publication date: august 2003 · 2004. 3. 1. · a datamonitor white paper prepared for publication...

Intelligent Interaction

Applying customer intelligence to customer contacts

A Datamonitor white paper prepared for

Publication Date: August 2003

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ABOUT DATAMONITOR

Datamonitor plc is a premium business information company specializing in industry analysis.

We help our clients, 5000 of the world’s leading companies, to address complex strategic issues.

Through our proprietary databases and wealth of expertise, we provide clients with unbiased expert analysis and in-depth forecasts for six industry sectors: Automotive, Consumer Markets, Energy, Financial Services, Healthcare, Technology.

Datamonitor maintains its headquarters in London and has regional offices in New York, Frankfurt and Hong Kong.

Intelligent Interaction © Datamonitor (Published August 2003) This report is a licensed product and is not to be photocopied

Introduction

INTRODUCTION

1. Understand the importance of quality data

This white paper describes the importance of using customer data to power intelligent interactions. However, if the data being used is not of sufficient quality then attempting intelligent interactions can cause more harm than good, leading to unhappy customers and lost sales.

2. Understand the concepts of customer lifetime value and customer profitability

Treating all of your customers in the same way is not always the right strategy. Every company has good customers and bad customers, but how do you identify the good ones, and what do you do with the bad ones?

3. Understand how customer intelligence can be used to drive more effective marketing campaigns

Using customer intelligence is vital for powering effective outbound marketing campaigns. This paper describes the different processes that create an effective marketing strategy.

4. Understand how customer intelligence drives more effective inbound communications

Customer intelligence can also be applied to inbound contacts in order to get the right contact to the right agent and increase customer satisfaction. In addition, knowing about the customer before speaking to them means that you can personalise the interaction more effectively and increase cross- and up-sell rates.

5. Understand the importance of operational analytics

All organisations must strive to constantly improve the quality of their customer service, and one way to achieve this is with operational analytics. Organisations must close the customer service loop if they are to reap the full benefits of intelligent interactions.



CUSTOMER PROFITIBILITY

The principle aim of any customer relationship management (CRM) strategy should be to improve customer profitability. Companies must be able to segment their customers based on their value to the company and serve them accordingly. This white paper describes how organisations can achieve this goal by applying intelligence to every interaction they have with their customers.

The importance of data quality

In order to determine customer profitability, companies must analyse customer data. The quality and accuracy of this data is of paramount importance, and companies must address data quality issues before they can start using this data to determine customer profitability. Basing an intelligent interaction strategy on poor quality data would in fact be detrimental to the organisation’s CRM strategy. Data quality (DQ) directly affects:

• Outbound marketing – sending outbound marketing to the wrong address or the wrong customer wastes resources and leads to reduced response rates;

• Analytics – using inaccurate data for analysis means that special offers and customer retention strategies are directed at the wrong customers;

• Customer service – miss-spelling a customer’s name or targeting them with offers that they have already declined leads to upset customers and wasted resources when they contact the organisation to complain;

• Revenue generation – poorly targeted marketing campaigns, misused analytics and poor customer service all eventually lead to customer attrition and consequently lost revenues.

Implementing data quality

In the early stages of data warehousing, CRM or indeed any other enterprise data management project, data from varied sources must be pulled together, consolidated and mapped onto a new system or integrated with existing systems. The procedure behind doing this often results in miss-entry or integration problems. Therefore it is



clear that any organisations seeking to integrate a number of different data sources needs to have an accurate understanding of the DQ issues it may entail.

The simplest method of implementing DQ is to ensure all data entered is in an absolutely consistent format: i.e. to use DQ as a preventative method rather than a cure. Prevention helps but most organisations will need to acquire a DQ tool, especially if they operate a data warehouse of any size, have some other form of decision support software or hold historic data that needs to be reformatted. Data warehouse volumes commonly exceed 100Gb and many exceed 1Tb. As volumes increase, the number of permutations and hence the possibility of errors increase.

Figure 1 illustrates the processes that companies must follow in order to improve data quality. The data is gathered from multiple sources and split into its constituent parts in order to standardize the various formats. The data from various sources can then be matched and consolidated before being moved into data warehouse.

