Category: Retail

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Data Retail

How Retailers Use Geospatial Data To Create Better Marketing Campaigns

If you’re a retailer, you know the importance of marketing your business digitally. You also know that your ability to effectively market to a consumer is what wins it for you. But, have you considered location-based marketing through geospatial data?

In this article, we are going to go over what geospatial data is, the benefits of utilizing geospatial data, and examples of retailers who use GIS for location-based marketing. 

First, let’s define geospatial data. What is it? 

 

What is Geospatial Data? 

Geospatial data is the use of technology, such as GPS, to create and store digital maps that help retailers better understand their customers. This information can be used to geotarget based on location and demographic information allowing marketers to create better-customized marketing campaigns in the long run.

The main use case for geospatial data is to segment audiences based on proximity to a location, but that’s not all it can do. Geospatial data can help you understand who your customers are, where they live, and how they spend their time outside of work hours — which can help you create marketing campaigns that drive results. 

Now that we went over the basics of what geospatial data is, we are know going to discuss some of its benefits as it relates to marketing. 

 

3 Benefits (and Examples) of Geospatial Data for Marketing

Did you know that 95% of executives across the globe believe that geospatial data is critical for achieving business success? Well, it’s true! Geospatial data is one factor that can really send your business over the edge allowing you to make better data-driven decisions for your overall business. This includes digital marketing as well. 

Still on the fence about marrying the concept of  GIS and marketing together? Here are three benefits of GIS marketing and examples of some of the top-name retailers who use GIS to improve their marketing efforts. 

 

Brings More Foot Traffic to Stores

Geospatial data is used to understand where shoppers are, where they’re going, and what they’re doing as they move around both inside and outside their stores. Retailers can then use this information to create tailored marketing campaigns that draw in more customers to their stores. In addition to this, geospatial data can also be used to keep customers in your stores longer.

 

Example: Sephora is Able to Better Segment Customers with Geospatial Data

Beauty retailer Sephora uses geospatial data to send its rewards members a pop-up notification anytime that a customer is in close proximity to one of its stores. The pop-up will generally have a marketing offer to come in for a “free mini makeover” making it almost too enticing to pass up — especially if they are already in the area. 

Once they are in the store, app users can visit the app to get personalized recommendations and to view reviews and product features in the easy-to-use platform. 

 

Better Targeted Advertising

Geospatial data allows your marketing teams to create better, targeted advertising campaigns that’ll drive your bottom line. 

For instance, let’s say you have a flower shop. You would want to pinpoint important location information of what neighborhood your ideal customer may live in and work in to ensure that any advertisement you launch online is shown to those specific groups of people. 

Through the power of geospatial data, incorporating target market data, you’ll be able to set more precise targeting parameters in your digital advertisements.

 

Example: Under Armour Uses Location-Based Marketing through App

An example of a company that uses geospatial data for digital marketing is Under Armour. Under Armour uses GIS through its Map my Fitness app to give its users better-targeted advertising. From tracking the type of activities you do to knowing geographically where you’re located, the Under Armour fitness trackers are pretty robust in their tracking features allowing the company to market its users more effectively. 

How does this look in action?

If you use the Map my Fitness app to track your runs, you may start to get more advertisements for Under Armour running shoes. If you actively use the app in a location with a colder climate, you may start to see more advertisements for the Under Armour base layer. The list goes on. Through the app, the company is able to up-sell and cross-sell seamlessly without coming off as too “salesy”. 

 

Enhanced Personalized Messaging

Personalized messaging has been proven to have significantly higher engagement rates than non-personalized messages. In fact, 90% of consumers find personalized marketing more appealing than the latter. 

Geospatial data allows your teams to send out personalized messaging based on where your customers are located and what they’re doing at any given moment. Pretty neat right? Let’s take a look at how Ritual creates personalized messaging by using geospatial data. 

Example: Ritual Ordering Food App utilizes Personalized Messaging with GIS

Ritual food ordering app is known to connect users with restaurants in the area based on historical purchasing habits. (It’s similar to DoorDash or Uber Eats.) 

Ritual does a great job when it comes to personalization. The company will send personalized notifications to its users with food recommendations based on both area and taste preferences. These simple but powerful pop-up notifications make the customer more likely to open the app and place an order. They may not exactly purchase from the restaurant you suggest, but it gets them curious (and hungry) to find the right food place to make an order at. 

