Marketing has changed. It’s no longer enough just to measure the campaign, how much it cost, how many sales were made, what profit or contribution was generated. Business has changed because the consumer has changed, and how (and why) the consumer engages with a business has changed too.
Of course, this has all been driven by technology, particularly the near-ubiquity of the internet. Businesses have had to learn to talk to their customers in different places and in different ways because of that. But it’s also meant that businesses have been able to gain a far greater insight into their customers (and others like them) than at any other time in the history of business.
The internet has led to an explosion of information about consumers, their likes, interests, annoyances, affiliations. All that information can be captured, stored, investigated, learnt from and profited by. But it’s also led to a massive stratification of those same consumers, who are now sorted out into tribes and sub-tribes, yet remaining cross-tribal and tribal-fluid, all at once. Consumers these days (as can be seen by their Facebook feeds) value experiences over things, enjoying the smugness that comes from being on the winning side of Facebook-envy. Yet still they’re buying and consuming stuff at the same rate they ever did. Consumer spending still makes up more than 60% of GDP in both the UK and USA. [1] (It’s remained firmly in the 60-70% range in the USA since WW2. [2])
As we saw in a previous post, there are three kinds of analytics:
Descriptive analytics, shows the basic data about customer behaviour, what happened (and why);
Predictive analytics, which takes the basic descriptive analytics a stage further, generates more insights from them, and infers possible outcomes: what might happen in future, or what ways customers are likely to have behave in future (based on what they did in the past); and
Prescriptive analytics, where things get very strategic, looks at how the company can better reach its various goals in future. Prescriptive analytics are model-rich – employing mathematical and statistical models, and often also using computer programming – to identify the best course of action to take in order to optimise a particular variable (such as increasing sales, maximising profits or making the company more competitive).
In the modern business environment, the consumer-facing company needs to take charge of the information available about its customers – the data structures and technologies used in collecting, storing, manipulating and exploring it. Business success will be won or lost on how the company can handle all the components associated with managing, analysing and making effective strategic decisions from all that data.
In the 21st century, it’s not so much what you know about your customers that counts, but how you will use that knowledge to differentiate yourself from your competitors, to engage fully and persuasively with your customers and drive home to them your uniqueness and difference. More and more, it is about how you choose to leverage data collecting, handling and analysing that will be the determinant of your success.
References:
[1] Consumer spending as % of Gross Domestic Product 2014: USA 62.7%; UK 63.8%. (Sources: www.tradingeconomics.com & www.statista.com, quoting statistics from Bureau of Economic Analysis, USA and Office for National Statistics, UK)
[2] USA GDP breakdown 1929-2011 (Source: www.forbes.com quoting Bureau of Economic Analysis statistics)