In today’s increasingly competitive marketplace, businesses are starting to recognise the importance of customer centricity and insights. However, in recent CMO research, while 73% of senior executives believe customer centricity is crucial to success, only 14% believe it’s a hallmark of their brand. This is a huge discrepancy and something that insight experts have battled to close for years, because achieving true Customer Salience requires not only a cultural shift but also the right tools to gather, analyse, and act on customer data quickly and effectively.
With recent technological innovations in artificial intelligence (AI), we now have some of the tools we need to collect insights, uncover patterns and make good decisions that embed customers at the heart of strategies all in faster times than ever before.
1. Real-Time Customer Insights
The current timelines for research projects and insight generation is faster than ever before, but it still could take days, weeks or even months until stakeholders get their hands on actionable insights. There are multiple artificial intelligence tools around the market research marketplace to help cut this time down, and even provide real-time actionable insights. These tools span the full range of the market research experience, from research request and design to implementation and insight activation.
Tools such as FlexMR’s PromptAI and TextMR use artificial intelligence models including Generative AI and LLMs to intelligently probe and analyse customer sentiment faster than traditional data gathering and analysis techniques. These tools have sped up the pace of research exponentially, with the ability to gain just as valuable insights at the speed of business as long as they have researcher supervision to ensure that no mistakes are being made. Combine this with dashboard tools that pull through live data, this equals real-time actionable insights at the touch of a button for stakeholders who have access to the dashboard.
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| Customer Salience is more achievable than ever with a wide range of AI-based tools available to collect insights, uncover patterns and help stakeholders make the right decisions. |
2. Advanced Customer Segmentation
One of the defining elements of a successful Customer Salience strategy is the facilitation of deep customer understanding. To do this, we must understand all segments of a business’ customer base, their demographic data, behavioural data, and their wants and needs. Artificial intelligence can help insight teams and stakeholders segment their customers in more meaningful and accurate ways, using more advanced parameters.
For example, AI programs such as ChatGPT4 can analyse downloaded data from sources such as browsing patterns, primary consumer data from communities and more to identify:
- High-value customers from within and outside of the target customer segmentation parameters
- Customers who are at risk of churn due to any unmet needs or negative experiences
- Highly engaged customers who could become brand advocates or great research participants
With this more nuanced understanding of the customer base and consumer patterns, stakeholders can make better decisions with the right information, that resonates with the target customer segments.
3. Predictive Analytics for Proactive Decision-Making
One areas of market research that AI can help with is predictive analytics. Using existing methods and statistical algorithms, AI can identify patterns and forecast future customer actions based on historical data. However, when it comes to predicting the future, there is no such thing as a 100% success rate. There are still the same biases and cautions to be aware of, it all just happens at a faster pace. What this does is provide a great starting point and range of likely scenarios to take into account when making crucial decisions.
For example, AI and advanced algorithmic software embedded into tools such as smart devices can capture real-time consumer behavioural data, and through the Internet of Things, AI has access to a wealth of data to spot patterns and identify opportunities for more customer engagement. This data can also provide marketers with the data to create truly personal interactions, micro personalisation opportunities and the ability to spot gaps for new product or service development that could be necessary for customers in the future.
Proactively addressing customer needs based on predictions made by AI is becoming all the more commonplace, and this data has already helped businesses respond to evolving target customer segments.
4. Enhanced Personalisation Strategies
As mentioned above, companies with AI algorithms have access to a wealth of data and insights already, and with that comes the chance to create highly personalised customer experiences. Personalisation has become a cornerstone of modern marketing strategies, with AI-powered research tools able to provide insights into how exactly businesses should deliver individual customer experiences at scale in a way that their customers will thoroughly appreciate.
AI tools embedded in CRM for instance, already have the option to:
- Understand who engages in emails and when, with advice focussing on how to improve emails in line with customer data. Whether that’s the content themselves, the formatting or the optimum times to send for the best open rate possible.
- Determine where sales teams should spend their time based on sales forecasting insights, customer history, as well as how to optimise the sales pipeline with KPI tracking and live customer sales monitoring.
- Analyse each stage of the customer journey, identifying key touchpoints and pain points through monitoring customer interactions across multiple channels. Through this, we can make data-driven decisions to optimise the customer experience.
There are also AI-powered chatbots that automate conversations and direct customers to the right place based on real-time and historical data. Yes, most mentions of chatbots are met with eye rolls because of their history of doing things so wrong, but the software is improving at an exponential rate and chatbots are becoming great tools to filter customers to the right department, right landing page, or answer common queries right then and there supported by the right customer data.
Making AI Work for Customer Salience
Customer salience strategies are multi-layered and revolve around one key principle: activating insights. In the past few years, artificial intelligence technology has exploded into every area of market research and business processes, so there are undoubtedly many many more tools that would help implement and enhance customer salience strategies. The key is figuring out which ones you need for your strategy and which tools wouldn’t add the value required to justify the costs. To make AI work for your Customer Salience Strategy, it must supplement your current and future activity.
Ultimately, when implemented well, AI tools can lead to more relevant, responsive, and customer-focused strategies that build lasting relationships and drive long-term success.
