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THE DATA IMPERATIVE
We are living in an age of information overload. In the perennial needle in a haystack analogy, the haystack is growing at a much larger rate than the needle. So how can we successfully look for the needles and glean out actionable insights, has become extremely important.
Customers leave a trail everywhere they go. If she browses an e-commerce website and purchases a few products, that is captured as part of the transactional data. If she simply adds products to the shopping cart without making a purchase, she leaves behind a trail. Inside a supermarket, with sensors attached to certain products, a trail is left behind, of the products she touches. All this is valuable information that can be used effectively by marketing managers.
While there has been increasing noise and buzz around data science for some time now, very few companies have gone ahead and leveraged data to its full potential. The trend, however, has been changing in the last couple of years. Increasingly, several companies have started to focus on advanced analytics and assess how to derive business benefits from data. Many companies have started discovering the “Dark Data” lying somewhere within the organization, waiting to be processed. All this has not happened merely by chance. This has been triggered by multiple things coming together – Data storage costs have been steadily going down and processing capability has been increasing with the mushrooming of cost-effective platforms. There has also been a realization by many companies that data can be put to smart use, thanks to some exemplary work done by a few innovators.
Data scientists today can lay their hands on huge volumes of data (not to mention the variety) that improves the predictive power of their applications. Be it, a marketing manager who is trying to predict the next best product for a customer, or a retention manager in telecom who wants to know in advance the customers who are likely to churn, the possible areas where analytics can be put to practical use is immense. Data science has potential use across every industry and can be used to improve customer experience, improve revenue, as well as improve operational efficiency. Analytics can play a part in every stage of the customer journey, right from customer acquisition to customer growth as well as in customer retention.
Data, when leveraged efficiently across the organization, create impacts across all key areas of the organization – product & services development, pricing, marketing & sales, after sales service, retention & win-back, and loyalty & advocacy.
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Alignment is the Key
A clear alignment of data strategy with the organization’s business strategy not only uncovers powerful and actionable insights, but also helps in setting up the right business priorities across multiple teams and functions in the organization. Data strategy is one of the strong foundations supporting business growth and an incomplete or faulty data strategy can result in poor decisions and performance for the organization. A good data strategy will define what data to collect, how to structure it, and how to build a system that enables its access across the organization.
Breaking the Silo
Many organizations today treat data in an ad-hoc way and suffer from the data-in-silo syndrome. It is collected, stored, and used by different stakeholders in a disjointed way. This reduces the tremendous potential that data offers for these businesses. At the core of data strategy lies the purpose to support and drive organizations’ business goals. This can only be achieved by developing a strategy for collecting, organizing, and leveraging data that supports the goals of the company rather than the individual goals of each team.
Agility and Scalability
Data strategy should have the flexibility to adapt to the changing needs of the organization that arises due to changes in customer preference and behaviour. The data strategy should keep on evolving to provide continuous advantages.
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Leveraging Micro Moments in the Customer Journey
A customer journey or path to purchase has multiple stages - Initial thoughts when they are considering a product, evaluation stage, purchase stage and post purchase. Consumers demonstrate visible intent at every stage of the journey. Marketers need to recognize the customer’s need in these “micro-moments” and engage with the relevant tools and content at each stage.
Customer Single View
Customer DNA is a 360-degree view of the customer, capturing her demographics, purchase behaviour, needs, preferences, as well as motivations.
Not only that, but this can also be used to provide contextual recommendations based on where and how the customer is accessing information. This enables brands to provide hyper-personalized communication to customers in real-time, at the point of decision.
Hyper Personalization
Targeting can be much more precise with the help of hundreds of customer attributes and behavioural signals collected from multiple sources almost in realtime.
Hyper-personalization can be accomplished through a well-integrated architecture of multiple tools and processes which involve:
Selection of target audience Selection of the triggers Finalizing the content Delivery mechanism including channel selection Measurement & feedback Redefining Customer Experience Through Experiential Marketing
The consumer of today wants to experience a product before they commit to it.
