How Retailers Can Get the Most Out of Data and Analytics
The UK retail landscape in 2018 is highly competitive. Customer behaviour and how people choose to interact with and purchase from brands has shifted significantly in recent times. The problem is that many retailers have found it difficult to adapt and keep up with these developments. They lose out on market share, and in the worst cases, go out of business. But for those that fold, there are also others that are thriving. They have found ways to stay ahead of trends and maximise their profits.
The difference between those that are successful and those that are not is in the way they embrace data and analytics. Retailers have been producing enormous amounts of data for a relatively long time. Analytics has also been a part of this for a long time as well. But it is the way in which this is conducted that counts for modern retailers.
Descriptive and diagnostic analytics will no longer be the difference needed. Retailers must embrace and implement predictive and prescriptive analytics. Tools are now readily available that will allow this sort of analysis to happen in real-time and be shared instantly across hundreds of locations. Trends can be spotted, predicted and prepared for before they happen.
Create Brand Ambassadors
One of the biggest challenges facing retailers is how to improve customer loyalty. Modern consumers are often, famously, brand-agnostic. They shop where it most convenient, cheapest or they receive the best service. It is rare for consumers to become loyal to brands. Rare, but not impossible.
The best way for retailers to inspire this sort of loyalty? Increased personalisation. There are so many competitors out there now, to stand out, retailers must tailor the experience of every customer if they want to create ambassadors. How? With analytics. Every customer, or potential customer, has multiple touchpoints with a business now that can be monitored. Social media, website visits, store visits. This data can be analysed for what products they were looking at, how long they spent looking can tell you about buying intent.
Delighting customers is about solving their problems for them, without them having to ask you to. Giving them a personalised experience – showing them a selection of products based on what they’ve been looking at for example – can do just that.
Personalisation of service can also be extended to retailer’s marketing strategy. Robust analysis of purchasing history, for example, can identify what ideal customers may look like, what products they are most interested in, and even what other topics or hobbies they may enjoy.
All of this can be used to better focus marketing campaigns. This data can inform how specific adverts are targeted and what products they show. Analysis of what products perform better in different regions may affect your location targeting for example, or a deep dive into customer demographics may expose a new audience that had previously not been targeted. These tactics are only possible when used in conjunction with effective analysis of large data sets.
In an increasingly competitive landscape retailers are driven to save costs and make process improvements wherever possible. A lot of those savings and improvements can be found in how retailers manage stocks and their inventory. Predictive analytics is uncovering insights that can inform wider strategy.
Generally, large stock warehouses are the reserve of the big giants now, smaller operators simply cannot afford to do this. Predictive analytics though means they can now identify areas of high demand, spot any developing trends and capitalise on opportunities. The real-time nature of modern analytics means these opportunities can be acted on instantly.
Knowing when to push new products and adjust pricing is critical for retailers. Especially during nationwide and even global sales events like Black Friday or January. Historically, prices are dropped at the start of these periods because that’s how it has always been done.
Predictive analytics can allow retailers to identify the perfect time to drop prices and the best way to do it. Gradual decreases, rather than large drops, for example, can be more effective. Just as dropping prices before other retailers can cause a large spike in sales.
Retail, like many modern industries, must find new ways of making use of its large data sets to adjust to changes in customer behaviour. Retailers are becoming aware of this though. According to a 2018 Gartner survey, 54% of retailers are focused on deploying analytics technologies as part of their digital transformation projects over the next 18 months.
Retailers that embrace the predictive and prescriptive aspects of analytics will be those who adapt best to the changing landscape. No other method will allow retailers to improve personalisation and engagement with customers, focus their marketing strategy more, and drive cost savings at the same time. The benefits are too difficult to ignore.
If you’d like to talk to one of our expert team about the benefits data and analytics could bring you, give us a call on 0800 652 4050.