The Use of Data Analytics in Recruitment
March 11th, 2019
When we conducted our recent ‘State of Data’ survey, we wanted to find out how businesses were using data. Not surprisingly, more than 50% of survey respondents said they are using data to understand customer behaviour. What’s more, nearly 80% believe they should be.
The gap suggests that while organisations understand that better insight into customers can lead to improved profitability, they are still evolving in terms of how best to mine, analyse, and apply customer data.
Many analysts struggle with the challenges of customer data, as an explosion in buying channels, the use of devices, overseas trade, complex supply chains and faster rate of new product launches make customer behaviour harder than ever to analyse.Gone are the days when analysts could rely on a standard RFM model; the modern consumer is less predictable, there is more choice available to them and margins are more squeezed. At the same time, sales and marketing departments recognise the need for hyper-personalisation but often lack the skills or resources to implement such strategies.
Even the definition of what is a customer has got murkier – people who’ve only bought once might be seen as triallists to some, or perhaps they’ve have made such a considered purchase that they represent the most loyal, engaged audience a business has.
Use of Net Promoter Scores is still widespread as a measure of customer satisfaction, but social media engagement measures are catching some off guard. Is a Facebook ‘like’ equal to a personal recommendation? What value can you put on a retweet? What constitutes real brand engagement on Instagram? Data mining from social channels is critical of course, but it’s an area ridden with obstacles, one that calls for intensified analysis to distinguish signals from noise generated by bots and spam.
Those who are successful in this area are able to pinpoint the customer characteristics that lead to profitability (previous buying behaviour, original lead source, brand engagement and so on) and filter out the red herrings. They’ll then use this information to improve the customer experience, whether that’s as simple as using the communication channel of their preference or a game-changing new product launch.
As long as customer data insights translate to product or service development, more compelling propositions and appropriate spend on most and least valuable customers, they are a direct link to increased profitability.
Does your organisation still have a way to go with successfully using customer behaviour data? What do you see as the obstacles to better customer analysis? Please share your insights with us here.
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