailers need the ability to do more with the ‘compliantly gained’ data they do still have access to. With 87% of marketers claiming data is their most under-used asset, there is evidently a need to harness greater insights from the available sources.
One key challenge in doing this stems from companies’ dependence on standalone Business Intelligence (BI) applications. Typically, these require an extensive amount of upfront, manual work to transform disparate data sets into a unified and valuable asset, which can then be used to enhance retailers’ digital strategies. Finding a single source of truth is a must to succeed in the current landscape, but traditional BI platforms often obscure this.
By using machine-learning tools that organise all data in a central, accessible location, retailers are not only able to establish this single source of truth, but also draw actionable steps for their marketing teams through augmented analytics. This was the case for renowned market leader, IKEA, which significantly improved its data accessibility through such tools, gaining greater detail and a higher level of data granularity as a result. Augmented analytics produced additional insights, which directly boosted the performance of its digital campaigns by maximising the potential of its available assets.
When retailers manage data manually, one of their greatest outlays is time. Processes such as reporting contribute to this and teams will find their skillsets used in an investigative, exploratory capacity rather than a proactive one. In a digital landscape where consumer behaviours are constantly changing, brands need stronger forecasting capabilities to allow their marketing teams to focus on actions that drive results.
By adopting technologies that automate data preparation, reporting, and analysis, retailers are able to rapidly process data – at scale and with greater accuracy – and reallocate marketers’ time on more impactful tasks. In particular, predictive analytics can be utilised to foresee future challenges and rapidly present solutions that minimise their impact on retailers’ bottom lines, which teams can then implement. Additionally, these tools are vital for discovering and leveraging new opportunities that offer an edge over competitors, especially in regard to enhancing the consumer experience.
For IKEA, its marketing department successfully optimised its media mix and ad spending after deploying AI-powered augmented analytics. It was also able to keep ahead of market changes within its sector and be proactive in responding to them. Using data technologies in this way is essential for freeing up the time and the manpower to ensure retailers can continue innovating their position in a notoriously dynamic space.
Despite the challenges that retailers encounter when making the most of data, advanced solutions exist to help them unlock the best ways of connecting with their consumers. Innovation doesn’t only rely on technology, however, but on brands shifting how they approach and manage their current assets. High quality personalisation will remain a crucial component of retailers’ digital strategies, but the means through which they implement it can significantly boost performance and optimise the consumer experience. By being proactive and forward-thinking, marketers can be as digitally savvy as their customer bases and stay ahead of the data curve.
Alexander Igelsböck is chief executive and co-founder of Adverity