In the face of economic uncertainty, platforms are returning to their foundational strengths for commercial success, emphasising accurate and scalable user identity as they brace for the deprecation of third-party cookies in 2024.
The most well-known marketing theorists of our time typically advocate two broad approaches to marketing. Target the whole category over the long term because mass marketing, penetration, and scale is “how brands grow”. But, for shorter term goals, that approach isn’t necessarily as efficient. Not everyone in a category wants to buy immediately. The latter makes sense.
Consumers don’t evaluate advertising or marketing activity based on the number of people in a category that were made aware over ‘the long term’. Their focus is more short-term. They are evaluating advertising and marketing on how well their needs are met across the customer lifecycle, and all the different factors which influence each stage within the lifecycle. These factors are probably best summarised by one word: relevancy.
Relevancy can mean many things; seeing a message at the right time, seeing it enough times, having a message appeal to you, having a message meet a need, having the right brand experience, price being set the right point… the list goes on.
And one tactic that has been intrinsically linked to creating (and is oft viewed as a short cut to) relevancy, is increased personalisation, be it truly 1:1, or more segmented in approach.
Why? Well, making marketing communications more relevant and personalised, works.
- 80 percent of people are more likely to do business with a company if it offers personalised experiences, 90 percent find personalisation appealing, and these consumers who find personalised experiences appealing are ten times more likely to be a brand’s most valuable customer.
- 77 percent of consumers have chosen, recommended, or paid more for, a brand that offers a personalised service or experience.
- Personalisation reduces acquisition costs as much as 50 percent and increases the efficiency of marketing spend by 10-30 percent.
- A segmented approach to advertising can double revenue. Rip Curl generated 93 percent more revenue per campaign by segmenting customers interested in surfing and wetsuits and sending tailored creative to each.
The examples above each illustrate the potential benefits linked to increased relevancy, more personalisation, and improved customer understanding. Something we at dentsu Media see regularly here in Aotearoa:
- When we compare the performance of the different display inventory types we buy on behalf of our clients, we can see up to 8x the ROI (Source: Google / DV360) when buying deterministic display inventory (i.e. inventory whereby we have data that allows us to 100 percent determine who we are targeting) when compared to buying probabilistic inventory (i.e. inventory where we are using statistical probability to infer who we are targeting. This is because when we can determine who we are targeting, we improve our chances of being relevant to them.
- By using a client’s first-party data (demographic, purchase behaviour, product preference, and loyalty programme engagement); in conjunction with third-party (intent) data, we were able to create an advanced segmentation and messaging matrix, comprised of over 600 segments and 900 different comms messages, to ensure all segments targeted received the right message. This approach encouraged over 300,000 lapsed customers to shop again. Not bad when your average transaction value is $250.
- For another client we analysed three years of loyalty data, to better understand customer shopping behaviour, including: average basket size, percent discount likely to trigger a purchase (i.e., 40 percent off at the start of the sale, or a 70 percent final markdown), and preferred point of purchase (in-store versus online). This gave us unparalleled insight into their customers, allowing us to understand and model the drivers of propensity to purchase, inform our advertising, and improve relevancy. This approach led to an 84 percent improvement in ROAS, which was worth millions to their business.
- By combining loyalty data, purchase data, and purchase triggers, with feed technology, and SA360’s machine learning capability, we were able to better understand behaviour, and deliver personalised shopping ads to customers. This encouraged a higher frequency of purchase, generating millions of dollars in incremental revenue for a retail client.
These are all large benefits, and they are all predicated on something called ‘identity resolution’; the process of connecting and consolidating customer data across various touchpoints and devices to create a unified view of a customer.
Identity resolution helps businesses, marketers, and agencies, understand, and recognise their customers across different channels and interactions, enabling in the very least more personalised, targeted, and relevant marketing efforts, at scale. But if you think bigger, it goes beyond marketing and advertising, and can inform the entire customer experience across the customer lifecycle.