 

Transform Your Digital Marketing Efforts with Geospatial Data

All in all, geospatial data is everywhere. It’s one of the most important elements for marketers and advertisers to understand if they want to accurately target their audience. 

As a recap, geospatial data can: 

  • Bring more foot traffic to your store
  • Help you create better-targeted ads
  • Enable you to send out more personalized messaging

As more and more location-based data becomes available to retailers, it has become even more important now than ever before for retailers to use that data to their advantage. If not, you are missing out on the opportunity to improve your reach to those who need to see your message the most: potential customers. 

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Finance Marketing & Advertising Retail

What Is Footfall Data? All You Need To Know About Foot-traffic In 2023

Footfall data is something that has been around for a while now. But what do we mean by footfall?

This kind of dataset has changed depending on the use case and industry.

In fact, footfall has moved beyond simply measuring the number of people that enter a location.

We’ll take you on a deep dive into footfall data. We’ll show you what it is with detailed examples, as well as what it can be used for across many industries.

What is footfall?

Before we look at footfall data, we need to explain what footfall is.

For us, we have always defined footfall as:

The way that a group behave and move in the real world.

This explains the who, what, when and why of how this group of people visit a location.

This could be different for each business.

But mainly footfall can tell you:

  • Trends around behaviour
  • Changes in demographics
  • Visits to real-world locations
  • Anonymised data trends

Essentially footfall means understanding how people move and behave in the real world.

 

So what is footfall data, and what does it look like?

Footfall data is sometimes referred to as foot traffic data. It’s a data set that will usually contain a number of entries. 

The dataset as a whole will signify a number of visits to a real-world location.

These are aggregated and delivered in a few different ways.

 

Aggregated visits to a location

This will be a data set in which the number of visits to a location is aggregated. This is usually done by some kind of time window, such as hourly, daily, weekly or monthly.

 

Individual visits to a location

Similar to the above, but this time each row will signify a visit to a location. This will usually come with a timestamp and will be up to the person receiving the data to aggregate the data as they wish.

 

Characteristics of visitors at a location

In this dataset, the visits to a location are overlayed with demographics data to understand the calibre of person visiting the chosen location.

 

Comparisons of visitors to a location

This dataset will contain a comparison between two locations based on the desired metric. This could be demographic or an hourly number of visits.

 

Where is footfall/foot traffic data generated from?

These datasets can come from a myriad of sources. It’s important that you understand where your footfall data is generated from, as this can affect its accuracy. The most common sources are as follows:

 

Geospatial/Location data

Data is usually generated from a mobile device. This is collected and aggregated to protect user privacy. A good amount of versatility as a single data set can be used to measure visits to numerous locations. A good balance of scale and accuracy.

 

Sensory data

These are usually physical sensors that are placed in entrances to stores. Very accurate but limited mainly to retail and requires stores to install physical tech, so not very scalable.

 

Purchase data

This kind of footfall data involves taking payment data to understand changing traffic in stores. It can be scalable but is not very accurate. This is mainly due to the fact that you are measuring purchases as opposed to visits.

 

How can footfall data help my business?

Traffic and movement trends

One of the main use cases for footfall data is for understanding changing traffic and movement trends.

These kinds of insights are valuable for businesses that are interested in physical locations. 

Examples of this use case are:

  • A retail location understands the changing number of visitors to its location. This could be a store or a real estate planner.
  • A city planner understands macro visits changes to plan infrastructure.
  • Financial companies looking to identify trends in behaviour for investment purposes.
  • OOH media owners measure how many people have seen their ads.

 

Visitor demographics

As mentioned, with overlapping datasets, it’s possible to show the demographic of visitors to locations. These demographics are features such as age, gender, interests. 

This use case traditionally sits more on the side of marketing and advertising. 

Example use cases are:

  • Marketers target consumers who have visited a real-world location.
  • Building lookalike audiences in advertising platforms.

 

Competitive analysis

This is similar to our first use case, but the target location will typically be a competitor. 

Examples of this use case are:

  • A store measures competitors’ traffic to target them with advertising.
  • A new site planner understands competitive performance to decide where to open a new site or venue.

 

Training ML

Footfall data can also be used to train emerging ML models. These models are being used to power new tools that can help solve problems in the real world.

