How to unleash the power of machine learning in digital marketing
How to unleash the power of machine learning in digital marketing
Over the past five years, artificial intelligence (AI) and machine learning in marketing have come a long way. Yet many marketers who are using today’s programmatic and social platforms to reach their audience are not exploiting the full power of the algorithms these platforms can offer or the vast amounts of consumer data they collect.
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Many apply traditional media tactics, metrics and segmentation models to a digital world. However, data and algorithms are far better than any human – no matter how experienced or insightful – in identifying and targeting the right consumer with a message or experience that leads to a conversion. The reason for this is simple: the platforms have the power to analyse data at unimaginable scale and identify patterns that would escape the human eye.
Here are four practices that can help marketers to unlock the full potential of machine learning in customer engagement:
1. Feed the machine with a varied and nutritious data diet
The major programmatic and social media platforms – including Facebook and Google – have grown into data empires that understand customers’ behaviour at granular levels of detail. Their algorithms can help you identify someone looking for a car loan or a holiday booking, then target them with a timely message tailored to their needs and where they are in the customer journey.
Yet this powerful technology is available to just about anyone who can afford to pay for a paid search ad or a social media ad. Leading marketers are therefore also looking at how they can leverage second-party data (for instance, data shared by partners) and first-party data to get an edge.
Examples of first-party data that can help a brand to craft a more personalised, compelling engagement including customer relationship management (CRM) data and data signals collected from offline channels such as the point of sale or call centre. This data can make the platform algorithms even more precise, leading to better customer engagements, lower customer acquisition costs and more conversions.
Companies that want to delve deeper into using first-party data face a range of challenges. They need robust data governance processes to ensure they comply with regulations such as the Protection of Personal Information Act (POPI) and the Global Data Protection Regulation (GDPR). They also need to think about how they will safeguard their proprietary data within today’s connected data ecosystems.
2. Trust the machine to be on target
It’s tempting for marketers to think that they know best, when their idea about whom the target customer is comprised of a blend of months-old market research, some historical assumptions about the customer base, and educated guesswork. Research and segmentation may, however, embed some presumptions that limit the brand’s ability to reach some of the most promising prospects.
At a time when marketers need to spend every rand efficiently, it’s best to trust the algorithm to identify customers according to a range of behavioural signals and other markers, then target them with appropriate messaging using dynamic ads. Learn from the likes of Netflix and Amazon, which use deep learning to target content to people based on their behavioural preferences rather than age, gender or other demographic factors.
3. See beyond funnel vision
Every marketer is aware of the traditional marketing funnel, which moves from awareness and engagement to consideration, conversion, and finally, loyalty. They often think of video or social campaigns as mechanisms to create awareness and spark consideration, tap into search and remarketing to tip customers from consideration to conversion, and use CRM-driven direct marketing to build loyalty.
Most understand that customer behaviour in the real world isn’t as linear and tidy as the funnel model would suggest. Consumers may vacillate between consideration and conversion for months before making a buy, for instance, or be lured away by a competitor at the loyalty stage. Yet delivering customised messages and engagements for the many permutations of possible customer behaviours and needs at different parts of the customer journey was impossible before machine learning.
With machine learning and dynamic ads, however, it’s no longer as necessary to follow a rigid funnel model to engage effectively with customers and prospects. Instead, marketers can look beyond the funnel and deliver the right message for the customer’s context on the fly. They can use triggers such as scarcity or authority to encourage customers to convert, based on their behaviour.
4. Prepare for big data to get bigger
Forward-thinking marketers are starting to look further than traditional digital platforms when it comes to fuelling machine learning with customer data. Voice and visual search are starting to play a key role, though the platforms have some distance to go to offer an integrated approach to managing voice, visual and traditional search to drive better outcomes.
This trend is accelerating not only because of the use of augmented reality and voice search on phones, but also because of the explosion of Internet of Things devices like smart cars and smart home technologies. Marketers should be thinking ahead to how embedded cameras and speakers in nearly every home device could change customer engagement in the years to come.
The bleeding lines between offline and online are also likely to lead to an explosion in the data available to marketers. The likes of Amazon Go and Alibaba today offer experiences where people can check into a store with an app, take the products they want, walk out and be charged without needing to pay at a point of sale. Scanners and cameras watch shoppers as they move through the store and the AI keeps tabs on the items they have taken from the shelves.
South Africa lags this emerging trend so far, which is understandable, given that the technology remains complex and expensive. However, now is the time for local organisations to think about how they can start to bring together offline and online channels and data. The leaders that get it right have the opportunity to offer an omnichannel experience that is consistent and personalised, whether people are shopping in-store or at home.
Leave behind the old limitations
With the technology and data digital marketers have access to today, they no longer need to limit their potential target audience to a set of personas or segments derived through customer research.
