If you’re a Category Manager who manages thousands of products, you’ve probably got your hands full. With days packed to the brim with meetings, email ping pong and supplier negotiations, focus has got to be on the short term – there’s often too little time to create a viable long-term strategy, and even if there were, the existing systems seem nigh impossible to overhaul or redesign. Many organisations are therefore turning to Category Analytics, a solution that dives into transactional data to answer questions such as the following:
- What products should we have as staples?
- What stores should have what products?
- What should the pricing be?
- How many product hierarchies do we need?
- How loyal are customers to any one product, and to us?
Here are some of the key components of a product strategy that you can do better with analytics:
- Price and discount products systematically
One good approach to pricing, particularly for organisations with tens of thousands of products, is to create an automated pricing and scenario model. By plugging in certain scenarios to the model (e.g. charging X for one product and Y for another, then switching it around to see the difference), you can test out what pricing strategy will work best for a given customer base or product range within your business.
Another component of pricing in large companies is discounting – to optimise this strategy, some companies use a solution known as Revenue Management to determine which are the best items to discount or have promotions for. Looking at past promotions can also help you determine which items drove spend throughout the entire store, compared to those that just spiked sales for the one product but didn’t actually make an impact on revenue.
- Evaluate customer loyalty to product brands
Your data can tell you what products and brands customers tend to purchase – but how do they respond when another brand is discounted? Or when their preferred product is out of stock? Or when a different product flavour is released? One way to tackle this is called a Substitutability and Loyalty Analysis, where we analyse sales data to see how people have responded to discounts or outages of their favourite brands and what they’ve purchased instead.
There are three primary that this is useful for:
- New product development – what niches are available to tap into? What is driving customers to purchase (brand, packaging, flavour etc.)?
- Commercial Negotiations – focus your time on supplier negotiations with only the brands that offer products that perform well on your shelves
- Promotions – identify items that are ‘substitutable’ when discounted, showing you which products you should and shouldn’t discount
- Create automated product hierarchies
Another big challenge in product and category management is creating hierarchies that are logical and usable by departments throughout the business. The maintenance of those hierarchies is incredibly challenging – especially if they’ve been around for a long time, or if multiple people have been updating them manually.
One approach to fixing this is through automated Product Category Grouping – the process of using insights from historical data to categorise products based on customer behaviour and product performance, either as new hierarchies or supplementary ones. Product category grouping is useful for hierarchy consistency, as well as usability across departments.
So where to start?
Moving the focus from short-term damage control to long-term strategy can seem like an insurmountable task – but with the right help and a focus on implementing data-driven processes, your product categories have the potential to function significantly better.
Interested in some of the work Datamine has done in Category Analytics? Check out this price sensitivity case study, or chat with me about enhancing your strategy.
Sally Carey is a Director at Datamine.
Sally’s 30 years of experience in strategy and quantitative decision-making ensures she can always be relied on to deliver extraordinary results. Sally is a co-owner and director of Datamine. Originally from the UK, she is a fellow of the UK Institute of Direct and Digital Marketing and holds a BA in Systems Analysis and MBA from Bradford University.