South African customers expect a return on their data

Increasingly, customers have begun to expect products and advice tailored to their needs. Couple this with the restrictions on personal interactions resulting from COVID-19, which have pushed customers toward digital channels for service and sales, and the result is a sudden acceleration of a 25-year trend towards digital interaction.

This, however, is not a one off. A number of shifts in consumer preferences are expected to emerge from the crisis. Meeting the challenge of ensuring your business adapts to embrace the “new normal” while addressing your customers’ emerging behaviours, comes down to five simple principles.

1. Personalisation and advice in exchange for data

To deliver personalised customer offers, organisations must create a single view of customer data by breaking down functional siloes

2. Unify customer data for cross-channel insights

3. Use AI to automate and enhance

Artificial Intelligence (AI) covers a wide range of technologies, which can be used to automate and enhance interactions by injecting valuable insight across the customer journey. 

Use AI to automate and enhance customer interactions

This ensures the relevance of business practices and keeps businesses connected to customers. Among others, this includes:

1. Efficiently analysing and integrating customer data across digital and physical touch points to understand customer context and sentiment
2. Predicting outcomes for customer targeting
3. Generating and delivering highly specific or event-driven actions and recommendations into agent guidance applications

As customer experiences across industries become digitised and largely automated, the remaining interaction points will become increasingly advice-centric, and important to deepen relationships and the competitive basis of organisations. AI and advanced automation capabilities will become increasing important pre- and post-purchase to:

1. Deliver high-quality, automated and personalised self-service experiences in real-time through digital channels 
2. Enhance the abilities of sales and service agents to offer relevant advice around complex decisions, and customise products and offerings to meet customer needs

Mass market customers will begin to expect “private bank” experiences as personalisation becomes more mainstream

Many financial services organisations are already using AI and advanced automation to accomplish the latter by:

1. Automating the generation of very specific insights to support private wealth advisers 
2. Supporting complex decision-making in commercial sales capabilities 
3. Using advanced recommendation engines to customise the features and pricing of products in banking and insurance
4. Tailoring rewards programmes to individual customer behaviour in real-time in the payments sector
5. Delivering personalised event-driven behavioural recommendations to relationship bankers and customers

The difference is that as the adoption of these technologies proliferates, these private bank-like experiences will increasingly become the expectation of mass market consumers.

4. Partnering to develop and apply insight as a team

There are a number of ways these technologies can be integrated into software applications and processes today. These include AI modules made available in existing sales and service applications, such as ServiceNOW and Salesforce, and free standing or bolt-on niche solutions that solve specific industry problems.

It is important to note that for emerging technologies such as these, capabilities continuously evolve and are often at different stages of maturity. To leverage the power of these, determine the business’s strategic intent and let it guide the experimentation, selection and combination of these:

1. Pinpoint goals for personalised sales and service and define the gains desired when adopting these solutions
2. Maintain a programme of active experimentation with constantly emerging and improving tools 
3. Break down barriers between digital, and sales and service teams to gain a deeper understanding of the agent’s role in better supporting customer goals

Iterative implementation staggers roll-out, allowing your business to gradually mature its use of data

Start by establishing cross-functional insight teams accountable for outcomes. These teams will purposefully measure the impact and optimise applied insight and technology use cases through active experimentation and learning. These teams will include business, data, IT, agents and specialist functions, such as compliance for data privacy considerations, to coordinate role-players and translate the business or customer need into viable solutions. Starting by:

Detailing specific instances along the customer journey as these emerge

1. Determining the data needed to enable them
2. Defining how and where data will be combined 
3. Identifying the technology systems that will use the data 
4. Developing a cross-functional data-governance council

5. You don’t need to tackle everything at once

As businesses embrace the “new normal”, they will learn from their customers, their own capabilities, and the market as a whole. It takes investment and effort to build a business that can use this to create specific insight and deliver competitive advantage. What is important, is that you don’t need to tackle everything at once.

In our experience, implementing practical pieces of each of the strategic data components iteratively allows you to deliver against your strategy, accelerate time-to-value, and mature your use of data and technology through shared learnings. 

We help our clients transform to become insight-led organisations. To get started, we can help business and IT to:

1. Develop a shared vision on what data will be used and how
2. Develop a sustainable, scalable solution architecture
3. Decide what to build and what to buy off the shelf

3. Use AI to automate and enhance

Artificial Intelligence (AI) covers a wide range of technologies, which can be used to automate and enhance interactions by injecting valuable insight across the customer journey. 

Use AI to automate and enhance customer interactions

This ensures the relevance of business practices and keeps businesses connected to customers. Among others, this includes:

1. Efficiently analysing and integrating customer data across digital and physical touch points to understand customer context and sentiment
2. Predicting outcomes for customer targeting
3. Generating and delivering highly specific or event-driven actions and recommendations into agent guidance applications

As customer experiences across industries become digitised and largely automated, the remaining interaction points will become increasingly advice-centric, and important to deepen relationships and the competitive basis of organisations. AI and advanced automation capabilities will become increasing important pre- and post-purchase to:

1. Deliver high-quality, automated and personalised self-service experiences in real-time through digital channels 
2. Enhance the abilities of sales and service agents to offer relevant advice around complex decisions, and customise products and offerings to meet customer needs

Mass market customers will begin to expect “private bank” experiences as personalisation becomes more mainstream

Many financial services organisations are already using AI and advanced automation to accomplish the latter by:

1. Automating the generation of very specific insights to support private wealth advisers 
2. Supporting complex decision-making in commercial sales capabilities 
3. Using advanced recommendation engines to customise the features and pricing of products in banking and insurance
4. Tailoring rewards programmes to individual customer behaviour in real-time in the payments sector
5. Delivering personalised event-driven behavioural recommendations to relationship bankers and customers

The difference is that as the adoption of these technologies proliferates, these private bank-like experiences will increasingly become the expectation of mass market consumers.

