A question that I get a lot from customers is around how Big Data can help augment CRM systems. The answer isn’t just about the ability to aggregate loads of information to produce much richer views of the data but also about feeding this data to produce richer digital analytics.
Why Combine CRM with Big Data
Customer Relationship Management (CRM) systems primarily resolve around customer information and captures a customers interactions with a company. The strength of CRM systems is their ability to work with structured data such as customer demographic information (Name, Identifiers, Address, product history etc)
Industry customers will want to use their core CRM customer profiles as a foundational capability and then augment it with additional data as shown in the below diagram –
- Core CRM Records as shown at the bottom layers storing structured customer contact data
- Extended attribute information from MDM systems,
- Customer Experience Data such as Social (sentiment, propensity to buy), Web clickstreams, 3rd party data, etc. (i.e. behavioral, demographics, lifestyle, interests, etc).
- Any Linked accounts for customers
- The ability to move to a true Customer 360 or Single View
All of these non traditional data streams shown above and depicted below can be stored on commodity hardware clusters. This can be done at a fraction of the cost of traditional SAN storage. The combined data can then be analyzed effectively in near real time thus providing support for advanced business capabilities.
Seven Common Business Capabilities
Once all of this data has been ingested into a datalake from CRM systems, Book Of Record Transaction Systems (BORT), unstructured data sources etc the following kinds of analysis are performed on it. Big Data based on Hadoop can help join CRM data (customer demographics, sales information, advertising campaign info etc) with additional data. This rich view of a complete dataset can provide the below business capabilities.
- Customer Segmentation– For a given set of data, predict for each individual in a population, a discrete set of classes that this individual belongs to. An example classification is – “For all retail banking clients in a given population, who are most likely to respond to an offer to move to a higher segment”.
- Pattern recognition and analysis – discover new combinations of business patterns within large datasets. E.g. combine a customers structured data with clickstream data analysis. A major bank in NYC is using this data to settle mortgage loans.
- Customer Sentiment analysis is an technique used to find degrees of customer satisfaction and how to improve them with a view of increasing customer net promoter scores (NPS).
- Market basket analysis is commonly used to find out associations between products that are purchased together with a view to improving marketing products. E.g Recommendation engines which to understand what banking products to recommend to customers.
- Regression algorithms aim to characterize the normal or typical behavior of an individual or group within a larger population. It is frequently used in anomaly detection systems such as those that detect AML (Anti Money Laundering) and Credit Card fraud.
- Profiling algorithms divide data into groups, or clusters, of items that have similar properties.
- Causal Modeling algorithms attempt to find out what business events influence others.
Four business benefits in combining Big Data with CRM Systems –
- Hadoop can make CRM systems more efficient and cost effective – Most CRM technology is based on an underlying relational database or enterprise data warehouse. These legacy data storage technologies suffer from data collection delays and processing challenges. Hadoop with it’s focus on Schema On Read (SOR) and parallelism can enable low cost storage combined with efficient processing
- This integration can focus on improving customer experience – Combining past interactions with historical data across both systems can provide a realtime single view of a customer thus helping agents work better with their customer.
- Combine Data in innovative ways to create new products – Once companies have deep insights into customer behavior and purchasing patterns, they can combine the data to create or modify existing service and products.
- Gain Realtime insights – Online transactions are increasing in number year on year. The onset of Digital Architectures in enterprise businesses implies the ability to drive continuous online interactions with global consumers/customers/clients or patients. The goal is not just provide engaging visualization but also to personalize services clients care about across multiple channels of interaction. The only way to attain digital success is to understand your customers at a micro level while constantly making strategic decisions on your offerings to the market. This essentially means operating in a real time world – which leads to Big Data.
To Sum Up…
Combining CRM with Big Data can help maximize competitive advantage across every industry vertical. These advantages not only stem from cheaply storing and analyzing vastly richer data. These business insights are deployed in areas such as marketing, customer service and new product ideation.