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The Use of Deep Learning across the Marketing Funnel

The Use of Deep Learning across the Marketing Funnel

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Artificial Intelligence and Machine Learning are no longer new concepts in digital marketing and managing the marketing funnel. Automating targeting, automating campaigns, personalized targeting etc have all been enabled by Machine Learning for a few years now. However, with the rapid evolution of Deep Learning, the impact of AI on marketing is now growing insignificance.

What is Deep Learning?

Deep Learning is a progression of Machine Learning which vastly improved the performance of AI models

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A significant step in the evolution of ML was the use of Neural Networks – Models build to mimic the Human Brain, with the ability to process large quantities of data, continuously learn from it and then provide highly accurate outputs – be it Classifications, Predictions or Segmentation/Clustering. Deep Learning is the next step in the evolution of the use of Neural Networks, enabled by the improved processing capacity of computers and the increased quantum of data available. 

Simply put, Deep Learning is an ML technique where very large Neural networks are used to learn from the large quantum of data and deliver highly accurate outcomes.  The more the data, the better the Deep Learning model learns and the more accurate the outcome. Deep Learning is at the centre of exciting innovation possibilities like Self Driven Cars, Image recognition, virtual assistants, instant audio translations etc. The ability to manage both structured and unstructured data makes this a truly powerful technology advancement.

So, what can we expect from the growing significance of Deep Learning, to enable marketing?

Let’s look at a simple construct of the marketing funnel and what marketers are trying to drive as objectives at each stage. Advances in Deep Learning are now impacting marketing at every stage of the funnel.

Building Awareness and Visibility:

Targeting and Segmenting Consumers: Deep Learning applied to historical marketing data is now enabling marketers to fine-tune target audience definitions, segments and even identify potential segments that haven’t been tapped as yet. The ability of Deep Learning models to combine multiple data sources and learn from them enables the identification of patterns and hence segments that neither traditional segmentation techniques nor even general ML techniques can achieve. This opens up new business opportunities and even product/service opportunities.

Eg. Coca Cola is using SMART vending machines, which have a virtual assistant to help customers create their favourite blend of different drinks.

Communication Development: Definitely at a nascent stage, but we are already seeing examples of ads being created through Deep Learning, Music created through bots, Image/Content selection for Dynamic placements, and important insight generation using data from social media, purchase baskets etc to identify more relevant triggers for comms development.

Personalization of Ads: While personalization is not a new concept and is already enabled by Martech platforms, the degree of personalization is limited. Deep Learning is now being used to develop personalization engines that can offer hyper-personalized deployment of ads/promos/offers that lead to a much higher level of engagement. 

Eg. Giving consumers recommendations for calendarized events like Valentines day etc has been on for a while. With Deep Learning, personalization can go a few steps further like recommendations for a friend’s birthday present, basis past interactions on social media. 

Building Differentiation: 

Differentiation not only comes from product proposition and comms but also how consumers experience the brand/service online. And here too strides in Deep Learning are enabling marketers with more sophisticated ways to create differentiation.

Website Experience: Based on the consumer profile and cohort, even the website experience can be customized to ensure that a customer gets a truly relevant experience creating more affinity for the brand/service. A great example of this is Netflix where no 2 users have a similar website experience based on their past viewing of content.

Chatbots: While not a new concept, Deep Learning is helping enhance the capabilities of Chat Bots, allowing for more intuitive responses to different kinds of customer queries, and ensuring that each consumer gets the relevant response that helps them decide on a purchase. Moving towards Conversational AI which is even closer to real human interaction.

Driving consumers to conversion:

More often than not brands lose consumers to competition, at this stage of the funnel.  And while there are multiple tactics that brands use to upconversion, Deep Learning is offering more sophisticated solutions. 

Recommendations Engines: Perhaps one of the most powerful use cases of Deep Learning in marketing /product, recommendation engines can be widely seen across eCommerce and OTT platforms. And they are only getting better as they feed more data from individual consumers into the Deep Learning models.eCommerce and OTT platforms all use recommendation engines which are becoming more sophisticated with time. 

Voice Search & Voice Shopping: Convenience will also drive more conversion. Voice searches have taken off in a big way in India – more out of necessity given that millions of new to the internet are not comfortable typing on their phones. And at the top end of the market devices like Google Home, Amazon Echo etc allow consumers to search and order products from eCommerce sites – all through voice commands. 

See Also

Driving customer loyalty:

Customer loyalty, for today’s highly demanding consumer, comes from not just meeting expectations but exceeding them consistently. This is a tough ask given the dynamic natures of consumer expectations, competition moves, new alternatives being launched etc. But Deep Learning is already providing solutions here

Predicting consumer preferences: In the fashion space, Deep Learning is using images of apparel posted by their target consumers on social media to predict the kind of apparel that will appeal to them or on which apparel should they offer discounts to existing customers etc

Anticipatory shipping: By analyzing consumers purchase patterns, their search behaviour, the price range of products purchased in the past, eCommerce companies can now use Deep Learning to predict likely purchases and therefore ship products in advance to their warehouses. This is already being used by Amazon in several markets. With Anticipatory Shipping the ability to deliver to consumers on time and consistently only ensures greater consumer delight and loyalty.

Encouraging Advocacy: 

A satisfied consumer is great, but a satisfied consumer who recommends the brand/service is even better! A huge amount of marketing effort is now being directed towards making existing consumers active advocates of the brand/service.

Identifying Potential Advocates: For brands/services with lacs of consumers using their online platforms, conventional ways of looking for potential advocates is not easy. With Deep Learning, brands can now micro-segment customers, basis past purchases, the value of purchases, frequency of visits, reviews they have done, number of customer complaints registered etc to identify clusters of audiences who are loyal, satisfied and high engaged. Then marketing can deploy tactics like special offers, unique content etc to encourage these users to become positive advocates of the brand/service.

Making advocacy easy: Consumers may often be very satisfied with a brand but don’t actually advocate it on their own social media assets due to the sheer effort required. Deep Learning can be used to help them create customizable assets that are easy to deploy on their social media handles. Eg Graphic based videos, audio clips, even personalized text recommendations. 

And all of this is just the tip of the iceberg. As Deep Learning involves its utility across the marketing funnel will only grow, enabling marketers to utilize the vast volumes of data they now have access to and improve marketing efficiency and effectiveness. 

However, Deep Learning is by no means an easy or cost-efficient solution for marketing. As much as it is a powerful stream of technology, it requires heavy investments in terms of data management, infrastructure and skilled resources to scale up.  So, organizations with scale can justify the investment today and will be the ones who are the early marketing adopters of Deep Learning.  But over time with more SAAS solutions being developed and accessible at lower costs, it is only a matter of time before we see the widespread use of Deep Learning across the marketing ecosystem. 

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