So you have created an information supply chain that takes your trusted customer information and feeds a data warehouse, which in turn populates a marketing repository for your outbound marketing using IBM Campaign.

You have exposed the customer profile from MDM as a service, so your inbound marketing can get real-time information.

The previous chapters describe how to establish a foundation for trusted customer information for inbound and outbound marketing. The trusted customer information is based on IBM Information Management capabilities including master data management, data integration and governance, and industry models. The inbound and outbound marketing is based on the IBM Enterprise Marketing Management platform.

This chapter describes several ways in which you can build upon the EMM platform to keep improving and optimizing the way you manage information, analyze that information for decision-making, and interact with customers.

If you are to deliver individualized, timely, relevant marketing that you can be confident in, then you need to know who you are sending offers to, how you know that the offer is appropriate, and whether you can easily determine the basis on which the offer was made. How can you be sure of this, when offers are made in real time, and using data from many sources that are constantly changing? How you gather, manage, store, and move and govern information becomes more important to be able to provide the right information, at the right time, to make the right offer to the right person. This is true for the offers you present to your customers, but also for all the other internal and external roles touched by your enterprise. This section describes some ways you can enhance your information supply chain to meet these needs.

You are in a new age, where there are many new types of data from the Internet, blogs, tweets, other social media, reviews, and comments. This data is often large in volume, full of opinion, collaborative and unstructured, and accessible everywhere on mobile devices. Influences on people’s opinions occur quickly, so a snapshot of this information might have a small time frame in which it is relevant. It is in this world that buying decisions are made. Your marketing efforts need to fit in with where your customers and prospects are and what they are doing. Big data can be characterized as having extremes of some or all of the following four dimensions: volume, velocity, variety, and veracity.

See the following image for more information:

One way to use this data is to capture it in stores such as Apache Hadoop (and IBM Hadoop Systems), then make sense of it by performing text analytics, for example by using the IBM SPSS® Analytic Server. Another way is to use InfoSphere Streams to look at the large flow of data, spot the interesting data, and perform analytics on it.

For more information. see the following sources:

In this book, you considered Party domain in MDM, which contains people and organizations. MDM can also manage other domains of data. A fruitful next step might be to set up a product domain, where you centrally store your product data in the MDM operational hub. You might also consider storing your accounts in the account domain. Imagine the power of having a central view of customers and everything they have bought and the terms and conditions associated with the account in an operation hub available in real time. Not only do you have these high-value business information sources stored centrally, but also the relationships between them, allowing you to manage lifecycles of each of these types of master data coherently and efficiently.

As you build your MDM capabilities, the master data becomes of more value across your enterprise because it has a single view of the trusted high business value information in an operational hub.

For more information, see the following sources:

As you embrace greater volumes of data, from an increasing variety of sources and store more in your master data repository, it becomes important to understand the data lineage of the data in your enterprise and also the governance policies with which you must comply. Master data management allows you to control the lifetime and quality of your master data; when information is “well groomed” it has policies and processes around it that provide governance. A strong governance story allows you to be able to define, implement, and enforce policies in line with legal and ethical considerations. For example, when you store data about people, you must be careful about what data you store, what permissions were sought and obtained, who has access to it and masking private data, so that data is not inadvertently exposed.

For more information about IBM Information Integration and Governance, see the following website:

With the move toward cloud computing, having information resources exposed in a virtual way allows data scientists and marketers to get their data the way they want it, without needing to know where it is. Data lakes manage and govern repositories of information for analytics use cases.

IBM Smarter Commerce™ is a set of rich capabilities geared around the customer to enhance buying, selling, marketing, and service to create an integrated value chain. This book describes IBM Campaign and IBM Interact, two products that are part of Enterprise Marketing Management (EMM). Figure 8-1 shows how these products fit in with EMM and Smarter Commerce.

To further enhance IBM Interact and gain insight, see IBM Digital Analytics:

With smarter commerce, things start with the customer. Having a single trusted view of a customer across your enterprise becomes a foundation on which to build your smarter commerce solution. Whether you are tying together supply chains, tracking products with Product and Accounts domains, or just making sure you know who you are marketing to and whether there was a subsequent sale with the Party domain, MDM has a key role to play.

For more details about Smarter Commerce, see the following website:

Analytics are increasingly becoming the differentiator between you and your competitors. Successful companies are using analytics and data-driven decisions. Folding analytic approaches into your EMM gives you the next level of insight into your customers and prospects.

For more information about IBM business analytics capabilities, see the following website:

Analytics can be categorized as descriptive, predictive, prescriptive, and cognitive.

Using business intelligence (BI), you can get real insights on your customers and their buying behaviors. These insights can be used as a basis for marketing campaigns.

Basing the marketing decisions on the way people have behaved in the past, you can use data mining techniques to understand how they might behave in the future. Given enough data (and there are masses of data in the world of social media), a data scientist can train an analytic model that can then predict values for new records and give a probability of the prediction being correct. For example, if the enterprise is concerned with customers leaving the company, and they have data about all their customers and what those customers have done, including which ones left, then a data scientist can use this information to train an analytics model to determine who is likely to churn.

When you have predictive analytics, you want the system to be able to act on that information when making a decision. For example, if you have churn information, you can use it in your marketing campaigns to take actions to reduce the chances of a customer leaving. Using this data-driven approach, you are much more likely to be able to give individualized offers and actions to encourage the desired action. In this way you are moving closer to the segment of one.

The next best action (described in 8.4, “Next best action” on page 159) uses predictive analytics.

The developerWorks blog defines prescriptive analytics as “A set of mathematical techniques that computationally determine a set of high-value alternative actions or decisions given a complex set of objectives, requirements, and constraints, with the goal of improving business performance.”

See the following web page:

Cognitive systems, such as IBM Watson™, have the ability to parse natural language in real-time and make decisions. So, after Watson is trained in your domain, you can ask it questions in speech and get intelligent answers!

For more information see the following sources:

The next best action is a real-time analytics solution, containing marketing and the customer master. The pattern includes a system of record exposed in operational hubs, for use by real-time analytics. It shows how various capabilities can work together, including big data, information gathering, marketing, decisions, and reporting, and how to coordinate the flow of information between them. Using this pattern, you can decide on a road map for how you want to move toward real-time analytics based on trusted information.

The next best action pattern in Figure 8-2 gives you an overview of the software capabilities that you might need and how they can work together.

For more details, see Smarter Analytics: Driving Customer Interactions with the IBM Next Best Action Solution, REDP-4888-01.

If you want to capitalize on the power of analytics in your business, you do not need to implement all of what is shown in Figure 8-2 before you can gain value. You can gradually adopt good practices for creating trusted data and building in analytic insight. Here are some considerations as you proceed:

This is an exciting time in the history of marketing and analytics. You are now better enabled to better understand where and how marketing works with big data, traditional data, social media, cloud technology and mobile. As you embrace and use these new technologies, you will need the backing of trusted governed data and information integration.