Masses of data and powerful machine learning tools can understand and predict people’s behaviour and needs with more accuracy than any tools marketers relied on in the past. Yet unleashing the potential of this technology is as much about embracing a new mindset as it is about learning new technical skills.
Published at Tue, 02 Feb 2021 04:07:30 +0000
What Are the Open RAN Benefits and Challenges
Open RAN benefits include more market competition and customer choice, lower equipment costs, and improved network performance. Open RAN challenges include achieving wide-spread adoption, tech support difficulties, system integration problems, and security risks.
Open RAN architectures are a type of virtual radio access network (vRAN), however, open RAN standards can still be used by non-virtualized architectures.
The Goal of Having Open Standards
The RAN market is dominated by a handful of organizations that do not design their RAN infrastructure to be interoperable. This forces network operators into vendor lock-in where operators must depend on equipment from exclusively one vendor. Without open interoperability standards, there is little space for new vendors at any level of the RAN market to get started.
The RAN vendors that dominate the industry can also benefit from having open standards. A multi-vendor model makes openings for managed service providers. The RAN vendors could partner with the smaller organizations and manage the multi-vendor RANs. Even still, the single-vendor model won’t likely go anywhere, because some organizations may prefer to have one place to turn to for tech support or to have a less-complex infrastructure.
Open RAN Benefits
When open standards are widely adopted, equipment vendors will have access to a wider network with more opportunities. Network operators will have the option to select the equipment and software with features that best fit their needs.
Historically, as competition in a market increases, prices for goods and services go down. This will benefit the organizations that need to purchase the equipment, software, and other infrastructure elements in a RAN.
RAN vendors typically only offer proprietary equipment and network functions. Organizations began developing open RAN standards to break out of that mold.
Proprietary products are typically more expensive than generic counterparts. Because there aren’t third-party RAN elements that can integrate into a RAN vendors’ infrastructure, a network operator is stuck with one RAN vendor’s products.
Open interface standards mean that third-party products can communicate with the main RAN vendor’s infrastructure. Network operators can then opt for the less-expensive third party product that runs on generic hardware. As network operators look to transition to a vRAN architecture for 5G, using open RAN interfaces can reduce the cost of deploying the new 5G technology. As well, when 5G technology advances and changes, network administrators working with open standard-based vRANs can easily send updates to the network infrastructure to accommodate for the changes.
The O-RAN Alliance is an open RAN standards body that is focusing on including artificial intelligence (AI) and machine learning in its standards. While this is not explicitly part of the interfaces connecting vendor products, standardizing how AI can work within an open RAN architecture can bring automation benefits.
Automated deployments save time and money because setting up the networking software requires less human involvement. AI is a big part of automation and maintaining the network. AI brings automation to operational network functions, further reduces human intervention, responding to traffic problems, and adapting to changing scenarios.
Open Standards Have Their Challenges
Before open RAN can bring these benefits, it faces the challenge of being adopted by the big players in the industry. As of this writing, only Rakuten’s 4G LTE network in Japan is using open RAN standards. However, Dish Network, with its purchase of Boost Mobile, is entering the mobile network game with the intent to use open standards in its own RAN.
A multi-vendor RAN model may be attractive to some organizations; however, there are challenges associated with the model. When an issue arises in the network, identifying and isolating the issues become more difficult because the environment is more complex. And even once the issue is found, a vendor can pass the blame to another vendor because of the complexity.
The role of system integrator becomes very important in a multi-vendor model because of the complexity. Those personnel have to work closely with the vendors in their system to make sure that everything can work together and is working together as well as it can. To try and mitigate this challenge, some open RAN standards organizations have created testing methodologies for the interfaces, established testing centers, and formed working groups to continue research into this topic.
Security is an essential aspect for any technology, and the threat surface area increases as more vendors are brought into a RAN, especially through their interfaces. Vendors should practice security best practices and customers should perform due diligence to ensure the vendors are doing so. Not all vendors will create sufficiently secure management interfaces for the service management and orchestration layer. The O-RAN Alliance is working to address this as well with its Security Task Force, which collects security requirements to create a security architecture, framework, and guidelines for its open RAN standards.
Open RAN Benefits and Challenges: Key Takeaways
- Open RAN standards have the benefits of introducing market competition, improving network performance, and reducing equipment costs.
- Open RAN standards are not currently widely adopted.
- The multi-vendor model promised by Open RAN introduces greater network complexity and difficulties maintaining system integration.
- Infrastructures made up of multiple vendors’ equipment increase threat surface areas.
- Open RAN standards bodies like the O-RAN Alliance are working to address these challenges.
Published at Mon, 01 Feb 2021 23:48:45 +0000
