Customer segmentation models explained

New Zealand
New Zealand New Zealand
Consumers make most of their payments by internet banking
  • 74%
    BFSI
  • 70.5%
    TELCO
  • 54.5%
    RETAIL
  • 46.5%
    BFSI
  • 39.6%
    TELCO
  • 40.7%
    RETAIL
  • A higher percentage make payments via internet banking to banks and insurance companies, telcos, and retailers, respectively, compared to the regional average
  • Impact: Anti-fraud capabilities critical to the increased digital transaction frequency and customers’ trust in banks
Australia
Australia Australia
Consumers are most satisfied with the post-fraud service of banks and insurances companies
  • More than 70% satisfaction rate compared to 59.7% on average
  • Impact: Increased trust in BFSIs
Indonesia
Indonesia Indonesia
Consumers that encountered most fraud incidents in the past 12 months
49%
34.7%

AP Average

  • 49.8% have experienced fraud at least once compared to 34.7% on average
  • Impact: Overall anti-fraud capabilities need improvement
Singapore
Singapore Singapore
Consumers have the highest trust towards government
AP Average
  • 75.5% choose government agencies, compared with 51.7% on average
  • Impact: Trust of personal data protection is centered around government agencies
Vietnam
Vietnam Vietnam
Consumers encountered most fraud incidents in retail and telco during the past 12 months
  • 55%
    TELCO
  • 54.5%
    RETAIL
  • 32.8%
    TELCO
  • 35.2%
    RETAIL
  • 55% and 54.5% have experienced fraud at least once in retail and telco, respectively, compared to 32.8% and 35.2% on average
  • Impact: Overall anti-fraud capabilities need improvement
Thailand
Thailand Thailand
Most Thai consumers believe speed and resolution are severely lacking (response/ detection speed toward fraud incidents)
AP Average
  • 60.5% think it is most important, compared to 47.7% on average
  • Impact: Response time as one of key factors to fraud management to retain customers and gain their trust
India
India India as standalone
Consumers have the largest number of shopping app accounts in the region
India
  • Average of three accounts per person
  • Impact: Highest exposure to online fraud
Hong Kong
Hong Kong Hong Kong
The least percentage of consumers with high satisfaction level toward banks and insurance companies’ fraud management
AP Average
  • Only 9.7% are most satisfied compared to 21.1% on average
  • Impact: effective response towards fraud incidents to be improved
China
China China
Consumers are the most tolerant toward submitting and sharing of personal data
AP Average
  • 46.6% compared to the AP average of 27.5% are accepting of sharing personal data of existing accounts with other business entities
  • Impact: higher exposure of data privacy and risk of fraud
alert
Japan Japan as standalone
Consumers most cautious on digital accounts and transactions
50.7% Actively maintain digital accounts’ validity
27% AP Average
45.5% Do not do online bank transfers
13.5% AP Average
  • More than 70% did not encounter fraud incidents in past 12 months, compared to 50% on average
  • Impact: Relatively low risk of fraud

Being Adaptable

Some adjectives get overused and eventually their original meaning gets lost in a ‘fog’ of marketing content. Dynamic is one such adjective. Used across the spectrum of marketing content, particularly to address any concerns about being outdated, its meaning has become muddied.  This is particularly true in the case of consumer segmentation. In the face of the perception that established methods cannot keep up with the myriad of influences on consumer behaviour, dynamic segmentation purports to address these issues by incorporating ephemeral data points that keep the segments up to date. But, a strong and effective segmentation of consumers relies on a stable and reliable data core. These two forces are often at odds with each other, and this fuels the perception that many established segmentation methods cannot keep up with the modern consumer.  An adaptable segmentation finds the right balance. By incorporating data sources that capture changing consumer sentiment and using that to refine the segments based on a stable and reliable data core, enables marketers to keep up with the changing consumers while maintaining a long-term effective segmentation solution.  

 

 

Keeping up with the changing consumer

There is a widely known, and almost a universally understood concept that drives the investment in and application of personalised data-driven marketing:  know your customer. It is more than reasonable to add to this: keep up with your customer.  

 

Savvy marketers have typically used segmentation to build audiences, buy the right media and calibrate conversion metrics. But in the face of hitherto unseen rich individual consumer data sources, in particular, those accessible in the digital marketing environment, the need and effectiveness of established segmentations has been called into question.

 

The segmentation doubters out there have a point; unless it helps understand consumers better right now and this is kept up to date and acknowledges the rapidly changing environment that our consumer lives in, then it’s just an academic exercise that only fits the purpose it was originally built for. And not much else.

 

But there is an important counter to this, as suggested in Experian’s ‘Avoid the Segmentation Trap’ whitepaper; insight into customer ‘state’ creates a response much more effectively than the next strongest factor such as offer, incentive or creative.

 

The seemingly contrary aspect of segmentation stability is an important criterion that is used to assess a segmentation’s success. A segmentation that is too dynamic is just as unsuccessful as one that stays static for too long. An adaptable segmentation is the most effective solution and finds the right balance by successfully using rich dynamic data sources to supplement the established segmentation model.

 

Creating Successful Connections

 

It is understood that building an emotional connection can create a huge payoff, but building those connections is often more guesswork than science.

 

A successful and accurate marketing message that shows you not only understand but are keeping up with customers is necessary to stand out against the ever-present background ‘hum’ of marketing messages. Our lives very rarely stand-still; and as we age, move to a new house, change jobs, buy a new mobile phone or subscribe to a new media provider; our lived experience changes and our needs and desires adapt accordingly.

