How brands are maximizing engagement with AI?

Since smart segmentation serves as the primer for personalisation, it's vital to create relevant and accurate segments before embarking on your engagement strategy.
technology

The Double Whammy Staring at Marketers
There was a time when only brands with massive financial clout could afford to run significant marketing campaigns, simply because Above the Line (ATL) marketing is resource-intensive. All of that changed with the digital explosion; now your neighbourhood mom and pop store could launch a Facebook or Adword campaign for as low as $50.

This resulted in an exponential rise in competition in the digital space for mindshare and customer loyalty. Unfortunately, the digital influx also pummelled consumer attention span to a scant 8 seconds (a sharp decline from 12 seconds in 2000). As a result, today’s marketers find themselves in an intensely competitive universe where thousands of brands are battling it out for the ever-shrinking attention span of the consumer.

The Rise of Digital Marketing
In the new digital universe, consumer attention and loyalty soon became an invaluable and coveted resource. And brands started pouring millions of marketing dollars to capture consumer mindshare. For a while, this worked, but the human psyche is incredibly good at filtering out irrelevant information, and that started a growing distaste for online ads. During the period from 2015 to 2019, the number of Americans using ad-blockers grew from 15% to 26%. 

Marketing 2: 0: Engaging the Connected & Empowered Consumer
Smart marketers soon realized that blasting consumers with a barrage of generic, irrelevant communication is hurting them twofold: it quickly drains precious marketing budget and also spreads the sentiment that the brand is apathetic to consumer needs. To gain mindshare in an attention-deficit consumer, marketers needed to make personalized communications A key pillar within this reformed marketing philosophy is personalization because it sets the stage and gives a contextual framework for a relevant and value-driven engagement.

The Holy Grail for Marketers: Hyperpersonalisation at Scale
In its purest sense, personalization is a targeted approach to customer engagement that delivers tailored, useful and relevant communication to every customer. For smaller businesses with small to medium customer sets, the personalization efforts are relatively simple and achievable with a good analyst team. But it gets increasingly complex and a herculean task at enterprise levels of data and consumer sets. In fact, less than 10% of top retailers say that they are good at effective personalization.

When done well, personalization accelerates sales and business growth for brands while increasing overall customer satisfaction levels. Personalization improves conversion rates by a whopping 70%, and it impacts ripples across the entire consumer lifecycle: from acquisition costs, engagement levels, average order values to repeat purchases.

The challenge to implementing personalization at scale basically boils down to two things: the pace of change in consumer behaviour and exponential rise in data volumes. Combine these two, and you get rapidly changing data, constantly shifting customer segments and frequent changes in the type of insights needed from the data.

Smart Segmentation with AI: Powering the New Personalization Paradigm
Since smart segmentation serves as the primer for personalisation, it’s vital to create relevant and accurate segments before embarking on your engagement strategy.

The Need for Smart Segmentation

While basic segmentations like demographics and location can be done manually, creating complex personas and purchase patterns segments can be tedious and time-consuming. Moreover, these generic segmentations cannot be used for building advanced purchase propensity modelling.

For successful smart segmentation, brands will need to layer primary segmentation attributes with ‘behavioural aspects’ of a customer. These include specific behavioural aspects like purchase history, search history etc. and implicit behaviour aspects like dwell time on a particular product page, heatmaps and other storefront interactions. Moreover, these behaviour segments need to be dynamic and adaptive based on evolving customer needs. This is where emerging technologies like artificial intelligence and machine learning can help brands optimize their data and resources to achieve their business goals.

AI-Powered Adaptive Segmentation

The real power of Artificial Intelligence lies in its ability to find complex and disparate correlations which are almost impossible to uncover through manual intervention. The need of the hour for brands is to value-driven experiences at every stage in the customer journey. However, multiple data sources and non-linear customer journeys have made it difficult for brands to create a seamless customer experience. Adaptive segmentation is a great way to develop centralized segmentation based on multiple factors like demographic data, behaviour metrics and time.

Adaptive segmentation is powered by an intelligent algorithm that continuously ‘learns’ more about the customer every time he/she engages with the brand – whether on social media, email, website or in-store. Adaptive segmentation also enables brands to optimize segments based on higher conversion probability rather than simple demographics metrics

A 4 Step Process to Smart Segmentation

AI-based segmentation typically comprises of 4 stages :
Pre-processing: The initial validation and cleaning of data sets. A ‘gold standard’ training set will need to be identified at this stage which will serve as a primer for future use cases.

Modelling: This is where you identify the variables that make up your segmentation. These variables are then stacked based on the order of importance and applied to the ‘gold standard’ training set to help the machine understand the characteristic properties for your segment.

The accessible segmentation attributes are :

Traffic source (website, PPC, email campaigns, social media etc)
New or returning customer
Platform (mobile, desktop)
Location
Average Order Value
Demographic, Likes and preferences
Search Behaviour
Past content interactions (product page, blog post)


Evaluation: At this stage, a confusion matrix is used to identify previously incorrect contacts and also evaluate the accuracy of the model. If there are unbalanced data sets across the segments, a statistical coefficient is applied to account for class imbalance.

Transformation: Output data is achieved, and your customers are now smartly segmented in accordance with the ‘gold standard’ training set.

Benefits of AI-Powered Segmentation
Eliminate human bias and stereotyping while segmenting
Create an almost infinite number of segments and sub-segments
Real-time updation of segments based on current behaviours and market trends
Uncover hidden patterns and trends (that goes against prevailing assumptions)
Highly scalability
Higher engagement rates and ROI
Despite the overwhelming success rate of AI-powered segmentation in driving personalization, its adoption rate has been low with 55 percent of marketers admitting to not implementing it due to siloed data sources and in some cases, lack of sufficient data itself. To stay ahead of the digital revolution and be consumer-ready, brands must create centralized, data pools using omnichannel analytics platforms and deliver a more personalized customer experience using AI-powered marketing solutions.

The article has been penned down by Vikram Bhat, their Chief Product Officer, Capillary Technologies

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