CRM Integration Strategies

Managing customer interactions often requires integration with other enterprise applications, processes and data sources. Building a 360-Degree client view, managing client’s communication history, marketing campaigns, product holding, cross, and up-selling processes, sales pipeline, and so forth, require a multiple integration strategies.

When it comes to CRM implementations, integration in general is one of the most versatile tasks for all stake-holders both for business and technical teams. As we have already discussed in chapter 2 of my book, “What Is CRM?”, and in chapter 3 , “Getting to Know Your Customer”, very often CRM projects will provide the unique opportunity for companies to unify historically grown silos that have created multiple data-sources, multiple process engines, multiple user-interfaces (UI), and overlapping functionalities.

We all know that there are a number of good reasons to bring and combine data from disparate sources into a single repository of customer and business data, such as to build a comprehensive 360° client view or automating client processes in sales, marketing, or client service. The integration in CRM is often done to provide business stakeholders the ability to slice and dice the data in building valuable and efficient client processes and analytical capabilities.

Traditionally, a data warehouse (DW or DWH) provides a central repository of integrated data from multiple disparate sources within a company. But what they don’t do is to provide the capabilities needed to manage all different kind of client processes, and here is the CRM the best tool to do that if it is well integrated to all key applications and data sources, including the DWH.

Meanwhile, in addition to the DWH concept, there are increasing implementations of data lakes. A data lake is a storage repository, such as Apache Hadoop. Apache Hadoop is a collection of open-source tools that leverage a network of many computers to solve problems involving massive amounts of data and computation. They usually hold a vast amount of raw data in its native form until it is needed for business. This is due to the fact that there is no time to process the vast amount of data using traditional integration tools.

Data lakes are an interesting concept because they are increasing the amount of data sources that companies can use to make better business decisions. As a matter of fact, these data sources can include:

?            Social networks.

?            Review websites.

?            Online news.

?            Weather data.

?            Web logs.

?            Sensor data.

All of these sources are resulting in rapidly increasing data volumes and new data streams that could all be useful for business orchestration. Organizations are looking to optimize the ingestion and use of their data, and so technology needs to adapt to the fast-growing business requirements that depend on these data streams.

With a CRM system, we need to not only integrate the external data from disparate sources and exchange information between other applications and CRM, but quite often we must (re)build the business logic related to the data, about why, how, when, and the frequency at which information exchange will take place.

In chapter 7 of my book, we explore all different ways of integration of applications and data into the CRM platform. We highlight a few important elements (with examples) regarding data integration within CRM systems along with recommendations such as performance and security. We also discuss the initial data load, the regular data load – both daily and nightly - and data synchronization within CRM, where we explore pros-cons of batch vs. real-time, the frequency of data import, data profiling, standardization, scalability, and enrichment.

You can purchase the book on Amazon .com https://lnkd.in/dk9GxjG , Amazon .de, or directly from the publisher https://www.packtpub.com/business/art-crm

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