Why should retailers include price optimisation and customer demand forecasting at the core of their digital transformation? Mo Joueid, Google Cloud Platform Practice Lead at Kin + Carta, shares some insights on this topic and how the Google Cloud team at Kin + Carta are supporting supply chain optimisation journeys.
This story begins with a pair of jeans. Like many of us, I used the recent holiday period to take advantage of the retail sales that normally take place around the holiday season. Call it a wardrobe refresh, should I get the opportunity to get back into the office and meet clients face to face again, or some well deserved retail therapy. But one of the items I have had my eye on for a few months, were a pair of jeans. But not just any jeans, but I seem to have picked up a soft spot for well fitted designer jeans over the years. And there’s perhaps nothing better than seeing that item of designer clothing you’ve had your eye on come down in price. And if I manage to need a smaller sized waist then all the better.
But as my luck would have it, when the price did come down, as did their inventory and I could no longer find the jeans in my size. I then watched as the days went by in hope that the situation would improve for the better. But what happened next had me puzzled. Aside from the sizing issue, the price for the item began fluctuating. Did I miss the memo that said that seasonal sales mean that prices come down but may also go up and down several times over a period of days/weeks?
I was soon to learn that my ‘preferred’ retailer was indeed a Google Cloud customer and I, of all people, should have realised that they were most likely making use of the many Artificial Intelligence/Machine Learning (AI/ML) features that the platform offers, including, but not limited to:
A ML model running on Google Cloud can take any number of data points to make a decision. At the very minimum, these tend to include previous sales and inventory related data. However, this may branch out and also incorporate environmental factors e.g. milder weather affecting cotton production, higher transport and logistics costs, trend setting that has sparked interest in a particular line of clothing or, as we witnessed more recently, the uptake in casual clothing as a result of home-working and less socialising. The event feeds can be broad and wide, but also need to take into account the subsequent implications of dynamic pricing.
In the case of my pair of jeans, how would the customer demand for size 34 fluctuate if the price were reduced by 5% versus 10%? How is the profit margin affected in each case? What impact would this have on inventory level? These are questions all modern retailers should look to answer, but deriving and scaling this kind of insight across such a diverse set of data points is challenging to say the least.
There is no one-size-fits-all model for retailers. Some fast-fashion, low margin retailers may find themselves price optimising on fewer data points, applying very minor price fluctuation, and placing more emphasis on customer demand and inventory management. Whereas a designer fashion retailer may rely on a completely different set of data points and factors, focusing on a niche clientele, and only needing to resort to price optimisation for a smaller number of line items.
In this blog, Part 1 of our Supply Chain Optimisation Series, we look specifically at how Kin + Carta leverages Google Cloud to help retailers optimise their pricing strategy to combat these very challenges.
We will now begin to look at the technical aspects of how we approach this with Google Cloud. The journey starts by working with clients to understand their business’ operating model. The initial objective will be to identify, explore, and assess data (quality) from source systems that will be integral to the ML modeling. Having identified these data sources, we begin the process of designing and building a robust and flexible Foundational Data Platform on Google Cloud that provides opinionated defaults, while allowing retailers to build and scale out data pipelines quickly and reliably. This will typically comprise multiple, segregated logical zones that provide elasticity:
