Introduction
POI data is information about a physical location. These locations can be restaurants, parks, malls, stores, etc. To be more precise, POI data is precise information needed to identify or define a venue. POI data provides a personalised experience based on the places customers visit and the nearby environment of those places. With detailed POI data, companies can deliver an intuitive, timely, and personalized service based on your current physical location.
Let’s imagine that in the morning, you will see a new message on your mobile that your French toast is ready for pickup. You will definitely wonder how that happens without placing an order. It’s because the restaurant knew that you would be passing by. This is feasible only because of POI data personalization. In this blog post, we will dive into POI data personalization and how it transforms customer experience.
What is The Importance of POI Data in Personalization?
POI data primarily focuses on physical locations and places where people generally engage with the world around them. It provides timely, useful, and accurate recommendations instead of a generic one. If you like coffee, but if the cafe is too far or closed, it will not be useful. Therefore, it is crucial that you factor in your context, like your past habits, location, timing, travel, etc., for the application to predict your needs and suggest a nearby cafe. POI data basically provides a more personalised experience by understanding your situation, and accordingly, it creates a seamless user experience.
Location-Based Insights For Personalised Customer Experience
Your Search history, advertisement clicks, and purchase behaviour can drive usual personalisation, which uses basic and static user data. However, they miss important offline behaviours that create preferences.
On the other side, POI data can be used to fill this gap. It provides valuable insights into the places customers visit and their routine activities. This additional benefit context allows brands to move from responsive personalisation depending on their past clicks to reach out to them before they actually ask, based on their movements.
How Can You Bring POI Data into Your Strategy?
You do have to monitor every move of the customer. Instead, you have to integrate limited but meaningful POI to gather valuable insights. There are several ways in which organisations are incorporating their POI strategies. Some use a mobile application to request location sharing for getting a clear value, like preparing an order at the time the customer wants. Others rely on the provider’s data, like the factory, which offers anonymised movement patterns instead of personal location.
Many times, collecting customer office or home location data with nearby POI can also provide strong signals without needing to be continuously tracked. Check-ins at a specific venue can also harness POI data. After collecting data, you can see its impact when you integrate it into your desired system or business process. Organisations can use POI data to improve their CRM profiles or recommend products to customers depending on location-based habits.
Key Metrics in POI Data
To utilise POI data at its best, businesses can rely on its key metrics. They are mentioned as follows:
- Foot Traffic: Number of people who visited any store or place, and their time of visit.
- Dwell Time: How much time the customer spends in a particular store. It suggests their interest.
- Catchment Area: POI data provides key metrics on the location from which the customer came.
- Demographics of Visitors: Age, lifestyle, and income can reveal customer visitation patterns.
- Cross-Visitation Patterns: Locations of customers visited before and after POI. This will enable you to know their habits.
Real Life Examples of POI-Driven Personalisation
Audience Segmentation: Retailers can use POI data to make customised promotions based on customer lifestyle. For example, stores available in suburban areas might focus on providing women’s products, whereas urban locations can think about promoting their electric items or processing the order. A bank can also leverage POI data to get address-level insights. Customers near the car showroom may receive information about a car loan, while offering a credit card to frequent travellers based on their lifestyle.
Contextual Marketing Moments: Brands can highly utilise POI data and deliver offers and discount related messages at the right time. If we consider a hotel app or a hospitality app, then it can offer seamless check-in to guests. A home improvement store provides a project suggestion to cater for the needs of customers who have just visited the showrooms.
Control What Customers Are Sharing: Companies can use general patterns from large groups to offer timely and relevant promotions. With the POI-driven personalisations, effective because it focuses on users with broader and anonymous behavioural patterns. POI personalisation indicates that the personal identity of the user is not revealed with their physical moment.
Location-Based Services: A Food and grocery delivery service app can recommend products or food items based on the user’s history. A travel app can also suggest places to visit for travellers based on places they often visit. Navigation apps can provide personalised points of interest based on user search history.
Urban Development: Government agencies can utilise POI data to understand how citizens are using their neighbourhoods to identify gaps in the services. With this approach, they can make decisions for making new roads, buildings, schools, hospitals, and more.
What Is The Impact of POI on Customer and Business?
PO data personalisation leads to improvement in customer experience. When you know exactly what the customers want, they feel more valued and supported. Customers can receive the offers and discounts they find useful by preventing spam. By using POI personalisation, businesses can get a higher conversion rate and deeper customer insights. This relationship can boost brand trust and offer services beyond their expectation.
What Is The Future Of POI Data Personalisation?
Personalisation has changed every aspect of customers, from anticipating their needs, their lifestyle and context-aware experience. POI just does not reveal where people go; instead, it provides information about why they go, at what time they go, and how much time they have spent. Let’s understand the prospects of POI in future.
Emerging use of AI and Machine Learning:
POI personalisations’ future will be powered by machine learning and AI models. They are beyond just descriptive analytics. Artificial Intelligence can seamlessly predict customer experience. It analyses customer visitation patterns to identify their future needs and provides them tailored-made experiments. If you are often visiting the maternity clinic, then the brand can predict and suggest to you a product like family care. POI data can be blended with AI to provide users more personalised experience. Simply, if it is raining, AI data will predict the weather and suggest nearby indoor dining restaurants.
Use of 5G and IoT:
The launch of 5G and IoT (Internet of Things) will have a great impact on how POI data can be collected. Together, it will provide you a fast and efficient way to get useful POI data. Brands will offer personalised offers to customers when they enter a POI. For instance, when a traveler enters the airport, they may instantly receive information about transport recommendations. IOT devices continuously feed into POI. This is helpful when a customer enters a shop, retailers can trigger personalised product suggestions on digital displays.
Augmented Reality (AR):
POI data and AR will transform how customers interact with the physical environment. Imagine you are walking in a mall and exploring it, AR glasses here will float offers and discounts above the store of your interest. Tourists who want to explore any place will receive AR narratives, landmarks, nearby attractions, restaurants, hotels, and more. In the jogging park, AI can prompt a hydration station for fitness enthusiasts.
Predictive Ecosystems and Anticipatory Personalisation:
Anticipatory is one of the existing features among all. Instead of providing products based on their actions, retailers can gather information about their interests before customers actually need them. A mobility app can prepare a personalised shopping list after detecting that the customer frequently visits the grocery store. A travel app may show destinations based on the traveller’s behaviour.
Industry-Specific Innovations: POI data personalisation can be helpful for distinct industries. For instance, retailers can merge AR and AI to deliver a personalised customer experience. In the banking sector, location-driven insights can be utilised to boost new financial products like credit cards.
Human-Centric Future:
POI personalisation is not limited to technology; it is more about the relevance, activity and empathy. The future is making customers happy by providing or suggesting what they want. Personalisations must be done ethically. By doing so, it will create an efficient and meaningful customer experience.
Conclusion
POI data is not all about a set of coordinates; it’s a channel between people, places, intent and action. By understanding the interests of people, where they are frequently going, and what surrounds them, a brand can deliver a personalised experience and relevant experiences to customers. Today, customers want a seamless and personalised shopping experience with minimal interruption. This can be done with the help of POI data personalisation.
As we move on, advanced technologies like AI, ML, AR, and VR will continue growing, and more will become stronger than ever before. By utilising these technologies and POI data, personalisation not only makes your customers feel seen, valued and understood, but it will also improve overall business value.
