Proximity Marketing & Ad Optimization

Related Topics: Consumer, Data & Analytics, Digital Engineering, Innovations, Uncategorized

In a recent eBook, Dr. Rob Nelson shared how to achieve business at the speed of now by driving business results with intelligent analytics and decision-making using real-time streaming data.

There are many sources for data appropriate to streaming processing and advanced analytics. Let’s dive into a specialized topic – Customer Location Intelligence – and look closer at proximity marketing and ad optimization which rely on sensor-driven real-time customer location data. This use case can be quite relevant and transferable to other types of data and circumstances. Plus, you can see how bringing different data sets into the mix with AI is very powerful.

Digitally Transform your business with Proximity Marketing

Consumer and retail companies are looking for innovative ways to transform their businesses digitally – a critical step toward competitiveness and enhanced profitability. Those enterprises that fail to cater to specific customer needs to lose out, and customers soon switch loyalties to their competitors.

The good news is that there are key technology enablers that support an enterprise’s digital transformation efforts, including intelligent analytics. Real-time insights and data about how consumers move through their world via intelligent analytics help to better understand and connect with customers.

Organizations are struggling to handle the enormity and variety of data channels, and the inability to correlate these disparate data streams to identify useful insights. Yet, despite ever-increasing data opportunities, organizations are puzzled as to when, where, and how product specific information should be communicated to customers to move them along a buying cycle.

Enter intelligent analytics via streaming real-time data and AI off of the data stream. This approach can uncover relevant insights from large amounts of data to optimize an organization’s marketing strategy implemented in real-time.

Companies can leverage analytics to become more familiar with customers and their requirements and to meet these, optimize communication so appropriate messages are pushed to them at the most opportune time and place, particularly in the consumer and retail setting.

The retail industry is undergoing rapid changes as the smartphone gives both the shopper and the retailer capabilities they never had before – and even more so as 5G comes online. Shoppers are able to receive information on demand wherever they may be via their smartphone.

A typical example of this is the use of streaming analytics for proximity marketing, incorporating beacons and mobile infrastructure to locate customers and analyze their behavior and enhance their experience by providing them with precisely what they need. This enables organizations to highly customize their offerings based on context, instead of pushing out random product offers.

Streaming analytics helps retail stores track the indoor location of their customers, discover the most attractive product in the shop, send an ‘offer of the day’ when customers within a certain radius, provide offers related to their specific outcomes of interest, suggest other products relative to what they are looking at right now, offer coupons based on customers’ previous purchases, and obtain more advanced statistics for the store manager to refine and redefine business strategies.

Why would you not want to gain a whole new perspective on your potential customers – an entirely new data set? A variety of technologies exist, such as beacons and sniffer tags that can sense and track Wi-Fi devices every ten milliseconds. When Wi-Fi devices are detected, they capture and encrypt the time, location, and a unique device ID or even an identified app.

  • Send that data over existing or dedicated networks to a cloud-based data repository.
  • Position and aggregate data for delivery, providing the processed data in the form of a data feed.
  • Ingest the data feeds into advanced analytics tools, machine learning models and are used to trigger events, alerts, or notifications to drive better marketing, promotion, and personalization.

The location data can even be used to correlate to active social media sentiments for potential service interventions.

There are many potential benefits beyond real-time customer promotion and personalization.

  • Location and contextual data can be used to create better customer experiences; create innovative new data-based products for your business; make more informed decisions in complex scenarios; carry out effective monitoring and analysis; optimize retail operations; customer behavior across different properties; detect even the smallest change and trigger immediate action; and extend your solutions to analyze the past, present, and the future. 
  • Intelligent streaming analytics elevates the product recommendation process by providing suggestions not just based on similar customers but also based on the customer’s purchase history, current seasonal trends, and product combinations that are not intuitive, yet can be found through data mining and machine learning.

These are achieved by correlating the behavior like real-time browsing data with summarized historical data, such as customer’s buying history, personal details as well as seasonal trends, inventory status, current promotions, and product correlations based on machine learning models.

This real-time streaming data, correlation, and analytics results in intelligent recommendations that have a higher purchase probability of being effective based on the customer or the current context.

Digital Ad Optimization Online and In-Person at the Point-of-Sale

Most enterprises struggle to achieve effective conversions through their digital marketing strategies as they cannot optimize ad placement contextually in real-time. Hence, most organizations rely on the static distribution of their digital marketing budgets to blindly place pre-determined amounts of different advertisements through multiple social media channels without being able to optimize ad placement in real-time based on which ads and channels that get more conversions.

By employing streaming analytics for digital marketing, enterprises can dynamically decide when and how to bid for digital ad space based on market penetration, real-time trends, purchase behavior, and available budgets.

Digital signage content can even be customized and served to customers’ presence based on their profile and behavior data.

Streaming analytics seamlessly solves this issue by correlating online user views/clicks and even real-time in-person omnichannel shopping behavior with user demographics on social media and available marketing budgets and make advert bidding decisions within milliseconds so ads can appear on the web page that the target customer is currently viewing within an average human reaction time.

As we can see in these case examples, when we enabled real-time customer location intelligence with machine learning models, we can offer real-time marketing interventions that consider the customer’s actual location as they move through the area. We can also use data for other purposes like digital ad optimization.

Implementations can include omnichannel interaction on mobile, in-app, and in-store digital signage and even menu displays.

We can even alert a sales associate to take an informed intervention.

Analytics has become vital to improve customer experience, increase market reach, optimize budget spend, enhance business processes, and find and eliminate anomalies. All of these eventually translate to improved revenue for any business.

Retail industry players can particularly benefit from analytics. It enables them to communicate more effectively with their customers in an era where customers are more informed and respond better to customized marketing efforts. Moreover, streaming analytics provides organizations with useful insights into customer behavior, which can help them refine their marketing strategies.

Provide High-Trust and Low-Trust Mobile Experiences

For a different use case related to how proximity monitoring can improve the mobile experience, check out this video on App Lifestyle Integration.

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