Data has always had particular relevance in marketing. Much of the pioneering work which kicked off the ‘Big Data revolution’ was conceived in supermarket loyalty programs, targeted direct marketing campaigns and the like. Today, Big Data allows marketing teams to extract valuable insights from the ever-growing mountains of data we are generating and storing, thanks to the connected world we now live in.
In the past, the data available to businesses or governments to inform them on vital issues would mainly have been structured, numerical data – figures, measurements and statistics. These days, a huge amount of value undoubtedly lies in the far larger amount of unstructured data which is increasingly available. This could be videos, social media posts, customer feedback forms or voice recordings.
Decoding this messy data wouldn’t have been possible just a few years ago, but today – thanks to advances in machine learning (basically, teaching computers to teach themselves) and natural language processing (teaching computers to understand and communicate with us in our own language) – this previously ‘dark’ data is starting to hand over its secrets.
Marketing’s challenge, now, is to capitalise on this and simultaneously play its part as a cog in the wheel of an organisation’s overall data and analytics strategy.
In my new book, I discuss three core ways businesses can funnel data to drive growth and positive change:
- Improve decision-making
- Improve operational processes
- Monetisation of data itself
As far as marketing is concerned, the most obvious opportunities lie within the second channel, and the process which should be focused on is understanding customers and developing and delivering innovative products and services.
The retail giant Target famously deduced from customer data that a particular customer – a teenage girl – was pregnant, before she had told her parents – leading to them becoming informed of impending grandparenthood through the promotional mailing their daughter received. Data-driven marketing has come a long way since then. Today, social sentiment analysis tells marketers about their customer habits and behaviours in more intimate detail than ever. Image recognition can be used to scan the millions of photographs uploaded to public social media profiles every day, picking out brands and then interpreting the context of the scene they appear in. Is a particular brand of soft drink most frequently photographed at youth music festivals or family theme parks? Does a person tend to wear a particular clothing label when they are travelling alone in foreign countries, or when they are slouching in front of Netflix with their friends? It’s clear to see that data like this has huge value for planning marketing campaigns.
The role of data in marketing campaigns: personalisation
Another marketing-related way data can be used to improve business processes is increasing customer retention rates. One telecoms company I have worked with analyses phone and text use patterns, as well as social media, to predict which customers are most likely to be in danger of cancelling or not renewing expiring contracts.
Once identified, those customers can be targeted with personalised incentives based on their usage (for example offering to reduce the cost of their tariff if they renew and removing unused features from their package). Traditionally, this is done on a one-by-one basis by customer retention staff, but technology now allows us to automate this process, allowing human workers to dedicate more time to tasks that machines still can’t do (and, in customer services, there are still a few!)
Data is also used for matching products to customers, working out how to reach them, and then predicting what they are likely to want to buy next.
Aside from inaction and the subsequent pummelling by competitors that it would likely lead to, the biggest danger posed to marketers by this new age of data-driven innovation is lack of strategy.
Data collection and analysis is not, in spite of the increasing number of open source and integrated platform solutions, a cheap matter. This is particularly true if you are dealing with personal data or data which is commercially valuable – which Big Data, due to its nature, often is. This means further expense in terms of data protection and compliance, as well as hefty legal liability if things go wrong. Despite their good intentions, many businesses still report that they are failing to make progress with large-scale data-driven transformation, in terms of delivering bottom-line improvements. This means they are spending a lot of money and taking a lot of risk, for literally no reward.
In my experience, this is often due to the simple fact that they did not have a comprehensive data strategy in place, making an overall assessment of how data could be used to impact every arm of the business, from marketing to HR, operations and logistics. The first step is to take a solid look at every challenge facing the business and understand what factors are at play. It should cover all three of the possible uses of data I mentioned previously – making decisions, improving processes and monetisation of data. Once opportunities have been identified there, a data strategy can be put into place – covering what data will be collected, how it will be analysed and what platforms and tools will need to be put in place to make it all possible.
Bernard Marr’s new book, Data Strategy: How to profit from a world of big data, analytics and the Internet of Things, is out now.
Once this is done, I believe any business can start to see the benefits of implementing change through data and analytics. Marketing has already proven itself to be a great driver of innovation in this regard and it will be exciting to see what new developments arise in the future.
Bernard Marr talks about some of those developments in this video (it lasts about half an hour – and is worth watching throughout – Ed):
Read also:
How data drives 5 key steps to maximising account-based growth
Five simple steps marketers should take when employing Big Data