Category selection and SKU rationalization using retail sales data
Nielsen, Panel data, IRI
Dear category/retail managers,
Few days back, I have ask for your suggestions for the next topics to be discussed here. Thanks for your kind words and inputs. If you haven’t shared the feedback, request you to do so. Your feedback will be helpful for me to decide on what topics to be discussed next.
One of the readers ask about how Nielsen, IRI data is used for category analytics purpose.
In the previous post , I have talked about importance of data driving category decisions. What all questions we need to ask from the sales data / traffic data. I have also shared a dataset to play around.
Let’s take it up a notch. Today we are going to discuss about SKU rationalization and how internal + external datasets ( Nielsen, IRI data) can help in SKU rationalization.
WHAT IS SKU RATIONLIZATION?
SKU = store keep units / items
SKU rationalization is a decision-making process to determine whether to keep the SKU in your product portfolio or to discontinue it.
It is about analyzing the impact of adding / discontinuing / keeping the product on operation / overall business.
WHY SKU RATIONLIZATION IS IMPORTANT?
If there are too many products in your product portfolio, it’s difficult to manage, there is a cost involved.
For example, let’s say - you are an grocery retailer. there are 10 SKUs under flour sub-category in staple food category.
You have wheat flour, multigrain flour, rice flour at different price range with wheat flour being the lowest one and of 3 brands.
As a category managers, you can have all 10 but then either you have a shelf space allocation problem in hand (offline retailer) or there is a supply-chain complexities / hidden cost involved of working with multiple SKUs / brands. ( In online retail)
Too many suppliers / brand to work with
Too many data to collect and analyze
If you are a manufacturer, that means you have too many production process going on; machines, resources to work with.
If you remove the product (not aligned to your category goals => either it is slow-moving or lower margin, low revenue, not a destination category, not what your customer wants) without hurting your category performance, you would be able to focus on the top-performing products.
P.s. In pandemic times, this is quite evident. with broken supply-chain, production issues, you need to prioritize.
Factors in SKU rationalization - (Margin, inventory turn-over, lead time, product return, erratic demand, shelf-space, currency risk, decline in demand, manufacturing defects)
Some Criteria to work with :
What SKUs are most critical for your category growth?
Which SKUs are the most difficult to source?
Which SKUs have the longest lead times?
Which SKUs have a higher potential for pilferage?
Which SKUs have a greater margin?
How would it affect customer retention if the SKUs is removed from the product mix?
Few important points :
You need to know if the SKU as a discontinue candidate falls under destination traffic category - Products that brings customer to the store (that are not necessarily available in other stores). Even if going by matrix (margin, revenue, slow sellers etc.), it doesn’t make sense to keep the SKU, this SKU might lead shopper to buy other product categories when they come specifically to buy this particular SKU.
It’s important to identify your most loyal or valuable consumers. Products that are important to them are important to you as well - you need to ensure that SKUs that customers want stay and are not being eliminated (Customer-centric analysis is needed). Customers will move on to something else if they don’t get what they are looking for. (For example- I buy only if the yoghurt is available with online retail store. Many times I left cart in JioMart and opted for grofer because yoghurt was available on grofer)
Watch out for Cross-category/subcategory product affinity. For example, it is probable that people buy pizza base with cheese. So let’s say you discontinue pizza from your portfolio, chances is that you are going to loose on cheese as well.
Consider substitution model / brand Loyalty factor. If you remove one item, does sales transfer to similar products in your category (demand transference)? If the SKU that a customer want is not available and customer is buying substitution item that would mean that the sales is not lost.
You need to consider localization as a part of the eqn. Whether you are running an online or offline retail store, need to consider location store, talk to store manager to understand the local demand. SKU rationalization is although a centralized exercise for big organization (Like Walmart, Amazon) , need to have geographic specific understanding.
And the most important, the data available with you is a history but you need to work with the future run rate.
Read these articles in addition :
SKU rationalization is tricky. It can hurt your topline / bottom line.
https://hbr.org/2012/11/which-products-should-you-stock
https://hbr.org/1994/09/extend-profits-not-product-lines
https://hbr.org/2006/04/growing-by-cutting-skus-at-clorox
https://blog.wiser.com/what-is-sku-rationalization-and-how-can-you-get-it-right/
HOW TO DO SKU RATIONALIZATION?
You require data to perform SKU rationalization. Traditionally, many category managers who spent years in a category, they act upon leveraging their years of experience and gut feeling. But with so fierce competition in the market, and volatility in market, you need to go by what data tell you.
Internal data : Sales / transaction data. Traffic data to your store (Online, offline)
External Data: You can buy the retail sales data from different market research companies. ( Nielsen / IRI / SPINS )
There are mainly 4 type of combination of retail sales data available based on data source (Direct and syndicated) and data focus (Store vs shopper).
FOCUS - STORE
Retail direct data - You will be able to get details of your product sales under a particular retail store with this dataset. This will help you to identify how your brand / product is performing under a particular retailer. (Ex Walmart retail Link)
Only your product information will be available, you will not be able to analyze how your competitors are doing
Syndicated store data - This data is an aggregate of all stores data in a geographic market or channel and capture all products in most major categories. You will be able to analyze your competitors performance, and can even drill down to characteristics of products in a category.
FOCUS : SHOPPER
Syndicated shopper data / Household panel data
Retailer direct shopper data from loyalty card data
These are consumer-level data. Help you to create a complete picture of important demographics / consumer characteristics.
Syndicated data is a pool dataset.
Syndicated data refers to general market data that isn't specific to any one client. An aggregation of retailer and product data, syndicated data is generally collected by market research firms and then purchased by businesses who have a vested interest in the market.
Lets take a case study.
You are an online retailer. Assume that these are the dataset available with you.
How would you improve the sales using the Nielsen data / External agency data? How would you combine these dataset together and conduct SKU rationalization? You have SKU level sales data as well as discontinue product data- lists of product that brand has discontinued for some reason.
Would wait for your analysis before I post my thoughts on this. Watch out for this space. Happy solving, let me know your solution / or any follow-up questions.
Reference: