Mar 26, 2021 | 5 minute Read

The Advantage of Real-time Analytics for BOPIS

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The global pandemic has expedited the adoption of digital channels for consumers almost overnight. Not only has it enforced a need to practice high standards of hygiene but has also brought on board a large consumer base to adopt technology—one of the stronger catalysts in the overall adoption of digital channels. Retailers have leveraged this for BOPIS (Buy Online, Pick-up In Store) and BORIS (Buy Online, Return in Store) too.

BOPIS has been growing significantly over the last few years. While there have been a few qualms about it, it continues to be a mainstay, as revealed by these telling stats:

- While BOPIS is fast becoming a popular way to shop, it can also generate additional in-store revenue. 90% of brick-and-mortar retailers are expected to offer BOPIS by 2021.[1]

- A footwear manufacturer’s No-contact Curbside Pickup, where online shoppers can pick up orders, boosted total revenue by 7% to reach a staggering $9.6 billion. Digital sales increased by 36% as well.[2]

- A range of companies, from pet retailers to variety stores, have seen an increase in their overall sales figures thanks to the introduction of a BOPIS option. 48% of a home improvement company’s online sales in 2018 were generated by BOPIS.[3]

Clearly, everyone is investing in it. BOPIS quite hits the sweet spot between ‘instantaneous gratification’ and ‘convenience’.

All that’s great, but what have been the traditional challenges with BOPIS?

- Customer service continues to be a strong detriment to adopting BOPIS on a large scale. Long queues or the unavailability of an item when you walk up to the pickup point can strongly affect customer experience, leading to customer churn

- Inventory visibility or the lack of a standardized inventory database create challenges for retail store associates in managing stock and delivering customer experience

- Not enough marketing information on options available to the consumers slows adoption

And how can analytics help?

A host of real-time analytics solutions can help bridge these gaps:

- Inventory data standardization: Build a global view of inventory across retail channels and empower retail store associates with information through handhelds for a real-time view of inventory stats. Complex halo effects of product interactions especially across the multi-channel paradigms can be better addressed when store associates have a more real-time view of products and inventories. Automated alerts for store associates would be another advantage here, bridging customer experience gaps

- Consumer queue management: Build queue management systems, increase analytics on the utilization of pickup kiosks, and optimize workforce allocation across the retail floor and pickup kiosks

- Forecast consumer traffic: Automated real-time analytics on estimated traffic forecasts for pickup kiosks will allow reduced congestion and a better store experience. This can be further enhanced by integrating signals from customers when they plan to initiate pickups, which can reduce lead times between arrival and the eventual pick up of products

- Predict wait times: Provide real-time views of estimated traffic and congestion at pickup points to help customers plan their pickups better

- Digital marketing: Targeted marketing initiatives to incentivize the first BOPIS transaction during online purchases. Real-time price models will enable experimentation and marketing strategy analysis

In true consultant fashion, the next step would be to map it on a feasibility and impact matrix. Let’s do that now:

But what does all this mean?

It is clear that BOPIS is here to stay and that the global pandemic has only made the shift more sustainable. That said, some of the traditional challenges will only scale as more and more customers switch to this mode of purchase. As businesses struggle to cope with this shift, real-time data and analytics can help smooth the transition and even bring in more customers.

Retailers will have to react fast and experiment to hit the sweet spot between ‘convenience’ and ‘instant gratification’. Data science roadmaps, which can handle both the scale and diversity of problems, can very well define who stands the test of (these strange) times.


AUTHOR BIO

Pranav Sharma | Delivery Manager

Pranav Sharma’s expertise in the field of data science ranges from supply chain, customer success, design thinking, and storyboarding to predictive analytics and management consulting. At TheMathCompany, he is a Delivery Manager developing and implementing cutting-edge AI & ML solutions for leading organizations. Outside of work, he can be found writing fiction and enjoying a good book.

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