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Customer Analytics

Virtuoso handling of customer data: Customer analytics

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.

Today, 80% of all customer data comes from sources such as social media, blogs, forums, tweets, web shops and internet sites. Combining, enriching and permanently associating such structured and unstructured data from the Big Data pool with customers provides new insights into customer behaviour.

Having a 360° view means constantly keeping all data from all sources up to date and to a predefined quality and making this information available for use. The following information categories are of particular interest in customer analytics:

  • Customer characteristics (preferences, needs, desires)
  • Customer interactions (offers, click streams, notes)
  • Customer activity data (orders, payments, length of stay)
  • Data that describes the customer (special features, self-evaluations, demographics)

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.

A well-known example of analytics is the "This might also be of interest" recommendations seen in web shops when completing a purchase. These are based on a rule set and predictive model and serve as examples of cross-selling and / or upselling.

By having a 360° view of the customer, companies achieve the following, diverse benefits:

  • Reduction in customer acquisition costs
  • Prioritisation of highly prized customers
  • Proactive customer loyalty programmes
  • Upgrading of customers from lower to high-value segments

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. After all, customers want to know whether a site visitor, influencer, blogger or (re)tweeter is already known to them as a customer and, ideally, how profitable they are. To find this out, intelligent algorithms for customer identity resolution are needed, so that all available data – structured and unstructured – can be associated with the customer and analysed. 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.

With the aid of customer master data management combined with customer analytics, the goal of making a digital customer's footprints readable and available for analysis can be achieved.

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

Improved customer service and increased sales

Lower churn rate

Increase in cross-selling and upselling potential

Visible representation of how customers perceive your corporate brand

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