CRM is all about revolving the business ecosystem around the customer. Every possible interaction of the customer with the company needs to be captured and stored. This helps make customer services and relationships better. With internet and social media penetration, the amount of data flowing through the formal and informal channels that can be collated becomes mind-boggling. When the concept of Big Data comes into the picture, it tends to give a sort of semblance and order to this mammoth scale of data. It encompasses all the rapidly moving data via the digital mediums which cannot be contained with traditional storage or collation techniques. Transactional data, video, text, audio, visual images, all make interesting stories if analysed properly. Automatically this carves out a path for use of Big Data along with the CRM solution for a company for better decision making.
Sources of Big Data:
Big data can be both structured or unstructured. The popular relevant sources for a company are listed, but not limited to, the following:
- Social media: This is the most talked about and obviously has far-reaching tentacles. This can be harnessed to get an idea about the actual customer sentiments from live feedback.
- Gelocational: This data can be very helpful to understand the global foot-stamps of the customers, their movement patterns, and accordingly optimise inventory and logistics.
- Web visits and clicks: This can reveal customer profiles, customer segmentation etc.
- Sensor and Machine generated: Like IoT, data here can help in predictive/ preventive models.
- Logging during various processes in the organization’s (and other authorized) servers: This can be used to understand the operational flow for handling customer service for instance. The response times could be improved, efficiency in processes and operations achieved, thereby giving a thrust to responsive IT.
- Customer service contact points: This includes information from email, chats, omni-channel medias, or call-centre records.
CRM systems, now and in the future, are leveraging the analysis of data for bettering the consumer and customer experience. This is easier said than done. Collating huge pools of data is one thing, making sense out of it is another thing altogether. This is where techniques like data analysis tools and algorithms play an important role. The idea is to tap all the points where the customer comes into contact with the business, directly as well as indirectly.
Related: Know More About Big-Data
What is the target?
The end-goals of Big Data analytics for CRM include:
- 360-degree view of the customer, both from inside the business systems and from outside
- Finding new ways to manage customer relationships
- Identify new sales opportunities, the ones that will have better buy-in from end-users
- Get innovative and scalable ideation for new products and services
- In-depth analysis of customers
- Data Mining for shaping efforts towards higher levels of customer engagement
- Metric based customer services can be targeted to measure and improve the customer-facing operations
- Call-centre productivity can be improved with detailed insights, so can internal processes be streamlined for higher efficiency and reduced costs.
In fact, data analysis within CRM can act as strong indicators of the performance of a business. Companies can use these to benchmark their own values as pitched against the competition. This could include cost vs. revenue measurements, customer retention and other parameters which can be used to improve self in order to capture sizeable market share and improve customer perception.
How to go full throttle
Technically, companies that intend to use data-driven ways to improve their CRM need to ensure the following:
- Quality of data: especially from a variety of sources needs to be monitored for its effectiveness. A phased approach to incorporate data from various sources is best to gain maximum control.
- Quality of the tools: The analysis is as good as the tools and techniques used. A tailor-made solution is the best, but may not be financially viable. The selection of the tools and off-the-shelf products needs to be reviewed and evaluated thoroughly and revisited periodically to assess the effectiveness.
- Models: Predictive models and preventive models are the natural by-products of data analysis, yet parallel models like causal analysis algorithms will need to be put in place for a holistic view that looks at past, present, and future.
- Profiling: This is not simply customer profiling as seems obvious. Data itself needs to be classified into meaningful groups, with those with similar attributes being grouped together for better data management.
Analysis over multiple facts:
- Regression analysis can be used to detect anomalies or the behaviour of a specific section or group from amidst a larger population. This can be used for target campaigns’ planning and execution as well.
- Market basket analysis tries to group items that tend to be bought together and put them in ‘baskets’. This can be used for innovative cross-selling and upselling efforts.
- Customer sentiments try to set scores for customer sentiments and customer satisfaction levels. These can be further used to set target goals and measure results of actions taken.
- Customer classification analysis tries to segregate the customers in different categories extrapolated with the views of the business needs.
How it all adds up
The synergy of Big Data and CRM is a definitive advantage to all types of industries. The power of deep analysis is thereby affordable with many tools being available today, ensuring that the CRM investments are optimised and profits are increased.