In other words, identity resolution is foundational when it comes to meeting customer needs. And looking ahead to 2024, identity resolution is becoming more important, more difficult, and more complex, as the landscape we operate in evolves:
- The proliferation of digital channels and devices means customer journeys have become more complex. Identity resolution must incorporate data from multiple sources such as websites, mobile apps, social media. Equally, offline interactions from bricks and mortar stores, call centres, and more, are also required, meaning new and more advanced identity resolution techniques are needed.
- Marketing efforts are shifting towards a customer-centric approach, aiming to understand and serve individual customers rather than segments or demographics. Doing this at scale means that identity resolution will become more reliant on advanced technologies such as artificial intelligence (AI) and machine learning (ML) to enhance accuracy, efficiency, enable better matching, real-time data processing, and predictive modelling, for more precise customer identification. This in turn means more data is required.
- As identity resolution becomes more sophisticated, ethical considerations surrounding data usage, privacy, and potential biases are gaining attention. Businesses are increasingly focusing on responsible data practices and ensuring fairness and inclusivity in their identity resolution processes.
- Google have also announced (in part because of the above) the sunsetting of third-party cookies in 2024 – currently one of the primary means of identifying △ people in digital marketing channels.
The knock-on effect? Businesses are looking to overcome limitations in, and/or expand their first-party data sets to enhance their identity resolution capabilities.
How are they doing this? What do you need to do to improve your own identity resolution capability?
Well, it starts with a first-party data strategy. Choose which data to collect, establish how you will analyse and activate it, and establish clear customer experience goals that are aligned to business objectives.
A first-party data strategy is dependent on your customers sharing information about themselves in the first place, and your data strategy must make it easy for customers to see the benefit in sharing their data with you. A value exchange is required, be it value through exclusive offers, loyalty programmes, early access, or a superior customer experience when interacting with your brand. If you have a value exchange in place, your customers will be more willing to share their information (aka their data).
Once you have the data, you need the right infrastructure, processes, and people in place to capture it, organise it, understand it, orchestrate it, and make actionable within your business, to create value. When this is done correctly you will have a clear view on who your customers are, how they are interacting with your business, and most importantly how you as a business should be engaging with them.
That might sound expensive and may well be if you are an enterprise-level business with a complex amalgamation of channels and data sources. Because in this instance there is often a need for enterprise-level technology (such as a CDP) to tie everything together.
However, the point about having a CPD is also a bit of a misnomer. We wouldn’t necessarily advise our clients to invest hundreds of thousands of dollars in a CPD. To reap the benefits of identity resolution there are cheap, or no cost, alternatives available that will allow you to leverage your first-party data and create positive experiences for your customers.
One that we use heavily at dentsu is GA4, Google’s privacy-first analytics platform that allows you to capture how customers interact with app and web assets, and then use that information to create addressable audiences in media, web personalisation, and inform decisions on how to better optimise your website or app, product suite, or price point.
If you then send your GA4 data to BigQuery, Snowflake, AWS (any data warehouse) to merge, blend, and analyse first-party data against your other datasets such as CRM or POS, you effectively have an identity resolution solution in place, which will enable more relevant marketing and advertising:
- You can create churn and engagement scores for your customers so that you can use marketing to reach customers when they are likely to churn from the business and keep them coming back.
- You can use the autoML feature in Big Query to score audiences based on propensity, to understand when an audience is more likely to buy, adjust audience investment in a more segmented way, and ultimately sell more product.
- You can suppress advertising (owned and/or paid) for any customer or audience segment that is deemed non-valuable (for any reason), which can vastly increase the amount of budget you have available to grow your business.
- You can push GA4 data and audiences into web optimisation tools such as Optimizely, to create personalised onsite experiences with your customers (e.g., product recommendations) to improve CX and conversion.
It is these types of techniques that were used to generate the returns outlined in the case studies above, and they all had one thing in common. They all started with a first-party data strategy, the most important building block for identity resolution.
So, to summarise, as the landscape we operate in becomes more complex, as customer expectations increase, and as privacy law evolves, identity resolution will take centre stage in 2024. But it doesn’t have to be expensive, it isn’t unobtainable, all it really requires is a first-party data strategy. What’s yours?
This article was first published in the NZ Marketing Magazine December/January 2024 issue.
