In a world where data is constantly being generated and where businesses have an unprecedented amount of insight into their customers, predictive analytics has never been so important. This form of analytics uses data, algorithms and machine-learning techniques to identify how likely a future outcome will be based on what has happened in the past.
Predictive analytics, therefore, has now become a vital part of business strategy. Businesses can analyse data from past activities in order to identify what is likely to happen in the future, and therefore pivot their services accordingly – transforming datasets into actionable insights that make a real impact.
When it comes to marketing, marketing departments have come to the forefront in business, helping drive profit and growth. The ability to use predictive analytics has become integral to today’s marketing jobs in order to assess campaign effectiveness and drive stronger results.
Thanks to increasing amounts of data and the software available today – such as self-service visual analytics – anyone can see and understand their own data to gain new insights. Only a few years ago predictive analytics was a skill often exclusive to ‘data scientists’ who could write extensive and complicated code. This is no longer the case. By having access to data and the technology to both see and understand the data, companies will be one step closer to adopting a data-driven culture.
Predictive Analytics In Practice: HelloFresh
Predictive analytics must act as a catalyst that drives a business strategy. And its ability to make an impact goes well beyond the marketing department. While marketers can use predictive analytics to identify the ‘why’ behind what was or wasn’t successful, they are most effective when involving other departments to collaborate and determine the best approach moving forward.
Take for example, the customer insights team within the marketing department. The team can delve into datasets, identifying what content, products or services were most interesting to the customer during a specific period. After identifying these insights, that team can share findings across departments in order to inform and improve the various parts of future campaigns – from supply and operations, to sales and customer service – therefore adopting a truly customer-centric approach.
In short, the data analysed by the marketing team can have a ripple affect across the whole business. By analysing past data, brands can improve future customer service and – most importantly – retain customers by keeping them engaged.
HelloFresh, the Berlin-based food-delivery start-up that’s expanding into seven markets internationally, centres its business strategy on predictive analytics. The company uses self-service visual analytics across the business to understand customer preferences when it comes to recipes, as well as manage the stock level and delivery of fresh ingredients. Every week, the company gets feedback about recipes and ingredients from thousands of customers.
With this information, teams can easily spot patterns and identify outliers within the data, sharing their findings with the head chefs who tweak and improve recipes based on the data provided. Knowing what recipes are the most popular also helps HelloFresh from an operations perspective. For example, the data will allow teams to forecast demand for specific ingredients to ensure these are freshly supplied while avoiding food waste.
Predictive Analytics In Practice: MUJI
Another strong example of a company that is using predictive analytics to ensure growth and retain customer loyalty is the consumer goods retailer MUJI. The company accessed more than 300 million rows of data to enhance its understanding of activity and results across its 640 stores, as well as online and on its mobile app. From this, the retailer can quickly understand which channels work best for which demographic, enabling them to focus their efforts on reaching those most engaged with the brand. Analysing data has brought wide-ranging opportunities to MUJI, from improving promotions to increasing multi-channel sales.
Are There Any Limitations?
The use of data analytics in business has started to raise questions about how data and human emotion interact. Data analytics reveals insights into human behaviour, from online engagement and purchase decisions, to a better understanding of customers’ pain-points – but it doesn’t paint the whole picture.
Most businesses understand that data is a huge asset; however, it should not replace human intuition. As in the case of HelloFresh and MUJI, data can provide actionable insights that will lead to the development of the business, but these data-driven insights should be coupled with an employee’s own contextual judgement to interpret, understand and make a final decision.
For example, if there’s a large gap between what employees originally thought and what the data shows, there should be an attempt to explore why that conflict exists, where it comes from, and the best way to resolve it. Predictive analytics provides businesses a sophisticated point of view, leading to a more holistic approach to business problems. However, data only tells part of the story. Human intellect and intuition should be used hand-in-hand with analytics to get the full story and drive the most business impact with data.