To Affinity and Beyond!
Relationships. The way we interact with the products we buy and browse, the way we relate to other consumers and the way products associate with other products. Relationships are one of the most meaningful aspects of life and have a fundamental impact on the decisions we make.
Relationships are also at the heart of affinity analytics, a data science technique that underpins the way in which businesses can truly understand customer behaviour and needs.
When deciding to create a podcast, video series and blog, affinity analytics emerged as a recurring theme, underpinning much of what we admire about how major brands such as Netflix, Spotify and Airbnb create insight on which to base critical business decisions.
And so, To Affinity and Beyond became the title for this series. In our first episode we have dived straight into what affinity analytics is, how it can be applied and some of the leading companies pioneering its use. This blog is a summary of the discussion and builds on the some of the key points.
What’s the Value of Affinity Analytics?
By understanding how customers buy and browse products, the need states they’re looking to fulfil and the language they use to describe products, retailers and brands have the insight they need to offer a compelling product range, price it effectively, merchandise it in a way that drives on and offline conversions and personalise marketing communications to make them relevant and powerful.
Ultimately affinity analytics helps deliver on 3 of the key thing’s consumers look for in their value exchange with retailers and brands – ease, speed, and value for money. If a business can understand what their customers want to buy, they can surface products faster, make them easy to purchase and price them to convert. In doing so customers will spend more, shop more frequently and show greater loyalty.
By understanding which products are similar or complimentary, affinity analytics also helps organisations to inspire customers to find new products they didn’t even know they wanted, shopping across the breadth of the range. For companies with lots of products this is key. Consumers have become jaded by scrolling through the endless online aisle and crave relevant recommendations. Take note Amazon and eBay!
Affinity analytics can be applied wherever there are customers and products. As well as fashion, grocery and other forms of retail, media, travel, and finance are other prime areas for applying these techniques.
Which Brands are Winning?
Media & Content
Netflix have long championed the cause when it comes to understanding us as consumers and giving us a better product as a result. Every interaction we have with them – browsing, rating, watching – all factor into how they engage with us as individuals. Aggregating all that insight about our decisions and behaviours tells Netflix a huge amount about their customer base, helping them make decisions right across their business.
Using affinity analytics to explore the relationships between customers and the content they consume has helped Netflix create sub genres of content they didn’t know existed and serve customer needs that were previously hidden.
By creating content and feature attributes Netflix also have a language with which to understand customer preferences, be that lead actor, genre, or type of content. Plus, attributes provide a way of describing why a relationship exists between two pieces of content.
By understanding the rich tapestry of interests each of us have, many of which might not seem connected at first, Netflix make informed decisions on what content to create, buy and then promote to each of their customers. For example, providing insight for new product development through Netflix Originals, enabling them to serve growing needs and trends in the market. You can find out more about the cutting-edge ways Netflix use data at https://netflixtechblog.medium.com/.
Fashion & Apparel
Very, the home shopping retailer, use weather data, based on a customer’s physical location to recommend appropriate products. That could be that umbrellas for the traditional British climate of rain, or bikinis for the occasional summer’s day. This adds a real time element to customer need states.
ASOS, the fashion and cosmetics retailer, have introduced Save Boards which allow customers to save products they browse online, with the ability to create their own categories. This feature empowers customers to create their own product hierarchies and need states, many of which ASOS didn’t know existed.
ASOS can use these categories, which actual consumers are creating, to personalise content and recommendations, creating new attributes and need states to act as the basis for personalisation and get the most relevant products in front of each customer.
Direct to Consumer
The fast-growing area of D2C represents a significant opportunity for the application of affinity analytics. Nike, who are on a much-publicised drive to build relationships directly with their customers, have used affinity type analytics to personalise their product range in stores, such as Melrose Place in Los Angeles – https://www.youtube.com/watch?v=wSZDndRHqbE.
Nike analyse the behaviour and needs of customers within the catchment of their Melrose Place store, typically based on web browsing behaviour, and merchandise the store accordingly. They use targeted offers to drive customers into store, where they doubtless browse and buy additional products to the ones they came for. This is a fantastic example of combining digital and physical commerce, driven by intelligent use of data.
That said, Nike have some way to go on delivering their D2C vision. On the positive side they offer compelling personalisation and customisation of products, leveraging Nike ID, but on the down side they struggle to meet consumer expectations when it comes to offering breadth of product range, which is often much more comprehensive through their retail partners.
Coke is another brand investing heavily in their D2C offering. Like Nike they face the challenge of convincing consumers there’s a strong enough reason to buy from them directly, rather than consolidating their product into a bigger shop with a retailer. Whether that’s a cheaper price, exclusive products or some other value add, affinity analytics can be an important tool to generate the customer insight required to understand what makes it worthwhile going direct.
Applications of Affinity Analytics
Pricing
A key aspect of affinity analytics is understanding customer needs. However, businesses also must look at the commercial side of things too, such as getting pricing right. For example, what will happen to demand if the price of a particular product is changed? Or the impact of changing the price of certain products in terms of cross elasticity throughout a customer need state or the entire range. The commercial implications of getting pricing right, or indeed wrong, are huge. In retail, where margins are often slim, this is a big deal.
