“There is no such thing as marketing analytics”

If you do analytics right, you get marketing right, says Mohan Jayaraman of Experian Credit Information Company of India

“There is no such thing as marketing analytics”

Organisations are increasingly depending on analytics to understand consumer behaviour, improve operational efficiency and cut flab. But what are the dimensions it will assume in the future? How should it grow out so that it can impact every aspect of business, leading to effective business decisions and strategies that ultimately translate into higher RoI and customer satisfaction?

As per Mohan Jayaraman, MD, Experian Credit Information Company of India, there is no such thing as ‘marketing analytics’. While marketing is focussed on vertical output, analytics is all about what you do with numbers for business output with a methodology and process in place, rather than just vertical output. If you do analytics right, you get marketing right. The broad dimensions associated with analytics of the future are:
Infrastructure completeness
• Completeness of approach
Complexity spectrum
• Latency progression

Infrastructure completeness
It is necessary to have a certain environment to put analytics in place. Every organisation looking at analytics is at a certain stage of maturity. The amount of progression in terms of infrastructure has to move from data mart to data warehouse with basic data initially, gradually moving on to complex transactional data, and then process information.

The richer the tools to look at data are, the better off an organisation is. Analytics in some form should link back to the organisation, interface with core systems which will end up using it. Ensure analytical completeness and also appropriate ability to use it within core systems.

Completeness of approach
Detailing in the framework of analytics is essential. There should be a holistic approach to analytics. Completeness of approach can be ensured through four steps:
Understand the business problem
Map it into an analytical problem
• Translate it into metrics that you can track – what are my opportunities and by how much can I influence the different variables; quantifying each element enables to prioritise
Track these elements with the objective/targets set

The ideal way to bring in a completeness of approach would be to look at it from the perspective of the whole business rather than taking one aspect of a business problem and trying to work it out. Solve it in entirety rather than in parts. More successful analytics have a completeness of approach and not a part-by-part approach.

Complexity spectrum
Analytics has to move from simple to complex; from mere reporting to strategy management and strategy science. It has to move up the maturity chain. Rather than just perceiving it as an analytical problem, it is important to look at it from the strategy point of view. Find the right place and continuum. Don’t get too complex. Apply the right technology to get optimal results.

Latency progression
The gap between the time when an event takes place and the time taken to act on it must be reduced. When the time between the two is longer, it lowers the desired impact. Reduction of latency is essential. This also translates to increasing the predictability of an outcome. How likely is a consumer to place a deposit with you when you approach him close to the event of him putting in some money into his account? That is latency reduction.

It is also important to evaluate the cost v/s return factor. Cost relates not only to money spent on the campaign but also for retention of customers. It is important to identify customers who will react. So, there should be objective decision-making, centralised analytics in some form, it should be consistent, and empower the front-line. The values expected out of analytics will remain the same provided it started out with the right objective to get optimal results through constant tracking. The four factors are hence critical to the delivery of the objective.

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