Must Read CX Articles from February

 

Here is the round-up of hand-picked articles from various sources about customer experience (CX), customer analytics and big data –

Leveraging Data in a “Real-Time” World

Driving more customers through the door is not the catalyst for strong retail growth — increasing share of wallet with each interaction is. Merchants should explore the best real-time data solutions that can deliver on that goal.

Big Data And The Creative Dividend

We know that personalization is planted firmly in the crossroads of data and creativity. But how often do we consider which our businesses are favoring? What we lead with? And, most critically, will a sudden shift in focus onto the former has any impact on our support for the latter, and what that might mean for our companies?

Chinese Banks Benefit From Customer Analytics

China CITIC Bank (CNCB) designed, implemented, and optimized marketing campaigns and gift and loyalty programs to increase card activations. By revamping its customer analytics capabilities to better understand customer profiles and manage customer relationships, CNCB increased its card activation rate and lowered the bank’s marketing costs.

When Big Data Projects Go Right

In this article, the Forbes contributor Howard Baldwin has commented on the idea of big data projects going right. It’ll get your minds racing about the big data possibilities.

Getting big impact from big data

New technology tools are making adoption by the front line much easier, and that’s accelerating the organizational adaptation needed to produce results.


Here are the blog posts published on VOZIQ blog in February –

Effective Management of Customer Churn

Controlling customer churn is vital for the success of any business. To improve customer retention and customer loyalty, companies need to first analyze customer churn and quantify its impact. This provides insight into the different customer groups that may need to be better addressed or need specific attention. Based on the industry vertical and the market, companies need to design a predictive churn model to identify potential customers who have a high probability of churn.

Who handles your Customer Analytics?

Handling Customer analytics is a complex task and different companies have different departments managing them. This largely various across different industries and regions. For some it’s the business functions who are In-Charge of analytics while for others it’s the IT department processing the customer data. Some have a separate group with customer analytics experts working independently in them. Which according to you works best?

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