Why you should forgo your ‘gut feeling’ and use data to guide your business 

Effective selling requires more than just meeting a consumer’s needs or wants. In today’s competitive marketplace, understanding and individually serving your customer makes the difference between them buying from you or from one of your competitors.

To gain a better understanding of consumer behavior, a marketing professional can  review purchasing history and customer interests, combining them with wider societal and industry trends.

But can you determine how each individual user will interact with your products and promotions? How can you better forecast consumer appetite and commercial opportunities?

Too often, marketers just “join the dots” and forecast based on presumptions, ultimately going with their ‘gut’.

What is typically the difference between presumption and prediction?

Presumption: forecasting formed from subjective opinion based on our own experiences; assessing limited - mainly historic - data and setting up hypotheses in the system through A/B tests to see what works.

Prediction: utilises significant amounts of data and algorithms to calculate the probability of behaviour at an individual user level, including how they will react to interventions. This allows more accurate forecasting of future events and their drivers.

 

From data analysis to predictive analytics

With the rise of big data, data analysis has evolved. Past presumptions were made based on experience, intuition and critical thinking, whilst today’s predictions are based on a technological analysis of multifaceted data.

Predictive analytics ideally involves data mining, multivariate statistics and machine learning to anticipate the future. Applying predictive analytics can improve understanding of consumer behaviour, allowing for more effective and personalised marketing.

Understanding consumer preferences and intent enables digitally transformed companies to present the most relevant products and promotions to their customers, encouraging interventions that result in improved sales and customer satisfaction.

The role of data

Consumers provide a great deal of personal information through their consumption patterns and online activities. This data can be gathered to create unique consumer profiles, enabling you to understand their needs, interests and intent, and predict buying and engagement actions.

Particularly in the fast-moving digital landscape, companies need insights about what is happening now, and predictions on what will happen. Big data and machine learning technologies are able to consolidate, crunch and act on consumer signals in real-time.

Seeking new avenues for responsible 1st party data collection enables a deeper understanding of your customers and will let you respond quicker and faster to their needs. The more data – particularly real-time customer data – you have, plus integrating multiple platforms and technologies, the more accurate and meaningful your predictions of future consumer behaviour will be.

Predictive analytics

Obsession with the future

Predictive analytics marks a progression from simply identifying known patterns in data to making predictions based on unknown patterns, possibly also taking into account individual consumer sentiment. This has advanced the practices of data mining, and the insights that can be obtained from ever growing amounts of data.

More than ever, sectors including travel need to look forward: rather than exploring retrospective patterns, a focus on prospective and anticipatory patterns – what will happen – is today’s level of ambition. This cannot be based on what happened in the past, instead consider what consumers will do based on patterns of signals they communicate.

To apply predictive analytics at scale requires automation, and using these insights to implement recommended actions in real-time. Using consumer patterns to understand behaviour, you can enable your online distribution platform to respond to these signals more intuitively for each and every consumer.

    Driving the prediction agenda forward

    Predictive analytics is a fast growing discipline: companies and consumers are rightfully cautious about data security, with some of the world’s largest brands suffering data breaches. Misuse of data is a key concern, with consumers increasingly alert to their rights and eager to avoid spam. Despite these fears, 74% of consumers see the value of data-generated ‘living profiles’ to provide tailored information and experiences.

    It’s important to communicate your value proposition and application of predictive analytics to your customers: this shows not only that you value their data and rights, but highlights your own understanding of how data is used. Companies need to invest in their teams and tools to maximise how data is smartly collected, stored and used, to benefit from the power of predictive analytics.

    You can continue to presume what will happen or you can take your business intelligence forward and respond faster and at scale to individual consumer intent by predicting your opportunities.

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