How To Use Data Mining To Improve Your Consumer Business
The more you know, the more you can do, and this mantra applies to business just as much as it does anything else. Data mining — the process of extracting, interpreting, and understanding trends, patterns and anomalies from data sets — is often considered a technical attribute, but can be used in a whole host of ways to benefit those who aren’t technically minded or focused. For consumer businesses, there is a lot that can be learnt from data mining in order to improve product, service and customer experience offerings to the advantage of both consumer and company alike.
Customer insights can be gathered from a range of different sources, and can be used and examined through various methods to provide a tangible, workable output. From sentiment analysis for product reviews to marketing campaign targeting, there’s lots of information that can be drawn from customer interactions. From here, businesses can adjust their development, direction and growth to best fit and meet customer need. But how? Let’s see…
Sentiment Analysis is the (usually automated) process of analysing text with Neuro-Linguistic Programming (NLP) methodology to judge whether or not the overall content relating to a topic, product, service or brand is positive, negative or neutral. This is done through machine learning and allows a business to identify areas for improvement or concern, as well as for further development. Sentiment Analysis can be completed either by manually feeding text content into a computer program and stipulating set rules for the language used, or from an existing service such as consumer review facilities that automatically analyses the words written by customers.
If a theme emerges that is constistently a pain point for customers, the business can move to resolve the issue. If it emerges that a certain product is a real success, the business can move to market it more and capitalise on the positivity.
Purchase Point Strategy
Understanding consumer behaviour at the point of purchase with a business is extremely important, especially if, for whatever reason, a customer doesn’t continue buying or using the service. Although rates vary between industries, it’s believed that around 70% of online shoppers ‘abandon’ their cart before completing a purchase. Data mining can help identify both why consumers leave at this point and how best to encourage a return to the purchase point. The less customers depart at this point, the better, and the more sales can be made.
Customer surveys can uncover why carts are abandoned, and this data can be used to improve the checkout process (for example by being upfront earlier on in the purchase process about shipping costs, or accepting more methods of payment). Communication and engagement data can help highlight how to promote the return of departed customers (for example through cart abandonment emails or targeted discounting).
The insights provided by data mining can be of vast benefit to marketing departments as they try to understand the demographics of the audiences they’re attempting to make contact with. Understanding the media that the audience consumes, the price points they can afford and the marketing that the audience is susceptible to can all help businesses to tailor their campaigns accordingly.
Simply placing an advert in a magazine because a competitor did, or showing up at an event because you think someone similar might, is not necessarily conducive to reaching the desired people. Opportunity can only truly be uncovered when the audience is properly understood – and that can be achieved through the insights presented by consumer demographic data sets.
Banks and financial services companies have long used data mining in their fight against fraud, highlighting unusual transactions in order to screen for them and ensure user authenticity. Businesses that sell high-value or premium products or services can introduce similar systems to ensure they’re able to quickly identify unconventional activity. For example, if a customer repeatedly makes purchases off just under a £1,000 when they could buy in bulk (£1,000 is often the threshold at which banks introduce money laundering checks), this could be flagged as suspect purchase behaviour.
Similarly, if a very small payment goes through just before one that is considerably larger (a tactic often used by those who have stolen card details and need to check that it still functions), the business could be notified. Data mining and the extraction of purchase behaviours allows businesses to better define what ‘normal’ activity is for them and their customers, and benchmark transactions against it.
Although the phrase ‘data mining’ sounds superbly complex and technical, there are now numerous products available online that give access to business insights for those who don’t employ data scientists. As they say, knowledge is power – so why not start gathering it?