Pricing is key for other industries too, for example travel, where Airbnb are an exemplar for their use of affinity analytics. When you offer your property up for rent with through Airbnb you decide how you want to price it. Airbnb offer a handy guide price based on people who have properties like yours and have successfully rented them out. This recommends the range you should price within to secure a rental.
Airbnb consider the attributes of your property, for example the number of bedrooms, bathrooms, and features, such as a pool or outside space. They take into account the types of people who have stayed in or browsed your property, it’s location and other meaningful attributes. They also look at seasonality and the real time dynamics within the rental market, for where your property is located. All of this is based on affinity style analytics.
Like Netflix, Airbnb are at the forefront of data science and share much of their thought leadership via https://medium.com/airbnb-engineering.
A further travel example is how operators price their flights and hotels. These decisions are often made in isolation, however, that’s not how consumers think. Affinity analytics give operators the ability to analyse consideration sets for a customer need. For example, shall I fly from Liverpool or Manchester? And which of these 5 hotels shall I book in Majorca?
The impact of pricing on each flight and hotel must be considered in terms of the alternatives being considered. Affinity analytics helps understand those relationships, attributes tell us which flights and hotels are likely being compared and need states inform us how customers are looking to make their purchase decision. All of these are key to offering a compelling customer proposition, priced in a way that’s commercially attractive to both customers and the company offering it.
Product Range Optimisation
An area where affinity analytics has been deployed with great impact is assortment optimisation within grocery retail. It’s an industry that’s well suited to this type of analytics, given grocers have many customers and products. Other sectors, such as fashion, travel, betting, and gaming have been slower to adopt affinity analytics but have significant value to release should they start.
Affinity analytics helps retailers and brands work out what to sell in their overall range and at a local level, be that in a store, on a website or via a partner. The insights created also enable the curation of product ranges to individuals, to make them as relevant and compelling as possible.
When analysing customer data to make range decisions, companies will typically look at what their competitors are doing, and rarely examine product substitutability within their own range. By neglecting this key angle, they may miss important insights, such as how many products are meeting the same customer need state.
If lots of products are satisfying the same need, there’s potential to remove some and trim the range, or replace with other products which meet different need states, ensuring the right size and mix of products are offered. This gives the opportunity to tap into growing trends or provide inspiration through new product development.
Recommendations
Recommendation engines are another common use of affinity analytics, delivering personalised content and offers to consumers. Spotify’s personalised end of year summary outlining what you’ve been listening to is an example of a brand using customer data to deliver a wealth of value back.
Spotify analyse customer behaviour and the attributes associated with songs, artists, and genres to not only provide an informative view of your previous 12 months listening preferences, but also to help you discover new music which you’re much more likely to enjoy. It addresses one the biggest worries many people have with algorithmic customer experience, the loss of serendipity.
Spotify works on periphery of tastes, trying to tempt you into new things. It’s an amazing way to coax people out of their comfort zones but keep them highly engaged and moving deeper into the platform.
Amazon, for all their incredible business success, cause frustration for many when it comes to recommendations, frequently recommending products when customers have already fulfilled that need with a purchase from them. Affinity analytics and customer need states can solve this problem. By understanding need states, and when a purchase addresses it, Amazon could stop promoting similar products, unless of course it’s a repeat purchase, such as a consumable product.
Supply Chain
Where Amazon clearly do use affinity type analytics extremely well is in their ultra-slick supply chain operation. Amazon Prime, with many products delivered next day, is a huge driver of success for Amazon, and relies heavily on moving stock around the country, between depots, to fulfil customer demand.
Affinity analytics can analyse purchase and browsing behaviour, by customer type and location, to help with supply chain planning and forecasting, ensuring products are where they need to be to maximise customer satisfaction and minimise the cost of logistics operations.
The same principal can be applied to the travel industry. Understanding demand across the entire range of flights and accommodation helps operators better forecast demand, and in doing so, have their transport and people in the right locations to satisfy that demand. For anyone who has ever travelled abroad, it’s clear there’s great potential for the industry to improve in this area!
Beyond Affinity Analytics
The full discussion summarised in this blog can be found by searching for To Affinity and Beyond on your favourite podcast platform, including.
- Spotify – https://open.spotify.com/show/1kFHG93zu9A0nBrjeFB3pp
- Apple – https://podcasts.apple.com/gb/podcast/to-affinity-and-beyond/id1568595127
- Google – https://www.google.com/podcasts?feed=aHR0cHM6Ly9hbmNob3IuZm0vcy81NjMyOGYxMC9wb2RjYXN0L3Jzcw==
- Anchor – https://anchor.fm/toaffinityandbeyond
- Breaker – https://www.breaker.audio/to-affinity-and-beyond
- Pocket Casts – https://pca.st/s3evwyrv
- Radio Public – https://radiopublic.com/to-affinity-and-beyond-GKgeLv
We have lots of exciting guests to follow, with strong opinions on customer insight, decision making, data analytics and business growth. If there’s a topic or guest you’d like to see featured, please get in touch via peter@hyper-group.co.uk.
