A quick history of predictive analytics

Believe it or not, predictive analytics as we know it, has been around and in use from as early as the 1940s, appearing with the advent of the early computers. Prior to this, early forms of predictive analytics (long before the computer was invented) were used to help voyagers at sea and farmers in their fields, and modern technology has rapidly taken the concept from strength to strength. 

The 1950s was the first time the technology was used to predict the weather, and in the 1970s we saw the earliest examples of real-time analytics being used to fight credit card fraud. Nowadays, it plays an essential role in almost every aspect of our lives, from recommending the films we’d like to watch next, and tracking our health cycles to suggest when is a good time to exercise, to deciding how much you’ll be willing to pay for a flight - and you can bet that the prices are changed accordingly.

In the decades since its invention, this branch of business intelligence has grown into a particularly invaluable element of retail and something that’s necessary to retain a competitive edge. It’s safe to assume that it’s what all of your competitors are doing.

Why do you need predictive analytics?

As the name suggests, predictive analytics involves collecting data, analysing it, and using it to predict what will happen in the future. For retailers, this includes forecasting trends, reducing risks, and optimising marketing campaigns. Not using predictive analytics is the equivalent of making all of these big decisions with your eyes closed - not only do you run the risk of missing opportunities, but you could actually do your business serious harm.

Retail is an industry where the changes come hard and fast - ignoring predictive analytics, or not bothering to use them in the first place, could leave you vulnerable to being left behind, or ignored in favour of your competitors. This could be due to poorly allocating your resources, not taking a personalised approach with your customers, or making inaccurate pricing decisions.

How can they change retail?

Here are seven ways in which predictive analytics could transform your retail business.

1. Increasing personalisation

An obvious example of predictive analytics feeding into personalisation is its use in customer service. Looking at previous customer interactions, past purchases and any feedback or reviews they have left can tell you a lot about how they are likely to behave in the future - and give you an advantage when it comes to communicating with them. Even something as simple as following up with a customer and asking how they’re getting on with their last purchase can feel like a personal touch. 

This also extends to personalised messaging and advertising. Analytics can help you to predict specifics, such as what your customer’s next purchase might be if they’re likely to be going on holiday soon, and even what kind of images they’re more likely to respond to. Combine all of this knowledge, and you’ll be recommending products with such precision that your customers will think you’ve read their minds.

Smarter personalisation like this is proven to result in an uplift of profits - we know, because we helped to make it happen for the well-known clothing retailer New Look.

2. Better customer targeting

Predictive analytics can give you the information you need to identify customers who are likely to churn (stop interacting with your brand) - for example, if they have stopped opening your emails or interacting with your social media. You can then focus on exactly what measures you need to take in order to reel these customers back in and keep them interested in your products. This can involve using methods such as dynamic pricing - offering different customers personalised discounts, discerned from data gathered on their previous purchases.

These are both examples of how predictive analytics can help you to better understand (and therefore target) your customers. It ensures that you’ll be spending time engaging with the customers who need and deserve it the most. Without predictive analytics, you run the risk of investing time and energy in the wrong customers - which will drain your resources, distort your customer insights, and ultimately lower profitability.

3. Building customer loyalty

Everyone wants loyal customers - these are the customers who are likely to go on to become valuable brand ambassadors and spread the word of your products far and wide. Not every customer will become an advocate (as nice as that would be) - so how do you decide which customers deserve your attention?

Loyalty rewards programmes, based on preferences discerned from predictive analytics, are a great place to start. By knowing which products and perks tend to appeal to your customers, you can heighten the value of your loyalty schemes, and make your customers more likely to engage with them enthusiastically. You can also use predictive analytics to determine every customer’s lifetime value - this will help you to identify the customers that are likely to be worth investing in.

4. Creating customer segments

What do you do with all of the data you’ve gathered for your predictive analytics? Use it to aid the segmentation of your customers. This data can come from sources such as transaction history, customer surveys, browsing behaviour, and geographic location, and be used to predict how these different groups of customers might respond to new products.

There are five key types of segmentation: psychographic, demographic, behavioural, geographic and technographic. All of these are areas from which you can collect data about your customers - everything from where they live, what industry they work in, and what their shopping habits mean for your business. So long as all of this data is collected compliantly, you’ll be free to use this information to sort them into segments.

Not only does good segmentation mean that you’re better able to understand your customers - it can actually help to improve your product, by teaching you more about the people who use it, what their next problem might be, and how you can solve it for them.

5. Enabling deeper customer analysis

Every time a customer has walked into a shop, seen something, and felt like it was made for them, you can almost guarantee that somewhere along the line predictive analytics was involved. Customers like to feel known by the brands they support - they want to be shown things that appeal to them, through the channels they prefer to interact with, for a price that fits their budget. Gaining this kind of insight into your customers through predictive analytics such as basket analysis, behaviour analysis, and churn prediction can only help your business.

This is because 83% of consumers care just as much about how a brand treats them as the products they sell them. As well as this, they’re more likely to spend money on a brand that they like, even if they consider it to be expensive. Feeling understood and personally catered for is a must to achieve this connection.

6. Improving stock management and staff timetabling

On a less personal and more practical level, predictive analytics is an essential part of logistical planning in retail. It can help to predict the amount of seasonal stock you should buy when the holidays roll around, and by how much you should discount these products when the holiday passes and the sales begin. 

When it comes to managing staff, predictive analytics can show you when you’re likely to be busy (and in need of all hands on deck), and when it’ll be quiet enough to tactically ‘understaffed’ yourself. 

While many of the previous points have been about the long game, and in particular about keeping customers engaged with your brand and products, these examples will have an immediate financial impact on your business.

7. Forecasting revenue

All of the above result in the ability to forecast revenue. With the data you’ve gathered, you’ll be able to simulate multiple scenarios and predict the impact they would have on revenue should they come to pass. Machine learning models are able to do this for you, continually absorbing and reacting to new data to give you real-time updates on your revenue forecasts. You can also easily compare the performance of your online, in-store, and third-party presence, to see where you’re the most successful.

Outside of your own company, predictive analytics is also used to take into account market trends and other external factors that could impact revenue. The behaviours of your competitors and shifts in the economic climate will contribute towards these predictions - and this knowledge can fuel your decisions as you move forward.