Figure 1: Data Quality

Source: Datamonitor D A T A M O N I T O R

Various incoming

data sources

Parse,

correct,

standardise

and enhance

Match ConsolidateData

Warehouse

Various incoming

data sources

Parse,

correct,

standardise

and enhance


Warehouse

Various incoming

data sources

Parse,

correct,

standardise

and enhance


Warehouse

Defining customer profitibiity

On the surface, the idea of customer profitability seems very simple:

Customer profitability = Customer spend – cost of products sold



Companies face difficulty in assigning values for customer spending and the cost of products sold. There are many inputs, fixed and variable, that feed into production, and determining the final cost is a complicated process. Despite the challenges, every company must price its goods and services for the market, and therefore should understand the cost of production. However, further complications arise when one takes into account not only the cost of production, but also sales, distribution and support, and it is these costs that we are concerned with in this white paper.

Determining the amount that a customer has spent with a company is not as simple as it sounds and there are a number of challenges that companies face:

• For organisations with multiple product and service lines, getting a complete picture of a customer’s spending can be difficult, especially if the information is held in multiple IT systems;

• In many industries, especially retail, purchases are very often anonymous so companies have no way of tracking spending by customer;

• A ‘customer’ is not always one person buying goods and services; it could be a company with multiple purchasing points or a household containing a number of people.

Customer lifetime value

Measuring the short-term value of a customer, for example on a transaction-by-transaction basis or on an annual basis, is perfectly acceptable in many environments, but in others this can only paint half of the picture. In industries where the relationship with the customer can be longer lasting it is important to consider not just the customer’s present value but also their potential or lifetime value. A classic example is a student bank account: the account holder will be unprofitable whilst studying, but after graduation he or she will more than likely earn an above average income and generate significant income for the bank through mortgages, credit cards, etc.

Maximising customer profitability

Once a company has identified customer spend, the cost of the goods and services sold to the customer and the cost of serving the customer, it is possible to determine customer profitability. As Figure 2 illustrates, it is possible to increase customer profitability in three ways: by increasing revenue per customer, by decreasing the



cost of the products and services you provide, and by reducing the cost of servicing your customers.

The focus of this white paper is intelligent interactions, and how these can help increase revenues and reduce costs. While it is not possible to reduce the cost of producing goods and services through intelligent interactions, it is possible to reduce the cost of customer service and potentially to increase revenues. As figure 2 illustrates, once the value of a customer has been identified and the cost of the goods and services they purchase has also been identified, the easiest way to increase their profitability is to reduce the cost of serving the customer. Assuming that the green dotted line is fixed, reducing customer service costs will move the blue line down and increase the profitability of the customer.

Figure 2: Customer profitability

Customer spend

Total customer cost

Customer value

Rev

enue

/ co

st (£

)

Customer service costs

Cost of goodsand services

Customer profitability

Customer spend

Total customer cost

Customer value

Rev

enue

/ co

st (£

)

Customer service costs

Cost of goodsand services

Customer profitability


What Figure 2 illustrates very clearly is that for low-value customers, customer service costs must be kept to a minimum if the customer is to remain profitable. However, companies can afford to provide the higher levels of customer support that are required to retain higher value customers and still remain profitable.



USING CUSTOMER INTELLIGENCE TO DRIVE INTELLIGENT INTERACTIONS

Once a company has managed to identify the value and profitability of its customers the next stage is to use this information to increase the efficiency and profitability of its operations. There has been a lot of talk about CRM over the last few years, and much of the attention has focused on the need to improve customer service levels. However, this is only part of the story.

The real aim of a CRM strategy should be to improve the profitability of each customer. This means striving to achieve the best possible levels of service for all customers, but it doesn’t necessarily mean treating everyone the same. On the one hand, companies cannot afford to spend lots of money serving customers that only generate marginal profits. On the other hand, they cannot afford to treat their best, most profitable, customers badly or they will switch to the competition. The aim, therefore, is to use customer data to improve customer profitability, and this means more intelligent interactions.

Intelligent inbound interactions

Customers can now communicate with organisations through a number of channels, and many companies and government bodies are suffering from channel chaos as a result. It is not uncommon for the department that deals with emails to have no contact with the call centre or the branch. The resulting confusion and poor customer service leads to inefficiency, unhappy customers and lost sales opportunities.

The answer is to integrate channels, to use one unified customer database and to build a central knowledge base from which to serve customers. Creating a multimedia contact centre allows organisations to serve their customers more effectively and efficiently, but it also allows them make each interaction and intelligent interaction. In a multimedia contact centre all types of contact (email, phone call, text chat, etc.) are placed in a single universal queue and are routed to the agent or self-service system using a single set of business rules. The criteria for the contact routing could include customer value (e.g. platinum card holder), agent skills (e.g. languages spoken or sales/service agent) and media type (e.g. phone call, web chat).

