There are many valid reasons for building consumer decision trees for your categories. For one, they allow you to organise your products logically. They also allow for a more enjoyable shopping experience for your customers. Of course, before you can begin building a consumer decision tree, you need the correct building blocks.
The blocks or fundamentals are key. However, it’s worth pointing out that while we’ve listed each fundamental, there is no specific order to their importance. In reality, you cannot expect to have an efficient consumer decision tree if any one of the below elements is missing.
About the contributors
Justine Brown joined DotActiv in mid-2019, creating data-driven planograms for Makro, a wholesaler chain and subsidiary of Massmart that has international ties to Walmart. She currently works on the Makro Food and Liquor account.
Leané Mulder joined DotActiv in 2019 as a space planner, working on various ad-hoc accounts. Since then, she has been promoted to account manager. Today, she oversees work on our Motus account.
Nadia Cloete joined DotActiv in 2017 as a space planner, creating data-driven planograms for Makro, a wholesaler chain and subsidiary of Massmart that has international ties to Walmart. She currently works on the Makro General Merchandise account.
Rina Wilkens joined DotActiv in 2018 as a space planner. She has since worked her way up to the position of account manager. Today, she oversees the Health Department at Dis-Chem, which includes categories such as Vitamins, Sports and Foods.
1. You need clean and classified data
As we noted in a previous article, a consumer decision tree depicts the decision-making process that your customer would undertake whenever they purchase a product. In that article, we also mention the importance of data analysis.
That should make sense if you consider that one of the foundations of your consumer decision tree (CDT) is your retail data.
Of course, it’s one for you to have retail data. The fact that you sell or supply products means you have data available. However, it all counts for nothing if you can’t make sense of it. And that’s where clean and classified data comes in.
Let us explain.
Clean and classified data allows you to accurately allocate all of your products to a specific level of your category or product hierarchy. It also ensures that your data is free of any inaccuracies such as duplicate SKUs or misspelt words.
The converse is also true - by classifying your data incorrectly or failing to take care to clean it, you'll end up with an inaccurate consumer decision tree.
For example, if your data has products from two different categories and you don’t clean it, you might find yourself attempting to classify them within the same CDT. However, this isn't how your customers would usually purchase them and so what you end up with is a shelf laid out with products that don’t make any sense to the shopper.
There is also the point that clean data is an accurate depiction of the sales you generate as well as the amount of SKUs used in trade. If your data isn't clean or classified, you can't use it to justify why a product deserves more space or why it should be merchandised in a particular position on the shelf (like at eye level).
So how can you ensure clean and classified data?
For one, it's critical that you first fill out any blanks with your data so that you have all the necessary information. All of your data must be in the same format. For example, all CAPS to ensure that you don't create duplicates when populating your data fields.
Also, ensure that all product descriptions are accurate according to the product so that it depicts what that product is. Another point is to ensure that all the data used for sales, for example, use the same period or store/region. If your data differs, it renders any project invalid and you can't use the data if you want to complete year-on-year growth comparisons.
2. You need access to a tried and tested methodology
While data is a must when building a consumer decision tree, it's critical that you also have a methodology to follow. And there are a few reasons why.
Tried and tested methodologies provide you with proven strategies that you can implement to target a specific market to influence them. Think about the fact that you can encourage customers to purchase more or try new products in a category.
Another reason is that having a methodology to follow can ensure that when you set up your CDT, it's one based on the needs and wants of your target market. That means a more enjoyable shopping experience for your customers, which can encourage them to return, knowing their needs will be fulfilled.
That feeds into another reason - a methodology is critical when building any consumer decision tree because it can allow you to assess whether or not to continue presenting a category using a specific strategy.
For example, with your methodology (and using data such as current sales), you could look at a category and determine the strengths, weaknesses, opportunities and threats of the category. From there, you could assess how consumers shop in the category and learn what methodology to implement.
When it comes to a process or methodology to use when building a consumer decision tree, it depends on a few factors. One factor is to ensure you know and understand the category - a point we unpack later on in this article. Another is to use any information provided to you by the retailer (if you're a supplier or third-party).
As for where to begin, a good suggestion would be to start at the top of your category hierarchy. So, go from category to subcategory to brand to sub-brand until you've laid out your entire CDT. How in-depth you go is entirely up to you. When determining your consumer decision tree, you must establish whether the average consumer first shops by brand or sub-category.
Let's look at the Deodorant category to illustrate what we mean. A consumer likely identifies the deodorants and roll-ons separately. Therefore, splitting the category into two sub-categories (deodorants and roll-ons) at a high level will simplify the shopping experience.
3. You need to have a good understanding of shopper behaviour
Since the consumer decision tree is a forecast of how shoppers will make a purchase decision in a specific category, understanding how they behave should be a given.
