Advanced analytics help determine customer behavior and engage inactive customers

In an interaction with Dataquest, Gaurav Khurana, CMO, PAYBACK India, gave insights on how data analytics can make a concrete impact on the bottom line and how PAYBACK help customers and retailers by using data analytics. Excerpts

1. How and where does PAYBACK help customers and retailers by using data analytics?

– The Payback Platform help retailers in manifold ways:

– We integrate our solution with the existing POS/ERP infrastructure

-We capture customer data to the last mile (basket & product level data)

-We run in-depth analytics driven by multi-dimensional view of spend & usage patterns

-We enable retailers to gain real-time insights into customer behavior and propose ways to engage and retain

-We provide integration with the Payback rewards database

-We personalize multi-channel communications

The insights are used for targeted engagement campaigns through a multi-channel ecosystem; E-mailers, SMS, web, social media and so on. These enable retailers to plan interventions throughout the customer lifecycle and plan retention and engagement activities instead of a single-minded one for all approach. This business intelligence (BI) is relevant specifically in cases of banking and retail industries which move a high volume of data and need to create much sharper offerings in order to create differentiation in the competitive landscape.

As part of the retention strategy at each retailer, various experiential offerings are also built in into the transactional ecosystem to create a distinction which may include home delivery, separate queues, variable benefits, privilege access etc. at pre-defined retail outlets. For PACKBACK apparel and fashion is the biggest segment, followed by grocery. This segment sees high value in loyalty programs as it ensures customer stickiness. Further advanced analytics help determine customer behavior and retailers are able to customize programs for each of their customer segments and also engage inactive customers.

2. How to use both qualitative and quantitative management techniques around data analytics?
There are two main types of user research: quantitative (statistics) and qualitative (insights).

Quantitative has quaint advantages, but qualitative delivers the best results at a lower cost. Furthermore, quantitative studies are often too narrow to be useful and can be misleading.

Various techniques are adopted by brands to capture consumer preferences and tastes. Primary among them being a CRM interface with sophisticated technology that can track the customer journey from the first transaction to the entire lifecycle including individual tastes, preferences to buying patterns, enabling businesses to predict demand. Efficient CRM systems are geared to record specific data with respect to unique visits, transaction patterns and frequency , location, basket size, product/category level data etc., which can then be used to build customer insights and create better value proposition suited to individual tastes.

Advanced CRM’s have a loyalty component built in to be able to incentivise the customer and create unique propositions to engage and retain them. Such advanced analytics in the loyalty domain enable businesses to understand and track customer behaviour and draw insightful inferences for the brand partner. Additionally, social media is another engaging channel of customer outreach and engagement which enables brands to track user journey and provide compelling insights to be able to segment and create sharper propositions to drive both customer acquisition and retention.

1 comment

  1. Riyaz

    Hi Ruchika,

    Thanks for sharing. The points mentioned above are worthy to execute and can help a business to gain more customer getting converted. Mr. Gaurav has enlighten very important points here. One can make most of it by using Social media in the proper way.

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