Predictive analytics has never been more important. A wealth of data is now available to businesses, but this doesn’t mean that everyone is getting the most value from it. This where data analytics and, more specifically, predictive analytics comes in. It uses data, algorithms and machine-learning techniques to showcase the probability of a future outcome based on past occurrences.

With this in mind, it’s not hard to see why predictive analytics has quickly become a crucial part of business strategy. If businesses can analyse data from past activities, they can predict what might happen in the future. They can then make adjustments in real time, working with agility by leveraging data.

What’s more, predictive analytics is a vital component of marketing jobs today – enabling marketers to assess how effective a campaign may be and drive stronger results.

Today, there is more technology available than ever which allows a greater number of people to get value from data, meaning that anybody can see and understand their own data to gain new insights. It was previously the case that predictive analytics could only be utilised by ‘data scientists’ who specialised in complex code. But this is no longer the case.

Predictive analytics and HelloFresh

It’s no secret that marketers can use predictive analytics to discover why a particular campaign was successful or unsuccessful, but what more people don’t know is that predictive analytics can be most successful when implemented across an organisation.

Let’s look at how this might work. Take, for instance, the customer insights team within the marketing department. It can comb through the data collected about what specifically was of most interest to customers during a set period – whether it be a product, service, or piece of content. Armed with this knowledge, that team can share results across other departments in the business. The result is a customer-centric approach across the company, as future campaigns are adapted to customers’ wants and needs.

HelloFresh, the Berlin-based food-delivery start-up, is just one example of an organisation that is putting predictive analytics at the heart of its business strategy. HelloFresh uses self-service visual analytics across the business to understand customer recipe preferences, manage stock levels and ensure delivery of fresh ingredients. The company receives feedback from customers on ingredients and recipes each week – clearly useful marketing information when leveraged properly.

The company can use this information to spot patterns and identify anomalies within the data. This helps the head chefs adapt their recipes to better serve customers.

Predictive analytics and the retail sector: MUJI

It’s not only the food industry that benefits from employing predictive analytics. Consumer goods retailer MUJI has discovered the benefit in boosting customer loyalty and continuing growth. MUJI tapped into more than 300 million rows of data to improve not only its results but also its understanding of activity across its 640 stores, in addition to online and on its mobile app. Predictive analytics provides them with a better picture of which channels work best for different demographics, allowing them to target efforts on reaching those most engaged with the brand.

But are there any downsides? If so – what?

While there are a number of benefits associated with incorporating data analytics into business, its use has raised questions about how data should link with human emotion. Data analytics provides a window to what we think and how we act – for example, what customers buy and how they engage with online content – but it would be wrong to suggest that it offers marketers a complete overview.

Data is an invaluable asset for businesses, but it has to work alongside human intuition. Data can offer a perspective on possible actions or decisions, but ultimately marketers should also incorporate their own judgement, based on intuition, previous experience and contextual understanding.

If, for instance, there is a big difference between a worker’s initial prediction and what the data actually shows, they should take every step to ascertain why this is the case, and what can be done to rectify the situation.  As valuable as data is, it does not paint the full picture. If companies wish to have the biggest possible impact on business, they must pair predictive analytics with human judgment. Only then will marketers have the best approach toward tackling business problems and developing a more engaging customer experience.

 

By Susan Graeme, senior director of EMEA marketing at Tableau Software


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