This principle does not only ring true for B2C marketing but equally for B2B — although the process and means to engagement may be different. Consumer insight around culture, purchase behaviour, brand sentiment and advocacy are some of the drivers behind relevant consumer engagement.
Ask any marketer, salesperson or creative director, the desire and need for consumer insight is one of the most
important elements in their arsenal.
As marketers, we live in a golden age because there is no better time
to gain insight into consumer behaviour and to improve our segmentation approaches. The reason for this is simple; the amount of marketing data that is available to us is immense
Data, along with technology, has truly become the fuel that enables more relevant consumer interactions at scale. That said, despite the significant stride marketers have made over the past decade, capitalising on this opportunity has remained elusive for many.
Sure, techniques such as A/B testing and ever more sophisticated segmentation strategies have allowed us to tailor brand interactions for more granular groups of customers. This is improving the relevance of engagement for each consumer.
But these rules-based or manual approaches are limited because they don't scale well or enable us to create a truly
tailored brand experience for each individual. Enter Artificial Intelligence!
AI holds the key to more human and relevant marketing experiences at a great scale. Its real power lies in the way that it can crunch through massive datasets to learn about customers, then use this information to create a personal and relevant message for each one.
Most marketers know that there is no shortage of data about customers. People are telling brands what they want, need, prefer and expect through countless brand interactions on the web, social media, mobile apps and retail channels each day.
But making sense of this data to take real-time action is where the real challenge lies. With today's data volumes, it's simply beyond the abilities of any
AI and machine learning are the tools marketers need to process big sets of data rapidly and
accurately to drive truly personalised interactions with customers in real-time.
By observing data signals about people's behaviour and leveraging information that they supply about themselves, AI can make sense of their needs and preferences.
Using algorithms, AI can analyse and score customer traits, preferences and behaviours — along with contextual identifiers, such as their location or the time of day. There are few
limits to the set of variables that AI can leverage to understand the customer.
Equipped with such data, the AI system can deliver a more relevant and personal experience for the customer or give marketers access to insights that help them make better strategic decisions.
Here are three examples of AI in action: 1. Personalised recommendations, content, ads and messaging
Brands today might target content at people with varyingt demographics or who have shown different inferred interests or behaviour online. But AI takes this to new levels.
It can automate
the process of targeting the right
content to the right
person and at the right
time, based on numerous variables including contextual data, behaviour, demographics and expressed interests.2. Granular customer segmentation
Most customer segmentation is still done at a relatively coarse level. AI can help markets break customers down into more granular groups, based on insights from big data.3. Predictive analytics
The next step from understanding how the customer behaves is to predict what he or she will do next
. For example, AI-powered tools can help brands to predict that a customer might be about to churn, because of their behaviour. It can then target them with a personalised retention message or promotion.
According to research from Google, 90% of leading marketers say personalisation significantly contributes to business profitability and 61% of people expect brands to tailor experiences based on their preference. AI will have a key role to play in the future as this need to personalise marketing interactions continues to grow.
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