Ecommerce growth has transformed retail operations in recent years, but how many retailers have fully optimised the new logistics models? Online activity now comes in a variety of shapes and sizes – from monthly/weekly subscriptions to one hour convenience deliveries – retailers have a range of ever expanding, complex and costly operational models.
The challenges associated with fulfilling high levels of customer demand and expectations, combined with rising wages and energy/fuel costs, continue to squeeze margins. In the current climate, logistics innovation is fast becoming a key differentiator for retail brands – it is vital to gain rapid, accurate insight into the cost and customer impact of different ecommerce models. How economically viable is a convenience delivery service? What are the options for eco-friendly delivery for brands with a strong environmental commitment?
With the quality of logistics operations becoming increasingly vital to both retail profitability and customer perception, Andrew Bithell, sales team lead, CTS, explains why cloud-based analytics, Machine Learning (ML) and Artificial Intelligence (AI) technology are enabling retailers to leverage deep data resources to transform retail logistics, maximising profitability and delivering essential innovation.
Playing catch up
For most retailers, the pandemic-inspired escalation of ecommerce inevitably outpaced any planned investment in new logistics solutions. Traditional warehouse operations could not seamlessly shift from a ‘pick to store’ to a ‘pick to consumer’ model, leaving retailers to explore options such as in store fulfilment, the creation of smaller local warehouses, even outsourcing direct customer fulfilment to third parties, while retaining traditional store fulfilment operations in house.
Yet still the problems remain – from the financial and environmental cost of returns, to difficulties in meeting customers’ delivery expectations and the impact on repeat business. With the fast evolution of new forms of ecommerce, including subscription and convenience deliveries, many retailers are struggling to catch up.
Determining where to prioritise investment presents a significant challenge. When online-only retailers can deliver an optimised, accurate customer service from dedicated fulfilment centres, the best approach for omnichannel retailers is less clear. Will they have the edge in providing convenience deliveries when they can be picked quickly in a local store? Can an online-only provider offer a ‘greener’ service that will entice a certain customer demographic?
Whilst retail logistics presents challenges, it also offers a key area of competitive differentiation. Every retailer will have its own specific set of customer demands and business opportunities – with delivery expectations now added to the traditional merchandising, marketing and customer experience requirements.
The ability to make data-driven decisions, fast, is key. Retailers are awash with information that provides essential insight both into existing logistics performance and opportunities for innovation. However, many are still constrained by traditional, siloed data resources which make it impossible to make truly accurate inferences about the impact of decisions and performance across the business. Are customers motivated by environmental options such as e-vehicles, for example? Are they still expecting free delivery and returns or will they pay more for delivery certainty? And with costs continuing to rise, how can a retailer meet customer expectations and still retain any margin?
Without a holistic view of the complete customer experience, how can a retailer determine the true impact of a late delivery on a customer’s long-term commitment and buying behaviour? How can a business undertake robust ‘what if’ analysis to assess innovations such as the use of e-bikes for in town deliveries from stores?
The use of cloud-based analytics to pull together, visualise and understand multiple disparate data sets from a single lens can rapidly transform a retailer’s logistics expertise. Retailers can add insight from deliveries, route optimisation, picking accuracy and returns to product, inventory, staff, merchandising and customer buying behaviour, for example, to provide a detailed view of the cross-business implications of specific actions and decisions.
In addition to leveraging this data to support strategic innovation, retailers urgently require rapid, accessible insight to support day-to-day operations, not least to address spiralling costs and on-going delivery disruptions. With logistics now contributing significantly to the overall cost of retail – indeed, last mile delivery is estimated to be more than 50% of total supply chain costs – retailers need to understand the impact of decisions on multiple levels, from the immediate financial cost to the long-term impact on customer attitudes and repeat spend.
Meeting delivery expectations has never been more important – according to KMPG 67% of organisations consider meeting customer expectations for speed of delivery as a critical force impacting the structure and flow of their supply chains over the next 12-18 months. With the traditional seasonal peaks and troughs in demand now being affected by changing customer behaviour – such as the growth of convenience deliveries – retailers need better analytics capabilities to understand the best way to meet customer expectations while retaining profitability.
Staff need a simple, effective and highly visual way to understand the hugely complex network of operational activities and their influence on the customer. With this insight – preferably in an intuitive dashboard – individuals can rapidly understand and take action. Indeed, using ML and AI, many of these actions can be automated – such as responding to a delivery crisis by automatically prioritising high value customers. Automation wherever possible will become ever more important to ensure retail logistics operations are continuously reaching performance goals while retaining profitability.
Ecommerce hasn’t changed the rules of retail. The goal is still to attract as many people as possible to buy as much as possible – and come back again and again. To achieve that goal, retailers now require not only a very efficient and innovative logistics operation but one that can flex and scale in line with changing demand and expectation.
Retailers cannot achieve the essential step change in ecommerce model innovation without a way to quickly and effectively combine and visualise multiple data sources. Insight offered by cloud-based analytics is essential in ensuring every step across multiple ecommerce models is optimised to maximise profitability and deliver on customer promises – from identifying the perfect moment to spin up a dark site to meet customer demand without incurring additional costs to assessing the potential of new multi-format stores, including gyms and creches and where goods are ordered for next day delivery, to attract a new customer base.
Retailers that embrace cloud-based analytics to understand and manage logistics data sources, alongside the complete retail experience will be best placed to optimise logistics and explore the opportunities for innovation that will increasingly deliver competitive differentiation.
Andrew Bithell, sales team lead, CTS