Examples of this use case are:

  • Predictive insights into footfall
  • Complex financial predictions

 

Example of footfall data

Get started with best-in-class footfall data today.

 

Categories
Retail

Leveraging Local Intelligence In The Retail Industry

In this modern era of technological advancements, it would be wrong to say that the retail market isn’t seeing similar growth. The number of online customers is evolving swiftly, and demands for more personalization are emerging. Retail industries are becoming more powerful as they are integrating with the latest artificial intelligence tools.

In 2019, the global market size of location intelligence was valued at USD 10.6 billion, and it is estimated to expand at a CAGR of 15.2% over the forecast period. Moreover, according to GSMA, the total number of IoT connections accounted for 9.1 billion in 2018 and is estimated to reach over 25.2 billion in the same period. 

Location intelligence is becoming more crucial for retailers with every passing day in providing measurable offline results, scalable audience insights, and better audience targeting. 

Here are the five ways how location intelligence proves to be revolutionizing retail industries. 

Customer Insights

If retail leaders start utilizing location intelligence, this can prove beneficial for them by provoking valuable customer insights. Technology can be used for this purpose called geo-enrichment. This technology efficiently converts customer’s residential addresses into useful insights. This way, retailers can utilize modern geographic information systems to generate smart maps and identify customer’s popular locations accurately and effectively. Retailers can accumulate information about numerous customer demographics and their behaviors. This helps business leaders to develop a better understanding of customer localities that are loyal. Business owners can optimize staff performance and have a better customer behavior experience by incorporating location intelligence in retail industries. 

Indoor mapping of retail shops is another thing that can immensely help with customer insights. Using this technology, retail stores can learn buying patterns of customers and then use this data to push different kinds of products and promotions.

Trade Area Analysis

Trade area analysis is essential for retailers to select suitable locations of a new store, define sales targets, and study competitor strength. Retailers can choose appropriate stores where they can easily access their target audience with trade area analysis. Location intelligence helps business owners have real-world insights about different customer demographics and behaviors around prospective retail sites. 

Also, business leaders can better understand which other locations their customers will consider for shopping. Thanks to location intelligence, retailers can collect data to measure customer trends and whether the customer demographic is growing or shrinking in a particular area. Location intelligence can offer data-driven approaches for trade area analysis in retails. 

Asset Tracking 

Retail businesses require a wide range of products to meet the ever-changing demands of customers. Late product delivery set’s a wrong impression on customers. This immensely affects customer delivery and staff productivity. Retailers can continuously track the location of their delivery staff and for the management of supply chains. Thankfully, location intelligence can also immensely usher identity verification of retail store staff members during asset tracking. 

Retailers can develop custom location applications that can be easily installed on mobile devices of their delivery staff. Location intelligence helps with asset tracking and can prove to be beneficial for business retailers. Business owners can track the precise location of a delivery executive in real-time and calculate accurate ETA. This way, retailers can track the location of all delivery executives on a centralized system.

Virtual Geo-Fences

Retails can set up virtual geo-fence for marketing. Business owners have the liability to define a certain radius around their store based on their store size. By utilizing this, retailers can push notifications regarding ongoing product discounts, deals, and offers when customers get in and out of geo-fence. Retailers can alert their customers by using this approach about products and discounts when they are in proximity to the store. Moreover, retailers can utilize innovative machine learning and artificial intelligence algorithms to improve sales and product suggestions. 

Let us consider an example. Suppose a customer wants to purchase coffee. There’s a high possibility that the same customer wants sugar and milk. Location intelligence is utilized for enhanced product suggestions and allows retailers to prepare themselves for ever-changing customer preferences. 

Competitive Strength Analysis 

Analysis of the competitor market is mandatory for retailers and business owners before selecting a new retail site. Location intelligence proves to be a game-changer in this scenario. Location intelligence assists business owners in measuring competitor’s market share and perforation in a particular geographical location. Also, this helps retailers to understand customer’s loyalty to themselves and their competitors. If their customers tend to visit other stores, the retailers can utilize this opportunity in the best possible way and capture less loyal customers of their competitors. A retailer can use Data-driven approaches with the help of location intelligence. 

