According to Forrester’s recent report, consumers expect for the Internet of Things products to be continuously monitored, personalized and constantly upgraded with bunches of new features.
But to provide seamless and predictive experience with smart devices, companies have to make sure they have a holistic approach to collecting valuable data and converting it into actionable insights when interacting with customers.
The volume of such data is growing exponentially. With tons of information and events logged within the given IoT ecosystem, your task is to understand why you are recording this data and how you can benefit from it. Applying data analytics, you can get reports on the device performance and status, visualize customer input, notice patterns and anomalies. Making sense of this info will help you trigger right actions towards cooperation with consumers, boosting their engagement and your business advantage.
Now, let’s check how it works.
Setting Up Processes
To learn more about your audience and its habits, start with collecting data. First, define the main sources of information and distinguish the data flows from each one:
- Servers: hosting applications and services on operating systems, they automatically save the events performed on the OS.
- Desktops and screens: these are the endpoints of user interactions with backend applications, so help to gather structured and specific customer data.
- Applications: they generate useful server, proxy and AV logs.
- Networks like WiFi routers, BLE hubs, firewalls, switches, wireless APs, IDS, and IPS: they allow both to generate data individually and to correlate information with other system logs.
- Sensors: a flow of data collocated with the time sequence.
What to Collect?
- User login/logoff
- Time consumers spend with the system
- How often they interact with your product
Understanding how people connect with your brand and using their behavioral data allows you to segment different user groups and achieve a new level of product customization. By leveraging collected data, you can recognize customer needs and wants to craft better orders.
Smart tech provides highly personalized product experiences and increases the interconnectivity between humans and machines. Knowing when and why users enter your system may give you insights on their peak activity time. It helps with customer engagement and target messaging through push notifications or system alerts.
With all the bulk of IoT data comes another challenge – storage! You don’t need to save every byte of information you get from your sources forever, as different data chunks can be valuable for different periods. For example, real-time use cases from sensors can be valid for a month for the marketing team, while your R&D department may want to archive some issues for several years even to get historical data for future product research.
Prioritizing and sifting data is another way to reduce unnecessary storage: some “hot” cases have to be instantly analyzed for real-time feedback, while others can be archived for later use. The following technologies will help you deal with the data storage:
- NoSQL databases
Open source NoSQL databases, including Apache Cassandra, MongoDB, and Couchbase, are popular due to their flexibility and support of low latency and throughput, allowing to add new data dynamically and in real-time.
- Time series databases
One of the most crucial assets of the comprehensive data analysis is that it can help predict and identify challenges before they happen. The best analytics tools today are preventative and predictive, forecasting not only the advantages consumers get from products, but eliminating potential risks as well. Make sure you alert customers about system updates and provide meaningful context as to why are you doing this.
Automated data analytics tools, IoT platforms or open source frameworks can help you with data processing and further alignment of this data with IoT solutions. Analytics methods include:
- Distributed analytics
The distributive approach to analytics enables more insightful solutions, processing data at scale. Distributive analytics shares workloads between multiple nodes in server clusters instead of loading one single node to cope with a big issue. When the processing is completed, datasets are collected back into a single unit to generate a unified insight. This is a huge advantage, as making more nodes solving the same problem significantly speeds up the process.
- Real-time analytics
Real-time analytics means fast and quick processing of high-volume data streams as soon as it arrives. It allows businesses to react on the input without delays, seizing opportunities or eliminating risks before they occur.
- Edge analytics
Edge analytics processes raw device data, filtering out trash files and unnecessary information prior to detailed analysis. It is usually performed at the acquisition stage directly on the edges of the network, IoT or gateway devices.
As the digital age is rapidly evolving, it brings up the nation of tech-savvy and digitally native consumers who dictate new rules and make businesses act accordingly. Today, companies have to let customers take the lead and give as many touchpoints with the brand as users need. Amazon Alexa Echo, for example, decided to fulfill customer requirements for seamless and instant access to its services and launched an Accessory Kit to bring the system to portable and on-the-go devices.
However, don’t follow only common behavioral patterns when interacting with customers. Usually, businesses shape their solutions based on trending preferences, but the majority doesn’t represent everyone! If you’ve launched an IoT-powered device and noticed that one of the thousand of customers keep using it at night, pay specific attention to this case. With such an exceptional insight you might want to add some night backlight or create a different voice tone to satisfy each and every customer.
Monitoring Interaction with Customers
As modern consumers are constantly connected and use multiple channels to communicate with brands, their customer journeys get more complex and not so easy to comprehend and predict. It is vital for organizations to perform cross-device tracking not to miss any detail.
Keeping track of every touchpoint customers use to interact with your brand, products or services helps develop an understanding of how to design better user experience. This knowledge allows you to take control over the improvements and changes that can be made according to the monitored interactions.
If you aim to launch an IoT product but still not sure how to choose and implement the right approach to data collection and its conversion into tangible assets, contact us for consultation. Don’t hesitate to visit our blog as well: having rich IoT expertise, we share practical insights that may help you further.