Topic: Customer Experience
How do you determine what customer preference data is valuable for your company to collect? How do you collect it? After collection, how is it used? You must define the business rules by which data is collected, interpreted and used. Then, use these rules to drive the decisions made about any marketing technology solution. That includes your solution for preference management and collection. If business rules aren’t clear, all data (including preference data) is at risk of being misunderstood or misused.
A lot of companies think one main challenge to getting a preference management system will be coordinating with the old systems already there. These companies often discover that the real challenge is creating consistent rules. Effective rules recognize the unique needs and goals of the enterprise itself. The “what” and “why” of collecting data from customers and how that data will be used later.
Key considerations include:
1. Definition of terms: Basic vocabulary is the first challenge. How does your company define a prospect? At what point does a prospect turn into a customer? How do you define the behavior of a prospect who is close to becoming a customer? Unless your company can agree on basic vocabulary, you’ll be left with vague results. Unclear results can often conflict or fail to present a cohesive whole.
2. Establishment of specific goals: Your goals have to be specific. Don’t say “We want to increase engagement.” Increase engagement HOW? What qualifies as an increase? What counts as “engagement?” A generic goal is doomed to failure. An undefinable destination is a goal a company will never reach. So, be specific, have an effort to reduce opt-outs. Maybe a goal is to increase targeted opt-ins. Those goals are trackable and reportable – and a project worthy of budget and personnel. For example, we often see global opt-outs reduced between 60-95%. How big of a reduction in opt-outs do you need to see to consider the effort worthwhile?
3. State a unique value proposition: Don’t ask for every bit of data because you have the customer’s attention. If you’re selling something generic like scissors, you don’t need to ask a customer’s age. But many companies see an opportunity to ask for lots of information because it’s easy to ask all at once time. Just because you have a method of gathering the information doesn’t mean you have to ask 20 questions. Consider what is actually necessary or valuable to know. Limit the collection, storage and liability concerns that customer data requires. This is especially important now in light of new regulations like GDPR. GDPR provides for data protection of consumers. Also, data shows that conversion rates go up when a form has 6 fields or less. You don’t want your customers bailing on the process because it’s too big a pain! So, ask only what’s relevant.
4. Prediction of customer lifecycle: What’s the sale cycle of your product or service? Is it the kind of product that the customer needs once a year, like a tax software? Is it information about new models of the car they own, which they may only need to look at every several years? Is it something they need often, like a recurring order of pet food every couple of months? Subscription-based services company (such as a cable provider) have yet another model. They may need to contact customers weekly or daily. Consider the natural cycle of your customer interactions. Recognize the tempo and context of your relationships, and pace your communications in a similar way.
For example: if you’re going to argue for more departmental budget (no matter what your project initiatives are), you have to show how your goals are being reached. You can’t know what goals are being reached until you know what your goals are. Likewise, to justify your preference management project, you have to show identifiable targets. What type of preferences are you trying to collect? Who is the target of the campaign(s) etc? To maximize return on investment, you have to know what those goals and targets are. You can’t measure ROI if you don’t know what to track.
Unless you have a plan in place, your company will have trouble justifying the expense of a preference management solution. If you can’t put a full system in place, you may only make marginal progress in impacting the overall customer experience.