What will a t-shirt on them look like in an outdoor setting? How will the sofa look in their living room? Experiential Marketing through Augmented Reality, Virtual Reality, and Mixed Reality, is redefining customer experience.
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EXPERT OPINION: CAN PRIVACY AND PERSONALIZATION CO-EXIST IN THE COOKIELESS WORLD?
PRASHANT SINGH
Country Manager, India RTB House
Since Google announced its plan to phase out third-party tracking cookies by 2023, we are now looking at a new reality of digital marketing in the coming years.
With the existing privacy regulations, from the EU’s General Data Protection Regulation (GDPR) to the California Consumer Privacy Act (CCPA), ad personalization will become more challenging. The key to success moving forward is businesses’ ability to adapt to the cookieless future.
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Why Are Third-Party Cookies Essential to Digital Advertising?
Third-party cookies have been on their way out for some time now, and Google isn’t the first to make a move towards a cookieless future. With rising user privacy awareness, many major browsers such as Mozilla Firefox and Microsoft Edge have already shifted to cookieless, without offering any alternative solutions for advertisers.
Thanks to the rich data that third-party cookies contain, advertisers are able to precisely match users to products that they may be interested in and make highly targeted ad campaigns with strong conversion rates. Losing third-party cookies is synonymous with losing access to that data, making it more difficult for advertisers to offer their customers personalized experiences.
Google has already pushed back its timeline for the phasing out of third-party cookies; however, it is in the process of developing the Google Privacy Sandbox, which is an anonymized alternative to the existing technology. This development provides advertisers with the opportunity to work on finding real solutions to the cookieless future.
Alternative Examples of Digital Advertising Without Third-Party Cookies
AdTech companies are currently exploring a range of options for the future. The first is to upgrade existing
marketing methods to reach users on the sites they currently browse. However, this method has a significant technological drawback. With the growing amount of online content, human-led solutions and traditional Machine Learning will not be able to keep up much longer.
Another option is to use Google’s own solution, which is the previously mentioned Privacy Sandbox. The anonymized intelligent advertising initiative is a response to the cookieless future of digital advertising. According to Google, this solution will allow advertisers to access user data without compromising over their privacy.
The third solution is using Individual Targeting, which relies on other ways to identify individual users instead of third-party cookies. There are two methods, the first of which recognizes an individual across various websites and devices based on a piece of Personally Identifiable Information (PII), such as an email address or login data. The second method uses statistical modelling to develop a user profile based on the version of the user’s browser, IP address, etc.
However, all of these forms of digital advertising come with a challenge – understanding and processing the data itself. The sheer volume of available information, as well as the complexity of these emerging datasets, will cause human and Machine Learning-based systems to struggle. This is where Deep Learning, the next generation of Machine Learning technologies, comes into play.
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What is Deep Learning Technology?
Deep Learning can identify patterns without preset parameters from a human operator. It does this by processing data through a series of increasingly complicated layers.
Unlike Machine Learning algorithms, Deep Learning algorithms are able to process significantly more complex, even unstructured, datasets. This means that it can operate far more flexibly than legacy solutions and it allows us to leverage the best of all available targeting methodologies.
Also, the more often you use it, the better it becomes. Deep Learning algorithms are able to identify patterns without human intervention, which makes for much more efficient self-learning.
The Benefits of Deep Learning Solutions in the Cookieless Future
Deep Learning algorithms have demonstrated their effectiveness in performance campaigns, but require a unique approach when being used in awareness and
branding efforts. To enhance the value of awareness and branding initiatives, algorithms can be tuned to maximize any combination of viewability, VCR, reach, or CTR. This significantly broadens the number of potential publishers a brand can reach whilst also improving the advert’s relevance.
At RTB House, Deep Learning technology is already working in the real world, with real clients. Its application in digital advertising campaigns has significantly improved results and allows businesses to target online users with the right ads, in the right place, and at the right time.
While we still have some time before third-party cookies disappear, RTB House and other AdTech suppliers are already boosting campaign outcomes with solutions and digital advertising for the cookieless future. This is the time to evaluate your current procedures and determine whether you are actually prepared today and for the cookieless tomorrow.
