Contact management is a hot topic that's only getting hotter as marketers look to take advantage of data big and small with effective technologies that can help them deliver a personalized experience for customers.
It's a clear goal, but for many organizations, getting started on a successful route to contact management can be a frustrating challenge. And then there's the challenge of applying it with contact optimization. Part of the problem is that it's often too vague, with half-formed notions and common misconceptions. It's time to clear things up.
The best place to start is with a working definition of both terms:
Contact Management is a well-governed set of principles that deliver an engaging, interactive dialogue with each customer that exists across the organization and supports both effectively. Contact Optimization is the way you apply the strategy of contact management, which can be performed in different ways with varying levels of sophistication.
Here are three contact optimization approaches that businesses can take:
- A Business Rules Approach: Most organizations fall into this category, a decision-tree approach that is easy to automate and easy to explain.Unfortunately, it's also the most likely to degrade over time due to changes in the business unless it is kept current and validated.It also relies heavily on the past to predict future performance, which may or may not fit organizations with changes in marketing, markets or consumers underway.
- A Mathematical Optimization Approach: Applies a greater level of predictiveness to the strategy.It allows modeling of a variety of circumstances and often delivers more precise targeting. Mathematical optimization takes longer to degrade in quality and predictability, but it can, however, require specialized resources and tools. It can also be more challenging for the organization as it removes subjectivity and creativity from the process.These are obstacles more to the culture than to the process.Most business people want to believe they can out-predict the math.Unfortunately, most of us cannot.Remember IBM's Watson on Jeopardy?
- A Mathematical Optimization w/Randomization Approach: (e.g., a Monte Carlo algorithm) This improves the ability of the contact strategy to deliver results by introducing variability to the model over time. While these types of techniques can be used to increase customer responsiveness and engagement as much as two years out, they cannot be completed as part of any standard marketing tool suite.
On the surface this all seems fairly straightforward and easy to understand. They're concepts that marketers have seen and heard before, therefore why are they so difficult to adopt and execute?For one thing, every marketer within an organization wants the same piece of the customer pie, like those high-valued customers.
Another common obstacle is that organizations have aligned campaigns to product groups and/or business units, or that marketers are driving campaigns based on budgets versus a customer- focused strategy. At many businesses, metrics are not clearly defined that drive an organization-wide goal. And it's way too common for organizations to still align campaigns vertically, not horizontally across channels and customer segments.
But these are organizational issues.The software, as good as it is, and the math, as predictive as it can be, can't solve these challenges.The solution hinges on people who are willing to examine new and better approaches and who are focused on business improvement.
To begin the journey toward improving your customer engagement through contact optimization, organizations must understand where they are at today. Start small and take a cross section of campaigns and contact history to create a lens into how customers are being contacted. From here you can begin to build out the case for improvement and take the next step in shaping your customers' experience.