- Design industry shaping loyalty programs
- Integrate easily and go live quicker
- Deliver hyper-personalized consumer experiences
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There was a time when only brands with massive financial clout could afford to run major 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 meagre 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.
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 there started a growing distaste for ads. During the period from 2015 to 2019, the number of Americans using adblockers grew from 15% to 26%.
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.
In its simplest 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 fairly 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 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.
Since smart segmentation serves as the primer for personalization, it’s vital to create relevant and accurate segments before embarking on your engagement strategy.
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 propensities modelling.
For successful smart segmentation, brands will need to layer basic segmentation attributes with ‘behavioural aspects’ of a customer. These include explicit behavioural aspects like purchase history, search history etc. and implicit behaviour aspects like dwell time on a specific 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 AI and ML can help brands optimize their data and resources to achieve their business goals.
The true 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 create centralized segmentation based on multiple factors like demographic data, behaviour metrics and time.
Adaptive segmentation is powered by an intelligent algorithm that constantly ‘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.
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, it’s imperative for brands to create centralized, data pools using omnichannel analytics platforms and deliver a more personalized customer experience using AI-powered marketing solutions.
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