In our work with many companies across industries, we have found that fully unlocking the strategic potential of customer insights requires three essential components. First, you must be able to identify and blend multiple sources of data into a single source of truth. Second, the capability to build technology and predictive models for optimization of results is needed. Third, your organization’s stakeholders must infuse every layer of decision-making with customer-centric insights.
Having worked with this for our clients, our mission has been to unlock a customer-centric strategy backed by data and analytics, to find intelligent solutions for acquiring the most profitable customers and maximize the value of existing customers. Note that this topic is complex and contains many important nuances, but for the purpose of this blog post, we will only explore the three components of the process explained earlier.
1. Establishing a single source of truth
Between CRMs, websites, mobile apps, order systems, ad networks and other channels, there are often various fragmented pieces of customer data all over the organization. This causes many organizations to sit on vast amounts of raw data without clear processes to assure its quality or built-out backend integrations.
At this point, you might ask yourself: “Why do I need a backend integration or data warehouse integration, whatever you call it”. Well, one must share data across systems in order to measure across the customer journey, thus unveil insights that ultimately may improve signals for bidding, targeting and personalisation of your customers.
2. Making the most out of transactional intent
The reality of today’s digital landscape is that decisions are most commonly made on single interaction instances, and to some extent by broad groupings of generic similarities in past behavior, such as remarketing lists.
How does one optimize strategic approaches like segmentation, targeting and personalisation in the context of customer acquisition and retention? Based on our single source of truth, we must model the behavior, paths and value of customers to understand our next best actions. What attributes affect their journey the most? Who are our top customers and are we prioritising around their needs?
This can be accomplished in countless ways, but mainly in two distinct forms:
Historical modeling
To model the historical value of customers without any attempt to predict what those customers will do next.
Predictive modelling
To model the predicted value of customers in order to determine what their future actions will be.
The output should cover detailed insights into high-value customers in terms of acquisition sources, tailored ad/website content and paths to purchase, and at its most sophisticated level, technology that supports predictive ranking and real-time path adaptation on site.
3. Cash rules everything around me
Hint: All of the previous components require resources and financial investments, which leads us to the last component. In order to convince your executives on leveling up your customer data activation, your actions need a clear connection to desired business impact. What is the most critical customer insight and are you measuring it? Whether it’s how online spend influences offline customer behavior, to gain cost efficiencies across channels, or build stronger connections to your end customers you need to define your mission statement and be specific about your purpose.
All companies can act on customer data, but performance improvements and competitive advantage requires investing in integrated data/analytics platforms and skill-sets to predict and model outcomes, to uncover and infuse these insights regularly and at scale.
