By: Nick Phipps, Optimisation Strategist at Rawnet
It’s no secret that customer expectations are on the rise. With almost 90% of consumers wanting more meaningful relationships with brands, marketers are being forced to move away from traditional marketing tactics that focus on promoting the brand, their offering and why customers should buy from them. Instead, they are prioritising a customer-centric approach as failing to build relationships with consumers is a costly mistake in today’s marketplace.
To do this, marketers need to understand their customers’ journey. However, this is easier said than done as organisations are often rich in data, but poor in insights. Capturing data is no longer enough, brands need to be able to take clear action from their data to drive success. For businesses, the first step involves defining the customer journey, which means connecting and mapping interactions across all touchpoints, in order to influence the buying experience.
As a result, brands need to create opportunities for more personalised, relevant and consistent messaging across all touchpoints. There are four key ways that businesses can optimise the customer journey with data-driven insights.
Creating a single view of the customer
The first way to optimise the customer journey with data-driven insights involves building a single view of the customer to better understand how the purchases, interactions and behaviours will drive future actions. This means that businesses can target them more effectively with the right messages, at the right time. Not only this, but these insights can help the brand to establish more effective strategies and marketing campaigns. This will drastically improve the quality of every customer engagement and craft content that resonates with them in the long term.
In order to take this to the next level, businesses need to share this single view across the organisation to ensure that everyone understands the customer journey and how this shapes their experiences. In addition to building a deeper understanding of customer needs, wants and preferences, brands will be able to drive greater value per customer through improved identification of cross and up-selling opportunities.
Interacting with customers in a contextually aware manner
With endless opportunities to engage with consumers, brands also need to make sure that it interacts with them in a contextually aware manner. This means delivering customer interactions in the highest degree of relevance possible, such as the customers’ needs, what’s happening in their business and what’s happening in the current market. Most importantly, marketers need to use their data-driven insights to understand and demonstrate why their product or service will provide value to their customers. This involves considering why they need it, why it differs from other solutions on the market and how they will interact with it.
Ultimately, contextual relevance is derived from businesses having the power to evaluate what a customer needs, and what product or service will have the greatest impact on them. Predictive analytic tools are now available to help marketers identify patterns of event types, market challenges and new opportunities amongst both existing and new customers. By improving understanding of relevance, brands will have an enhanced ability to interact with customers in a more contextually aware manner and optimise the customer journey.
Conveying consistent messages
What’s more, it’s vital to ensure that businesses are delivering consistent messaging, taking advantage of data insights to address customer needs at each stage of the journey. As part of this, brands need to ensure that different teams across the organisation are not running separate messaging across multiple channels and instead are utilising the same view of the customer journey. This requires different parts of the business to collaborate and work together, taking advantage of technology advancements.
Data-driven insights partnered with intelligent technologies can be leveraged to better align sales, marketing, business development and customer service. By improving the understanding of their customer, marketers can improve lead generation and prioritisation, drive conversion rates and identify new opportunities to add value throughout the customer’s journey. Furthermore, by bringing teams in the business closer together to achieve one common goal, they can better serve their customers and optimise their purchase journey.
Mapping the customer journey
Finally, companies can use data-driven insights to map sentiments and customer buying behaviours to create models that will correlate with long term success, helping to predict the next steps in the customer journey and keeping it moving forward seamlessly. Machine learning and AI algorithms can help marketers to build models based on previous patterns of event types, customer attributes and buying behaviour that have led to eventual success.
Over time, businesses will be able to fully understand what the next steps are to better predict success and avoid disruption to the customer’s journey. This will result in improved sales and marketing strategies, campaigns and product or service development. The advanced machine learning and AI tools will gather a high volume of data, improving precision and predictive capacity in the longer term. Therefore, brands will benefit from faster and more accurate predictions of customer needs, market challenges and new opportunities, to successfully optimise the customer journey.
Enabling success with data-driven insights
Optimising the customer journey is still a struggle for many businesses. But it’s now time for brands to use data-driven insights to overcome these challenges across all touchpoints. Every marketer and business leader needs to understand how creating a single view of the customer and delivering consistent messages in a contextually aware manner is vital. This will support the brand’s approach to mapping the customer journey to bolster customer experience management and the opportunities for success with data-driven insights.
