How AI & analytics makes Q-Commerce possible

Q-Commerce is the natural evolvement of E-Commerce, especially in the grocery segment. The always-on culture of smartphones made us more immediate including our purchasing habits. The Q-Commerce segment grew rapidly during the COVID-19 pandemics due to its convenience and fast delivery, up to 30, or even 15 minutes in some instances. It is like the corner shop but at your front door. How nice, isn’t it?

How Q-Commerce is possible?

Unlike brick and mortar retailers, Q-Commerce vendors have a limited assortment and a bit higher but still affordable pricing. They rely on so-called Dark Stores, a small warehouse full of groceries located throughout the city. As bikes, scooters, and bicycles are faster in congested urban traffic, customers can expect delivery within half an hour or less.

What brings the Q-Commerce boom?

It was about time for the natural expansion of E-Commerce to reach the shores of old-school grocery shopping. Combine this with the fact that urbanization has led to a surge in small or single-person households needing just small quantities of goods instead of large families focused on significant quantities at lower prices. The boom was ignited by the COVID-19 pandemic and lockdowns all over the world.

Q-Commerce is a phenomenon right now with 3 new Q-Commerce brands are emerging every month in Western Europe alone, a Euromonitor report says.

How hard is it for Q-Commerce to become profitable?

It is harder than it seems at first glance. According to a study, European Q-Commerce startups need to make at least 1500 deliveries a day with an average basket value should be about 30€ to reach profitability between 4 – 6% of the total revenue. This is a substantial amount for a newly formed retailer with huge marketing expenses on top of the investments required in inventory and Dark Stores / fulfillment centers.

Why data science & AI are crucial for Q-Commerce success?

Tiny margins and significant upfront investments make the equation for Q-Commerce managers genuine hardship. That is why they need to extract as much efficiency as possible. This is where Data Science and AI-powered decision automation solutions come in the picture. Real-time inventory management tools will have a critical role in the sector.

The three main levels of forecasting and planning are 1) Planning of the dark store inventory to maintain stock between delivery; 2) Hour by hour to plan personnel; 3) Planning of the DC inventory.

  • Forecasting future inventory purchases, timing, quantity, and locality

Category Managers working in a rapidly changing environment with strict limitations on space and waste is extremely hard. Add variables like different vendors with specific lead times, demand fluctuation between locations, as well as logistics, and the task, becomes close to impossible. Decision automation with business rules taken into account is a huge helper here due to two factors. First is the potential human errors limitation, the second one is the ability of the AI to adapt to early trends faster and to react according to them. This way, retail analytics solutions will be able to suggest the optimal purchase orders reflecting the demand and business specifics. A Category Manager wouldn’t know what, when and how much to stock in if he doesn’t know what and how much he is going to sell today, hour by hour. This is why highly accurate sales forecasting is a must-have for Q-Commerce merchants. AI and data analytics algorithms are highly successful in solving this particular problem. The precision of the demand and sales forecasting is essential and nothing is capable to deliver like good data analytics with smart AI behind.

  • Forecasting sales orders to plan Dark Store personnel and delivery fleet capacities

By forecasting demand and supply, Q-Commerce managers are also capable to plan personnel better. This is a crucial element since the Q in Q-Commerce stands for quick. You don’t need a team of idle people nor orders lagging committed time frames. Such an optimization can be executed by engaging AI.

  • Forecasting demand on category/brand level to plan replenishments from Distribution Centers (DCs) to Dark Stores and optimizing shelf capacities at Dark Stores

To be efficient, Dark Stores should be located in highly populated areas. The challenge is, the locations are small but expensive properties and costly to operate. This is why Q-Commerce managers can’t afford to load an incredible amount of stock and they start to play the supply-demand game with tiny margins. It is an efficiency needed due to the lack of storage space and it should be utilized as wisely as possible. Since us, the humans are not as good as machines at working with a huge amount of data and taking everything into account at any given moment, AI-powered analytical solutions are really useful here.

Get in touch with us to learn how A4E provide Q-Commerce retail solutions

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