There is also the point that the more you understand how a customer shops, the better you will be at merchandising your products in that category. After all, understanding shopping behaviour drives the objective of any CDT.
For example, with the right merchandising strategy based on shopper behaviour (established by looking at your retail data), you can increase sales and turnover of the category while simultaneously providing an enjoyable shopping experience.
By applying this understanding of shopper behaviour, it leaves little to no room for error, whether you are a retailer or supplier, to merchandise the category based on the wrong CDT principles.
If, on the other hand, a customer has difficulty navigating your store or finding products, you're inviting them to leave your store to shop elsewhere.
So how should you go about learning and understanding shopping behaviour?
One solution is to look at how you shop the category yourself. While not always scientifically correct, because you can't predict whether a customer will shop like you, it does at least give you a base from which to work. And you can do this across any category.
Let's use the example of buying groceries and you want to bake muffins. When buying items in this category, you might want to shop by ‘Size’ first (500g instead of 1kg). Then by ‘Brand’ since you are not brand-loyal. Finally, you'd shop by ‘Price’. Keep in mind that this thought process differs from customer to customer and therefore it is important to keep your target market in mind when creating your CDT.
Using this information and comparing it with any information received from a retailer or supplier, you can learn more about how shoppers behave when shopping in that category. Another solution to consider is doing market research and inviting persons that fit the persona of your ideal target market to answer questions about the category at hand. You can motivate this by providing incentives for their participation
One more solution is to learn how to analyse your data and do research. That includes completing a category analysis. When your data shows that the top contributing brand differs in the different sub-categories, for example, it is most likely true that the shopper does not tend to stay brand loyal when making a purchase.
4. You need to have a good understanding of the category
Understanding the shopper and how they behave when shopping in a category is one of the many aspects of designing a consumer decision tree that makes sense. However, you first need to understand the category.
After all, if you don't understand the category, you'll struggle to understand anyone who shops it. And if you understand neither, you will create product layouts that are unappealing to your customers and take up space that you could use for products that will sell.
Let's say, for example, you know that your customer only shops for towels when in your store. You would need to differentiate your range and price points so that you entice customers to enter your store. Once there, the layout should be determined by your consumer decision tree so that shoppers have the most convenient and easiest shopping experience.
Of course, it's not just about pleasing the customer. By understanding the category before building a CDT, you can ensure that you benefit from any strategy that you want to implement in-store. Also, you ensure that you choose a strategy that complements and helps you achieve your goals.
To get a better understanding of any category that you're working on, you could look at doing research. Doing so allows you to not only learn more but also be that much more effective when assisting your retail or supplier client with any merchandising exercise.
You could also look at performing a category assessment. Such an assessment can aid you to understand your category. That's done by looking at the market, consumer and category as a whole. From there, you can identify opportunities and put specific strategies and methodologies into place.
That includes researching the various products and brands found within the category and consulting any collected data.
Another option is to visit a store and shop the category as if you were a customer. Make notes on what you liked or disliked about your experience. Then take those notes and compare them against any data collected to see if there is a correlation between sales data for specific products and how you've merchandised them on the shelf.
5. You need to have a good working relationship with your retail client
If you have ever built an effective consumer decision tree, you’ll know that it’s not something you can do on your own. As we have already pointed out, it involves many different aspects.
One aspect that we haven't yet touched on is the relationship between the person building the consumer decision tree and the retail client. It needs to be strong and reliable.
Here's why: building a CDT is an interactive discussion between a retail buyer and the space planner. At least that's if you want to build one that delivers.
Having this interactive discussion encourages an open communication channel that will benefit you as a retailer as much as it'll benefit the space planner tasked with building the CDT. If you are the person tasked with creating a consumer decision tree for your retail client, you can better understand what they want and implement these changes.
As the retailer, you can implement more changes on the planogram based on any changes to shopper behaviour. It also helps you to develop trust. As soon as your retail clients trust you with key decisions on how to merchandise or display categories, you can learn more about any category that you are working on.
If you’re looking to improve your relationship, one way of doing that is through good communication. That includes honesty and compassion.
For example, as a space planner, be empathetic but don’t appear to be a pushover. Show them that you are not only knowledgeable about their categories but also passionate as this can build camaraderie and trust. If your retail client can see that you are knowledgeable about the category, they will be more accepting of any suggested changes to a layout or CDT.
Building a CDT creates an opportunity for you and the client to get to know each other better and have a clear understanding of how the category functions and how shoppers behave when buying products in the category.
It’s similar if you’re looking to improve your relationship with a space planner or third-party tasked with building planograms for you. Communication is key.
Building an effective consumer decision tree comes down to many different aspects as you would have read. Having these fundamentals in place can mean the difference between having a CDT that works for your business or one that works against it.
Interested in learning how? You can book a complimentary custom exploratory consultation with us here or visit our online store.