Establishing rules concerning contact frequency should be managed by marketing operations; you will certainly want to include field marketing in the policy definition mix. Policy compliance must involve any data management function responsible for access to contacts, as well as managing name acquisition and data quality. Touch policy development tends to be a work in progress, with many organizations putting in place basic rules to manage contact frequency and building toward more complex guidelines. We have generally observed four phases in this development, including:
1. Legal obligation only. Even an organization that puts no restrictions on how prospect and customer data is used must, by law, adhere to opt-out requests, and thus build a process to ensure opt-outs are processed quickly and correctly. These organizations should also be tracking opt-out levels over time against an initial benchmark (e.g. total percentage of database opted out, opt-out rates by month and quarter) as a way to heighten the need for more well-defined policies.
2. One-size, rules-based, honor system. The initial foray into formal marketing touch policy tends to be defined by both its simplicity as well as its faith in the (general) honesty of people. To reduce the amount of pounding of the database, the organization sets a blanket guideline for contact frequency by email (one per week seems to be a common rule) as well as by telephone and direct mail. Without any formal gatekeeper in place, marketers are relied on to police themselves. Such policies are often accompanied by efforts to educate marketers on why “overfishing” of the database can be harmful to their success in the long term; this at least helps to build awareness about the problem and encourage compliance. Similar to a no-policy scenario, opt-out levels must be benchmarked and tracked over time to help indicate whether marketers are staying true to the policies put in place.
3. One-size, rules-based, safeguarded. Our third phase sees similar types of blanket policies to those adopted in the second phase, but adds a dimension of protection in the form of an individual or team that manages access to contacts in the database, or technology that does so in an automated way. Some organizations have created a formal role (e.g. a data steward) to do the job, while others have appended the duty onto an existing marketing operations resource. This resource will receive list requests from individual marketers, then pull the list to ensure that all names comply with regulations.
4. Multi-dimensional, rules-based, safeguarded. Our fourth – and most complex – phase sees the evolution toward a more complex, rules-based policy that includes buyer preferences, roles and even account type to define the frequency of contacts. This level of sophistication tends to eliminate the human element, simply due to the fact there are way too many moving parts to manage. Companies without marketing automation platforms (MAPs) and/or contact data management capabilities will struggle to get to this stage, especially in distributed marketing organizations with limited visibility into what gets sent to whom.
Marketers are naturally drawn to think that more is more, meaning increasing frequency of communications can only improve results. The fallacy here is that those messages, whether delivered via email, phone or direct mail, are welcome in the first place. If communications are not sought by contacts, and/or if many are not relevant to their specific role and needs, increasing their frequency can drive blanket opt-outs. On the other hand, communicating too little with a contact can also hurt a marketer’s cause, as messages are so infrequent and unpredictable that the company is neither top of mind nor trusted. It also means bad contacts are kept longer, which gets expensive. Once a marketing touch policy is in place, tracking and measurement must then be established to understand the impact of message frequency both on results and the database. This is easiest for email communication: Look at both response measures (was an action taken) and deliverability metrics (did the message get to the recipient, and did the contact opt out). Looking at how these measures change by type of message and frequency of messages overall will show what the threshold is and when it has been crossed. Don’t forget to look at metrics by role to determine if different types of contacts show they prefer different frequency (or types of messages) by opting out at higher rates.
A marketer’s most valuable asset is his or her organization’s database, but it is a fragile ecosystem that decays quickly if not properly managed. Setting rules for the frequency of contact is a good first step toward making sure this decay isn’t an inevitable fact. The next step is to understand what prospects and customers need at various points in the buying process, so relevant options are offered and expectations set. The third – and most important – step is to encourage contacts to define their preferences so your messages are expected and more likely to be relevant. With both permission and preferences in place, your database will grow to become a competitive advantage rather than a detriment.
Ally Motz
Posted in B2B | |