In order to intelligently route contacts, a contact centre must integrate the universal queue with the customer database using computer telephony integration (CTI). A CTI



application links data to a telephone call so that it can be identified and then routed to the most appropriate location. A common use for CTI applications is a screen pop, where customer data is ‘popped’ on to an agent’s screen, allowing him or her to serve a customer immediately without having to go through the customer’s details in order to access account information.

Telephone calls and other media cannot be intelligently routed to the agent if they cannot be identified and categorised first. On the most basic level, this could be identifying the customer’s telephone number or email address and routing the contact based on their location. More sophisticated routing takes place when the contact is identified and then cross-referenced with the customer database. Once the customer has been identified, the call can be routed using a number of criteria, for example based on the customer’s profitability, or more simply an agent with whom the customer has spoken before.

Figure 3 illustrates some typical routing scenarios.

Figure 3: Intelligent routing criteria

Customer value

Agent skills

Media type

“Gold card French customer using web chat with a French-speaking agent”

“Outbound telemarketing agent using telephone to contact prospect”

“Platinum customer using IVR to check bank balance”

Possible criteria used in queuing and routing rules

Customer value

Agent skills

Media type

“Gold card French customer using web chat with a French-speaking agent”

“Outbound telemarketing agent using telephone to contact prospect”

“Platinum customer using IVR to check bank balance”

Possible criteria used in queuing and routing rules




Intelligently routing contacts has a number of clear and measurable benefits:

• Increased customer satisfaction - contacts are routed to the most appropriate place and can be answered more effectively. This could mean routing a call from a French customer to a French-speaking agent, or it could mean routing a call to an agent that the customer has spoken to before. During peak hours it could mean routing a call to an IVR rather making a customer wait in a queue for 15 minutes. The more effectively and quickly the customer’s request is answered, the more satisfied the customer will be.

• Cost savings - Routing contacts to the most appropriate agent or self-service system means that customers are more likely to have their questions answered the first time, which reduces both the average length of calls and call back rates. Customers can also be routed to the most appropriate channel for their level of profitability. A low-value customer could be routed to an IVR, while a high-value customer will have his or her call answered immediately by an agent so as to keep him or her as happy as possible.

• Increased revenues - Intelligent routing increases customer satisfaction, which itself leads to increased revenues, and routing contacts based on customer value ensures that a company’s best customers receive the most appropriate level of service. Routing contacts based on the customer’s likelihood to purchase additional products or services also leads to increased cross- and up-sell activity, vital in industries that are focusing on increasing average revenue per user (ARPU).

Intelligent outbound interactions

No company can survive simply by assuming that customers will come to them – outbound sales and marketing activity is essential if a company is to grow its business. The effectiveness of outbound sales and marketing activity varies greatly, and investing in technology that will increase this effectiveness will generate a significant ROI. A company sending out 100,000 emails to its customers that can increase its response rate from 5% to 10% will generate 5,000 more leads, for example.

Marketing effectiveness can be increased by improving operational efficiency and by targeting marketing campaigns more effectively. By intelligently using customer data a company can deliver a more personalised sales and marketing message and therefore build stronger customer relationships and increase responses to marketing



campaigns. One thing that should always be kept in mind, however, is that poorly targeted and unsolicited outbound communications (e.g. email ‘spam’) do not lead to increased revenues. In fact, they are more likely to cause customer dissatisfaction.

Using customer data to power marketing campaigns

Once a company has determined customer profitability and sliced and diced data to determine the right customers to target, it can use this data to power marketing campaigns. These campaigns could use static data to send out mass communications across multiple channels, or they could use real-time data to personalise content. Whatever type of marketing an organisation is involved in it is important to invest in a reliable marketing automation solution.

Multi-channel marketing automation

Figure 4 shows the marketing process at its most basic level. A message is sent from the organisation to the environment – typically existing and prospective customers. Feedback is given to the organisation, either through a decision to purchase, to upgrade or to leave in favour of a competitor. Since there is a cycle, the process is fine tuned in subsequent messages.

Figure 1: What is Marketing Automation?

