There was once a world where hunches and intuition were the life-blood of the marketing department. Passion led the way and the most convincing voices in the room were the ones that could paint the most compelling narrative. Now we are in the midst of a sea-change and the Don Drapers of the world are learning how to share the stage with Data Analysts. Marketing is becoming less about intuition, and more about data-driven proof points.
But in the modern world of marketing, advertising and sales, how are marketing teams marrying the idea of unpredictable creativity and water-tight data analytics? The answer is simple; by knowing in advance the potential areas of challenge, and being equipped to respond.
Great creative design, whether it’s UX, sales processes or clever retention marketing doesn’t have to exist in isolation to powerful data analytics. A Data Science Roadmap is the first tool that all great CMO’s are implementing to ensure their teams are ready to fight for their customer.
Creative & Data Science meet in the middle: At the customer insight
Marketing isn’t just about creative ideas in the same way that data analytics isn’t just about building models. The place where both disciplines meet is in the creation and identification of powerful customer insights.
The insight is the “why” behind specific customer behaviours and a good insight is something that will make both the marketer and the analyst sit up and pay attention. What customer profiles convert? Where are the design flaws in the onboarding process? Why do some customers leave us? Etc.
These insights can be gathered and shaped into a viable and realistic Data Science Roadmap, as we will now explore within the example of a customer lifecycle.
How are marketing teams marrying the idea of unpredictable creativity and water-tight data analytics?…By knowing in advance the potential areas of challenge, and being equipped to respond.
Stage 1: Customer Acquisition
Lead Conversion Funnel
At the first step in your Data Science roadmap having a Predictive Model for Lead Conversion will attribute a “conversion propensity score” to each lead. Sales teams can simply work the list from the top, focussing most attention on the high propensity leads. Low propensity leads can either be removed from calling lists or used for alternative means where conversion is less important e.g. awareness activities or brand building.
Early Churners & Early Cancellations
If you have a problem with customers signing up and cancelling soon after, you may want to understand what is driving this behaviour. Predictive Analytics work really well here as the algorithms will slice and dice the data automatically, mix demographics, sales processes, product usage patterns and will help to understand why cancellations happen.
For some products and services, you can observe customers paying but then having low or no usage. This is a prime example of a customer churn waiting to happen. You don’t need a Model to take action but you will probably want to know what kind of customers enact this behaviour so you can be ready and prepared when they do.
Stage 2: Nurture Relationship
By understanding specific, defining customer characteristics you can group your base into individual groups, or segments. Segmentation allows you to define specific offers, products, communications plans or even to develop innovative new solutions. Predictive data then allows you to understand and anticipate the unique behaviors of these groups. Segmented customers require segmented communications & sales solutions, and a clearly defined Data Roadmap will simplify the development of these tools.
You are likely to have different versions of your product/service with different price-points. Up-sell models will show you who is actively interested in paying more for improved products or services. This type of model is easy to build, quick to implement and provides particularly reliable data.
If you have several products or services that can be consumed at the same time, cross-sell models can segment your customers into “interested” and “not interested”. Working hand-in-hand with up-sell Models, they provide a powerful tactic for increasing revenue and customer loyalty.
Stage 3: Retention
Retention Analytics spans a much wider reach than just Predictive Models. This is a major decision-making space where you need quick answers to many questions and not all of them will convert into deployed models.
- What factors drive a customer to cancel?
- Why do some customers renew and some don’t?
- What can we do to retain them?
- Why do some customers lower their spend?
- Does seasonality affect our retention levels e.g. Christmas / summer etc.?
- Have customer churn patterns changed in the wake of recent competition activity?
When trying to retain customers the single most important factor is timing. If your models predict churn for tomorrow – it is already too late for any retention action. Some customers decide months before the renewal date that they will cancel.
Staying ahead of the action.
By building Models with a much longer horizon and analyzing what leads to such early decisions you can stay two steps ahead and pre-empt your customer behavior. Armed with these insights you can improve your offers or processes to keep your customers happy, loyal and away from the danger zone.
A New Era for Marketing
Data Science enters a new level of automation allowing us to get fresh Models & Insights within a few days instead of months.
This creates a new playing field for marketers where they can ask tens of questions instead of one or two and get up-to-date answers quickly without the pain of scoping every time a multi-month project.
It’s a numbers game and we hope the Predictive Analytics Roadmap will help you to navigate through it.