Navaid Khan

Is Omnichannel Data Integration the Key to Mastering CX?

Blog Post created by Navaid Khan Employee on Jun 18, 2018

From industry giants to bright startups, brands are frantic to deliver the best interactions with customers and to burn the churn. In other words, master the customer experience (CX).

The frenzy for telcos is to morph from staunch utility companies trying to keep millions of customers happy into innovative, tuned-in service providers that keep happy customers. To compete means being on par with OTT companies, which were born in the cloud and exemplify dexterity, whereas telcos did not have agility built into their DNA.

Gene Therapy

To uplevel CX, some telcos have successfully added OTT–like agility (or actual OTTs) to their DNA (T-Mobile/Layer3 TV, Comcast/NBC Universal, Verizon/Yahoo/AOL and pending AT&T/Time Warner). Some have partnered (Sprint/Hulu). Some OTTs offer solutions (Whisbi.com meshes live voice, video, chat and chatbot data in real time to create a personalized, conversational CX solution for savvy telcos and other enterprises).

Competitive relevancy, however, also demands a consummate understanding of every single customer. While companies are rightfully focused on providing omnichannel content, I’m looking at how "omnichannel data integration" will be the force shaping CX long into the future.

Omnichannel Data

CX 2018 predictions exclusively forecast the preciseness of deep intelligence of customer data. In a short time, we’ve seen data technology mature from summarized averages of customer behaviors to pinpoint accuracy, so you can zoom into every customer touchpoint.

One example is the net promotor score (NPS) index, which tracks, weights and packages each online imprint, call, complaint, purchase and contact per customer. A low NPS indicates that a particular customer needs to be saved or he will leave. A high NPS means this individual will promote your company or service. Computational frameworks applied to NPS systems provide a dashboard of, say an enterprise’s overall NPS.

Comcast recently paired with Convergys, a billing company focused on helping telcos improve CX by analyzing billing data, call center data and operations data. By fully understanding customer drivers and its own CX shortfalls, Comcast recently removed 25 million calls from its business: a huge win.

Bringing meaning to billions or trillions of data points across the endless parade of sources is anything but easy and requires more than trial-and-error. Data scientists can spend up to 79% of their time cleaning, organizing and collecting data sets[i]. But what’s the right data? While each datum has the potential to be correlated and actionable, not all data is useful.

Pentaho Orchestration

CX orders of magnitude require predictive data analytics nimble and comprehensive enough to extract intelligence and aggregate the most value from omnichannel data. Deftly integrating and mining data means knowing how and what to ingest and validate, which algorithms to use and how to prepare and blend traditional sources, machine intelligence, social media, etc. After all, we’re talking about reaching online audiences in real time at scale.

Figure out how to operationalize and capitalize on omnichannel data, and you’re looking at vast opportunities to master CX.

I believe Pentaho gets it right. Pentaho Data Integration (PDI) and Pentaho Machine Intelligence (PMI) use enterprise-grade orchestration capabilities to train, tune, test and deploy predictive modeling for big data and machine learning (ML) workflows.

Why is this important? Because these capabilities buffer ridiculously difficult tasks of big data onboarding, transformation and validation. Regardless of whether predictive models were built in R, Python, Scala or Weka, the Pentaho tools enable smooth collaboration for faster, more complete intel. Pentaho uses an impressive automated drag-and-drop environment that accelerates collaboration across platforms and mitigates recoding and reengineering.

Let’s apply Pentaho to the successes already mentioned. For NPS, Pentaho could streamline how scoring frameworks are computed and delivered, to readily adapt and capture touchpoints for added or changed customer products and services. Companies like Convergys and Whisbi can benefit from Pentaho’s supervised, unsupervised and transfer learning algorithms, to measure ROI of the software tools and customer behaviors being collected.

With a bring-your-own ML philosophy and transparency across algorithms, Pentaho integrates and mines omnichannel data in the most complete and meaningful fashion. Our Hitachi Vantara Labs has even been working on ML model management plug-ins for the Pentaho Marketplace.

http://www.pentaho.com/marketplace/The bottom line here is that a simplified data-in-data-out analytics approach can deliver optimal value and choice to customers, new revenue and monetized opportunities for telcos and the OTTs looking to help.

For more information, please contact me or visit http://www.pentaho.com/machine-learning-capabilities.

[i] Source: Survey of 80 data scientists conducted by Crowdflower, provider of a data enrichment platform for data scientists.

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