Becoming more customer centric & data driven

This is one of my articles which was published on Deloitte's blog as part of the series called- "6 Steps to transforming your retail & consumer business on cloud". This particular post was centred around building a strong data foundation to be able to understand your customer better. Full article is below.

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Build a common retail data model

Using data as a key driver for decision making has to be the foundational step in any business’s transformation. This means you need to connect multiple information sources across a hybrid technology infrastructure and process real-time streaming data across your value chain. Then you can create a common retail data model.

Once you have access to updated information, having the ability to access this information at scale without the complexity of major analytical skills becomes an important step towards democratising your data and establishing a System of Insight. This ultimately gives your organisation the ability to ask the right questions and look for operational efficiencies and innovation. For example, do we see any trends emerging from the data? How can we proactively engage and improve the customer experience with these insights? How can we improve demand forecasting and optimise our marketing mix to improve ROI ?

As you begin to ask these questions, it soon will become clear what your data foundation is lacking and what information needs to be integrated across the retail value chain to become more customer-centric. It could be modernising your data warehouse or leveraging a big data platform to process and analyse streams of information in real-time. Smaller retailers may find these steps to be too cumbersome and hence may prefer ready-made platforms and scalable, pre-packaged Cloud solutions that use Artificial Intelligence or Machine Learning models with a proven business impact but a low execution complexity. On the other hand, larger organisations may want to engage a strategic partner to build customised solutions based on their own datasets and requirements.

Becoming data-driven is key to digitally transforming your business model and surviving downturns in the retail sector. For example, when many IKEA stores were closed during COVID-19 they transformed their offering to online POS only. They quickly scaled up their technology infrastructure to manage large web traffic volumes and online orders, while transforming their closed stores into fulfilment centres. With traditional IT infrastructure this could have taken months to years; however, IKEA accomplished it within weeks using Google Cloud and other serverless technologies. This demonstrates that a strong, scalable digital foundation is crucial to an organisation’s ability to pivot quickly in a changing global landscape.

Step Two: Understand your customer better

Q How can retailers offer personalized recommendations for their customers?

Once data foundations are in place, organisations must fully understand customer journeys to make them more personal and drive engagement. A single view of the customers across multiple touchpoints, anticipating their needs in advance, predicting market trends and tracking inventories in real-time are some of the essential building blocks for your personalisation engine. The key benefit of this is an improved in-store execution, reduced operational costs and a better shopper experience.

According to McKinsey & Co, 83 percent of customers say they want their shopping experience to be personalized in some way, and effective personalization can increase store revenues by 20 to 30 percent. A 2019 Google study showed that 85 percent of retailers don’t believe their company is doing well in personalisation. This is primarily because on average, retailers are investing only a fraction of their revenue in personalisation and as a result do not have the ability to provide the differentiated experiences which the customers seek. Frankly, if you don’t invest in these capabilities, you will be left behind.

Once you have built the foundational elements, you can then begin to augment that data by integrating external data sources and demand signals from consumer behaviour, geographic and demographic data, or any other third-party signals. The key is to be able to contextualise this in real-time from your shoppers’ perspective so you can target and personalise recommendations to improve customer lifetime value.

Covid-19 has accelerated many retailers' digital transformation and their move into personalisation as they strive to meet the demands of ‘the new normal’ and find new ways to engage customers. Large retailers such as Bed Bath & Beyond understand the importance of establishing a single view of customer data across their portfolio and are thus doubling down in strengthening their personalisation capabilities. Another great example is apparel and lifestyle retailer Hanes Australasia. By using Google Cloud’s Recommendations AI they are able to react to customer behaviour and provide customers with personalised product recommendations and changes to product ranges, pricing and offers. They have built a strong data foundation and a platform which integrates the in-store transactional data along with the customer event data in real-time, enabling them to offer customers a unique shopping experience.

At the heart of customer-centric decision making is having the right information available at the right time. To enable this, a data strategy as a key foundational building block is a must. The pandemic has given retailers an opportunity to rethink their digital operations and get closer to their customers than ever before. It is clear that data will continue to play a pivotal role in retail strategy and will be the competitive advantage which will separate winners from losers.