Advanced analytics help determine customer behavior and engage inactive customers

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.

3. How to avoid some of the most common pitfalls around implementing and using data analytics?

More and more companies are recognizing that they’re accumulating ever increasing amounts of data but not necessarily gaining business insights from it. The missing link is the transformation of data into information that is comprehensive, consistent, correct and current. Some of the most common mistakes around implementation and using data analytics are:

- Ignoring data shadow systems

-Not building sustainable and on-going processes

-Not dealing with change management

-Focusing on technology instead of the business need

- Not executing a cost-benefit analysis

-Running environments in business-as-usual model

What gets organizations in trouble is how they actually go about implementing data analytics programs. A combination of factors usually derails data analytics implementations. Problems and failures occur due to factors including strategy, people, culture, capacities, inattention to analytics details or the nuances of implemented tools, all exacerbated by the rapid advancement of the digital economy.

4. How data analytics can make a concrete impact on the bottom line?

The analytics and information provided by a coalition loyalty program such as PAYBACK, helps in forecasting customer behavior which is helpful. Data analytics is slowly changing the dynamics of the retail industry. It is leading to more targeted and sharper segmentation basis consumer behavior and usage. Big Data reveals the trend, opportunities, and challenge areas, and importantly, customer focus and segmentation through passing in the loyalty program filters. At PAYBACK, we identify the customers and lapsers based on the analytics gathered and we customize programs according to their needs.

5. Do you think it is proving difficult for some organizations to implement data analytics? If yes then why?

Data analytics is not easy. We all know how much experience, expertise, and industry knowledge it requires to derive value adding insight from a data set. But implementing a platform so that data quality is constantly re-evaluated and defined is an intimidating task for many.

In November 2011, PAYBACK and Future group came together to manage the latter’s loyalty segment. The partnership proved to be a winning one drawing on PAYBACK’s strength in data analytics and Future Group’s large format retailing outlets. Future Group been able to understand trends, and customize offers to suit customer needs and aspirations with the help of PAYBACKS Data Analytics.

Big Data has helped in studying the traits of Indian consumers relevant to FG, that PAYBACK identified and customized the program/offers/campaigns for. PAYBACK also helped FG formats like Big Bazaar & Central with customer insights to segment their transacting customer base on the basis of their Regency, Frequency & Value (RFV model). This has helped FG formats in segmented targeting which has resulted in increasing the overall response rates to the campaigns. Big Bazaar Diwali gifting program in 2012 was exclusively launched for PAYBACK members, which helped in increasing the value penetration from 35-40% range to 50% during the festive period.