Home Retail Expert Speak: Using data mining for bumping business profitability

Expert Speak: Using data mining for bumping business profitability

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As retailing moves at an increasing velocity towards multi-channel, the need to understand the customer across channels has never been greater. Customer expectations of of relevance and personalisation are also growing…

Using data mining for bumping business profitability
Data mining is largely used in several applications such as understanding consumer research marketing, product analysis, demand and supply analysis, e-commerce, investment trend in stocks and real estates, telecommunications and so on

The amount of data being generated is far more than we can possibly handle. Almost every single activity or interaction leaves a trail that somebody somewhere captures, stores and analyses. Just the size of this data has gone beyond human sense capabilities and at this scale it’s almost impossible to detect patterns just by looking at the data. This is where comes into the picture – it automates a part of this process to detect interpretable patterns.

As retailing moves at an increasing velocity towards multi-channel, the need to understand the customer across channels has never been greater. Consumer interaction with a retail brand now spans stores, e-commerce, mobile and social media.

As the number of multi-channel interactions increases (with the volume, the velocity and the variety of data sources all expanding rapidly), so customer expectations of relevance and personalisation are also growing.

How can we narrate our brand story through data?

In short, how can my current data help me take better decisions for my brand?

Every brand is trying to make some sense of their business data which is piling up at the speed of light. A decision on which data is relevant and which data is just noise is the first step that companies need to take if they want to make sense of all the data that they are capturing

How does it help businesses?

For example, with data mining a company can identify their most profitable customers, offer that customer base better prices, also helping the company to accelerate its product innovation cycle.

Data mining can help companies understand their current supply chains better and optimise it more effectively. Retailers are well-known users of data mining techniques.

Retailers offer free loyalty cards to customers that give them access offers not available to non-members. The cards make it easy for stores to track who is buying what, when they are buying it, and at what price. The stores can then use this data, after analysing it, for multiple purposes, such as offering customers coupons that are targeted to their buying habits and deciding when to put items on sale and when to sell them at a full price.

Key metrics like net profitability, customer performance, channel profitability, customer trends – all this data and more – tells you the true story of your customer base. You aren’t guessing at their buying behaviour – you can track and measure it.

Data mining is largely used in several applications such as understanding consumer research marketing, product analysis, demand and supply analysis, e-commerce, investment trend in stocks and real estates, telecommunications and so on. Data Mining is based on mathematical algorithms and require analytical skills to drive the desired results from the huge database collection.

Technology Play

Multi-channel retailing brings about numerous IT and software challenges. Channel neutrality at the front-end needs support from the underlying infrastructure to make sure the customer is visible throughout the sales process.

At the back-end, systems need to ensure that the product is where it is supposed to be, at the right time and right price. Indeed, connection and visibility is king.

Many retailers are now turning to a single integrated platform that can connect to all facets of the business to allow for functional precision as well as front-end and supply chain visibility.

As well as the actual point of sale (PoS), interactions with customers have changed. It is now normal for a customer to interact with a retailer through multiple channels in the course of one purchase cycle, which complicates issues further.

Integrating both operational systems and data then becomes a key requirement of Omnichannel retailing, so that both customers and products can be identified and seen, no matter where they are within the retailer’s systems.

In practice, this means bringing data out of silos and sitting it on a single platform where it can be seen by all – from the front end right through to the back office. It can then be taken and used appropriately by the various operational components of the retailer. But the important thing is that everyone is seeing the same thing and has one single version of the truth.

More than technology, for an organisation to drive change, it needs vision, skills, incentives, resources and an action plan. If one of those elements is missing transformation is not possible. Successful organisational change is an adaptive process that requires the coordinated efforts of a wide range of people at all levels of an organisation that are collectively seeking the same positive outcome.