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We are going to tell you the real case of how a small food delivery company managed to stay afloat thanks to predictive analytics. Despite having a good food offering, the company was not attracting enough customers and its revenue was insufficient to cover its costs.
Their marketing campaigns weren't all bad, but they weren't reaching their target audience and their competitors were light years ahead of them. What were they missing, what were their competitors doing that they weren't? On the verge of giving up and desperate for a solution, they gave predictive analytics a try. That's when everything changed.
They started collecting and analysing data about their customers and their business, including information about restaurants, orders, prices and opening hours. They also cross-checked this with external data on their competitors, the market, weather, social media, calendar, etc.
With this information, they were able to identify patterns and trends in food demand and accurately predict which dishes and restaurants would be popular in the future, on which dates and when demand would increase.
This enabled the company to make informed decisions about what food to offer and at what prices, resulting in increased sales and profits. In addition, they were able to identify the best times to launch targeted promotions and advertising campaigns, which further increased their customer base.
In a short time, this business became a great success. Their revenues increased significantly and they began to expand into new cities. Predictive analytics not only helped them survive, but allowed them to thrive and become one of the leading companies in their sector.
This story is just one example of how predictive analytics managed to help a one-off company. But what if we were to transfer this case to many others? Or rather, what if we extrapolated this situation to your own business? Don't stop reading because we are going to tell you all the benefits you can enjoy thanks to predictive analytics:
This technology uses advanced data analysis techniques that identify patterns and trends in your historical data, allowing it to predict future events and enable you to make better, more informed decisions about those events.
For example, with predictive analytics, a company can detect patterns in the buying behaviour of its users, assess which procedures help or hinder the benefits of those patterns, and thus help to decide what are the best measures for the organisation. Imagine you are a fashion retailer and your predictive model predicts that denim is going to be a trend this winter; it would not be very smart for you to start producing corduroy garments, would it? Surely, you would make the decision to produce clothes in denim, which is going to give you the best results.
Thus, predictive models provide such valuable information that they enable organisations to identify opportunities and make strategic, operational and much more timely decisions.
Every sector faces a different degree of risk in its day-to-day operations, but in any industry, managing risk properly is essential to ensuring your success and staying in business.
Predictive analytics is a great ally here because by analysing historical data and making predictions about future events, they are able to identify potential risks before they occur. Thus, companies can anticipate danger and take preventive measures to avoid or minimise the impact, reduce recovery time and costs in the event of a crisis.
Take the example of the insurance industry: all insurers are exposed to fraud risks, be it false claims, false billing, unnecessary procedures, staged incidents, withholding of information, etc.
Predictive analytics can anticipate fraudsters: by analysing patterns of behaviour, fraudulent claims can be identified through modelling based on past cases of deceptive activity and measures can be implemented to prevent them.
On the other hand, risk assessment also helps to reduce industry losses and improve industry performance. Predictive analytics analyses your clients' policy documents and predicts the likelihood of claims or accidents. This enables them to make informed decisions about who should receive insurance and the appropriate amount of premium and coverage. This significantly improves underwriting efficiency as lower-risk policies can be processed faster.
Thanks to advanced data analytics techniques, a company can predict market behaviour and future demand, allowing them to adjust inventory to avoid overstocking or shortages, pricing and production, and execute marketing strategies that better meet customer needs and increase efficiency in demand planning.
For example, with predictive analytics, a hotel can predict the number of customers it will have on a given night and, based on that, optimise pricing, maximise occupancy and increase revenue.
By optimising inventory and production, predictive analytics can help you reduce costs and increase operational efficiency.
Inventory management is crucial to ensure the supply flow of products, optimise warehouse management and improve resource planning. Sales analytics, known as "sell out", provides detailed information about in-store inventory and sales, orders in transit, supplier discounts, mark-ups and mark-downs, making inventory optimisation much easier.
With the help of predictive analytics, we can monitor the out-of-stocks of a product compared to others, calculate the size of safety stock, the volume of returns or disruptions.
As we have been mentioning throughout the article, thanks to the analysis of the large amount of data stored, you can identify patterns and trends in the behaviour, preferences and needs of your consumers. With this information, you get a better understanding of what exactly your customer wants and what their desires are, so you can segment your audience, personalise your message and offer and choose the right channels to reach them, thus optimising all your marketing campaigns to the maximum.
In addition, predictive analytics can also help you measure the performance of these campaigns, identifying which tactics work best and which do not. With this information, you can adjust and improve your marketing strategies to get better results and get closer to your customers.
Thus, predictive analytics allows you to deliver a much more personalised and trusted experience, which will help you to better engage your customers, improve your relationship with them and meet their needs accordingly.
Let's go back to the example of the food delivery company at the beginning. Predictive analytics helped it analyse customer data such as order history, time between orders, food preferences and delivery preferences. With this information, the company customised its offerings and services to better meet the needs of each individual user, such as offering discounts on one customer's favourite dishes or scheduling delivery at a specific time for another.
In addition, the company was also able to use predictive analytics to identify those customers who were at high risk of unsubscribing, such as those who had decreased their order frequency or had stopped ordering for an extended period of time. With this information, it was able to take better measures to retain these customers, such as special discounts, promotions or free trial offers.
In short, thanks to predictive analytics, your business will be ready to face any challenge and achieve success. With the valuable insights it provides, you will gain a competitive advantage that will help you make much more informed, accurate and strategic decisions, avoid risks (or minimise their impact), manage your resources more efficiently, reduce costs and improve customer loyalty.
So what are you waiting for?