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Integrating Data Analysis with Business Realities: A Story from Retail Horticulture



Some of you will know that, as well as spending the vast majority of my career working in data, I am also a qualified Landscape Architect with a Chelsea Flower Show Medal under my belt.


You’d have thought, therefore, that a data project involving all things horticultural should go off swimmingly. 


Of all people to get it wrong in this case, it shouldn’t have been me, had I had my designer hat on during the thinking process.


We were working on a fascinating project with a client in the DIY and retail horticulture sector. 


Our purpose was to adopt a customer-centric approach to their operations, ultimately enhancing the customer experience and driving sales performance by making the proposition more relevant and targeted to the right customers.


This was part of a bigger initiative to encourage the business to start thinking about the customer more and to focus on what they needed and liked, rather than defaulting to the trading-focused behaviours the business had developed over years of operations, which lacked passion or interest in the products they were sourcing.


It was a challenging project in many ways, as we had to preach what we had learned at Tesco to a group of retail executives who really didn’t want to hear about it and saw Tesco more as a foe rather than someone to learn from. 


It didn’t help that the majority of the executive team were not spending their weekends doing DIY or gardening and therefore had a very low level of connection with the business they were responsible for.


However, the CEO was a convert and passionate DIYer and gardener, and they were looking to us to help spread the customer-centricity love and convince the naysayers in their peer group. 


Our goal was ambitious but clear: we would harness data analytics to prove how a localised garden centre range and a demand-led approach based on customer behaviours and preferences would boost footfall, sales growth, and customer loyalty. 


We thought this would be an easy win.

The retailer ranged every store the same, irrespective of location, such as urban or rural, or local customer types, which we knew varied tremendously in both wealth and age. 


We started by developing a set of customer segmentations based on DIY and gardening projects with the goal of identifying unique customer needs and preferences. 


We did this by understanding what the individual products in each customer’s basket told us about the work they were doing, as well as interesting insights into their skill levels in various tasks.


Using this as a foundation and integrating the local market data we had available through third-party data suppliers, we implemented local store-based category allocation and range plans within the garden centre, tailored to local demographics and basket behaviours. 


On the surface, the project showed a lot of promise, as the analytics and investigation we were able to undertake with these new lenses into the business were revealing a host of opportunities to use space differently and optimise our chances to fill baskets.


However, as we progressed, a number of significant setbacks emerged, highlighting the need to always align data-driven strategies with the realities of day-to-day retail operations and never underestimate the need for in-house and product expertise.


Ecological Oversight

One of our first major hurdles was an ecological oversight that was particularly galling for me. Our analysis didn't fully account for the varying regional conditions essential for plant growth, such as soil types and climate variations. 


A really popular plant with amateur gardeners in the UK is the acidic soil-loving Rhododendron. 


Unfortunately, there are large swathes of chalky soil in the UK where these lovely shrubs just don’t thrive, and any gardener worth their salt knows this. 


Our analysis didn’t factor in this kind of thinking and, as such, this oversight meant that some stores ended up stocking plants that weren't suitable for their specific locations, leading to unsurprisingly disappointing sales and customers probably wondering why we had so many shelves stocked full of the wrong plants.


I remember visiting a particular store in a coastal area where the soil was sandy and saline. 


Our data, because it knew no better, had suggested stocking a variety of plants popular in inland regions, completely disregarding the local soil and climate conditions. 


It was a harsh lesson that data, no matter how sophisticated, needs to be grounded in practical realities.


Supply Chain Constraints

Next came the supply chain constraints. Our project hadn't fully considered the logistics of the supply chain in horticulture. Plant nurseries are not like factories or warehouses; they are dealing with a very fragile product. 


Most nurseries specialise in specific plant types based on their individual experience and expertise as well as the location factors of their site. 


While we clever data analysts had devised perfection in terms of unique and tailored plant ranges for each store, the reality was that the existing supply chain setup just couldn't handle this level of customisation in a month of Sundays.


This led to significant fulfilment challenges and delays, and then unimaginable workarounds to fill empty spaces in store by the diligent retail teams, which played havoc with our results measurements. 


I recall a conversation with the supply chain manager, who was pulling his hair out with the logistical nightmare our tailored plans had created. 


The bespoke plant ranges were a great idea on paper, but they clashed with the long-standing processes the supply chain team had perfected over the years. It was clear that we had to rethink our approach.


Lessons Learned

This experience underscored the need for a holistic approach and to not let yourself get carried away by the data. 


Data analytics must be complemented by an understanding of both the operational aspects of the business and a proper understanding of the products or category you are dealing with. 


It's not enough to rely solely on numbers; we need to integrate practical knowledge and local expertise.


At Beyond, we make a point of getting into our clients' stores and getting to know store managers and the real product or category experts. Data-driven decisions must be vetted through the lens of practical experience and sector-specific expertise. 


I found that the best insights often come from the people on the ground, those who knew their products and customers inside out.


Sorry to say it out loud, but Head Office can sometimes be a bit divorced from reality.


Data is a powerful tool, but its application must be tempered with practical business understanding and market realities. 


This project was a stark reminder, as well as a little embarrassing given my personal horticultural experience, that while data can provide incredible insights, its true value lies in how it's applied in the real world.

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