CRM Value Chain Enablers: Data & Analytics and Marketing/Sales/Service Processes

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By Francis Buttle[1], Julie Jones[2], Merlin Stone[3]

The CRM Value Chain (CRM VC), shown in figure 1, aims to demystify, characterise, and conceptualise CRM. The CRM VC is made up of three Core Processes supported by five Enablers all of which contribute to CRM’s goal of driving up customer profitability. In our previous two articles, we have first presented an overview of the CRM VC, and second, we highlighted CRM’s 3 Core Processes.

In this third article we identify and shed some light on two of the 5 Enablers – the conditions or resources that enable CRM to deliver on its promise of customer profitability: data and analytics, and marketing, sales, and service processes. We discuss the other 3 Enablers in our final article in this series.

Figure 1: CRM Value Chain

The Five Enablers

As shown in Figure 1, the 3 Core Processes are supported by five Enablers. These conditions or resources enable the Core Processes to contribute effectively and efficiently toward CRM’s goal of creating profitable customer relationships. The five Enablers are:

  1. Data and analytics.
  2. Marketing, sales, and service processes.
  3. Network relationships.
  4. Organizational culture, and
  5. People

In this article we investigate the impact of data and analytics, and marketing, sales, and service processes on CRM performance.

Data and analytics

CRM is dependent on customer data, the most basic of which is held in corporate databases. Companies may have more than one database containing customer data. These capture customer data from different functional perspectives – sales, marketing, service, logistics, and accounts – each of which serves different operational purposes. These databases might record quite different customer-related data – opportunities, campaigns, enquiries, deliveries, and billing, respectively. The data may be held in different forms – spreadsheets, relational databases, accounts systems, for example – and a major challenge in CRM for many firms can be data integration to create a single view of the customer that is then used for operational CRM purposes. The single view allows any customer contact person with appropriate permissions to access an entire customer history.

Where managers need to interrogate customer data, the conventional practice has been to copy operational data across into a data mart or data warehouse where analytics can be performed. Currently, some leading companies are experimenting with ways to use a single database to support both operational CRM and analysis.

Customer data might be independently maintained by managers running operations in channels such as company-owned retail stores, third-party retail outlets, and online. Similarly, different product managers might maintain their own customer data. Customer data can have a current, past, or future perspective, focusing, for example, upon the current sales pipeline, historic sales, or potential opportunities. Customer data might be about individual customers, customer clusters, market segments, or entire markets. They might also contain product information, competitor information, regulatory data, or anything else pertinent to the development and maintenance of customer relationships.

In addition to customer data, successful execution of customer strategy may require data about the business network, especially but not only up-stream and down-stream supply chain partners. Sometimes network relationships be quite complex. For example, a blow-moulding company may supply windscreen wash assemblies to an automotive manufacturer, who may in turn supply the blow-moulding company with fleet vehicles. Relationship data can help to increase partner/network lifetime value, improving efficiency and effectiveness throughout the whole business network. We have more to say about business networks in our next article.

Most of the data maintained in corporate databases are structured. Structured data are stored in a fixed and named field in a record or file. Structure is provided by a pre-defined data model that specifies the data to be stored in each field, how that data should be recorded (alphanumeric codes or literal text, usually), and how the fields are related to each other. Commercial CRM applications usually come with pre-defined data models. The types of data that are relevant to a banking industry CRM application are quite different from a life sciences CRM application, and the data models are therefore quite different. CRM users often customize pre-defined data models to make them fit better with their own business and customer context.

If internal data are insufficient for CRM purposes, external data can be imported to enhance the customer record. External data can be imported from numerous sources including market research companies and corporate database companies such as Nielsen and Experian, and credit scoring agencies.

Unstructured data, by comparison, are data that do not fit a predefined data model. Unstructured data take the form of textual or non-textual files. Textual corporate unstructured data includes emails, PowerPoint presentations, Word documents, text messages, PDFs, spreadsheets, and agent notes on a customer’s service history. Non-textual data includes recorded telephone calls and other MP3 files, images in JPEG and other formats, video in MP4 and other formats, and multi-media messages. The massive increase in unstructured corporate data not only creates storage costs but also presents challenges to privacy and confidentiality.

There has been a huge lift in the amount of unstructured data in social media such as Facebook, Instagram, TikTok, YouTube, and Twitter. Other forms of unstructured data are metering data, climate data, mobile phone GPS signals, and stock ticker data. The momentum in the growth of unstructured data is expected to accelerate into the future. This type of data has become known as Big Data. Big Data presents an opportunity for businesses only if the data can be analysed, interpreted, and acted upon.

