Technology has disrupted the way people shop and the challenge for retail marketers is very complex – how do I find, retain and understand my customers when the path to purchase has an increasingly complex web of customer touch points? Understanding this customer journey and enabling a seamless customer experience throughout that journey will dictate the future success of retail marketing where marketers will evolve their digital strategies and architecture to create omnichannel content and experiences that deliver value to consumers.
There’s now a raft of conveniences around e-commerce driven by mobile that can be enabled in-store that is changing the relationship between what people do in the store and online and how retailers can use online to better serve the customer and their staff.
We have seen transformative customer behaviour with in-store digital whether that’s smart dressing rooms that record customers size, shape and preferences for future recommendations, using social media to ask a retailer a question via WhatsApp with immediate responses often delivered by Chatbots, interacting with an immersive experience using AR for deeper product knowledge, touch screen kiosks or simply browsing and then using click and buy. Sales staff are also using online as part of the sales process to be as customer friendly as possible. This fluid retail ecosystem is being fuelled by rich data and retailers must have faith in the accuracy of the data being captured and analysed to drive business performance.
Therefore, the challenge for the dynamic retailer is to match its stock with the customer’s needs, and present offers in an entertaining and engaging way by linking the retailer’s enterprise IT layer with the marketing application layer.
In the enterprise IT layer, retailers store the mass of their operational data – in it they host their customer data including previous purchases with recency and frequency information; their product data, including stock, pricing and warehousing; their ordering and supply chain data, and plenty more.
The application layer includes the tools common to the digital marketer – email, programmatic display, landing pages on the retailer’s web and mobile sites, SMS, customer service and call centre operations.
The science of matching retailers’ core customer data to the application layer is what makes this ecosystem come alive for retailers and this is a gradual and ongoing process that makes data accessible at the point of demand, based on the latency and the demands of the tools.
For example, web page personalisation is an important opportunity to show different content to visitors based on the signals we have gathered about their interests and previous browsing history. We can use AI to create segmentations based on the most efficient paths from the homepage to purchase. In this way a retailer can test a range of offers on a consumer with a certain set of traits and analyse the content and offers that are most successful.
These analyses need to be carefully constructed, so that all the scenarios are planned for, and the required range of creative options are in place to run the test. It may take time for sample sizes to be large enough for the retailer to decide whether a test has succeeded or failed; and the data may take days to be assembled and deploy. In our post-GDPR world, there is also need to integrate marketing and technology to ensure that the messaging complies with data privacy, GDPR and consent requirements.
Alternatively, customers who have just viewed a product on their phone and then conduct a search, require an immediate response. For example, a retailer can use data to identify that a customer who has a long history of loyalty is searching for a product on Google. This demands action from the brand literally in real time, to ensure that the customer sees our offer and we have a chance to retain them as a customer, by raising our bid for that search query. In this example, the data – including the customer profile and the bidding tactics can only be effective if it is provided in real time, via a data feed, so that the digital marketer can increase the search bids and react to the opportunity immediately.
The goal of this data work is to automate as much of the data retrieval processes as possible, to let marketers do their job. The job should be about increasing sales through skilfully matching client needs with offers and exciting ideas – not spending whole days building audience sets to be uploaded into a tool.
Retailers who master these skills of automation and efficiency can then make data and analytics a competitive advantage. Retailers and brands need to build a data ecosystem that’s easy to use by all their customer-facing staff to sell the next purchase to their customers – not to hand-feed the data into machines!