Making great commercial decisions is hard. Especially amidst the cost of living crisis, ongoing supply chain issues and a rapidly changing retail market.
Decision intelligence is the commercial application of data science and AI. It helps business users make better, faster and more unified decisions – driving profits, reducing operating costs and increasing customer loyalty.
But how can you get started with decision intelligence as a retailer or brand?
Strong foundations are the key to long-term success.
Think of decision intelligence like building a house. Before you can start making physical progress by building the walls (ie making decisions), you need to start with strong foundations.
Affinity analytics, attributes, customer need states and segments are the cornerstones of great commercial decisions.
Identifies the underlying relationships between products and products; customers and customers, and customers and products. For example, affinity analytics examines the products customers browse and purchase, in the same session or order, or across multiple sessions or orders.
Affinity analytics helps to paint a picture of which products are in a consumer’s consideration set across multiple shopping missions; which products they substitute for each other; which products they purchase together, and which products they remain loyal to.
Words or phrases describing the detailed features of products, customers and/or their relationships. For example, a pair of jeans may have the attributes ‘denim’, ‘casual’, ‘fashionable’, ‘expensive’, ‘young’ and ‘high waisted’.
Attributes can be built from anything, from product meta data such as size, colour or brand, to product review data (using techniques such as natural language processing to capture consumers’ descriptions). Attributes help to create an understanding of how customers start to bring products into a consideration set based on their detailed features.
Customer need states.
Affinity analytics helps to create customer needs states or missions, whilst attributes help to describe the products that make up those need states. By understanding how products ‘affine’ with each other, you can create clusters of products that similarly satisfy a customer need. For example, you may stock 10 different black leather jackets that are extremely substitutable for each other. You could consider this in a couple of ways: (a) you’re providing the consumer with a variety of choices within this particular need state (‘black leather jacket’), or (b) you have too many options that are ultimately taking up valuable shelf space, online or in store.
Customer need states help you understand how consumers group products together into decision mindsets or missions. They also help uncover how your product range satisfies those needs across every category.
Groups of customers with similar characteristics, behaviours or preferences. For example, a retailer might have a segment for loyal customers with a higher-than-average order value.
Segments of customers can also be created based on the product attributes or customer missions that describe their purchase and browse behaviour.
Segments can be macro (broader groups with similar behaviour), micro (broader segments cut further by detailed behaviour) or individual (every consumer is a segment with discrete behaviour facilitating personalisation).
Put together, affinity analytics, attributes, customer need states and segments create the foundations for true decision intelligence. Each piece of foundational insight is interlinked. Building it layer by layer creates sophistication and drives more intelligent decision making.
Affinity analytics creates the foundations for customer need states. Attributes explain in detail what those need states are, and segments bring everything back to the customer level, either on a macro, micro or individual level.
In action, affinity analytics could find six pairs of women’s branded white trainers at a high-end price within a retailer’s range. The customer need state could be defined as ‘luxury white trainers for women’. In this case, the attributes could be ‘luxury footwear’, ‘expensive’ and ‘branded’. A micro segment could capture all shoppers interested in expensive footwear, with a propensity for buying branded products.
So, why’s this important? Retailers and brands can use this information to personalise customer experiences. After all, personalisation improves customer experiences and boosts loyalty.
Layering the building blocks.
Affinity analytics, attributes, customer need states and segments are also the building blocks for personalisation. 71% of consumers expect personalised experiences, so they’re an essential part of brands’ business strategies.
These building blocks can be layered to build transformational insights across the business:
Retailers can use customer need states to establish how many products serve the same need. Any unprofitable lines which don’t serve a unique need but have obvious substitutions can be removed from a range. Affinity analytics also helps predict demand transference.
The endless aisle can lead to eternal scrolling – and no one wants that. Retailers can use customer need states to reimagine their merchandising hierarchy. They can also create curated ranges, providing shoppers with relevant, frictionless experiences.
Pricing and promotion.
Customer need states help retailers understand promotional cannibalisation. Meanwhile, affinity analytics helps optimise different price points within a need state. Using data (rather than gut feel) to find the optimal price point drives customer engagement.
Marketing and media.
Affinity analytics, attributes and customer need states work together to surface personalised recommendations for customers. Segments help create micro or individual lists to send relevant marketing communications that drive genuine engagement.
After offering personalised ranges, retailers need to be able to fulfil orders and delight customers. Supply chain forecasts can be carried out at customer need state level – any products in a need state are substitutes for each other and share a demand profile.
Putting data at the heart of decision making.
Ultimately, data is key.
Without a data-driven culture, businesses will never be truly customer-led. They risk making misguided commercial decisions. They risk delivering poor customer experiences. At worst, they risk obsolescence.
To create decision intelligence, retailers must place customer and product data at the heart of their decision making. After all, great commercial decisions help brands compete – and succeed.
Our decision intelligence platform is now available. The first module helps create deep customer insight, including affinity, attributes, customer need states and segments. Armed with this insight, retailers can make key marketing decisions to drive revenue and loyalty.