1 April 2021
The most successful brands are those who are completely in tune with their customers, understanding what their needs are before they even realise it themselves. Artificial intelligence solutions which model human behaviour enable us to anticipate actual human behaviour and are becoming a central part of how we gain detailed shopper insights, allowing us to see into the minds of our customers.
But just how can artificial intelligence provide an insight into the minds of shoppers? And how can you use that all-important shopper insight to your advantage?
When we talk about shopper insight, we’re talking about getting inside the minds of the shopper. That doesn’t just mean what they’re buying, but also why they’re buying it and how they arrive at their purchasing decisions.
Shopper insight often combines both attitudinal and behavioural data to provide in-depth insights into the purchasing thought processes of the customer. Businesses can then use this information to make decisions about how their sales funnels are constructed and where adjustments can be made to increase shopper attention and maximise sales potential.
Many people wonder whether there’s really a difference between shopper insights and consumer insights but there’s an important difference. Shopper insights focuses on the person making the purchase, whereas consumer insights focus on the person actually using the product.
The shopper and the consumer may be the same person. However, it’s important to remember that they could also be different people from completely different demographics. For example, a parent may purchase a pair of toddler sandals, but they aren’t the consumer of the sandals. In this situation, the parent would be the shopper whilst the toddler would be the consumer.
When we’re thinking about shopper insights, we’re trying to get into the mind of the person doing the shopping, who may or may not be the person who is actually using the product.
The type of retail analytics that you’ll use to gain shopper insight will depend on how your business operates and how your customers interact with and purchase from your business.
There are three key types of retail analytics:
In-store analytics focuses on analysing customer behaviour within a physical retail store, to allow meaningful insights to be drawn. This type of analysis typically focuses on footfall, dwell time, attention, basket data and conversion rates.
Customer analytics focuses on the full customer journey. This includes your customer retention rate, churn rate, customer loyalty and Net Promoter Score (NPS). The goal of customer analytics is to understand the overall attitude towards your brand so that you can improve customer satisfaction and loyalty.
If your business is run online, you’ll need to focus on your web analytics. This should include online traffic, bounce rates, sales, conversion rates and customer return rates. Understanding how your online store is performing can enable you to enhance your processes and maximise your ROI.
Retail analytics take the guesswork out of shopper profiling and analysis by providing specific and detailed information about shoppers. Through retail analytics, you can find out exactly who your customers are, what they want and how, when and where they buy it.
Collecting retail analytics can help you to optimise every area of your business, as well as helping you to make strategic decisions about the direction of your business. Retail analytics could make the difference between a profitable store and going out of business.
Some shopper insights examples that you may wish to collect include:
Understanding how, when, where and why your customers shop can help you to predict and measure purchase intent. This can then be fed into your marketing, operations, stock control and customer experience processes to increase your efficiency.
However, it’s important to remember that many customers are now moving to an ROPO (Research Online, Purchase Offline) and vice versa, show-rooming models, meaning that retail analytics need to take these behaviours into consideration. This means that you’ll need to use both online and offline tools for conducting your shopper analytics.
This trend also means that it’s more important than ever to manage your customer relationships and your customer experience across every channel. Both your online and in-store experience need to be successful to give your brand the best possible chance of achieving a high conversion rate.
The benefits of retail analytics are countless, from enhancing your customer experience to optimising your in-store operations. Here are just a few of the benefits that retail analytics can bring to your business.
Shopper insights technology gives you a powerful awareness of the experience of your customer. This can help you to enhance their experience, both in-store and online, enabling you to attract more customers and to retain a loyal customer base.
Through retail analytics, you can begin to understand where your in-store operations are effective and where they can be improved. This includes identifying inefficiencies and opportunities for improvements, such as within your in-store displays. In-store analytics can also help you to streamline buyer experience, ensuring that the in-store purchase process is as smooth as possible.
When you utilise the power of shopper marketing insights, you can build a clear picture of exactly who is buying your products. By fully understanding your customer demographic, you can tailor your marketing to target your customer base, as well as ensuring that your processes are tailored towards your customer demographic.
Whatever your product or service, it’s essential that you fully understand your customer journey. Retail analytics can help you to understand the behaviour of your customer throughout their buying journey, from interacting with your social channels and conducting research online, through to visiting a store and completing their purchase.
When you fully understand your customer journey, you can begin to tailor advertisements and offers to reach your target customer at each stage of their journey.
In retail, it’s important to be able to anticipate future demand. Customer insight tools based on retail analytics will provide you with the insight that you need to forecast and plan for future demand through understanding customer buying behaviour. You can then use this information to plan promotions and effectively manage your supply chain.
Many customer insight tools rely solely on point-of-sale systems and foot traffic analysis. Whilst these are useful tools for making data-driven decisions, they are not enough to enable you to create long term strategies. That’s where predictive analytics comes in.
Predictive analytics is helping to make shopper insights smarter and more efficient than ever before. This AI-based technology uses past data to predict the future actions of your customers, generating insights that will help you to make strategic business decisions whilst optimising your retail operations.
A powerful example of predictive analytics is predictive attention heatmaps. Whilst traditional eye tracking measured eye movements and fixation durations, predictive attention heatmaps use artificial intelligence to detect saliency, producing heatmaps, at a fraction of the cost of eye-tracking. This makes predictive attention heatmaps much more accessible to many businesses than eye-tracking analytics.
Predictive attention analytics can help you to understand:
This is particularly useful for optimising in-store displays, ensuring optimal product placement and ensuring that promotional materials are as effective as possible.
Dragonfly AI’s innovative predictive attention heatmaps can help you to understand how customers interact with your in-store displays, in real-time.
The Dragonfly AI app can be downloaded onto your mobile device, allowing you to measure the effectiveness of the shopper environment at the touch of a button.
The app uses your device’s camera to measure the performance of in-store displays. By simply pointing your camera at your display, packaging or promotional material, you can quickly see how your materials are attracting user attention.
You’ll be able to see the performance of your in-store display in real time on a live heatmap. The cold areas, or the areas which are lacking attention, are displayed in blue whilst the hot areas, or the areas which grab shopper attention, are displayed in red.
Through the app, you can also see your Share of Attention on the shelf compared to your competitors. This can help you to optimise product placement and assess the saliency of your packaging.
Using the heatmap and Share of Attention data, you can adjust your in-store displays to maximise your on-shelf attention and increase your conversion rates. Customers associate positive experiences with quickly being able to find what they are looking for, so by increasing your share of attention, you’ll also increase your customer satisfaction rates.
Artificial intelligence is playing an important role in modern shopper insights technology, allowing brands to see into the minds of shoppers. By utilising customer insight tools such as Dragonfly AI’s predictive analytics app, businesses can optimise their in-store displays and packaging to increase their Share of Attention and retain loyal customers.
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