Virtuoso handling of customer data
Collecting customer data is getting easier and easier for companies. The current challenge, however, is the useful and profitable integration of this data into existing business processes.
Merging different data sources with Big Data data sources in a completely integrated environment such as Pentaho enables companies to gain a 360° view of their customers, on demand.
Companies that collect data aim to know "everything about each customer". The benefits of having such a 360° view of a customer can only be reaped, however, if we succeed in converting this knowledge into target-oriented measures and actions for every customer interaction. This can be achieved with customer analytics.
Practical tips for implementing a customer analytics project
Before any data can be analysed, it must first be extracted from the various source systems. And when consolidating data, it is important to remember that it can sometimes exist in heterogeneous structures. This is why structured data from operational systems such as a CRM system has to be combined with unstructured data from social media platforms, which in turn makes data processing and cleansing more complex.
Also, it should be ensured that customer data is always up to date so that up-to-date decisions can be made. It may even be necessary to process data in real time. It's also worth bearing in mind that in addition to the increased demands on data diversity and speed of processing, high data volumes also present a particular challenge to databases.
In order to deal successfully with such data volumes, an efficient data storage system – one that is scalable even with increasing data volumes – must be implemented. Predictive analytics models that recognise patterns in the data stock are necessary to be able to derive forecasts for future developments from the data pool.
Ultimately, it must also be possible to visualise the data volumes in a suitable form as part of any data analyses. To this end, dashboards or reports are available that, at a glance, provide users with information about customer behaviour.
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- Virtuoso handling of customer data
- Practical tips for implementing a customer analytics project
- Specific recommendations on technology for customer analytics use cases
- Benefits and applications
- Your advantages of having a 360° view of your customers
- Our service: Providing added value with a Pentaho customer analytics solution
Benefits and applications
Against the background of the three time-centred customer views:
The Past: Evaluation and historicisation of past customer interactions
The Present: prompt reactions to current customer interactions
The Future: Deriving future customer behaviour (predictive analytics)
The 360° view of the customer needs to be re-orchestrated
Considering the broad range of sources as well as the large amount of data, a new approach to the technology-based "360° view of the customer" must be found.
A crucial factor is having a professional master data management system for customer data. The use of metadata in this context is particularly important, because only by using this can customers be uniquely identified via the various contact channels. This gives rise to the so-called "golden record": a dataset that covers all information about a customer and provides a 360° view of the customer at the customer data level.
Expert recommendation
Before the data from the various sources is even processed, data quality management tools ensure that the data is clean and current. Right from the start, when information is being entered into the system, quality mechanisms are used that ensure the consistency of customer data across all data sources.
Your advantages of having a 360° view of your customers
Our service: Providing added value with a Pentaho customer analytics solution
- A single data pool containing all customer data makes fast queries possible
- Business users find all key metrics in one central location
- Joining up previously isolated data, avoiding selective integration
- Combining traditional data sources with Big Data
- Creating extensive analyses (visualisations, reports, dashboards, ad hoc analyses)
- Embedded analytics, making operative information directly usable
- Development of predictive analytics models for database pattern recognition