Enterprise

Phase 2: Feedback

Message

CommunicationsChannels

SalesChannelVARDirect Sales

ForceDistributorPartner

MarketingChannelAdvertisingPromotionsPRTelephoneInternetDirect Mail

ServiceChannelCustomer

supportCustomer

surveysCourtesy

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Phase 1: Marketing

Marketing Resource

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supportCustomer

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supportCustomer

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supportCustomer

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Marketing Resource

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Marketing Resource

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The four main components of a complete marketing automation suite are:

1. Marketing resource management (MRM): For planning, coordinating campaign design and execution and measuring the impact of marketing efforts. The main components of an MRM solution are: storage of marketing materials, project management for developing multiple campaigns, and collaboration tools for marketing professionals to work together from disparate locations;

2. Real-time recommendations and personalisation: Ensure the right product / service is offered to the customer at the right time, based on interaction information and recent purchase history. This is often tied into service automation systems, to turn complaints and enquiries into sales opportunities;

3. Customer and marketing analytics: Analysis tools for pre-campaign prospect targeting and post-campaign analysis to determine effectiveness. This can vary from simple alerts and a dashboard to sophisticated analytical techniques (e.g. data mining, visualisation and scoring);

4. Campaign planning and execution: Design and deployment of campaigns across multiple channels including email, Website ads, SMS and mobile ads, direct mail and all offline adverts. Increasingly common among smaller companies is the in-house development of email campaigns coupled with outsourcing of the execution phase to a trusted third party.



OPERATIONAL ANALYTICS: CLOSING THE LOOP

Using customer intelligence to power interactions enables organisations to increase customer profitability – but only if done correctly. Organisations that use poor quality data to apply the customer intelligence can actually experience increased costs and falling customer satisfaction and potentially revenues. Therefore, organisations must monitor and analyse the effectiveness of their intelligent interactions, and that means investing in operational analytics.

What are operational analytics?

The term operational analytics refers to the set of technologies that are used to monitor, analyse and improve the effectiveness of a business process, in the management of intelligent interactions. The use of operational analytics is a continuous process, and there are three key stages in the improvement cycle:

• Monitor – First companies must monitor the success of their customer interaction strategy. This must be done in a number of different areas:

o Interaction recording allows the organisation to record telephone calls and other interactions that take place both in the call centre and the wider organisation;

o Monitor key performance indicators (KPIs), including financial performance and call centre metrics such as first time resolution and average queue times;

o Monitor customer satisfaction using an independent third party;

• Analyse – Once all of the data has been collected it must be analysed and strengths and weaknesses must be identified;

• Improve – Once the strengths and weaknesses have been identified, the weaknesses must be improved upon. This can happen in a number of areas, including improving processes, investing in people and technology, and improving the breadth and quality of raw data.



Closing the loop

As Figure 5 illustrates, operational analytics is a constant process and organisations should strive to achieve an improvement cycle. The quality of interactions must be constantly monitored and analysed if they are to be constantly improved, and if the quality of interactions is not constantly improved, customers will become dissatisfied and sales will start to decline.

Figure 5: Closing the loop – the improvement cycle

Monitor

AnalyseImprove

Monitor

AnalyseImprove




DATAMONITOR CONCLUSIONS

Companies must be able to identify their most profitable customers

It is not possible for a company to use intelligence to drive interactions if it has not first identified its most, and least, profitable customers. Companies must be cautious, however, not to dismiss customers that are not profitable now, as their lifetime value may be significantly greater than their present value. It is also vital to ensure the data is of good quality, as using poor quality data to drive interactions leads to increased costs and reductions in customer satisfaction and potentially revenues.

Intelligent multi-channel outbound campaigns increase revenues and reduce costs

Companies waste millions of pounds with marketing campaigns, both through operational inefficiency and missed opportunities. If the customer data that is used to conduct the marketing campaigns is of a better and more relevant quality in the first place, marketing effectiveness can be significantly increased.

Applying customer intelligence to inbound contacts increases revenues and reduces costs

If an organisation can identify a contact before it deals with it, it can be routed intelligently. This can help reduce costs by driving efficiencies in the contact centre. Enabling a company to prioritise its best customers and improve customer satisfaction by answering queries more effectively can also lead to increased revenues.

Constant monitoring and improvement is essential

All of this investment will be wasted if the organisation does not monitor the quality and effectiveness of its customer service strategy. It must monitor and analyse organisational effectiveness and act on this information to improve processes, and this must be a constant cycle if the organisation is to achieve the maximum benefits of an intelligent interaction strategy.


publication date: august 2003 · 2004. 3. 1. · a datamonitor white paper prepared for publication...

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