Invest Today For Long Term Business Protection

Location intelligence has great potential to protect your business against criminal acts and fraudulent activities in the long term. Today, everything is prone to digitization due to the rapid evolution of technologies. The utilization of data and insights from experts for efficient and accurate business decisions is becoming a new normal, proving to be revolutionizing the world. 

In this current competitive environment, if you want to enhance your business’s security, it’s high time to invest in location intelligence technology. The utilization of local-based data helps the identification of those areas where you are overspending and underspending. Moreover, it helps develop a better understanding of your business operations and identity area of enhancements and how your retail store operates. It is much more straightforward to prove your compliance to stakeholders and customers with precise and versatile map data. Just start investing today for the long-term protection of your businesses. 

Categories
Retail

Big Data In Retail – How Data Analytics Can Transform Retail

The retail industry is evolving rapidly. The way that consumers shop is changing. The line between online and offline is blurring and more retailers are adopting a data first strategy, helping to understand how their customers are behaving and ensuring that they can match the right person the best product.

Retail data analytics is the new normal. The brands that have access to high-quality data, and know how to use it, are the ones that will deliver unprecedented value to their customers. Let’s look at how big data can be used in retail analytics to gain a powerful competitive advantage in a highly competitive space.

 

Complete understanding of customers

Big data analytics can help retailers understand customer trends in great detail. Behavioural analytics help retailers to predict what the next big trend will be based on these data sets. This kind of insight can have a positive impact across the retail business strategy.

This helps brands identify new preferences much quicker, helps them to avoid churn and ensures that acquisition costs are kept to a minimum. Data sets based on how consumers behave online can help retailers to predict what will be the next must-have items, for example.

Other predictive datasets include location, which provides an understanding of the demographics that visit your stores. This allows retailers to adapt to their changing customer base. For example, there might be a growing number of millennials visiting your stores. Learning this in real-time allows retailers to be proactive. This could be in terms of the in-store experience -it could make sense to open a specific area with products relevant to these demographics.

 

Customization and personlized promotions

Retail marketing means matching customers to a product like never before. Big data is making this easier than ever before. Predictive analytics can help you to find the right customer for your product with incredible accuracy.

Data sets that include location can help you understand exactly how consumers behave. This helps retailers to create a better understanding of their customers and create better targeting solutions that provide value and personalize communications.

In-feed targeting can be a hugely effective way to reach the right customer with the right message when the right data sets are used. Data sets that can tell you how customers interact with your brand online and offline allows retailers to provide highly contextual communications.

This level of personalization also helps to optimise media spend and ensure that you are reaching the right consumers with the right message.

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Get in touch to see how data can transform your retail strategy

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Physical store layout

Personalisation also includes the in-store experience. Big data provides retailers with high-level insights around how consumers move in their retail stores.

These datasets allow brands to analyse store behaviour and measure the impact of marketing spend in store.

Big data in retail stores can provide a competitive advantage in cross-selling and it can significantly boost the power of in-store promoting.

As well as this it can provide a powerful method of understanding how to layout physical shopping experiences to maximise engagement and ensure that consumers are provided with optimal smart shopping experiences.

 

E-commerce marketing strategy

E-commerce is a huge part of the modern retailer’s arsenal. It’s vital that brands can leverage retail data analytics to help make the experience seamless.

Customers want an online shopping experience that follows on naturally from their store visits. They want the two to work together without any issues. Services like click and collect as well as abandoned baskets messages are seen as a something consumers expect from top brands.

Big data can help to fill in the gaps that allow retailers to link the Ecommerce world to the physical retail store and provide a seamless shopping experience for consumers. Linking Ecommerce to the physical store and ensuring that consumers can find solutions easily is a major benefit of big data in retail.

 

Order management

Behind the scenes, big data can help to inform the supply chain and optimize production. This means that customers aren’t disappointed when a trend takes off and the product is out of stock.

Using big data in retail supply chains is instrumental in predicting trends and ensuring stock is in the right place. When big retail events hit, such as Black Friday, data set such as location insights can be instrumental to monitor areas where there is increased demand to allow retailers to react to changing demand and trends.

 

Big data in retail helps brands to answer questions that are crucial to growing a modern retail business. It helps you to answer these questions quicker than ever before:

  • Who are your customers?
  • What motivates them to visit?
  • What engages them?
  • How do they behave outside of your store?
  • How can you target them?
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To find out how location data and location intelligence can help your business please get in touch with our team.

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