“Social CRM” – From Customer Relationship Management to the Intelligent Customer Relationship Network

Customer Relationship Management (CRM) is the management of information about people and the communications with them that can be used by a business for a range of purposes but typically supporting a customer base, generating new sales or maintaining a pool of people with particular skills that can be drawn upon.

In any CRM system, there are defined relationships between the people and between their contact records. In traditional CRM software these relationships are mostly fixed and prescriptive e.g. a person works for a company which is part of a group of companies and the company is a customer. These are simple relational and hierarchical relationships. This standard form of CRM is appropriate for most practical applications but it relies on the owner of the system to source, input and maintain the data and relationships. It is also “closed” in the sense that the data is not augmented by external sources of information about the people and other entities in the system. Therefore, the knowledge that can be derived from this data to manage the business is limited by the information that has been input.

With the rise of on-line social and business networks some CRM systems have attempted to embrace these sources of additional data. This has been termed “Social CRM” (see Wikipedia definition below). However our research shows that current commercial CRM software is limited to acquiring basic supplementary data to augment contact records. We believe the potential is much greater.

Social CRM From Wikipedia, the free encyclopedia

Social CRM (Customer Relationship Management) is use of social media services, techniques and technology to enable organisations to engage with their customers. As an emerging discipline, interpretations of Social CRM vary, but the most frequently quoted definition is from Paul Greenberg:[1]

“Social CRM is a philosophy and a business strategy, supported by a technology platform, business rules, workflow, processes and social characteristics, designed to engage the customer in a collaborative conversation in order to provide mutually beneficial value in a trusted and transparent business environment. It’s the company’s response to the customer’s ownership of the conversation.”

Graph Theory

Social and business networks are based on the principles of Graph Theory, an established mathematical paradigm for modelling relationships between entities. As a graph evolves it can be traversed to identify how entities are related to each other. This is the basis of social and business networking sites which have enjoyed a massive growth curve in the last five years. In the social context of Facebook, for example, a person may have 50 friends but be connected to 10,000 people who are no more than two connections away from them i.e. friends of friends or friends of friends of friends. Similarly, within business networking systems such as LinkedIn, a network of 100 connections may provide access to hundreds of thousands of useful contacts who are closely connected by just a few links in the network. This provides a potentially rich source of additional contacts. At present, business networking sites like LinkedIn can be used in an ad hoc way to find additional contacts but this is still labour-intensive. By combining this data with a CRM system, it will be possible to “intelligently” semi-automate the growth of a contact network, adding value way beyond the confines of traditional CRM. The graph-based contact network becomes a “living” entity that evolves to add value to a company’s business activities e.g. identifying sales prospects and cross-selling opportunities, augmenting contact records with data that fulfils “know your customer” objectives, and recognising new relationships between people that can drive additional business opportunity.

“Any” Relationships

Social CRM systems will not restrict the relationships that a company may choose to have between the entities it is managing. Instead, any kind of relationship may be defined and established between entities on a 1-to-1, 1-n or n-1 basis e.g. a contact in our system, John Smith, is a customer, but he is also a prospect (for a new service) and a supplier [traditional CRM software makes it difficult for contacts to have multiple “roles”]. John Smith may also be Managing Director of his company with ten employees. He also has 200 active business connections and is a member of a trade association.

In this way, complex data structures are established that relate all the data in the system in different ways, from which new knowledge can be inferred and actions proposed. The data relationships are dynamic and not simply modelled with a traditional relational database where the entity relationships are essentially established in advance. With “any relationships”, a completely new relationship could be devised by an end user of the system without affecting the fundamental integrity of the database.

Building Intelligent Contact Networks

By combining the traditional benefits of CRM with the power of online networking, business owners, managers, marketeers and salespeople will have a tool that enables them to evolve their contact networks. They will be able to augment the data with additional information from business network sources, profile actual and potential customers and create new opportunities, without traditional “hard-selling” techniques, by finding prospects via networking. Crucially, this process will be semi-automated. The system will be designed to “intelligently” identify valuable connection opportunities within people networks using connections, relationships and metadata. For example, I may have a contact within my CRM system who is based in Bristol, works for an engineering firm and has recently purchased my product. If I later set up a visit to this customer, the system might search the networks and tell me about 5 other people who are connected to my customer, are based near Bristol and may have a similar need based on their business type, suggesting I send an introductory email with a view to obtaining a meeting or introductory phone call.

For high volume users of CRM systems, such as telesales people, efficiency is all important. Social CRM systems will be able to automatically add general metadata to contact records to eliminate the need for background research e.g. it will add or infer a company’s web address, perhaps with key information and pictures “scraped” from the web site, along with “likes” and interests obtained, where available and shared, from social media (data privacy compliance and data security is taken as read).  The system may even be able to identify personality types based on publicised preferences. This information would be used to build up rapport during a cold/warm contact.

In this way, a “rich” contact record is created.

These concepts are expected to embrace established approaches such as “money mapping” and customer segmentation which aim to make a business more efficient by directing its efforts at marketing to the subset of customers that has the greatest potential to deliver optimal sales. This is also very closely aligned with Pareto’s Law – otherwise known as 80-20 – which is used as a best practice selling technique – “80% of business is derived from 20% of customers”. Additionally, Social CRM systems will recognise that contact networks grow best by referral and so potential new contacts that are “connected to” or “friends of” an existing customer will carry a greater weight than “cold” contacts with whom there is no identified close relationship. “Relationship” in this context, however, could be “any relationship” e.g. they may be related by geography, business type, technology used or even historical buying pattern.

These systems will also “learn”. For example, if the system identifies that a high proportion of successful sales or customer transactions have followed a particular pattern or process, or come from a particular source, it will suggest this pattern, process or source again. For example, it may generate an alert such as “You have closed 3 product sales to engineering customers in the South West in the last 3 months who received the sales brochure. I have identified 20 additional similar contacts who are closely connected to these customers – send them a brochure?”.

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