4. Partnering to develop and apply insight as a team

There are a number of ways these technologies can be integrated into software applications and processes today. These include AI modules made available in existing sales and service applications, such as ServiceNOW and Salesforce, and free standing or bolt-on niche solutions that solve specific industry problems.

It is important to note that for emerging technologies such as these, capabilities continuously evolve and are often at different stages of maturity. To leverage the power of these, determine the business’s strategic intent and let it guide the experimentation, selection and combination of these:

1. Pinpoint goals for personalised sales and service and define the gains desired when adopting these solutions
2. Maintain a programme of active experimentation with constantly emerging and improving tools 
3. Break down barriers between digital, and sales and service teams to gain a deeper understanding of the agent’s role in better supporting customer goals

Iterative implementation staggers roll-out, allowing your business to gradually mature its use of data

Start by establishing cross-functional insight teams accountable for outcomes. These teams will purposefully measure the impact and optimise applied insight and technology use cases through active experimentation and learning. These teams will include business, data, IT, agents and specialist functions, such as compliance for data privacy considerations, to coordinate role-players and translate the business or customer need into viable solutions. Starting by:

Detailing specific instances along the customer journey as these emerge

1. Determining the data needed to enable them
2. Defining how and where data will be combined 
3. Identifying the technology systems that will use the data 
4. Developing a cross-functional data-governance council

5. You don’t need to tackle everything at once

As businesses embrace the “new normal”, they will learn from their customers, their own capabilities, and the market as a whole. It takes investment and effort to build a business that can use this to create specific insight and deliver competitive advantage. What is important, is that you don’t need to tackle everything at once.

In our experience, implementing practical pieces of each of the strategic data components iteratively allows you to deliver against your strategy, accelerate time-to-value, and mature your use of data and technology through shared learnings. 

We help our clients transform to become insight-led organisations. To get started, we can help business and IT to:

1. Develop a shared vision on what data will be used and how
2. Develop a sustainable, scalable solution architecture
3. Decide what to build and what to buy off the shelf

3. Use AI to automate and enhance

Artificial Intelligence (AI) covers a wide range of technologies, which can be used to automate and enhance interactions by injecting valuable insight across the customer journey. 

Use AI to automate and enhance customer interactions

This ensures the relevance of business practices and keeps businesses connected to customers. Among others, this includes:

1. Efficiently analysing and integrating customer data across digital and physical touch points to understand customer context and sentiment
2. Predicting outcomes for customer targeting
3. Generating and delivering highly specific or event-driven actions and recommendations into agent guidance applications

As customer experiences across industries become digitised and largely automated, the remaining interaction points will become increasingly advice-centric, and important to deepen relationships and the competitive basis of organisations. AI and advanced automation capabilities will become increasing important pre- and post-purchase to:

1. Deliver high-quality, automated and personalised self-service experiences in real-time through digital channels 
2. Enhance the abilities of sales and service agents to offer relevant advice around complex decisions, and customise products and offerings to meet customer needs

Mass market customers will begin to expect “private bank” experiences as personalisation becomes more mainstream

Many financial services organisations are already using AI and advanced automation to accomplish the latter by:

1. Automating the generation of very specific insights to support private wealth advisers 
2. Supporting complex decision-making in commercial sales capabilities 
3. Using advanced recommendation engines to customise the features and pricing of products in banking and insurance
4. Tailoring rewards programmes to individual customer behaviour in real-time in the payments sector
5. Delivering personalised event-driven behavioural recommendations to relationship bankers and customers

The difference is that as the adoption of these technologies proliferates, these private bank-like experiences will increasingly become the expectation of mass market consumers.

4. Partnering to develop and apply insight as a team

There are a number of ways these technologies can be integrated into software applications and processes today. These include AI modules made available in existing sales and service applications, such as ServiceNOW and Salesforce, and free standing or bolt-on niche solutions that solve specific industry problems.

It is important to note that for emerging technologies such as these, capabilities continuously evolve and are often at different stages of maturity. To leverage the power of these, determine the business’s strategic intent and let it guide the experimentation, selection and combination of these:

1. Pinpoint goals for personalised sales and service and define the gains desired when adopting these solutions
2. Maintain a programme of active experimentation with constantly emerging and improving tools 
3. Break down barriers between digital, and sales and service teams to gain a deeper understanding of the agent’s role in better supporting customer goals

Iterative implementation staggers roll-out, allowing your business to gradually mature its use of data

Start by establishing cross-functional insight teams accountable for outcomes. These teams will purposefully measure the impact and optimise applied insight and technology use cases through active experimentation and learning. These teams will include business, data, IT, agents and specialist functions, such as compliance for data privacy considerations, to coordinate role-players and translate the business or customer need into viable solutions. Starting by:

Detailing specific instances along the customer journey as these emerge

1. Determining the data needed to enable them
2. Defining how and where data will be combined 
3. Identifying the technology systems that will use the data 
4. Developing a cross-functional data-governance council

5. You don’t need to tackle everything at once

As businesses embrace the “new normal”, they will learn from their customers, their own capabilities, and the market as a whole. It takes investment and effort to build a business that can use this to create specific insight and deliver competitive advantage. What is important, is that you don’t need to tackle everything at once.

In our experience, implementing practical pieces of each of the strategic data components iteratively allows you to deliver against your strategy, accelerate time-to-value, and mature your use of data and technology through shared learnings. 

We help our clients transform to become insight-led organisations. To get started, we can help business and IT to:

1. Develop a shared vision on what data will be used and how
2. Develop a sustainable, scalable solution architecture
3. Decide what to build and what to buy off the shelf