As the retail industry is highly visual, product categorisation is one of the most critical aspects that Store Manager accomplish in sync with Brand will. This entails, often, some simple tasks as separating a long-sleeved polo shirt from a short sleeved one, isolating a belt worn over jeans, or knowing what in the database was technical sportswear versus athleisure, for example.
As merchandising in retail continues to evolve with the data integrations and other analytical solutions, merchants needs to become much more nimble and ready to fulfill customer needs.
To date, algorithms are commonly used for tracking customers behaviours and journeys, analyze their purchase history for a better product upsell/cross selling recommendation, they help them to find the right product during their search intent and autogenerates category or the homepage to serve the best informations during their shopping experience.
An interesting article I read on Wall Street Journal and some others newspaper about using artificial intelligence and big data at H&M.
I want to focus on 3 interesting points that actually can be implemented, right now, on the whatever ecommerce platform:
- Customize the Visual Merchandising: they are using Big Data and AI to customize the merchandising mix of individual offline stores to reduce markdowns through store receipts, returns and loyalty-card data.
- Analyze sell-out: in Stockholm stores, H&M analyzing purchases and returns discovered that the store’s customer base was primarily women, and that fashionable items like floral skirts in pastel colors and higher-priced items sold better than the retailer expected.
- Optimize the catalog: last year, H&M cut the number of SKUs in the store by 40%, and eliminated most menswear products.
Ok, what can I do that on my ecommerce?
You can easily export your analytic and marketing data from your Google accounts in order to analyze your online business.
With right proportions, even a company not as big as H&M, it could deliver something similar on its ecommerce with a consequent competitive advantage.
– Customize the VM
How can I get a personalized experience to my customers about visual merchandising on the ecommerce?
For implementing this feature you have 2 roads on your way:
- set it up through a developer implementations
- set it up by your own using a easy product that facilitates setting
Regarding the first solution, I suggest to use Recombee because it’s super simple to use it, since its implementation on your platform (whatever).
You can read this simple article in order to implement a merchandising machine learning driven in only 10 minutes.
On the other way, you can run some “ready to use” solutions like:
- Nosto UX
- Search Spring
They provide some connector for different ecommerce platform and once connected, they will analyze all your history datas for their recommendation system, showing best products and informations based on your different customer segmentation or their purchase history, injecting the code directly in category, product page and homepage.
– Analyze sell-out
How can I analyze correlations between my customers, products and purchase history?
In order to get informations on correlation between customers and purchase, on of the best solution is to create a RFM analysis: it’s a part of the predictive statistical techniques that it’s able to associate a score with your customers.
By itself, the analysis, does not tell you if you are making money, but it’s used to classify your customers for targeting purposes and therefore to plan the most relevant communication possible.
Frequency: how recently did the customer purchase?
Recency: how often do they purchase?
Monetary value: how much do they spend?
Why you should need a RFM analysis?
There are many reasons for creating this analysis, here I listed 3 above all important, in my opinion:
- customers who have recently purchased are more receptive to subsequent promotions than customers whose last purchase is far in the future
- understand your customer segmentation based on monetary and frequency evaluations: VIP, loyalty, active and inactive
- Customer Lifetime Value
In order to achieve these informations we should go deep inside on ecommerce datas, aggregating and normalizing the different sources and create the RFM report. It’s not really simple as it seems :)
But, I’ll try to do this using only Google Analytics and Google Datastudio, showing them as simple as I can in just one table:
In this article I’ll focus on understanding how to extract these two useful informations in an easy way, but, in the next articles, I’ll describe how to create a RFM analysis from different sources: stay tuned and subscribe at my newsletter!
Link your Google Analytics account (check the advanced e-commerce options was active) and analyze that correlations using this dashboard:
Once you clicked on, you’ll see the screenshot above with sample Google Analytics data. If you want integrate yours, you need to click on “Create a copy” button and then, select the source on the top left of the dashboard.
If you have a created different segment and metrics regarding customers, you can edit this table and substitute “User Type” dimension with yours.
– Optimize the catalog
How can I understand what items I need to cut off from my inventory?
In the same Data studio dashboard, you can see the section “Most selling products” where you can order the data to show what you need. The table is ordered to show to answer the question above.
If you’re interested to analyze which products are driving your business you need to order the table by “Products Revenue per Purchase”.
Otherwise, if you’re interested to learn more about which products are viewed most but they aren’t sold (for some reason), you have to order by “Product Detail Views” and see “Products Adds to Cart” to understand which one is the most NOT added to the cart.
If you need to setup your ecommerce with right metrics and dimensions or some other detailed information for your business, feel free to comment and ask your point of view or questions.