CRM analytics for structured data are well developed. Simple statistical procedures such as computation of totals, averages, modes, medians, and ranges are the foundation of many of the descriptive standard reports generated by and for CRM practitioners. As questions become more complex and shift from description to explanation or prediction, the analytical procedures required to generate answers also become more complex. Online analytical processing (OLAP) and data mining are two ways of interrogating structured data to deliver answers to these more complex questions. Whereas OLAP queries allow CRM users to drill down into the reasons why a particular piece of data – say a salesperson’s exceptional performance – is as it is, data mining tools draw on a well-established array of statistical procedures, such as correlation, regression, decision-tree, clustering, and neural network routines to produce insights for users.

Analytics for unstructured data are still evolving. However, some analytical tools – voice recognition, predictive analytics, social media monitoring, and text analytics, for example – are with us today. One of the more advanced forms of unstructured data analytics currently is text analytics. Text analytics extracts relevant information from unstructured text files and transforms it into structured information that can then be leveraged in various ways. Unstructured textual data is found in call centre agent notes, emails, documents on the web, instant messages, blogs, tweets, customer comments, customer reviews, questionnaire free-response boxes, social media posts, transcripts of telephone calls and interviews, and so on. When we write or speak in natural language, we use slang, dialect, jargon, misspellings, anachronisms, short forms, acronyms, colloquialisms, metaphors, grammatical idiosyncrasies, and even multiple languages in the same stream. This presents challenges to analysts, but text mining tools can help.

The boundary between human and computer-based decision-making in CRM is shifting. Artificial Intelligence (AI) is increasingly deployed in CRM. AI is used in the analysis and targeting of customers, offer creation and messaging for unique customers, and pricing to maximise yield. In the future, we expect AI will be widely employed in strategic decision-making, for example, which business models to use, which strategies to follow, which markets to target, which products to market, which channels of communication and distribution to use, what pricing and competitive positioning strategies to follow.

Marketing, sales, and service processes

The second Enabler in the CRM Value Chain comprises the processes used in the customer-facing functions of marketing, sales, and service. CRM software applications are used to routinize and automate the marketing, selling, and service processes that target, onboard, monetize, and retain customers. A fundamental misunderstanding about CRM has been to equate CRM with these software applications. They are not the same. Elsewhere, you may have heard or read commentary about the size of the CRM market: these reports usually provide data about spending on software applications. For example, a 2022 Fortune Business Insights report claims that the global CRM market was valued at US$57.83 billion in 2021. Our view is that these applications support and enable CRM strategies to be executed, but they are not CRM per se.

Before CRM software applications were developed, marketing, selling, and service processes were usually ad hoc. For example, different marketers, even those working in the same business, would go about their planning, implementation, and reporting processes in different ways; a few companies, notably leading FMCG firms, required brand and product managers to follow approved processes. Today’s CRM software applications can be deployed right out of the box or customized to suit a particular business’s requirements; in both cases, these applications allow standardised marketing, selling, and service processes to be used. Most CRM software developers now have a cloud-first approach to software development. In other words, CRM applications are provided from the cloud as Software-as-a-Service. CRM users no longer need to install CRM software on their own servers and devices, but instead, use the app’s functionality online.

Broadly speaking, marketing software applications are used mainly for customer acquisition, sales applications for customer development, and service applications for customer retention. These applications offer a massive amount of functionality that supports CRM practitioners, as shown in Tables 1, 2, and 3. Deployment of these applications often aims to increase sales, reduce cost, improve customer experience, enhance productivity, and generate better management reports.

Table 1: Functionality offered by Marketing Automation software
Table 2: Functionality offered by Salesforce Automation software
Table 3: Functionality offered by Service Automation software

Conclusion

The CRM Value Chain (CRM VC) models our contemporary understanding of CRM as a core business strategy that aims to initiate and retain profitable relationships with customers. The CRM VC is made up of three Core Processes supported by five Enablers all of which contribute to a firm’s goal of driving up customer profitability. In our previous two articles, we presented an overview of the CRM VC, and we explored CRM’s three Core Processes. In this third article, we have introduced two of the five Enablers which support managers as they execute the three Core Processes: data and analytics, and marketing, sales, and service processes. In our fourth final article, we comment on the remaining three Enablers: network relationships, organizational culture, and people.

Notes

[1][email protected] Formerly Professor of Marketing and CRM at Manchester Business School (UK) and Macquarie Graduate School of Management (Australia), Consultant in CRM, Customer Experience Management and Word-of-Mouth.

[2][email protected] Lecturer in Marketing at Aberystwyth Business School, Wales. Marketing Consultant and Chartered Marketer

[3][email protected] Formerly Professor of Marketing and Strategy, St Mary’s University, Twickenham, London, England

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