 

As consumers become accustomed to a personalised experience, they expect businesses to be good users of data to build connection and deliver appealing propositions. Building and deploying an adaptable segmentation will put you on the fast track to delivering relevant messages to consumers.

 

Building an adaptable segmentation

 

There are ways to ensure your customer knowledge accurately illustrates preferences and behaviours. These often involve real-time market research and new data sets and sources. But incorporating these dynamic data sources can contradict the reliable (and stable) data sets many segmentation solutions are built upon.

 

To ensure time doesn’t render valuable consumer insights obsolete requires careful planning and design:

  • Build a stable core: The inputs into a consumer segmentation solution should strike the right balance between stable and dynamic. Despite the inherent appeal, a solution that relies heavily on rapidly changing dynamic preference data can be quickly rendered ineffective. Utilising stable building blocks and complimenting these with attributes that measure consumer dynamism will result in a long-lasting solution that maintains its relevancy and accuracy as consumers change.

To achieve a stable core when creating your segmentation algorithm consider these questions:

  1. Can I rely on the long-term availability of this data point?
  2. Is the data likely to help understand the majority rather than a minority of my customers, current and future?
  3. What are the influences that may change the allocation of this attribute – and how quickly might they change?
  4. Are we convinced of the impact on consumer behaviour?
  5. Are there reliable linkages that will easily allow the segmentation to be applied to new and/or prospective customers?

Confident and positive answers to these questions indicate the identification of the right type of data to form a stable core of an adaptable segmentation.

  • Keep it fresh: Designing a program of segmentation ‘refreshes’ rather than rebuilds will result in a solution that remains accurate and robust long after its build date. This involves planning upfront to ensure that the data inputs;
    • Contain the right mix of stable and dynamic data types
    • The algorithm is constructed in a way that integrates dynamic data sources, but the core elements of segment allocation rely on robust and stable sources
    • Ensure supply and access to new data as it becomes available – the need for refreshes and the regularity they should occur will be driven by the accessibility of data sources
  • Research is your friend: Integrating market research data has long represented a challenge to many segmentation solutions. Indeed, many of the arguments regarding the best type of segmentation often come down to a disagreement on how to use consumer research – as consumer’s attitudes and preferences change which solutions best captures these changes? This argument often misses the point – currency of consumer attitudes and preferences captured using market research techniques are a rich and important source of data. Capitalising on this data and leveraging its power on top of the stable core to drive marketing results are essential to segmentation success.

Building an enduring segmentation

 

To ensure your segmentation continues to deliver results, there are new challenges keeping data-driven marketers ‘on our toes’

  • Keeping it up to date: The market doesn’t stand still; Consumer choices change, our habits change, technology and the way we use it changes. Segmentation solutions need to make sure they keep up with these changes to maintain their effectiveness, which can be achieved without significant costs to rebuild
  • Too much data: It might seem almost non-sensical, especially to those of us who have lamented the lack of data sources available to drive segmentation over the years, but more data doesn’t always mean better It is always crucial to keep your eyes on the prize: will this data source help achieve the outcomes I am after. Vendors and algorithm advocates will likely try to convince you that we now have the ability to throw everything at an algorithm, and with the capability and capacity at our fingertips we can let the machine decide what is and isn’t useful. In some cases, this may be true, but these outcomes come without the insight that is necessary to drive success and value.
  • The problem of scale: In the face of an increasingly restrictive regulatory framework and shifting attitudes to the appropriate use of online consumer data, the much touted walled garden of first party data appears to have claimed pole position in the race for most in demand marketing audiences. The benefits of these rich data sources are obvious, but they are no doubt limited in their scale. In these circumstances, the data conundrum often becomes a choice between quality and quantity. But it doesn’t necessarily have to be this way if an appropriate consumer segmentation is put in place. This will allow you to extrapolate the rich insights of 1st party data, on to a much larger audience.

Making the most of Change

 

Events occur in people’s lives that change our regular routines and habits. Understanding these changes is a crucial consideration that we must address when making short- and long-term marketing decisions.

 

Segmentation remains one of the most effective and efficient ways to quickly deploy consumer insights at scale and implement a targeting strategy. In the face of the fast-moving changes in the consumer data environment, the principles of building effective segmentation strategy remain:

  1. Be relevant:

    Make sure the message is appealing to the audience you are talking to. And this means that it is equally important to ensure the audience receiving your message is the most appropriate one.

  2. Be in the right place:

    A good consumer segmentation that truly understands your audience, means also understanding where to reach them.

  3. Be able to measure:

    Deriving fast and valuable insight from targeting your segments should be non-negotiable and can be straightforward.

Understanding consumers is a critical part of effective marketing: regardless of execution method, knowing your customer is the first port of call when developing successful campaigns. For many, consumer segmentation forms the bedrock of this consumer understanding. Using segmentation to build buyer portraits, select the best channel for communication, develop and design offers and often influence product and marketing strategy means it forms an integral part in business success. Understanding changes in consumer behaviour and preferences can prove to be powerful influences on improving the return on marketing investment (ROMI).

 

Segmentation delivers benefits across the entire spectrum of interaction with consumers (marketing, delivery, product development, logistics etc) and an adaptable segmentation undoubtedly adds to the effectiveness of its application.

 

Data driven insights into the Australian consumer are a vital ingredient to deploying successful marketing campaigns. But keeping up with changes in the things we like to do, our entertainment choices, the brands we buy and the way we feel can challenge even the most advanced analytics teams. Building an adaptable segmentation, by separating the important fundamental influences on consumer behaviour from the ephemeral influences is crucial if you want to get the most out of your consumer insights.

 

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Experian

By Experian 07/21/2021

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