‘The most important insight is the one you don’t have yet’ sounds like a line from a futuristic movie, but it neatly sums up the aim of predictive intelligence. Predictive intelligence helps companies deliver what customers want before they even realise they want it.
What is predictive intelligence?
It’s a way of delivering experiences that are uniquely tailored to each individual. By monitoring a customer’s behaviour and building up a profile of their preferences, companies can predict what they will want next – and offer it to them. Predictive intelligence goes much further than personalisation because it anticipates a customer’s intent and provides the organisation with tailored recommendations on actions to take to improve the experience and improve their key metric whether it’s customer satisfaction, average spend or intent to buy
What are text analytics?
Before the development of text analytics, collected data was analysed numerically, and text responses painstakingly trawled through by a human. Text analytics software uses natural language processing algorithms and text mining to extract meaning from vast amounts of text across many channels.
The most advanced software can even read sentiment and emotion – you’ll quickly know what your customers are feeling and be able to pick up complaints early on. Traditional numerical surveys would not pick up subtleties. Advanced text analysis makes sure you can easily get the most important actionable insights out of every piece of feedback.
How predictive analytics can be used in market research
So-called ‘big data’ – your organisation’s collection of unstructured internal and external data gleaned from traditional and digital sources – is a treasure trove of insight waiting to be discovered.
Predictive analytics not only makes sense of this unstructured data, it also turns it into intelligence, creating prediction models that can be actioned in real life:
Qualitative research: subgroups within customer data can be targeted, and preferences, perceptions and motivations identified.
Quantitative Research: Using predictive analytics, customer groups can be identified and segmented. Variables such as customers’ intent to buy new products, preferences for product features, promotions, messaging, and prices can all be predicted. Online behaviour, geographic, sociodemographic and economic variables can then be linked to survey respondent data. The resulting predictive models can be used for decision-making, marketing and customer targeting.
Being able to mine your historic market research data (both structured and unstructured) and apply predictive models you will be able to predict what will happen in the future. For example, you’re developing a new smartwatch. You could run product testing, and the predictive modelling would reveal the biggest driver of consumer behaviour. You could then predict how a 5% price increase would affect sales/profit among your key demographics, or what effect of adding a new feature such as making it waterproof would have.
This kind of modelling removes guesswork in everything from product development to brand and marketing research. By making sense of all your data points, predictive intelligence is able to accurately model the impact of your actions before you make them. So you can business decisions safe in the knowledge that they’ll have the desired impact and that your investments are going to pay off.